International Journal of Communication Networks and Information Security (IJCNIS)

An Ensemble Based Astrological Prediction Model for Profession and Marriage Using Machine Learning Strategies

2024-08-28T02:41:39+00:00

The fascination with astrology, an ancient and conventional form of prediction, continues to grow despite the absence of universal astrological prediction rules or principles globally. While accuracy is not guaranteed, astrologers prioritize offering high-quality services over establishing universal standards. In contrast, machine learning yields superior outcomes across diverse applications through its capacity to handle large, noisy, complex datasets via classification and prediction. This paper aims to present a scientific method that addresses the shortcomings of traditional astrology, identifies universal prediction rules, and employs classification techniques—Neural Network (NN), Import Vector Machine (IVM), Random Forest (RF), and Iterative Boosting—to validate the reliability of astrology in predicting profession and marriage outcomes. We computed Correctly Classified Instances (CCI), Incorrectly Classified Instances (ICI), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Relative Absolute Error (RAE) using cross-validation with 10, 12, and 14 folds. Additionally, we evaluated F-Measure, Precision, True Positive Rate, False Positive Rate, and area values for MCC, ROC, and PRC. For three-class labeling of professor, businessman, and doctor, we determined the true positive rates, false positive rates, accuracy, F-measure, PRC, and ROC area. We gathered birthdate, birthplace, and time of birth data from one hundred individuals across these professions, creating horoscopes using software. Data analysis involved building a datasheet in .csv format and employing the Weka tool to assess various parameters, including classifier accuracy, to identify the most effective classification method.

Creativity and Innovation on the Adoptions of Creative Arts Activities: Attitudes and Perceptions of Kindergarten Teachers in Yunnan

2024-09-02T09:13:59+00:00

The study aimed to explore the influences of attitudes and perceptions of creativity and innovation of kindergarten art teachers on creative art activities in the kindergarten art classes of the first-class public demonstration kindergarten schools in Qujing City, Yunnan Province. Questionnaires were employed to collect data from 261 kindergarten art teachers. Descriptive analysis and multiple regression were used to analyze the data. The findings revealed the statistical differences among six variables of the adoption of creative art activities, which included: 1) Creativity in Creative Art Activities; 2) Evaluation of Perception of Teaching Activities; 3) Specified Art activities in Art Education; 4) Planned Classroom Goals; 5) Planned Classroom Activities; and 6) Adopting Creative Art Activities into Creative Art Teaching. For future research, longitudinal and cross-regional comparisons to track long-term changes with combined research methods can be applied to ensure the efficiencies of kindergarten creative art teaching activities to promote Chinese kindergartens’ artistic senses and skills.

An analysis using a structural equation model to assessthe various factors influencing the Iraqi construction industry, with a specific focus on the moderating of organizational culture

2024-09-02T13:15:18+00:00

The construction industry is of great importance as it is able to achieve cost savings and promote economic development worldwide. Regardless of a country's level of development, be it an underdeveloped country. Nevertheless, there are a number of constraints and hazards that hinder the start or progress of a construction project, and which usually have a significant negative impact on the overall project. In a previous study, the influence of a company on construction performance was investigated, leaving out certain factors. This study aims to fill this research gap by using the methodology of organizational culture and the various factors including stakeholders, communication, cost, technology, top management support and local authority support to investigate the impact on the Iraqi construction industry. The data pertaining to the research was gathered through a survey questionnaire administered to multiple construction project practitioners in Iraq. The research objective was achieved through structural equation modeling (SEM). The study operator a quantitative approach to gather data, which includes a survey questionnaire administered to construction project practitioners and interviews conducted with academicians who specialize in the construction industry. The results obtained from the SEM analysis indicate the model is appropriate for the characteristics of variables and data under investigation. The further analysis of research outcomes demonstrated that the hypotheses (H1, H2, H3, H4, H5, H6, H7, and H8) all the results were found to be statistically significantand had positive findings. A survey instrument was utilized to obtain information for the research from many construction companies in Iraq. The data have been analyzed, and an SPSS AMOS 26 software-based structural model has been constructed to test the results of the hypotheses.A moderate relationship can be inferred between organizational culture and the construction industry in Iraq, as indicated by a positive correlation coefficient of 0.036. A positive association is denoted by the positive sign that is an increase in one variable is typically accompanied by an increase in the other. A correlation coefficient of 0.08 indicates a positive relationship between organizational culture and stakeholder factors. Although the correlation demonstrates statistical significance, its magnitude suggests the strength of the relationship. A relationship exists between stakeholder factors and their influence on the Iraqi construction industry, as indicated by a positive correlation of 0.080. Alterations in construction industry developments might be correlated with stakeholder factor changes, as indicated by the positive correlation; however, the relationship is not definitive, noting that correlation does not imply causation is essential. Although the statistical relationships presented offer valuable insights, further investigation and analysis are required to comprehend the fundamental mechanisms and factors that underlie these correlations within the organizational culture and construction industry of Iraq.

Artificial Intelligence Driven Customer Relationship Management: Harnessing the power of technology to improve business efficiency

2024-09-03T13:12:16+00:00

This paper investigates how the Artificial Intelligence (AI) has significantly affected Customer Relationship Management (CRM), with focus on the transformative potential of AI tools like chatbots and predictive analytics in transforming customer-business interactions. Companies that integrate chatbots can personalize their assistance 24/7, thus improving client involvement and satisfaction. Additionally, another benefit from predictive analytics, is, the successful interpretation of customers’ behaviour patterns and their future requirements to enable early or precise tailoring of the experience. It also strengthens the existing business relationship with the customers and makes business efficient and effective in terms of increased sales turnover and revenues. This study by applying the mixed method approach underlines the crucial role of management and clients’ centric approach in conditions of the intensified competition of the nowadays high-stake market environment. The research results show that the use of AI in CRM systems can be critically beneficial for a business since such a system can enhance customer experience and provide decision-makers with tools to enhance their understanding of consumer conduct and behaviours. Such competencies help companies increase their long-term performance on the market since they uncover the potential of AI in CRM. Altogether, the findings are highly beneficial as it reveals the opportunities of leveraging AI in CRM systems to deliver the clear perspectives for improving interactions with clients and organisation’s performance.The research findings demonstrate that AI-powered CRM systems offer a significant competitive advantage by enriching customer interactions, uncovering deep insights into consumer behaviours and supporting better strategic decisions making. These competencies enable companies to strengthen their market position for long-term performance by revealing the true potential of artificial intelligence in CRM. The findings are highly beneficial as it brings forth insights into AI-powered CRM systems provide a clear roadmap for enhancing customer engagement and operational efficiency.

Integration of Artificial Intelligence in Activity-Based Project Costing: Enhancing Accuracy and Efficiency in Project Cost Management

2024-09-04T06:49:30+00:00

Activity-Based Project Costing (ABPC) has long been recognized as an effective method for managing project costs. However, the increasing complexity of modern projects demands more sophisticated approaches. This study explores the integration of Artificial Intelligence (AI) into ABPC to enhance cost estimation accuracy and project management efficiency. By utilizing machine learning algorithms and big data analysis, it has been developed an AI-ABPC model capable of predicting project activity costs with higher precision, identifying hidden patterns in historical data, and providing real-time cost optimization recommendations. A case study of 50 large-scale construction projects showed that the AI-ABPC model improved cost estimation accuracy by 30% and reduced cost analysis time by 40% compared to traditional ABPC methods. These findings pave the way for a revolution in project cost management, enabling faster and more accurate decision-making in dynamic project environments. The implementation of AI in ABPC not only enhances project financial performance but also fosters innovation in overall project management practices.

Recommendations for Changes in Education Practice in Sociology for Students

2024-08-31T12:32:52+00:00

The fundamental subject behind various professions is education beliefs. Both learning and teaching are complex tasks. An effective communication between the student and the teacher is essential to implement these tasks. A proper language, symbolism and technical vocabulary is essential for realizing the basics of instructions in Sociology. There are many difficulties faced by the students in learning Sociology such as; complexities with abstract direction and time concepts, mistakes like recalling, reading and writing numbers, reversals, omissions, transpositions, substitutions and additions. This paper of research has been designed to identify the existing difficulties faced by the students in learning Sociology and recommend some solutions and changes in the education practice, which could be made in Sociology teaching for the students. The research study encapsulates a survey, which comprise of 14 teachers of Sociology and 200 students. Both open-ended and closed ended questions were present in the questionnaire, which was designed for the proposed research. Some of the common difficulties encountered by the students in learning Sociology is identified in the present scenario and also the perceptions of the teachers about the mathematical difficulties faced by students were identified and recommendations were made finally, which were contemplated on the learning strategies and the beliefs of the students.

A Literature Review on Software Defect Prediction: Trends, Methods, and Frameworks

2024-09-05T08:41:57+00:00

Identifying possible problems at an early point in the development lifecycle is one of the most important things that software defect prediction can do to enhance software quality and minimize development costs. This is one of the most crucial roles that software defect prediction can play. Of all the functions that software can perform, this is one of the most crucial ones. This literature review aims to offer a thorough examination of the research trends, methodologies, and frameworks utilized in the field of software defect prediction. This study analyzes a broad range of scholarly publications. These publications cover a wide variety of topics related to defect prediction, including dataset features, prediction models, assessment measures, and prediction approaches. Within the context of minimizing the negative consequences of defects on software quality and project schedules, the review emphasizes the significance of software defect prediction. This investigation identifies significant research themes such as the use of machine learning algorithms, feature selection approaches, and ensemble methods in defect prediction. The paper also scrutinizes the challenges and limitations associated with the diverse defect prediction methodologies currently in use. These include the imbalance of the dataset, the bias in feature selection, and the overfitting of the model. Additionally, it highlights the development of research fields and the opportunities for future study, such as the incorporation of domain knowledge, the incorporation of varied data sources, and the development of advanced approaches to predictive modeling. Furthermore, it acknowledges the existence of these opportunities. In its entirety, this literature review provides researchers and practitioners working in the field of software engineering with critical insights into the present state of the art in software defect prediction.

Deep ConvBi-LSTM: A Robust 3D Room Layout Estimation Model for Indoor Environment

2024-09-02T11:03:33+00:00

Room layout estimation is importance in recent times due to its extended application area. This process is highly challenging due to several factors affecting the room image such as clutter, occlusions, illuminations, etc. It is important to accurately identify the 3D layout of the room from a single 2D room image. The available techniques focused on determining the 3D layout but with limited number of features. It is important for a model to be fed with large number of features to result in successful predictions. To this extent, the proposed model introduced a robust 3D layout estimation framework for indoor environment. Initially, the input image is pre-processed and then subjected to layout estimation where our proposed model predicted both the edge maps and semantic labels for the image. For prediction, the proposed framework utilized the Deep ConvBi-LSTM model and a score function is defined and maximized by remora optimization algorithm (ROA) to obtain the optimal 2D layout from the candidate set. Finally, the 3D output is reconstructed from the 2D layout based on the layout coordinates and camera orientations. The experimental results of the proposed model proved the efficiency of the model in providing the desired performance.

Deep Transfer Learning for Masked Face Reconstruction and Hybrid DCNN-ELM Framework for Recognition

2024-09-05T05:22:52+00:00

Facial reconstruction has always been a pivotal aspect of medical and forensic science. The increasing use of face masks in recent years has posed new challenges, making traditional facial recognition techniques less effective. To address this, our research explored innovative methods for reconstructing faces from images obscured by masks. We focused on post mask face reconstruction and facial recognition using cutting-edge techniques. We assess the effectiveness of three key unmasking algorithms: edgeconnect (EC), gated convolution (GC), and hierarchical variational vector quantized autoencoders (HVQVAE). Using two synthetic face datasets, MaskedFace-CelebA and MaskedFace-CelebAHQ, we rigorously evaluate the quality of the reconstructed faces based on metrics such as the PSNR, SSIM, UIQI, and NCORR. Among these, the Gated Convolution algorithm stands out as the superior choice in terms of image quality. For facial recognition, we employ a novel hybrid framework that combines a deep convolutional neural network and an extreme learning machine (DCNN-ELM). We tested five classifiers (Vgg16, Vgg19, ResNet50, ResNet101, and ResNet152) in combination with ELM and a support vector machine (SVM). Our comprehensive ablation study revealed that ResNet152 combined with ELM achieved the best performance, with a facial recognition accuracy of 60.9%, suggesting that the reconstructed faces were of high quality. Our paper presents a novel approach to image classification utilizing five classifiers within the DCNN+ELM hybrid framework and provides a complete ablation study of these classifiers. This research underscores the importance of face reconstruction in the current field and its potential to enhance facial recognition techniques.

Advancements in Natural Language Processing: Enhancing Machine Understanding of Human Language in Conversational AI Systems

2024-09-07T06:02:24+00:00

This paper aims to review the recent developments in Natural Language Processing and their implications to the improvement of current comprehension in conversational AI interfaces. The comparison of four leading NLP models, namely BERT, GPT, T5 and XLNet is carried out systematically with respect to the major tasks of conversational interfaces including text generation, sentiment analysis and question answering. Based on the performance predicate that I used, it is clear that BERT has a 91% accuracy level. 5%, GPT 88. 2%, T5 89. 6%, and XLNet 90. 3%. Precision scores were precise to the second decimal place for BERT at 92. of reserves as 1%, while keeping GPT at 87%. 9%, T5 at 88. 5%, while last comes the averagely performing XLNet at 89. 7%. On the aspect of recall rates, BERT had a slightly better performance at 90.8%, GPT at 86. 5%, T5 at 87. ; 87% BERT, 88% RoBERTa, 89% XLNet. 2%. The recognized F1-scores were as well highest in BERT where it obtained 91. 4%, and XLNet at 89%. 5%, T5 at 88. Metcash Group Ltd at 17, Coles Group at 28, Woolworths at 10, Metcash Ltd at 6% and GPT at 87. 7%. This paper shows that BERT surpasses GPT-T5 in terms of accuracy and precision; however, GPT and T5 are better suited for text generation applications. These models contain theoretical and practical value in analyzing their advantages and drawbacks, which makes the base for choosing the suitable tools of NLP for definite uses and developed future conversational AI appliances.

Next-Generation Wireless Communication: Exploring the Potential of 5G and Beyond in Enabling Ultra-Reliable Low Latency Communications for IOT and Autonomous Systems

2024-09-07T06:16:56+00:00

The current study aims at exploring the development of wireless communication technologies especially 5G and the future 6G to deliver ULLC for IoT and auto-mobiles. By the use of simulation models and real-life examples the research assesses gains resulting from these next generation networks. The outcome indicates that 5G network means a set of numerous improvements as compared with the previous technologies and it possesses the latency of 1. 2 milliseconds and the throughput of 10 Gbps. In the future, 6G technologies have been expected to increase performance even more as the forecasted latency of 0. 8 milliseconds, packet loss rates getting down to around 0. 01%, and throughput which could go to up to 15 Gbps. The study also presents artificial intelligence, the edge computing system, and other high-advanced beam-forming technologies that assist in enhancing network performance and dependability. Another actual example showed how 5G can be used in the control of traffic, which reached a latency of 1. 1 millisecond with the reliability rate of more than 99 %. 98%. Essentially, these research discoveries indicate how next generation wireless networks may revolutionize key applications as well as progress the way toward more reliable connections.

Tax Transparency Moderates the Effect of Green Supply Chain and Green Accounting on Corporate Reputation and its Impact on Financial Performance Risk

2024-09-07T06:43:55+00:00

A company's reputation is influenced by its adherence to good or bad business ethics. Tax avoidance is a decision that reflects poor business ethics. Management's efforts to enhance tax transparency signal to investors that the company upholds strong ethical standards by being transparent about its taxes, which helps reduce tax avoidance. This study examines the impact of green supply chains and green accounting on corporate reputation and how these factors influence financial performance risk, with tax transparency serving as a moderating factor. The study uses a sample of 658 companies over a two-year period, selected through purposive sampling, and applies moderation regression analysis. The findings support three hypotheses and reject two, indicating that the green supply chain positively affects corporate reputation, and tax transparency strengthens the relationship between the green supply chain and green accounting with corporate reputation. The practical implication is that green supply chains can serve as a strategy to enhance corporate reputation, and tax transparency can reinforce corporate environmental policies, further improving corporate reputation.

Machine Learning Based Framework for Unmasking Bogus Reviews in Online Shopping

2024-09-07T06:59:50+00:00

This research introduces a robust machine learning framework that utilizes the K-Nearest Neighbors (KNN) algorithm to detect fake reviews in Amazon product feedback. The model capitalizes on KNN's ability to assess the proximity of data points, integrating a diverse range of features derived from the textual content, temporal patterns, and contextual elements of reviews. By thoroughly analyzing these features, the model is able to identify subtle discrepancies that distinguish genuine feedback from deceptive ones. Rigorous validation on real-world datasets demonstrates the model's high accuracy in detecting fake reviews, while also maintaining a balance between effectiveness and computational efficiency. The model's design ensures it is adaptable across various product categories and scales well within Amazon's vast ecosystem, addressing the complexities of diverse product offerings. Furthermore, the approach is engineered to be resilient against evolving deceptive tactics and variations across different regions and time periods, showcasing its robustness and long-term applicability. The study highlights the importance of adopting KNN-based methodologies as a critical tool in the ongoing battle to preserve the integrity of online feedback systems. By enhancing the reliability of reviews, this framework empowers consumers with trustworthy information, enabling them to make informed purchasing decisions. The findings of this research advocate for the broader implementation of KNN-driven approaches to fortify consumer trust and ensure the credibility of e-commerce platforms.

Wireless Energy Harvesting (Weh) and Spectrum Sharing In Cognitive Radio Networks

2024-09-07T10:27:44+00:00

It is detailed how one possibility exists for making use of wireless energy in the context of a decode-and-forward relay-assisted secondary user (SU) network that functions according to the guidelines of a cognitive spectrum sharing paradigm. The maximum power that the source and relay in the SU network can transmit from the harvested energy, the peak interference power from the source and relay in the SU network at the primary user (PU) network, and the interference power of the PU network at the relay-assisted SU network are the power constraints that were used to derive an expression for the outage probability of the relay-assisted cognitive network. According to the findings of the research, a relay-assisted network that makes use of the recommended wireless energy harvesting protocol has the potential to operate with an outage probability that is less than 20% for particular applications that take place in the real world. The performance limitation that was placed on the primary system is what is utilised to establish the optimisation challenge that has to be met in order to maximise the area throughput of the secondary system. After analysing the performance of the system with the help of the stochastic geometry theory, we developed a method that evenly distributes the available bandwidth and time resources in such a way as to make it possible for electromagnetic transmission as well as data transfer. Data on performance are provided to highlight how the various system parameters interact with one another and to assist with our theoretical research.

The Influence of Emotional Design and Demographic Characteristics on Homestay Inn Satisfaction

2024-09-09T09:49:31+00:00

Objective The burgeoning homestay inn industry has diversified and personalized accommodation offerings. This study investigates the interplay between demographic variables (such as consumers' gender, age group, educational attainment, occupation type, and income level) and emotional needs (encompassing the utilitarian, visual-aesthetic, and reflective dimensions). The aim is to assist homestay inn in delivering more precisely tailored services and designs. Methods Employing a cross-sectional research approach, this study gathered data from a sample of 916 homestay inns consumers across the nation via online surveys. The data underwent analysis through SPSS, encompassing descriptive statistics, correlation analysis, and regression analysis. Initially, the research focused on delineating the basic characteristics of the respondents and their emotional needs. Subsequently, it probed the existence of correlations between demographic variables and emotional needs. Lastly, the robustness of the regression model was assessed. Results The findings reveal that the primary demographic of homestay inn patrons skews towards younger individuals aged 30-45, with a higher educational background (college degree and above) and higher income brackets (5001-10000 yuan), albeit with diverse occupational profiles. The average score for emotional needs hovered around 8 points, suggesting a generally positive reception towards emotional design among respondents. A statistically significant correlation was observed between demographic variables and emotional design. Nevertheless, the regression model's explanatory power proved limited, indicating the need for more in-depth exploration to unravel the complexities of these relationships.

Android Malware Detection and Classification Using Machine Learning Algorithm

2024-09-10T09:39:37+00:00

The cyber security approach that is being offered in this project addresses the growing dangers that rogue applications that target mobile devices are posting. With several forms of malware, such adware, spyware, and ransomware, multiplying quickly, these threats to Android users throughout the world are getting worse. This paper aims to create a thorough classification framework that uses both static and dynamic information to detect Android malware effectively in response. Our method seeks to provide a comprehensive understanding of malware traits and actions by fusing dynamic runtime behavior monitoring with static analysis of APK files. To develop a powerful classification model that can reliably classify various kinds of Android malware by utilizing machine learning algorithms such as Gradient Boosted Trees (GBT) and Ridge Classifier. APK files' metadata, permissions, and code structure are extracted using static analysis, but runtime behaviors including API calls, network traffic, and system interactions are captured using dynamic analysis. Our suggested methodology shows promising results in terms of categorization accuracy, precision, recall, and F1-score after comprehensive testing and evaluation on real-world Android malware datasets. A thorough understanding of malware behavior is made possible by the combination of static and dynamic features, which makes proactive threat detection and mitigation techniques in mobile security easier to implement. Persistently exploiting gullible people with false links, URL phishing is a cyber threat that can result in financial loss, theft of identities, and data breaches. The objective of this work is to create and deploy a strong defense against URL phishing assaults and mobile security procedures against new and emerging Android malware vulnerabilities.

Detection and Analysis of Disease from Brain MRI Image Using Machine Learning

2024-09-13T07:36:56+00:00

Now a day’s tumor is second leading cause of cancer. Due to cancer large no of patients are in danger. The medical field needs fast, automated, efficient and reliable technique to detect tumor like brain tumor. Detection plays very important role in treatment. If proper detection of tumor is possible then doctors keep a patient out of danger. Various image processing techniques are used in this application. Using this application doctors provide proper treatment and save a number of tumor patients. A tumor is nothing but excess cells growing in an uncontrolled manner. Brain tumor cells grow in a way that they eventually take up all the nutrients meant for the healthy cells and tissues, which results in brain failure. Currently, doctors locate the position and the area of brain tumor by looking at the MR Images of the brain of the patient manually. This results in inaccurate detection of the tumor and is considered very time consuming. A tumor is a mass of tissue it grows out of control. We can use a Deep Learning architectures CNN (Convolution Neural Network) generally known as NN (Neural Network) and VGG 16(visual geometry group) Transfer learning for detect the brain tumor. In this paper, the design and implementation of a tumor detection system using two CNN models is considered. Digital image processing and Deep Learning technologies enable us to develop an automatic system for the diagnosis/detection of various kind of diseases and abnormalities. The tumor detection system may include image enhancement, segmentation, data augmentation, features extraction and classification; all these steps are discussed in details in the above sections. To work on CNNs, powerful GPU based system are required to speed up the process, lot of processing is carried out and also lot of RAM is required to process the images for testing. CNNs have also some options such as optimization technique selection, Number of Epoch, Batch size, iteration and learning rate. These options are tuned to get the optimal results from the CNN model. Learning rate is used to update the weights and bias in training phase, learning rate changes the weights. One Epoch is when the model see all images in training, as the training data maybe of very big sizes, the data in each Epoch is divided into batch sizes. Every epoch has a training and test session, after each Epoch the weights are updated according to the learning rate, optimization algorithms are used to update the learning of a CNN adaptively. When the best weights for training are computed, the model is said to be trained. All the experimental work is carried out in MATLAB simulation tool.

Hybrid Convolutional Neural Network Model to ascertain the Objects in Dynamic Cluttered Environment

2024-09-14T15:50:31+00:00

The field of computer vision has made significant strides in object detection in recent years, primarily because of the introduction of deep learning techniques, specifically Convolutional Neural Networks (CNNs). We have introduced a novel method for the multi-object detection in multi-scene cluttered environment in the proposed work. In order to build multi-scale andmulti-scene object detection, in our work we have provides a multi-scale neural network basedonthehigherresponse of FastR-CNNarchitecture. For the experimental work,we have considered different categories of different objects. The dataset is designed to facilitate the development of object detection techniques. It comprises 12,165 object chips, each consisting of 256 pixels in both azimuth and range dimensions. This dataset encompasses diverse primary backgrounds and object sizes. Furthermore, well-known cutting-edge object detectors that have been trained on real-world images are modified to serve as baselines, guaranteeing the availability of reliable and practical reference points. Experimental results indicate that these object detectors not only enhance various quantitative metrics but also achieve unprecedented levels of accuracy, surpassing the capabilities observed in prior studies.

Hybrid Convolutional Neural Network Model to ascertain the Objects in Dynamic Cluttered Environment

2024-09-14T17:03:01+00:00

AI, IoT, and Blockchain in Fashion: Confronting Industry Applications, Challenges with Technological Solutions

2024-09-14T17:10:50+00:00

The fashion industry is undergoing a transformation driven by the convergence of Artificial Intelligence (AI), the Internet of Things (IoT), and Blockchain technology. These cutting-edge technologies offer innovative solutions to a range of challenges that have long impacted the sector, from design inefficiencies to supply chain complexities and lack of transparency. AI enhances design processes, enables better demand forecasting, and delivers personalized customer experiences through advanced data analytics and machine learning algorithms. IoT facilitates smart textiles, connected garments, and real-time inventory management, allowing for improved operational efficiency and new, interactive customer engagement models. Blockchain technology provides robust solutions for transparency, traceability, and security by creating decentralized, immutable records that verify product authenticity and ethical sourcing throughout the supply chain. The integration of these technologies is not without challenges. Issues such as data privacy, cybersecurity threats, scalability, and the lack of industry-wide standardization present significant barriers to widespread adoption. Data collected through IoT devices and AI systems must be securely managed to protect consumer privacy, while Blockchain networks need to overcome scalability concerns to handle the massive amount of data generated in global supply chains effectively. The absence of common standards and protocols hinders seamless interoperability between various technological platforms. This paper explores the current applications of AI, IoT, and Blockchain in the fashion industry, highlighting their potential to enhance efficiency, sustainability, and consumer trust. It also identifies the critical challenges these technologies face and proposes practical solutions to overcome them, such as implementing advanced encryption methods, developing new consensus mechanisms for Blockchain scalability, and fostering industry collaboration to establish standardized frameworks. Ultimately, the successful integration of these technologies could lead to a more transparent, efficient, and customer-centric fashion industry, setting new standards for innovation and sustainability.

Navigating Security Threats and Solutions using AI in Wireless Sensor Networks

2024-09-14T17:13:47+00:00

Wireless Sensor Networks (WSNs) are increasingly pivotal in applications such as environmental monitoring, smart cities, and healthcare, yet their widespread use introduces significant security challenges. These challenges arise due to the inherent vulnerabilities of WSNs, including their wireless communication medium and limited resources. Key security threats facing WSNs include eavesdropping, where unauthorized entities intercept sensitive data; node compromise, where malicious actors take control of sensor nodes to disrupt network operations; and denial of service (DoS) attacks, which overwhelm the network with excessive traffic or tasks. Additionally, Sybil attacks, wormhole attacks, and sinkhole attacks further compromise network integrity and data accuracy. Artificial Intelligence (AI) offers transformative solutions to these security threats by enhancing threat detection, response, and overall network resilience. AI-driven anomaly detection leverages machine learning to identify deviations from normal network behavior, thus recognizing potential threats. Intrusion Detection Systems (IDSs) powered by AI analyze network traffic and node activities to detect and respond to unauthorized access or malicious behavior in real-time. AI also optimizes secure routing protocols through reinforcement learning and dynamic adjustments, ensuring that data paths avoid compromised nodes. AI contributes to data encryption and authentication by selecting efficient cryptographic algorithms and improving authentication mechanisms. The integration of AI into WSN security also addresses energy constraints by designing energy-efficient solutions for encryption, monitoring, and response. AI techniques enable self-healing capabilities, allowing WSNs to predict and address potential failures autonomously. Despite these advancements, challenges such as scalability, adaptability, resource constraints, and privacy concerns must be addressed. This paper explores these AI-driven solutions and identifies future research directions to enhance the security and resilience of Wireless Sensor Networks.

XAI - Credit Risk Analysis

2024-09-15T13:24:52+00:00

This paper delves into the integration of Explainable AI (XAI) techniques with machine learning
models for credit risk classification, addressing the critical issue of model transparency in financial
services. We experimented with various models, including Logistic Regression, Random Forest,
XGBoost, LightGBM, and Artificial Neural Networks (ANN), on real-world credit datasets to predict
borrower risk levels. Our results show that while ANN achieved the highest accuracy at 95.3%,
Random Forest followed closely with 95.23%. Logistic Regression also performed strongly with an
accuracy of 94.68%, while XGBoost and LightGBM delivered slightly lower accuracies of 94.4% and
94.37%, respectively. However, the superior accuracy of these complex models, particularly ANN,
comes with a trade-off: reduced transparency, making it difficult for stakeholders to understand the
decision-making process. To address this, we applied XAI techniques such as SHAP (SHapley
Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to provide
clear and understandable explanations for the predictions made by these models. This integration
not only enhanced model interpretability but also built trust among stakeholders and ensured
compliance with regulatory standards. This study illustrates how XAI serves as an effective mediator
between the precision of sophisticated machine learning algorithms and the demand for clarity in
evaluating credit risk. XAI offers a well-balanced method for managing risk in finance, harmonizing
the need for both accuracy and interpretability.

Localized Modeling for Airline Price Prediction Using K-Means and Decision Tree Ensemble

2024-09-15T14:12:52+00:00

Both travelers and airline companies rely on accurate prediction of flight prices, nevertheless it is difficult to train machine learning models using large-scale, current flight datasets due to computational inefficiency and risk of overfitting. This paper introduces a novel two-pronged approach that combines K-means clustering with decision trees for effective localized flight price forecasting. First, K-means clustering is employed to segment the flight data on the basis of shared characteristics into meaningful clusters thereby reducing data dimensionality and speeding up model training. Then individual Decision Tree models are built for each cluster separately. Finally, this technique emphasizes on closely related data attributes which may enhance predictive accuracy for given routes or types of flights. The proposed method solves the problem of calculation load when working with large datasets without sacrificing any details necessary for catching peculiarities in pricing in various categories of flights.

Enhancing Stock Price Prediction: Improvising in KNN

2024-09-15T14:15:54+00:00

Stock price prediction is very crucial for informed investment decisions, involving forecasting future
stock values which are based on various factors. K-nearest neighbors (KNN) is a machine learning
algorithm that can assist in predicting stock prices by identifying patterns and similarities between
the target stock and its neighboring data points in a multidimensional feature space. However,
traditional KNN algorithms encounter challenges like sensitivity to irrelevant features and outliers,
potentially compromising predictive accuracy. To address this, integrating Density-Based Spatial
Clustering of Applications with Noise (DBSCAN) before KNN proves effective. DBSCAN identifies
and filters out noisy data points and outliers, refining the dataset for subsequent KNN analysis. This
integration not only mitigates traditional KNN issues but also uncovers underlying data structures,
improving overall predictive power in stock market analysis.

Intuitionistic Fuzzy Semihypergraphs

2024-09-16T08:17:25+00:00

A Semihypergraph is a connected hypergraph in which each hyperedge must have atleast three vertices and each pair of hyperedges has atleast one vertex in common. In this article, the intuitionistic fuzzy semihypergraphs, semi µ- strong, semi ?- strong, strong IFSHGs and effective IFSHGs are introduced and some kinds of IFSHG such as simple, support simple, elementary, sectionally elementary IFSHGs are discussed.

Unveiling the Impact of Pro-Environmental Behavior on Corporate Environmental Performance

2024-09-16T08:28:47+00:00

This study investigates the relationship between pro-environmental behavior and corporate environmental performance, focusing on data collected from 387 employees across ten highly polluting manufacturing industries in Jammu and Kashmir. The analysis was conducted using SPSS 23.0 and AMOS 20.0, with hypothesis tested through structural equation modeling (SEM). The results strongly support the hypothesis, revealing a significant and positive impact of pro-environmental behavior on corporate environmental performance. These findings underscore the critical role of employee-driven environmental actions in enhancing organizational environmental outcomes. The study highlights the need for organizations to foster and promote pro-environmental behavior within their workforce as a strategy to improve environmental performance and address stakeholder pressures effectively. Additionally, the study provides directions for future research, suggesting avenues for further exploration to validate and extend these findings in different contexts.

Effect of nursing knowledge on heat exposure risk in Mecca health centers in 2024

2024-09-16T16:58:02+00:00

Although classic heat exposure and heat stroke are among the oldest known human diseases, their early clinical manifestations, natural history, and complications remain poorly described. Heat exposure and heat stroke are life- threatening conditions characterized by a rapid increase in core body temperature to above 40°C and neurological changes such as delirium, seizures, or coma following exposure to extreme heat alone or in combination with strenuous physical exertion. Heat-related illnesses (HRIs), such as heat stroke (HS) and heat exhaustion (HE), are common complications of the Hajj. The Saudi Ministry of Health (MOH) has developed guidelines for the management of HRIs to ensure the safety of all pilgrims. Medical staff must follow the latest national guidelines for the management of HRIs before and during hospitalization. Effect of nursing knowledge on heat exposure risk in Mecca health centers in 2024. A descriptive cross-sectional study was conducted among nurses to investigate the risk of heat exposure and prevalence of heat-related illnesses among pilgrims who visited primary health care centers inMakkah from May 1, 2024 to May 30, 2024. The total sample size of participating nurses was (200). Relationship between nurses' knowledge of heat exposure hazards and prevalence of heat-related illnesses among pilgrims The relationship between the knowledge level of most participants was general knowledge (56.0%) followed by high knowledge (26.0%) but weak knowledge (18.0%) and total knowledge(100.0%), with significant relationships at P value <0.001 and X2 48.16. Conclusion: Heat exposure and heat illness are not common problems for Saudi Arabians. However, they are significant for pilgrims from other parts of the world during the Hajj season, which varies according to the lunar year. In recent years, the Hajj timing coincides with the summer months of July and August. The average temperature during the Hajj reaches 54 °C (130 °F).

The Revolution of Quantum Computing: Analyzing Its Effects on Cryptographic Security and Algorithmic Efficiency

2024-09-16T17:05:00+00:00

This study examines how quantum computing influences cryptographic security and algorithmic performance. Quantum computers, using qubits, superposition, and entanglement, present dangerous levels of risks to traditional cryptography and show promising potential for calculated speed. For predicting secure and efficient quantum computing, the current study adopts some machine learning models such as Decision Trees, Random Forests, and K-nearest neighbours. The Decision Tree model is the most accurate model with 100% precision, and there is a need to incorporate studies and research on quantum-safe algorithms and post-quantum cryptography for potential risks brought by quantum in the future.

Effect of Awareness Program on Stress and Anxiety among Parkinson’s Disease Patients

2024-09-16T17:18:38+00:00

Background: Parkinson's disease is a common degenerative neurological illness that reduces life expectancy and causes loss of independence. Aim: To evaluate the effect of awareness program on stress and anxiety among Parkinson's disease patients. Study design: A quasi-experimental research design was usedto fulfill this study using a pre-test and post-test one-group design. Setting: The study was conducted in the neurology outpatient setting at Sohag University Hospital. Subjects: the study included a convenient sampling technique of 100 patients with Parkinson's disease. Tools of data collection:Three tools were used for data collection; Tool (I): Structured interview questionnaire: This tool was made up of the following three parts: Part 1:Personal data of the studied patients: It contained information on the age, gender, education level, and place of residence of the patients, Part 2: Structured multiple-choice questionnaire (pre and post) to assess the patients' knowledge regarding Parkinson's disease, and Part (3): Patients' practice questionnaire (pre and post), Tool II: Perceived Stress Scale-10 (PSS-10), and Tool III:The State-Trait Anxiety Inventory. Results:There was a statistically significant difference in the total score of the knowledge and practices after the awareness program application among Parkinson's disease patients. Astatisticallysignificant difference and reduction were found between stress mean scores and anxiety at (P=0.001) pre and post-awareness program application. Conclusion: Theawareness program application has a significant improvement in knowledge and practice with a reduction in mean post-test stress and anxiety among Parkinson's disease patients. Recommendations: It is stronglyadvised to apply continuous training for Parkinson's disease patients about the importance of the awareness program application regarding stress and anxiety management strategies to be able to use them as a part of routine care.

Optimized Feature Selection and classification for Non-Portable Executable Malware

2024-09-17T06:01:03+00:00

Malware is a program that executes harmful acts and steals information. nowadays it is widely recognized as oneof the largest hazards. In this research work machine learning is used to identify and detect Non-PE file features. The variousdistinct aspects of the Non-PE files features can correlate with one another, being clean or affected, led to the identification ofsuch features. by using machine learning algorithms such as Ada Boost Classifier,Gaussian NB, KNClassifier,RF Classifier, SGD classifier, and feature selection produced the best detection rate also Prediction accuracy of thealgorithms is used to compare the efficacy and efficiency.

Global Research Trends and Citation Impact in Ceramic Decorative Pattern Art: A Comprehensive Bibliometric Analysis (1978–2024)

2024-09-17T11:02:50+00:00

Ceramic decorative patterns hold cultural and aesthetic value, blending craftsmanship with symbolism. Recent research integrates traditional art history with technologies like computer-aided design and pattern recognition, emphasizing the need to understand global research trends and citation impact. A bibliometric analysis of 395 publications (1978-2024) from the Web of Science explored citation impact, publication trends, and keyword co-occurrence, mapping themes such as cross-cultural exchange, craftsmanship, and technological innovation. Research output surged after 2008, peaking in 2014-2020 with 9.28 citations per article. Studies combining traditional and modern design, especially in cross-cultural contexts, had higher citations. Recently, emerging technologies like deep learning have gained attention. While traditional elements remain central, modern technologies are driving newresearch directions. Future studies should leverage these innovations to reinterpret ceramic art, with interdisciplinary approaches being crucial.

ANALYSIS OF THE INFORMATIVE TERM THERMAL TOURISM IN SCOPUS

2024-09-17T11:57:27+00:00

Objective. The objective was to evaluate the status of the term thermal tourism in the international scientific literature for consultation and research use by universities, institutions and decision makers. Methodology. A bibliometric method was applied with the support of the Biblioshiny tool, analyzing 152 documents extracted from Scopus from 1975 to July 2024. The indicators evaluated were: annual scientific production, most productive publication sources, most prolific authors, most productive institutions, most cited articles, most frequent keywords, co-citation network, and collaboration network. Results. The year with the highest production was 2017 (19 articles), with the main source of contribution being the Springer Proceedings in Business and Economics conference (6 articles). Additionally, the University of Vigo (Spain) leads in contributions (17 articles) and is also the core of the most robust collaboration network along with Portugal. Discussion. Despite the growth of thermal tourism following the COVID-19 pandemic, there is low interest from the community in researching this topic. Conclusions. There is limited participation from South American institutions, despite the region's numerous thermal resources, which can be partly attributed to a lack of awareness and inadequate preservation and management actions by authorities and the local population. Originality. This study contextualizes the current scientific landscape of thermal tourism, offering an opportunity for researchers to contribute to the sustainable development of this sector in the region.

ADAPTIVE PERSONALIZATION OF SOCIAL MEDIA FEED USING POSTCATEGORIZATION

2024-09-17T17:18:49+00:00

In today’s world, social media constitutes a significant part of everyone’s lives. It occupies so much of our time that it even kills our productivity. Social media applications consist of enormous number of posts that can be both informative and entertaining and belong to a wide range of categories. They can be made more user-friendly by the personalization and customization of feeds of users who consume it. Suggestions shown often consist of uninteresting posts, which can make the user experience bad and result in longer scrolling durations. The solution proposed here provides a customized and personalized feed for users based on user interests. Most of the social media applications are proprietary and the algorithms for adaptive personalization are not available publicly. The main objective of this work is to develop a model for deciding how to include or discard a new post from the users' feeds based on user interests collected earlier. The system uses concepts of clustering, informationretrieval and user profiling.

Analyzing the Behavior of Software Reliability Execution Time Models for Different Agile–Scrum Based Projects

2024-09-17T17:20:24+00:00

Software reliability is an essential component of software development stages. The reliability of a software system plays a vital role in the overall development and success of that software. Reliability is broad and linked to other areas of technologies and approaches. This requires a proactive mechanism, which includes not only technical aspects but also legal and ethical considerations. Maintaining reliability in Agile-based software is an arduous exercise. This happens because in an Agile-based project, frequent changes in requirements are expected during the development cycle. To develop high levels of reliability in an Agile environment, the Quality Assurance (QA) engineer needs to carefully select the appropriate reliability model. In this research paper, we studied the performance of two most popular reliability models namely Basic Execution Time Model and Logarithmic Poisson Execution Model. Since the basic idea behind developing software is quite different in an Agile environment compared to traditional software development processes; a dataset of 30 Agile-based projects has been prepared for the purposes of calculation. These 30 projects in this dataset are divided into three groups as low, medium and high-level projects based on the number of sprints required to complete the work. This paper presents a comparative analysis of these two reliability models on various parameters. As a result, we found that the Logarithmic Poisson Execution Model produces optimal results for most Agile-based projects in all 3 project categories.

AN MPPT-ENHANCED BATTERY CHARGING REGULATOR FOR HYBRID SOLAR-WIND ENERGY SYSTEMS INCORPORATING SUPPLEMENTARY ALGORITHMS

2024-09-18T04:43:48+00:00

A novel battery charging regulator for hybrid solar-wind energy systems, utilizing Maximum Power Point Tracking (MPPT) alongside additional algorithms to enhance energy efficiency. The proposed framework incorporates MPPT to adaptively modify the power output from photovoltaic (PV) panels and wind turbines, thereby guaranteeing optimal energy extraction in response to fluctuating environmental conditions. Complementing MPPT, additional algorithms improve the system's functionality by mitigating energy variations, preventing battery overcharging, and maintaining system stability. The integration of these methodologies results in enhanced energy management and storage efficiency, thereby bolstering the reliability and sustainability of hybrid renewable energy systems. Both simulation and experimental findings substantiate the efficacy of the proposed framework in optimizing energy harvesting and prolonging battery life.

The Influence of Organizational Culture on Performance, with Innovative Behavior, Job Satisfaction, and Work Motivation as Intervening Variables in Bireuen District Government

2024-09-18T12:05:38+00:00

Various factors cause an institution to have decreased performance. This research examines organizational culture's influence on performance, with innovative behavior, job satisfaction, and work motivation as intervening variables. The Regency government conducted this research with echelon II, III, and IV officials. The sampling method used a saturated sample, so 238 respondents were obtained. Two hundred two respondents returned the questionnaire. The data analysis technique uses SmartPLS software, structural equation modeling, or SEM. The study's findings clarify that organizational culture positively influences innovative behavior, job satisfaction, work motivation, and performance. It also positively affects innovative behavior's impact on performance, job satisfaction's impact on performance, and motivation work's impact on performance. In addition, organizational culture positively influences performance through innovative behavior, job satisfaction positively influences performance, and organizational culture positively impacts performance through work motivation.

Analysis of the Influence of Servant Leadership, Job Crafting and Quality of Work Life on Organizational Citizenship Behavior and High School Teacher Performance in Pematangsiantar City

2024-09-18T12:07:12+00:00

This study aimed to determine how servant leadership, job crafting, and quality of work life impact organizational citizenship behavior and teacher performance based on job satisfaction. This study involved 278 high school teachers in Pematangsiantar City. This study aimed to determine how servant leadership, job crafting, and quality of work life affect organizational citizenship behavior and teacher performance through job satisfaction. This study used a quantitative approach. This study used a sample of 278 high school teachers in Pematangsiantar City. This study obtained data from the results of filling out the questionnaire and then analyzed using the Structural Equation Modeling analysis technique with the help of the AMOS version 22 program. The results showed that satisfaction could positively and significantly mediate the influence of servant leadership, job crafting, and quality of work life on organizational citizenship behavior and teacher performance. Servant leadership partially did not affect OCB and teacher performance, and job crafting partially did not affect OCB and teacher performance. The results of this study indicate that satisfaction was able to positively and significantly mediate the influence of servant leadership, job crafting, and quality of work life on organizational citizenship behavior and teacher performance. Servant leadership partially does not considerably influence OCB and teacher performance. Job crafting hurts teacher OCB and is not significant on teacher performance.

DEVELOPMENT OF TANKER MANAGEMENT SELF ASSESSMENT SYSTEM IN TANKER SHIP OPERATIONS

2024-09-18T12:08:54+00:00

One example of a ship safety management system failure is a casualty that occurs in the ship system. This study is critical because it will study how the Tanker Management Self Assessment (TMSA) system works to ensure the efficiency and safety of tankers. The study shows that the tanker's safety culture, communication, safety performance, compliance, and Port State Control (PSC) Inspection Pass have improved due to the implementation of the TMSA system, which has reduced the accident rate. In addition, the system has increased safety awareness and culture among the ship's crew, increasing crew involvement in safety management. Limited resources, complexity, and limited training are some obstacles and limitations when developing the TMSA system. It is concluded that improving the development of the TMSA system for tanker operations is essential for crew safety.

Development Of A Hospital Selection Model With Service Quality As An Intervening Variable In Medan City

2024-09-18T12:10:46+00:00

Hospitals, as one of the institutions that facilitate the scope of health, are critical. However, at this time, many hospitals that were initially service industries have changed into business industries. So there is competition in providing services for business purposes only. This study will test the hospital selection model with service quality as an intervening variable in Medan. Data collection uses a quantitative method with partial least squares (PLS). This study used a sample of 384 people. The study was conducted in several private hospitals in Medan. The study results showed that Market orientation, customer emotional response, and trust were proven to significantly influence the implementation of marketing strategies and service quality at Medan City Hospital.

The Impact of Enterprise Risk Management and Competitive Advantage on Firm Performance: Future Research

2024-09-18T12:12:23+00:00

Enterprise Risk Management (ERM) has emerged as a key point in academic and professional spheres. While it is gradually being recognized to have potential benefits, there still exists a significant gap related to what exactly the relationship ERM and firm performance intensively means. This ongoing research endeavors to fill this void by exploring the influence of ERM and aligning its dimensions with concepts of competitive advantage. This paper aims to present a comprehensive overview of ERM, utilizing the Resource Based View (RBV) and Contingency theoriesas a lens to examine its effects on the performance of non-financial firms, where competitive advantage serves as a mediating factor. Building upon existing research, this study contributes novel insights, particularly in the intersection of strategy and ERM. The findings of this research not only dissect the pivotal components of ERM and their ramifications on firm performance but also provide practical insights. These insights can function as a roadmap, bridging the expectations of managers concerning ERM with its actual implementation within the context of competitive advantage strategies. This study thus seeks to enhance the understanding of ERM's strategic implications and offers actionable recommendations for effective integration into organisational frameworks.

Machine Inspired IOT based Framework for Real-Time Heart Disease Prediction

2024-09-18T15:29:39+00:00

The rapid advancements in Internet of Things (IoT) technologies have enabled the development of innovative healthcare solutions, particularly in the field of real-time disease prediction and management. This paper presents a machine-inspired IoT-based framework designed for the real-time prediction of heart disease. The proposed framework integrates IoT-enabled wearable devices that continuously monitor vital signs such as heart rate, blood pressure, and oxygen saturation. These devices transmit data to a central processing unit where machine learning algorithms analyze the information to detect early signs of heart disease. By leveraging real-time data and advanced predictive models, the framework aims to provide timely alerts to healthcare providers and patients, thereby facilitating early intervention and reducing the risk of severe cardiac events. The framework's architecture is built on a robust and scalable IoT infrastructure that ensures seamless data collection, transmission, and analysis. Machine learning techniques, including supervised learning models and ensemble methods, are employed to enhance the accuracy of heart disease predictions. The system also incorporates edge computing to reduce latency and improve processing efficiency, enabling real-time analysis even in resource-constrained environments. Experimental results demonstrate the framework's potential in achieving high predictive accuracy while maintaining low power consumption, making it a viable solution for continuous heart health monitoring. This work contributes to the growing field of smart healthcare by offering a practical and efficient approach to real-time heart disease prediction, ultimately aiming to improve patient outcomes through proactive healthcare management.

Impact of Coiflet Wavelet Decomposition on Forecasting Accuracy: Shifts in ARIMA and Exponential Smoothing Performance

2024-09-24T10:21:35+00:00

Accurate forecasting of electricity demand is crucial for effective
energy management and resource planning. Recently, time series
forecasting methods such as Exponential Smoothing (ES) and
ARIMA models have gained popularity due to their ability to detect
intricate seasonal patterns in data. This study examines how
various wavelet families, particularly the Coiflet wavelet, impact the
performance of ES and ARIMA models in forecasting electricity
demand. We observed that applying the Coiflet wavelet could
significantly enhance forecasting outcomes. The study evaluates the
effectiveness of different wavelets in improving the forecasting
accuracy of ES and ARIMA models, with a particular focus on the
superior performance of Coiflet wavelets. Our findings offer
insights into the suitability of wavelet-based methods for electricity
demand forecasting. Nonetheless, the choice between ARIMA and
Exponential Smoothing should be guided by the specific
characteristics of the time series data and forecasting goals. For
complex and noisy data, ARIMA combined with Coiflet wavelet
preprocessing proves to be a robust and effective forecasting
approach, demonstrating superior performance in our analysis.

The Role of Artificial Intelligence in Cybersecurity: Enhancing

2024-09-24T10:32:13+00:00

This study explores the use of AI with increased application of the
machine learning approach in improving cybersecurity.
Addressing the significance of the four most widespread
algorithms, CNN, RNN, RF and SVM, this work investigates the
effectiveness of these algorithms in the context of cyber threats
identification and counteraction. To assess the performance of the
models, the system was validated with a large quantity of datasets
with emphasis on the detection capability, false alarm rate as
well as response time. The findings also show that the proposed
CNN model attained the maximum detection accuracy of 96. 5 %,
while developing new features the RNN was at 94%. 2%, RF at 91.
5% while Naive Bayes is at 87. 7%, Random forest is at 87. 2% and
SVM at 88. 9%. The false-positive rates were reported to be at
lowest for CNN at 1. 8% more than Urban, thus testifying to its
increased reliability. Moreover, it took a considerably less amount
of time to give the response for CNN which was 0. 5 seconds,
compared to 0scaled up for comparison with 5 seconds of reading
a text online. 89 seconds for RNN, 1. That is 6 seconds in total
while the time taken for RF is 2 seconds, and 1 second for TF. 5
seconds for SVM. These studies reaffirm the possibilities of the
artificial intelligence and machine learning in improving
cybersecurity through optimized and more precise threat
identification and mitigation means. Subsequent work will be
devoted to continuing the model enhancement works as well as integration with live data
processing systems for the
increased effectiveness of
cybersecurity prediction and countermeasures.

Enhancing English Language Proficiency through Mobile Language Learning Apps: A Comprehensive Overview

2024-09-24T10:35:18+00:00

Background and Aim: The increasing importance of English
language proficiency has become a significant challenge in
today’s globalized world, where effective communication is
essential across various domains. This article addresses the
pressing need for innovative solutions to enhance English
language learning, particularly through mobile applications. The
aim of this study is to explore the pivotal role that mobile
language learning apps, such as Cake, Babbel, and Elsa, play in
meeting this need. The author aims to highlight the features,
target audiences, and language offerings of these applications,
emphasizing the critical role of student motivation in the
language learning process. Search Method: In conducting this
review, a comprehensive search was performed using recognized
databases and search engines. Keywords related to mobile
language learning applications, English proficiency, and user
engagement were utilized. The search was limited to articles
published between 2011 and 2024, adhering to internationally
recognized guidelines in language education. This approach
ensured a thorough examination of relevant literature and the
inclusion of diverse perspectives. Results: The findings from the
review reveal significant insights into the effectiveness of mobile
language learning apps. These applications demonstrate a
positive impact on language acquisition, particularly through
features such as gamification, personalized learning paths, and
interactive exercises. Empirical evidence from case studies
underscores the effectiveness of these tools in improving English
skills among learners. Conclusion: In conclusion, the challenges
surrounding English language proficiency necessitate innovative
approaches to language education. The results of this study
indicate that mobile language learning applications can
effectively enhance student engagement and motivation. By
fostering collaboration among various stakeholders in the
language learning community, this article aims to encourage
readers to consider the implications of these findings. The
purpose of this study is to compare the advantages and
disadvantages of the three software applications based on
previous literature, providing a nuanced understanding of their
effectiveness. The methodology employed involved a thorough review of articles published
from 2011 to 2024, ensuring
a comprehensive analysis of
the topic.

Analysis of Medical image fusion using Yager's heuristic fuzzy analysis for multiple modes

2024-09-24T10:37:16+00:00

Multi-scale image fusion is one of the most important fusion techniques in which multi-scale fusion
and subtraction tools play very important roles. Quaternion wavelet transform (QWT) is one of the
most widely used optimization techniques. Therefore, this paper introduces a new multi-modal
image fusion method using QWT and various features. First, we apply QWT to each image to obtain
low coefficients and high coefficients. Secondly, the weighted average fusion rule based on the phase
and amplitude of the low-frequency sub-bands and the spatial variance is proposed to fuse the lowfrequency
sub-bands. Then, the highest fusion rule is selected according to the ratio and the power
coefficient is aimed to be combined with high sub-bands. Finally, the final merged image is created
by inverse QWT. This method consists of multifocal images, medical images, high-resolution images
and remote sensing images. The results of the experiment show the effectiveness of this method.

“Enhancing Mental Health Assessments: The Role of Voting Classifiers in Evaluating Depression's Impact on Quality of Life”

2024-09-24T10:39:10+00:00

Depression continues to pose a significant global challenge,
ranking as one of the most prevalent and costly mental disorders
that substantially impairs quality of life, supported by a
substantial body of research. Enhancing our comprehension of
the factors influencing quality of life is paramount for optimizing
long-term outcomes and reducing disability in individuals
grappling with depression. This study primarily focuses on the
identification of depression based on lifestyle and livelihood
factors. It's noteworthy that depression can afflict individuals
across all age groups, genders, and backgrounds, often arising
from a complex interplay of genetic, biological, environmental,
and psychological elements. Furthermore, major life events,
chronic stress, trauma, or a family history of depression can
contribute to its emergence.
In the realm of healthcare, machine learning techniques are
increasingly employed to process and analyze diverse data types,
with the aim of better understanding the relationship between
quality of life factors and depression. Various classification
algorithms, such as Random Forest, Decision Tree, Naive Bayes,
Support Vector Machine, and PPMCSVM, have been utilized for
this analysis. However, existing approaches have encountered
challenges related to their accuracy in predicting depression.
Consequently, the primary objective of this proposed research is
to enhance depression prediction by leveraging an ensemble
technique that identifies the determinants of quality of life
among individuals affected by depression. To attain this goal, the
study employs KNN (K-Nearest Neighbour) and Voting Classifier
algorithms. The Voting Classifier aids in uncovering the root
causes of depression in each individual. The results of this
investigation reveal that the proposed model can effectively
predict the causes of depression, thus opening avenues for more
targeted intervention and treatment strategies.

IMPACT ON ENHANCING CLOUD DATA STORAGE SECURITY THROUGH BLOCKCHAIN INTEGRITY DEVELOMENT

2024-09-24T10:41:23+00:00

Cloud computing is becoming more and more popular, but
worries about data security and privacy stem from the
regularity of hostile assaults on wireless and mobile networks.
One of the goals of the created IAS protocol is to address these
issues. Access control, secure authentication, and identification
will all be integrated into this protocol. The proposed IAS
protocol, which was created to guarantee the secrecy and
integrity of data transmissions in cloud computing
environments, is based on blockchain technology. The
implementation of decentralized identity verification and key
recovery/revocation management is made possible as a result
of this. The effectiveness of this strategy can be evaluated by the
utilization of a cloud-based simulation of the proposed idea that
makes use of Identity management, access control, and secure
sharing based on block chains (BC-IAS). The simulation is used
to evaluate key performance characteristics such as the pace at
which data is accessed, the ratio of messages delivered, the
latency from beginning to end, and the amount of energy
consumed. When it comes to enhancing the privacy and security
of On top of blockchain technology, cloud computing and the
BC-IAS protocol that is being proposed appear to be promising
developments. To further improve cloud computing's security
and integrity, the BC-IAS protocol, which is constructed using
blockchain technology, is an appealing alternative. By virtue of
Because of the decentralized nature of blockchain technology,
data is stored in an accessible and immutable manner.
Furthermore, smart contracts allow for the automatic
enforcement of access control restrictions using blockchain
technology. By adopting identity verification and secure
authentication, which restrict access to sensitive information to
only authorized personnel, it is possible to reduce the likelihood
of data breaches and cyberattacks occurring.

Web Based Graphical Password Authentication System

2024-09-24T10:42:59+00:00

In order to provide security and assurance, programs commonly
make use of verification that is dependent on passwords. This is
done for the aim of providing security. However, human behaviors,
such as selecting terrible passwords and supplying passwords in
square measurements, are considered to be "the most delicate
association" in the verification chain. This is because human beings
are the ones who are responsible for these activities. As an
alternative to alphanumeric strings that are optional, it is feasible
that consumers will select passwords that are either short or
necessary in order to facilitate quick remember. Both of these
options are available to them. The expansion of web applications
and portable applications has made it possible for individuals to
utilize these programs from a broad variety of devices at any time
and from any location. This is possible because of the fact that these
programs are portable. In spite of the fact that this new innovation
provides an extraordinarily high level of simplicity, it also raises the
possibility that a password could be divulged to bear riding attacks.
An adversary can readily identify or use recording equipment that is
positioned outside of the client's location in order to obtain the
client's credentials. For the purpose of avoiding problems of this
sort, it is essential for us to make use of an alternative method of
confirmatory communication. Another method of authentication
that can be applied in this situation is graphical authentication. The
usage of the picture secret word is the most efficient approach for
managing sign-on, which is less complicated than the process of
memorizing and generating passwords that are basic. This method
is the most effective method. The process of signing in can be
completed by tapping the relevant places or performing the
appropriate gestures over a picture that has been pre-selected. Both
of these methods are acceptable.

Repeated Dexmedetomidine Infusion is a Two-shot Weapon for Pain and Pain-induced Mood Disorders in Chronic Pain Patients: A Placebo-controlled Randomized Prospective Interventional Study

2024-09-24T10:45:03+00:00

Objectives: This prospective study examined the effects of 6-
sessions of dexmedetomidine (DEX) for patients had chronic
musculoskeletal pain (CMSP) on the frequency and severity of
pain, and pain-induced depression, anxiety and kinesiophobia.
Patients & Methods: 80 CMSP patients were evaluated using
the short-form McGill Pain Questionnaire (SF-MPQ), Pain
Anxiety Symptoms Scale (PASS), State-Trait Anxiety Inventory
for measuring state and trait anxiety, short-form Tampa Scale of
Kinesiophobia and Beck Depression Inventory-II. Patients were
randomly divided into Group-C received placebo infusion and
Group-S received DEX infusion (0.7 ?g/kg for 1-hour) twice
weekly for three weeks. Evaluations were re-assessed at the end
of infusion (T2), 1-m (T3) and 3-m (T4) in comparison to scores
determined before start of infusion therapy (T1).
Results: At T2-T4 the scores of all the evaluated tools
decreased significantly in Group-S compared their T1 scores and
to scores of patients of Group-C. Moreover, 47.5% of Group-S
patients were independent on any analgesia since T2 till T4 with
significant difference compared to Group-C patients and to their
consumption rate and type of analgesia at T1. Satisfaction scores
of Group-S patients by the infusion therapy were significantly
higher compared to that of patients of Group-C and to their T1
scores by the usual analgesia. The re-assessed scores were
negatively correlated with the administration of DEX infusion
and positively correlated with the decrease in pain scores. ROC
curve analysis defined decreased kinesiophobia scores as the
significant predictor for the decreased depression scores to 0-13.
Conclusion: DEX infusion might break the circle of painpsychopathy-
poor quality of life of CMSP patients. All
psychological scorings were improved secondary to improved
pain scores but improved kinesiophobia is the significant
predictor for alleviation of depression.

Isolation and Characterization of a High Thermal Resistance Bacteriophage vB-KPP01 Infecting Antibiotics Resistant Clinical Klebsiella pneumoniae (PP464225) Isolated from Egypt

2024-09-24T10:46:54+00:00

Background: Antibiotics-resistant Klebsiella pneumoniae has
persistently developed greater resistance to groups of antibiotics
as ?-lactums, Sulfonamides, carbapenem, Aminoglycosides and
Fluoroquinolones. Nonetheless, bacteriophages are being
investigated as a potential substitute for antibiotics in the
treatment of bacterial infections due to host specificity, no series
side effects, without destroying normal flora of patient. In this
study, Bacteriophage vB-KPP01, isolated from local sewage of
Talkha, Egypt, was tested in-vitro to evaluate its lytic activity
against antibiotics resistant K. pneumoniae isolated from blood
of patient with pneumonia at the Sandoub Health Insurance
Hospital (SHIH). Methods: The
bacteriophage vB-KPP01 was assessed for its morphological
characterization, phage adsorption, growth kinetics, in-vitro host
range, temperature, dilution end point and pH sensitivity. Invitro
Lytic activity of phage vB-KPP01 was determined against K.
pneumoniae. Results: bacteriophage vB-KPP01 produced a
clear plaque with a halo (0.6 to 1.1cm) and had an icosahedral
head (127 nm) with short non-contractile tail (30.1 nm) was
classified as podoviridae. The phage was tested against various
clinical strains and results proved it to be host specific and had a
burst size of 490 PFU/cell. It was stable over a wide pH range of
4–11.4 with maximum activity at pH 8.1 and had relatively strong
heat stability up to 90°C. Phage vB-KPP01 demonstrated
significant in-vitro lytic activity against K. pneumoniae, resulting
in a maximum decrease in K. pneumoniae counts with 78.3%
after 9.5 h of incubation. Conclusion: These attributes suggest
that phage vB-KPP01 could hold therapeutic promise for the
treatment of K. pneumoniae infections.

The outcomes of cardiac surgery in patients with congenital heart disease in the Eastern Region of Libya between 2021 and 2023

2024-09-24T10:48:55+00:00

Background: Congenital heart disease (CHD) is a frequent
birth abnormality. As surgical and medical care improves, more
kids born with congenital heart disease survive to grow up in
developed countries. Hospitals in eastern Libya have
continuous to depend on operation of foreign cardiac missions
for emergency & surgical management of kids with congenital
heart disorders. The aims of this project are to assess both the
type & diagnosis of CHD that is surgically managed and the
challenges with pediatric cardiac operation facilities in the
eastern portion of Libya. Methods: The data were obtained
from medical reports & monitoring of 163 congenital heart
disease cases that underwent operating correction at the
National Heart Center and Benghazi Medical Center between
May 2022 and March 2023, including those with an age less
than 30.Results: Males (47.8%), females (52.1%), and patients
came mainly from Benghazi (46%) and Jebel Kadar (35.5%),
and the most frequent diagnoses were VSD (26.3%), CCHD,
and ASD (19.8%), TOF (17.4%), and ASD (12%). With an overall
morbidity rate of 7.4%, PS and AS reported the greatest
mortality rate (33.3%), followed by COA (12.2%), TOF (10.3%).
Conclusion: The collaboration among the mission team and
the local team improves the local team's care for CHD children.
One of the fundamental criteria for assisting children with CHD
in Libya is training local healthcare providers to participate in
cardiac humanitarian trips and to continue their training
through different workshops and programs. We need an
extensive treatment plan for these patients, along with ability of
the local team to treatcongenital heart disease cases on their
own.

Strategies to Solve People’s Poverty Sustainable with the Power House-Temple-Government of Banmai Subdistrict, Admnistrative Organization, Maharat District, Phra Nakhon Si Ayutthaya Province

2024-09-24T10:51:15+00:00

The purposes of this study were to study 1) To study the factors
affecting the poverty problem of people in the area of Banmai
Subdistrict Administrative Organization, Maharat District, Phra
Nakhon Si Ayutthaya Province.2) To create Strategies to Solve
People's Poverty Sustainable with the Power House -Temple –
Government of the Banmai Subdistrict Administrative
Organization, Maharat District, Phra Nakhon Si Ayutthaya
Province. 3) To evaluate the implementation ofStrategies to Solve
People's Poverty Sustainable with the Power House -Temple –
Government of the Banmai Subdistrict Administrative
Organization, Maharat District, Phra Nakhon Si Ayutthaya
Province. This research methodologies were quantitative
research and qualitative research. The sample group used in the
quantitative research consisted of 400 people.The sample groups
used in the qualitative researchhave three groups. 1)
representative from home 17 populations. 2) representative from
temple5 populations. 3) representative from government 5
populations, by cluster sampling. The tool used in quantitative
research was a questionnaire. Tools used in qualitative research
was an interview. The quantitative data were analyzed by
software package. Statistics used for data analysis were
percentage, mean, standard deviation, andt-test.
The results of research found that 1. The results of the
quantitative research found that the factors affecting the poverty
problem of people in the area of the Banmai Subdistrict
Administrative Organization, Maharat District, Phra Nakhon Si
Ayutthaya Province as a whole were at a moderate level.When
considering each aspect, it was found to be at a moderate level in
all 5 areas, arranged in order of average from highest to
lowest.First are economic factors, followed by personal factors.
Psychological factors Social factors and the least is a factor of government policy.2.
Strategies to Solve People's
Poverty Sustainable with the
Power House -Temple –
Government of the Banmai
Subdistrict Administrative
Organization, Maharat
District, Phra Nakhon Si
Ayutthaya Province were 1)
Philosophy of sufficiency
economy.2) Exploring indepth
information in every
dimension.3) Defining the
target group clearly.4)
Adding knowledge by going
on study tours.5) Promoting
additional careers to
increase income.6) Product
marketing promotion.7)
Creating community kitchen
logistics for the welfare of
the elderly.8) Creating a
FDA Standard Central Kitchen,1 subdistrict, 1 central kitchen. 9)
Development of local food toglobal food.10) Development of One
Subdistrict, One Team, OTOP Community.3. Evaluation of the
implementation of the strategy for solving people's poverty issues
sustainably with the power House-Temple–Government by the
Banmai Subdistrict Administrative Organization, Maharat
District, Phra Nakhon Si Ayutthaya Province. They were 1) The
strength or advantage is that it stimulates the community to gain
experience in analyzing the poverty problem in the community
itself, making them more aware of themselves. 2) The
weaknesses or disadvantages are that people still have opposing
attitudes and lack of continuity in leadership. Strategies for
solving people's poverty problems sustainably with the power of
implementation. 3) The opportunity: This strategy has a
possibility of being developed further. 4) The obstacleis the fact
that some people do not understand the process of solving
people's poverty problems sustainably with the power of
Buddhism.

IoT Map: A Comprehensive Framework for IoT Data Management and Analysis

2024-09-25T04:52:55+00:00

The Internet of Things (IoT) revolutionizes numerous industries by enabling real-time data collection and analysis from a multitude of connected devices. However, the vast amount of data generated by IoT devices presents significant challenges in data management, security, and real-time processing, particularly regarding data storage, real-time processing, security, and predictive modeling. This paper introduces IoTMap, a comprehensive framework designed to streamline IoT data workflows by integrating database management, live sniffing, exploitation testing, and modeling capabilities into a single platform. IoTMap addresses the inefficiencies and fragmentation seen in existing solutions by offering an all-in-one approach that enhances data handling, improves security, and provides real-time insights and predictive analytics. Through extensive testing with a variety of IoT devices, the framework demonstrated high efficiency in data management, effective real-time traffic analysis, robust security assessments, and accurate modeling of IoT environments. IoTMap’s unified and scalable approach positions it as a valuable tool for researchers, developers, and practitioners, facilitating more efficient and effective IoT data management and analysis while paving the way for advanced IoT applications and research.

A Comprehensive Tool for Legal Document Interpretation and Summarization using Large Language Models

2024-09-25T04:59:17+00:00

The proposed system in this paper introduces a user-friendly software solution leveraging cutting-edge AI technology called Large Language Models (LLMs) to simplify the understanding of legal documents and ensure fairness within the legal system. With LLMs at its core, the system offers two primary functions. Firstly, users can upload various legal documents, such as contracts or statutes, and ask questions related to their content. Using sophisticated natural language processing techniques, the system analyses these documents and provides accurate answers, aiding both legal professionals and individuals without legal expertise in navigating complex legal texts effortlessly.

By harnessing the power of LLMs, this software revolutionises how we interact with legal documents. Its advanced capabilities enable users to better understand legal papers and ensure they're fair and transparent. With its user-friendly interface and focus on leveraging LLM technology, the system aims to empower users to make informed decisions and promote fairness and accountability within the legal domain

AI-based Organic Farming as an Instrument for Environmental Protection

2024-09-25T06:04:14+00:00

The combination of AI and traditional farming methods shows potential for tackling environmental issues in agriculture. This research paper examines how AI-driven organic agriculture can serve as a powerful tool for safeguarding the environment. Through analyzing recent literature, case studies, and upcoming technologies, we explore the potential of AI in enhancing organic farming methods to lessen environmental effects, enhance resource utilization, and support biodiversity. The research investigates different AI uses in organic farming, such as precision agriculture, pest control, soil health tracking, and predicting crop yields. Our results indicate that incorporating AI into organic agriculture can make a substantial impact on promoting sustainable farming practices and preserving the environment. Yet, issues like data privacy, technological accessibility, and the necessity for farmer training need to be resolved in order to fully exploit the advantages of this method.

Caries Dental Detection Using UNet Deep Learning Methods

2024-09-25T06:08:31+00:00

Dental caries, a prevalent oral health issue, can lead to severe consequences if not detected early. This study explores the application of U-Net, a deep learning architecture, for the automatic detection of dental caries from radiographic images. U-Net's architecture, characterized by its encoder-decoder structure with skip connections, allows for precise segmentation and localization of carious lesions. We employed a dataset of annotated dental X-ray images to train and validate our model. The results demonstrate that the U-Net-based approach achieves high accuracy in identifying carious regions, outperforming traditional methods in both sensitivity and specificity. This method holds significant promise for enhancing diagnostic workflows and improving early intervention strategies in dental care.

TOWARDS EFFICIENT AND RESILIENT CONTAINER ORCHESTRATION: A LAYERED ARCHITECTURE FOR AUTOMATED SECURITY AND RESOURCE OPTIMIZATION

2024-09-25T06:13:06+00:00

In the rapidly evolving landscape of cloud computing, the management of containerized applications presents significant challenges, particularly in the areas of security and resource optimization. This research introduces a layered architecture for container orchestration, designed to enhance the efficiency, resilience, and security of cloud environments. Building upon the foundational principles of CloudDefense, this study explores the integration of advanced security orchestration and automation within the proposed framework. The architecture leverages cloud-native orchestration tools and incorporates resource management, adaptive intrusion detection and prevention systems (IDPS), and automated secret management to optimize security workflows. By automating incident response, fortifying threat detection mechanisms, and facilitating efficient remediation, the framework addresses the critical need for robust security in containerized environments. The study further investigates the role of dynamic network policies and continuous deployment with auto-patching in maintaining high availability and performance while ensuring compliance with industry standards. Through practical insights and case studies, the research elucidates the transformative impact of this layered architecture on modern cloud security practices. The findings demonstrate how this approach mitigates orchestrator-level vulnerabilities and optimizes resource allocation, offering a comprehensive solution for organizations seeking to secure and streamline their container orchestration processes.

A Hybrid Deep Learning Approach for ECG Arrhythmia Detection: GPT, GANs, and Triplet Loss Integration

2024-09-26T06:46:43+00:00

This paper proposes a novel deep-learning method to detect arrhythmias from the ECG data by adopting pre-trained GPT models and other powerful state-of-the-art DL algorithms. Most traditional ECG classification models face challenges in capturing complex temporal dependencies and handling class imbalances. To meet these challenges, our system leverages GPT to capture complex temporal patterns and contextual relationships within ECG signals, enabling us to better understand the more intricate dependencies in the data. Finally, the proposed system leverages data augmentation with Generative Adversarial Networks (GANs) to generate a wide variety ofcomplex samples, which help improve model capability and robustness. It also uses Triplet Loss, which shows it can work better on imbalanced classes and tiny differencesin different cardiac arrhythmias. Compared with other methods, our results exhibit great improvements in classification performance, particularly for rare arrhythmias. Model Interpretability is based on SHapley Additive exPlanations(SHAP) and Gradient weighted Class Activation Map (Grad-CAM), which interpret the model decisions.

State of the Art in the Use of Wireless Sensor Networks (WSN) and IoT Devices for Water Source Monitoring in Urban Environments

2024-09-27T12:24:42+00:00

Objective: The study aimed to analyze the use of wireless sensor networks (WSN) and IoT devices in the monitoring of water sources in urban environments, focusing on key applied technologies, advancements, and technological challenges. Methodology: A systematic review of 37 scientific articles indexed in Scopus between 2014 and 2023 was conducted, focusing on the analysis of IoT devices, SCADA systems, sensor networks, drones, and unmanned vehicles, as well as the integration of machine learning algorithms for resource use prediction and optimization. Results: The findings show that most research concentrates on the use of IoT and sensor networks for water quality monitoring and resource management. The implementation of drones and unmanned vehicles has enhanced monitoring capabilities in remote areas. Predictive models based on machine learning have improved the efficiency of detecting water-related events such as floods, in addition to enhancing decision-making regarding resource use. Discussion: Despite advancements in the development of water monitoring technologies, challenges remain in system standardization and real-time data integration, underscoring the need for further development of more robust and scalable technological solutions. Conclusions: This study highlights the importance of emerging technologies, such as IoT and sensor networks, in the management and monitoring of water resources, emphasizing their positive impact on the sustainability and efficiency of water systems in urban environments.

Unique Log Parsing Framework for Enhanced Anomaly Detection in Network Security: Lauki Log Parser

2024-09-27T17:08:51+00:00

The increasing complexity of information security demands effective strategies to protect data across various domains. Traditional system log analysis, relying on unstructured logs, employs data mining and machine learning techniques to detect network threats. However, existing methods often struggle with logs of diverse formats and structures, resulting in missed anomalies and vulnerabilities. This paper introduces LaukiLogParser, a novel real-time log parsing framework designed to address these challenges by processing both structured and unstructured logs from multiple formats, including JSON, Syslog, and CEF. By incorporating unique parsing equations, the proposed parser enhances the identification of network threats, insider threats, and system vulnerabilities. Through comprehensive testing on publicly available datasets, LaukiLogParser demonstrated a significant 15% increase in anomaly detection accuracy compared to traditional parsers, along with improved F1-scores, precision, and recall. The parser’s ability to handle a variety of log formats provides unmatched flexibility in real-time environments, making it highly effective for modern network security systems. The paper compares LaukiLogParser with existing parsers, such as LogParser-LLM, OpenLog, and LogPPT, showcasing its superiority in accuracy, scalability, and adaptability. The results highlight the limitations of current parsers, while LaukiLogParser’s novel approach offers a robust solution for enhancing anomaly detection and improving real-time security monitoring.

Efficient Dual-Level Encryption for Securing Data in HDFS Using Hybrid User-Defined Function (HUDF)

2024-09-27T17:12:50+00:00

Big Data is a new class of technology that gives businesses more insight into their massive data sets, allowing them to make better business decisions and satisfy customers. Big data systems are also a desirable target for hackers due to the aggregation of their data.The Hadoop Distributed File System (HDFS) stores massive data in the Hadoop framework. Since HDFS does not safeguard data privacy, encrypting the file is the right way to protect the stored data in HDFS but takes a long time. In this paper, regarding privacy concerns, we use different compression-type data storage file formats with the proposed hybrid user-defined function (HUDF)based on an XOR-Onetime pad with AES to securedata in HDFS. In this way, we provide a dual level of encryptionby maskingselective data and whole data in the file. Our experiment demonstrates that this scheme offersoverall data security and also faster processing time than the conventional methods. The proposed HUDF with ORC, Zlib (Z) file format (HUDF-ORC-Z) gives 9-10% better performanceresults than 2DES and other method. Finally, we efficiently utilized the space, improved query processing time,and decreased data load timewith the Hive engine.

Web Based Graphical Password Authentication System

2024-09-27T17:14:22+00:00

In order to provide security and assurance, programs commonly make use of verification that is dependent on passwords. This is done for the aim of providing security. However, human behaviors, such as selecting terrible passwords and supplying passwords in square measurements, are considered to be "the most delicate association" in the verification chain. This is because human beings are the ones who are responsible for these activities. As an alternative to alphanumeric strings that are optional, it is feasible that consumers will select passwords that are either short or necessary in order to facilitate quick remember. Both of these options are available to them. The expansion of web applications and portable applications has made it possible for individuals to utilize these programs from a broad variety of devices at any time and from any location. This is possible because of the fact that these programs are portable. In spite of the fact that this new innovation provides an extraordinarily high level of simplicity, it also raises the possibility that a password could be divulged to bear riding attacks. An adversary can readily identify or use recording equipment that is positioned outside of the client's location in order to obtain the client's credentials. For the purpose of avoiding problems of this sort, it is essential for us to make use of an alternative method of confirmatory communication. Another method of authentication that can be applied in this situation is graphical authentication. The usage of the picture secret word is the most efficient approach for managing sign-on, which is less complicated than the process of memorizing and generating passwords that are basic. This method is the most effective method. The process of signing in can be completed by tapping the relevant places or performing the appropriate gestures over a picture that has been pre-selected. Both of these methods are acceptable.

Examining the Role of Human Capital Management in Enhancing the Resilience and Agility of the Military Sector on Reserve Personnel of the Indonesian Armed Forces

2024-09-27T17:16:07+00:00

Purpose - The purpose of this paper is to examine the role of human capital management in enhancing the resilience and agility of the military sector in the Indonesian Reserve Military Personnel.

Design/Methodology/Approach - This study adopts a qualitative research method with a literature review (library research) approach. Data collection involves searching and reconstructing information from various sources such as books, journals, and existing research. The data obtained from observation and documentation techniques are then analyzed using data condensation, data display, and conclusion drawing and verification techniques.

Findings - Firstly, recruitment and selection in the Indonesian Reserve Military involve attracting potential candidates, assessing their qualifications, skills, and physical abilities, and choosing the most suitable individuals for reserve duties in national defense and security. Secondly, training and development focus on enhancing military skills such as marksmanship, combat tactics, and physical fitness. Thirdly, performance management involves goal setting, performance planning, regular feedback, performance monitoring, training and development, recognition and rewards, and performance improvement. Fourthly, succession planning aims to identify individuals with potential to take on key leadership roles in the reserve and develop their skills and abilities accordingly. Fifthly, talent management involves talent identification, talent development, succession planning, performance management, and retention strategies. To enhance resilience, the military reserve focuses on building the mental and physical strength of its personnel.

Research Limitations/Implications - Increased investment in human capital development, Expansion of military sector training and development programs, Effective measurement methods should be considered for future research.

Prediction of Lecturers' Satisfaction Using M-Learning by Fast Learning Network

2024-09-28T07:59:00+00:00

M-learning, or mobile learning, has seen remarkable advancement in the past few years owing to the growth of mobile technology. This development has greatly improved the features and usability of mobile learning applications. This paper aims to present and analyze the accelerated learning network model, which identifies the determinants of mobile learning satisfaction of lecturers at Southern Technical University. The model employs a questionnaire given to 250 participating lecturers using multiple variables to examine the factors that affect their satisfaction levels. The study results revealed that the suggested model was more effective than others, including ANN, KNN, and MLP, in predicting factors affecting lecturers' satisfaction regarding accuracy and specificity. The model also showed good accuracy and specificity, with the latter reaching 93.55% in the prediction of the satisfaction factors of lecturers on mobile learning, with an accuracy of 92.00%. It emphasizes the need to consider the different aspects of different assessment methods and lecturers in research within an m-learning context.

GENETIC COUNSELLING

2024-09-30T05:25:32+00:00

Genetic counselling is a service that provides information and advice about genetic condition. These are condition caused by changes known as mutation in certain genes and are usually passed down through a family. Genetic counselling is the process through which knowledge about the genetic aspects of illnesses is shared by trained professionals with those who are at an increased risk or either having a heritable disorder or of passing it on to their unborn offspring. A genetic counsellor provides information on the inheritance of illnesses and their recurrence risks; addresses the concerns of patients, their families, and their health care providers; and supports patients and their families dealing with these illnesses. The Heredity Clinic was the first genetic counselling service centre established in 1940 at the University of Michigan, USA. Since then, the many such centers have been opened around the world.

Design and Implementation of an Any Time Electricity Bill Payment System

2024-09-30T05:27:14+00:00

This work presents the design and implementation of an any time electricity bill payment (ATP), which is an unmanned system designed to collect payments from consumers by various modes such as cash, cheque, or demand draft (DD). The ATP operates 24/7 and provides a touch screen and multimedia-based interface to facilitate easy and convenient payment transactions. This paper discusses the objectives, simulation, output state diagram, area report, timing report, and algorithm for the Mealy Machine implementation of the ATP.

Opportunistic Scheduling Algorithms In 5G/6G

2024-09-30T05:36:49+00:00

Wireless networks have experienced rapid growth in recent years, which evaluates the importance of scheduling algorithms in the development and optimization of wireless networks. Opportunistic scheduling algorithms take channel condition as one of the factors when transmitting packets. The number of bytes which can be carried within a time or physical slot is determined by the channel condition which significantly impacts the throughput of wireless networks. This study aims to focus on the uplink and downlink schedulers and channel conditions which contribute to optimizing resources utilization, enhancing network throughput, and minimizing delays in network communication. In addition, this study also aims to compare and analyze existing scheduling algorithms and their impact on network throughput. In this paper, several existing opportunistic scheduling algorithms in 5G will be compared.

ROLE OF DOMESTIC AMINO ACID BLOOD SUBSTITUTE ON METABOLIC DISORDERS AND ENDOGENOUS INTOXICATION IN EXPERIMENTAL TOXIC HEPATITIS

2024-09-30T06:02:06+00:00

The development of effective agents to correct metabolic balance in critical illness is critical for the successful treatment of serious diseases. Critical conditions such as trauma, poisoning, burns, sepsis and surgery, as well as diseases such as acute infections and cancer, can lead to increased breakdown of substances and increased catabolism. Poor nutrition can lead to weight loss, poor physical performance, and metabolic disorders. Amino acid mixtures are the best way to influence metabolic homeostasis, since protein synthesis occurs only from free amino acids. They are widely used in medical practice, including for parenteral nutrition. The RGNPMCG of the Ministry of Health of the Republic of Uzbekistan has developed a new blood substitute containing amino acids and an antioxidant complex, which has a wide spectrum of action and is able to influence protein synthesis by body cells, optimize the functioning of physiological systems, and accelerate recovery processes in severe diseases associated with impaired protein -energy metabolism. The purpose of this study is to study the impact and evaluate the effectiveness of a new domestic amino acid blood substitute on metabolic disorders and endogenous intoxication in toxic hepatitis.

HYGIENIC ANALYSIS OF THE NUTRITIONAL CONDITION OF THE EMPLOYEES OF TEXTILE ENTERPRISE

2024-09-30T06:04:30+00:00

Flattened enterprise of workers real eating status hygienic analysis of the year cold ( winter-spring ) and in hot (summer-autumn) seasons done increased. Daily ration contained high risk to the group belongs to There are 10 types food of products comparative analysis take went being this? analysis to the results than the flour is cold 46,7-88,0% in the season and hot bakery products by 33,3-44,0% in the season desired analogous 46.4-71.8% and 35,2-54,5% respectively, pasta 16,4-20,0% and 9,1-24,0 %, confectionery products 17-20 grams and 2-7 grams , sugar 1,43-2,4 and 1,14-1,9 times, margarine 17-15 grams and 13-11 grams of salt and 7-6 grams and 4-3 grams excess consumption done was determined.

AGE FEATURES OF CHANGES IN THE ANGLE OF THE SPINE AND AGE FEATURES OF ANTHROPOMETRIC INDICATORS OF VARIOUS SECTIONS OF THE SPINE IN BOYS AND GIRLS AGED FROM 11 TO 16 YEARS

2024-09-30T06:20:14+00:00

The article examines age-related changes in the angle of inclination of various sections of the spinal column, as well as age-related features of anthropometric characteristics of the spinal column in boys and girls aged from 11 to 16 years old, who do not have pathological changes in the spinal column. [12].

The purpose of the study: to study the change in the angle of inclination in various parts of the spinal column, as well as age-related features of anthropometric indicators of various parts of the spinal column in adolescents.

Evaluating the Impact of Scheduling Algorithms on Performance and Reliability in Delay-Tolerant Networks

2024-09-30T07:00:07+00:00

Delay-Tolerant Networks (DTNs) are designed to support communication in environments where network connectivity is intermittent and unpredictable. Efficient scheduling algorithms are critical for optimizing the network's performance, ensuring data delivery, and reducing latency. This paper provides an in-depth evaluation of two widely-used scheduling algorithms—the Timely-Throughput Optimal (TTO) Algorithm and the Bundle Lifetime Criteria-based policy. Using a comprehensive set of simulation experiments, we analyze how these algorithms perform under various network conditions, including mobility, node density, and varying traffic loads. Our results demonstrate that the TTO algorithm maximizes throughput and data delivery in stable networks, while the Bundle Lifetime Criteria-based policy significantly improves reliability in highly dynamic and mobile environments. The insights derived from this analysis are vital for designing adaptive scheduling solutions that enhance both performance and reliability in DTN applications such as disaster recovery, vehicular networks, and space communications.

Incorporation of information systems in healthcare centers in the peruvian context: An exploratory systematic review

2024-10-01T05:54:15+00:00

The article presents an exploratory systematic review on the incorporation of Information Systems (IS) in healthcare centers in the Peruvian context, highlighting the importance of these technologies in the management of clinical and hospital data. The objective was to analyze the current state of IS implementation in healthcare centers in Peru, identifying the types of integrated systems, the development methodologies adopted, the purpose of the systems, and their impact on medical care. The methodology employed was an exploratory systematic review, in which studies were searched in academic databases such as Scopus. Inclusion criteria were applied, encompassing articles published between 2015 and 2023, in English or Spanish, focused on the implementation of IS in Peruvian healthcare centers. A total of 14 relevant articles were selected. The main findings reveal that the implemented IS include point-of-care medical information systems, electronic health records, and machine learning models. These systems have improved the quality of medical care, optimized hospital processes, and facilitated clinical data management. However, their adoption faces barriers such as a lack of technological infrastructure and resistance to change. In conclusion, the integration of IS in Peruvian healthcare centers has had a positive impact, but challenges remain that require further research and technological adaptation.

Leveraging Machine Learning and SMOTE for Diabetes Prediction: Implementation of an Application Based on Indonesian Hospital Data

2024-10-01T06:00:22+00:00

Diabetes mellitus is a widespread chronic condition affecting millions globally, including a substantial population in Indonesia. Accurate and early detection is critical for effective management and treatment, and machine learning offers promising solutions for enhancing predictive accuracy. This study evaluates three machine learning algorithms: Support Vector Machine (SVM), Logistic Regression, and Naive Bayes, with and without the application of Synthetic Minority Over-sampling Technique (SMOTE) to tackle data imbalance. Data were meticulously collected from an Indonesian regional hospital, including various medical parameters such as age, body mass index (BMI), blood sugar levels, blood pressure, and family history. Our findings reveal that the SVM model, without SMOTE, achieved an accuracy of 95%, precision of 95%, recall of 97%, and an AUC of 98%. With SMOTE, SVM's performance improved to an accuracy of 95.8%, precision of 97%, recall of 94.6%, and an AUC of 99.1%. Logistic Regression without SMOTE demonstrated an accuracy of 94.8%, precision of 96.2%, recall of 96.2%, and an AUC of 98.3%, while with SMOTE, it reached an accuracy of 95.6%, precision of 97.9%, recall of 93.3%, and an AUC of 98.7%. The Naive Bayes model showed an accuracy of 93.5%, precision of 98.5%, recall of 91.9%, and an AUC of 98.1%, improving with SMOTE to an accuracy of 94.3%, precision of 98.3%, recall of 90.2%, and an AUC of 98.6%. The best-performing model, SVM with SMOTE, was implemented into a desktop application. This application successfully validated the model's predictive capabilities, demonstrating effective and accurate diabetes detection in practical scenarios. Our study highlights the significant impact of SMOTE on enhancing model performance and emphasizes the importance of sophisticated machine learning techniques in advancing healthcare diagnostics. This work provides a foundation for further development and deployment of predictive models in clinical settings, contributing to improved patient care and disease management.

Investigating the Impact of Generative AI on Cybersecurity of Digital Marketing; A Solution-based Approach to Counter Threats

2024-10-01T06:02:06+00:00

It discusses the role of generative AI in the cybersecurity of digital marketing: it demonstrates the possible threats and assesses the effectiveness of defensive algorithms. The generative AI technology applied was Generative Adversarial Networks (GANs). GANs were used in simulations of various cyber threats, including fake ad campaigns, phishing emails, and spreading disinformation. The simulations proved how effective AI-generated attacks can be successful-good engagement can lead to significant financial and data losses. Fake ads accounted for significant losses in terms of ROI, phishing emails contributed towards several data breaches, and false information campaigns spread widely. The algorithms were advanced enough to resist such threats, which were tested. The algorithms could offer high accuracy and precision, recall, and F1 scores in the identification and mitigation of AI-driven threats. Success rates of AI-based cyber-attacks and robust performance of the algorithms in the defence against disinformation diffusion and fake ad and phishing email detection. Recommendations include strengthening AI-based detection systems, training employees, making them aware, putting in multi-layered security measures, and promoting collaboration between industry and academia. Future research directions include exploring cutting-edge AI defence mechanisms, studying the impact of AI on all marketing channels, designing real-time threat detection systems, and analyzing the ethical and legal aspects of AI in cybersecurity. The paper throws light on the need for perpetual innovation and interdisciplinary research to combat the developing threats from generative AI in digital marketing. The digital marketing environment will have a stronger security and resilience profile with holistic measures of security and digital literacy.

DESIGN OFMAC USING FEED FORWARD NEURAL NETWORKS

2024-10-01T06:31:00+00:00

This research investigates the usage of feed forward neural networks,traditional Indian Vedic multiplier,carry skip adder,parallel in parallel out register and MAC. An Artificial Neural Network (ANN) operates in a parallel information processing structure, comprising processing units. The efficiency of the network is determined by the processing unit. Hence, there is a need to design a processing unit that is both efficient and capable of delivering superior performance. This processing unit encompasses a MAC unit (Multiplication and Accumulation) and an Activation unit. The main focus of high-speed processors is to minimize power consumption and processing times. The

implementation of the design utilizes Verilog, a hardware description language, and the testing phase is carried out using the Modelism simulator.The study performance the 4 bit mac using feed forward network with 4 bit mac using logic gates.

Mathematical analysis of Dynamic Neural Network Fusion for Intelligent Transportation Systems (DNNF-ITS) on Internet of Things-enabled scalable data synchronization technique

2024-10-01T06:54:28+00:00

A comparative analysis of three important techniques in Intelligent Transportation Systems (ITS) is presented in this research. These approaches are the Dynamic Neural Network Fusion for ITS (DNNF-ITS), the Internet of Things-enabled scalable data synchronization methodology (IoT-SDSM), and the Intelligent Automated Software System (IASS). A growing need for optimal solutions in enhancing the efficiency and intelligence of transportation systems is addressed in this comparative study, which highlights the significance of the study and emphasizes its importance. The paper provides an overview of the difficulties that are inherent in each methodology and suggests a methodical approach to evaluate the effectiveness of these methodologies. These approaches are investigated for their potential applications in real-world scenarios, which include anything from the forecast of traffic to the synchronization of data and intelligent automation. A simulation analysis is incorporated into the study, which offers a more nuanced comprehension of the practical consequences and effectiveness of the techniques. When taking into consideration the distinct qualities and capabilities of each methodology, the purpose of this research is to provide decision-makers and practitioners with a roadmap that can assist them in picking the most appropriate way for progressing ITS.

Intelligent systems for project management in public institutions: a review of the state of the art in Scopus

2024-10-02T07:05:51+00:00

The study analyzed the use of intelligent systems in project management within public institutions, focusing on applied technologies, implementation challenges, and emerging trends. Through a systematic review of 29 scientific articles indexed in Scopus (2013-2024), technologies such as Artificial Intelligence (AI), Big Data, ERP, and Blockchainwere identified, highlighting their impact on the efficiency and transparency of projects. The results show that these technologies have optimized resource allocation and improved decision-making, while the modernization of legacy systems has facilitated the digitalization of public services. Additionally, increased efficiency in managing international projects has been observed through collaborative cloud platforms. However, challenges related to system interoperability, data security, and the need for staff training persist. The success of implementing these technologies depends on a strong governance framework and the institutions' ability to adapt to technological advances. In conclusion, intelligent systems present a significant opportunity to enhance project management in the public sector, although addressing these challenges is essential to maximize their impact.

Use of information technologies in administrative management: A bibliometric analysis

2024-10-02T07:07:21+00:00

The use of Information and Communication Technologies has significantly transformed administrative management in both public and private organizations. This article presents a bibliometric analysis of the scientific production related to the use of information technologies in administrative management, aiming to identify the main research trends, sources, authors, and influential institutions. To conduct this analysis, 1,772 articles were examined from the Scopus database between 2010 and 2023, using bibliometric analysis tools such as Biblioshiny. The indicators considered were annual scientific production, the most relevant publication sources, institutional affiliations, most frequent keywords, and the collaboration network between countries. The results reveal a notable growth in scientific production since 2018, with "Government Information Quarterly" being the most prolific source and the University of Brasilia leading in publications. The most recurrent concepts include "e-government," "digital transformation," and "artificial intelligence," while Spain, the United States, and China stand out as the countries with the highest international collaboration. In conclusion, research on ICT and administrative management continues to expand, reflecting the impact of digitalization on administrative processes.

Optimizing Human Resource Management Strategies for Cybersecurity Workforce Development in the Era of Digital Transformation

2024-10-02T10:07:10+00:00

The rapid digital transformation has created an unprecedented demand for skilled cybersecurity professionals. Human Resource Management (HRM) strategies must evolve to address this workforce shortage, especially as cyber threats become more sophisticated. This paper aims to explore the optimization of HRM strategies to effectively develop the cybersecurity workforce. By conducting an extensive literature review, we identify key HRM practices that influence cybersecurity talent acquisition, retention, and skill development. The findings highlight the importance of adaptive training programs, strategic recruitment, and fostering a culture of continuous learning. Furthermore, the research examines the challenges organizations face in aligning their HRM strategies with the dynamic nature of cybersecurity demands. The implications for HR managers and organizational leaders are discussed, providing insights into how to build a resilient cybersecurity workforce. This study contributes to the ongoing conversation on workforce development in cybersecurity, emphasizing the need for innovative HRM approaches.

"The Role of Political Connections on the Relationship between Corporate Governance and Management Accounting in Companies Listed on the Iranian Stock Exchange: A Machine Learning and Neural Network Approach"

2024-10-03T06:36:41+00:00

The main objective of this research is to examine the impact of political connections on the relationship between corporate governance and management accounting in companies listed on the Tehran Stock Exchange. The statistical population includes listed companies during the period from 2008 to 2023. This study is descriptive-correlational and utilizes both parametric and non-parametric statistical models. Various machine learning methods, including random forests, decision trees, SVM, and neural networks, were used for data analysis.

Results indicate that variables such as the percentage of institutional shareholders (IO) and the percentage of government ownership (GO), as indicators of political connections and corporate governance, have a significant impact on the application of management accounting. Additionally, the interaction of these two variables (IO*GO) shows high importance in the model, demonstrating the influence of political connections on the relationship between corporate governance and management accounting. Moreover, profitability (PROF) and company size (SIZE) were identified as important factors affecting the implementation of management accounting.

The neural network analysis results show that political connections play a significant role in shaping the relationship between corporate governance and the application of management accounting. The composite variable IO*GO (interaction between institutional shareholders and government ownership) has shown the most significant impact on this relationship. These findings indicate the profound influence of political connections on governance structures and management decisions in Iranian companies.

This research emphasizes the complexities arising from the intersection of political and economic interests, suggesting the need for a review of macroeconomic policies and the establishment of more effective regulatory mechanisms. This study can assist policymakers and regulatory bodies in improving transparency, increasing efficiency, and enhancing Iran's position in international business indices. In other words, the present research emphasizes the importance of reforming existing structures and creating effective control mechanisms in the country's macroeconomic policies. It also highlights the necessity of creating a healthy and fair competitive environment in Iran's economy and strengthening anti-monopoly and conflict of interest laws. This study can help policymakers and regulatory bodies improve the business environment, increase transparency, and enhance Iran's position in international indices.

The Role of Leadership in Project Success: A Quantitative Analysis

2024-10-03T06:51:30+00:00

This study explores the relationship between leadership and successful project results by offering a detailed quantitative analysis. The current study focuses on the leadership style that may be attributed to successful delivery and that has a positive relationship with project outcomes. Using a sample of 500 project managers from different industries and the adoption of validated measures to gauge leadership attributes, it follows there are clear success indicators in projects. Overall, we come up with significant strong statistical links of some leader behaviors with the performance variables of a project through correlation and multiple regression analyses. The results indicated strong links of transformational leadership and emotional intelligence with better project outcomes from the strategic decision-making ability of a leader. The research contributes to knowledge by providing empirical data on the critical role that leadership plays in project management and provides relevant practical insights into how the success of projects can be furthered with focused leadership development.

Gradient Directional Edge Coding (GDEC) for Expression Recognition from Facial Images

2024-10-04T05:53:15+00:00

Facial Expression Recognition (FER) requires an effective Expression descriptor that can provide sufficient discrimination between different facial expressions. However, the existing FER methods are susceptible to noise, distortions and not able discriminate flat regions from noisy regions. Hence, this paper proposes a new Face descriptor called as Gradient Directional Edge Coding (GDEC) which encodes the expression components through the directional edges. Initially, GEDC finds the gradients for each pixel and then encodes them with their neighbor pixel’s support. The support is assessed based on the deviation in the direction of corresponding neighbor pixel with the mean direction of a local region. Each pixel is encoded a 7-bit code word among which the six bits are belongs to the directions of neighbor pixels and one bit is sign bit. After describing the expression, the classification is accomplished through Support Vector Machine at different kernels. Experimental validation on Standard CK+ dataset shows an accuracy of 94.6300% which is outstanding compared to the state-of-the-art methods.

Early Prediction of Hyperglycemia Using Cat boost Ensemble Technique

2024-10-04T11:01:44+00:00

Hyperglycemia, characterized by elevated blood glucose levels, is a critical condition that can lead to severe health complications if not detected and managed early. This study explores the application of the Cat Boost ensemble technique for the early prediction of hyperglycemia. Cat Boost, a gradient boosting algorithm that handles categorical features efficiently, is employed to develop a predictive model using a comprehensive dataset comprising patient demographics, medical history, lifestyle factors, and genetic information. The dataset undergoes rigorous preprocessing, including data cleaning, feature engineering, and normalization. The model is trained and validated using an 80-20 train-test split and evaluated through cross-validation to ensure robustness. Key performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC are utilized to assess the model’s effectiveness. This study demonstrates the potential of the Cat Boost ensemble technique in the early detection of hyperglycemia, offering a valuable tool for healthcare professionals to identify at-risk individuals and implement timely interventions. The proposed model provides 86.15% in prediction of hyperglycemia.

Enhancing Pensioner Authentication Through Biometric Verification: The Integration of Fingerprint and Vein Recognition Technologies

2024-10-04T13:00:07+00:00

The article examines the implementation of a biometric verification system for pensioners that features fingerprint along with vein recognition technology. The purpose of the system is to increase the precision, security, and effectiveness of pension payments by providing a secure and convenient method for retiree verification. The system employs the integration of biometric data to overcome challenges associated with traditional verification systems, thereby cutting down on fraud and lessening administrative costs. The results imply that biometric technologies markedly improve verification reliability and can be adapted for use in many geographical areas. The study brings attention to the essential need for policymakers to carry out pilot projects and fully adopt these technologies in the context of managing pension systemsThis document analyzes the application of integrative fingerprint and palm vein pattern recognition methods in pension disbursement aimed at enhancing the security and precision in making announced payments By performing a thorough assessment of some typical use cases, the research explains how the dual-biometric verification can minimize the fraudulent claims and administrative mistakes in the pension systems. The studies recommend that these technologies should be implemented saying that they seem to work from the field enhancing the processes of distributing justice and efficiency in the pension processes. The research highlights the importance of effective diffusion and planning additional investigation to fine-tune these systems for specific populations and regions.

The Impact of Post-Quantum Cryptography on Secure Communication Networks: A Review of Current Trends and Future Directions

2024-10-05T16:34:37+00:00

As quantum computing advances, traditional cryptographic systems that underpin modern secure communication networks are increasingly at risk. Post-quantum cryptography (PQC) has emerged as a critical solution to secure data against quantum threats. This review examines the impact of PQC on secure communication networks, focusing on current trends, challenges, and future research areas. By exploring recent developments, challenges in implementation, and potential research avenues, we provide a comprehensive overview of PQC's role in safeguarding communication networks in the post-quantum era.

A Comparative Study of the Legal Systems of Iran and China Regarding the Enforcement of Arbitration Awards in Light of the 1958 New York Convention

2024-10-07T06:11:26+00:00

The China International Economic and Trade Arbitration Commission (CIETAC) is one of the prominent arbitration institutions in China, providing a fair environment for the facilitation and assurance of the enforcement of international commercial arbitration awards, in accordance with international standards, by offering clear legal guidelines for the parties involved. This institution plays a significant role in the development of international trade relations. CIETAC has established regulations based on modern arbitration principles and rules. The enforcement of commercial arbitration awards in Iranian law has also gained attention, emphasizing efforts to align with international standards while maintaining the autonomy of the parties involved.

At the international level, the recognition and enforcement of CIETAC’s and Iran’s arbitration awards are carried out in accordance with the 1958 New York Convention rules. Both legal systems, despite deviations from some international legal principles, adhere to these principles overall, though differences exist in the details.

This article offers a comparative analysis of the legal systems of Iran and China regarding the recognition and enforcement of international commercial arbitration awards from the perspective of CIETAC's regulations and Iranian arbitration laws, in light of the 1958 New York Convention. The principles, obstacles, methods of recognition, and enforcement of arbitration awards, along with the specific features and gaps in both legal systems, are examined using a descriptive and analytical method. Ultimately, the study identifies the characteristics and gaps in the enforcement of international arbitration awards in both systems, given their crucial role in the development and promotion of international trade.

Designing the Interior of Pars Rasht Hospital Using the Color Psychology Approach

2024-10-07T06:12:55+00:00

Nowadays, architects and designers are obsessed with designing beautifully appropriate environments using architectural models that would provide environmental comfort. As is known, such environmental designs could increase people’s quality of life in society. Since hospitals and treatment centers are used by humans and serve as places where humans interact with each other, it is essential to provide desirable designs for such places and the relevant environment that would increase the comfort of people, including patients and clients, as well as the treatment personnel. The method of the present study was descriptive-analytical, and library sources were used. Findings showed that surface colors should be so designed that they would not cause light reflection or glare, which will harm patients’ vision and cause related problems. Also, strong and dark colors should not be used on ceilings and floors. Colors used in administrative sections should be selected so as not to cause stress, nervous pressure, and fatigue for the employees. When designing the interior of hospitals, the impact of colors on patients should be considered. For example, cold colors may be useful for patients with high blood pressure and anxiety. Red color is recommended not to be used for patients with epilepsy, whereas blue color is not good for cardiac patients. It is thus important to design an intimate environment for children’s inpatient wards, which can be made possible by using happy, albeit neutral, colors.

Toward a Holistic Model for Adopting Cloud Computing in Organizations: A Systematic Review

2024-10-07T06:14:40+00:00

Regarding the innovation that occurred in the information technology industry over the years, the dependence on cloud computing has increased. In this regard, cloud computing has drastically developed in recent years. Many organizations usually rely on this technology for their business and use it as the backbone of their company’s IT infrastructure. Despite the extensive advantages of the could computing, many of these services are limitedly adopted by the companies and many companies are dubious to adopt this technology. The present study aimed to systematically review the factors effective in organizational decision-making for adoption of the cloud computing by the use of the Technology-Organization-Environment (TOE) framework. The TOE framework classifies the factors affecting an organization’s adoption of innovation into three groups: 1. Technology (meaning the system’s security and complexity), 2. Organization (meaning the organization size and chief managers’ support of functional systems displacement), and 3. Environment (cases such as market uncertainty and pressure from the government or competition).

Designing, Implementation and Evaluation of DBL-Based Learning for Undergraduate Students of Dental Faculty: An Experience from Low and Middle-Income Country

2024-10-07T06:16:00+00:00

Statement of the Problem: Traditional lecture-based teaching may not adequately prepare dental students for future challenges. Implementing a Discussion-Based Learning (DBL) approach, where students engage in problem-solving to create solutions, can improve learning and readiness for their careers.

Purpose: This study aims to explore the potential benefits of the DBL approach in dental education. The research involves the design, implementation, and evaluation of DBL sessions for undergraduate students in the theoretical course of Restorative Dentistry 1 during the academic year 2020-2021.

Material and Methods: A semi-experimental design with a literature review and expert panel input was used for tailored DBL sessions via Adobe Connect for 29 fourth-year dental students. Evaluation methods included a questionnaire and interviews. Mid-term scores of DBL participants (n=17) were compared to those using conventional methods (n=17). Qualitative data underwent content analysis, and quantitative data were analyzed with SPSS software 16.

Results: The DBL group of 17 students showed higher engagement levels than the routine teaching group of 56 students. DBL participants rated their engagement as "highly," "very much," or "somewhat." Mean total scores were 45.82±5.29, and average scores were 3.81±0.44. Students' perspectives on the advantages and disadvantages of the DBL method were extracted. The DBL group had a significantly higher mean grade point average (17.56±1.33) than the routine teaching group (15.34±1.56) with a P-value<0.05.

Conclusion: The study underscores the effectiveness of the DBL method as a valuable teaching approach in dental education, as evidenced by improved academic performance and positive student feedback.

Developing a Strategic Sense-Making Model of Social Responsibility at Mashhad’s Municipality

2024-10-07T06:17:35+00:00

Sense-making is a pivotal human process within organizations that contributes significantly to gaining key organizational outcomes such as strategic changes, organizational learning, and innovation. That said, this subject is a major and highly repeated theme in management research, as using sense-making in social responsibility is a new development. The main topic of this study was to develop a strategic sense-making model of social responsibility in the Municipality of Mashhad City, Iran, using the mixed research approach (a combination of quantitative and qualitative research). After reviewing the literature review and using the content and thematic analysis approaches, the researcher investigated the dimensions, components, and indicators of the strategic sense-making of social responsibility. The researcher also used the composite ISM-DEMATEL technique to rank and determine the type of variables and to investigate the relationship between the model variables, determine the intensity of the relations, and identify the extent to which the criteria affected and were affected by each other. The final model consisted of 251 basic themes, 16 organizing themes, and 4 inclusive themes. These indicators included the spiritual strategy of social responsibility (consistency with spiritual values, moral decision-making, spiritual leadership, society’s spiritual engagement), the interpretation of the sense of social responsibility (definition and comprehension, social responsibility process, social communication and awareness, cultural sensitivity), the spiritual optimization of social responsibility (holistic approach to social responsibility, resource allocation, development of innovation and creativity, continuous improvement of social responsibility), and the understanding of the sense of social responsibility (citizenship welfare, social justice, consistency with policies, global spiritual citizenship). In the next stage, to investigate the validity of the developed model, the structural equation modeling and partial least squares methods, as well as factor analyses using SPSS and Smart PLS software were used. Finally, the existing and desirable situations as well as the difference between these two situations to make sense of social responsibility in Mashhad’s Municipality were investigated by the components of the extracted model.

Multi-stage Fine-tuning Approach for AI-based Chest X-ray Abnormality Detection

2024-10-07T06:29:15+00:00

Correct detection and localization of thoracic diseases on chest X-ray images supports or even secures an early diagnosis and treatment-oriented planning. This paper proposes a version of the YOLOv5 deep learning model augmented with more sophisticated components, namely Spatial Pyramid Pooling (SPP), Path Aggregation Network (PANet), and Convolutional Block Attention Module (CBAM) to enhance overall stability and generalization for clinical use. The key components of the proposed model are first trained on the VinBigData Chest X-ray Abnormalities Detection dataset to improve feature extraction and adaptively stretch multiple image resolutions across different strides per pixel levels while focusing more attention on the region. Next, we make a multi-stage fine-tuning approach for real-world clinical data, which usually shifts domains in practical settings. Finally, the model is forced to be more resistant and less overfitting by performing real-world data augmentation instead of focusing on clinical variability. We further qualitatively assess the performance of our model on both the VinBigData test set and CheXDet dataset with only publicly available bounding box annotations on matching classes between the two datasets. Moreover, the model was integrated into a web application that could easily be employed in clinical environments for real-time chest X-ray analysis and may assist with more accurate diagnosis at an early stage.

FABRICATION AND INVESTIGATION OF BANANA FIBER AND JUTE FIBER REINFOERCED COMPOSITE MATERIAL

2024-10-07T07:36:23+00:00

In the current scenario Automobile industry focuses on enhancing the strength and reducing the weight of body parts. The fuel efficiency and emission regulation of two wheelers are two important issues in these days. The best way to increase the fuel efficiency without sacrificing safety is to employ fiber reinforced composite materials used for the two wheeler. In this study two wheeler mudguard which is used to protect the mud and rain water placed on front and rear wheel is considered for investigations. Mudguard is the one of the part having more weight and in my work, the existing steel/ABS plastic mudguard replace with composite mudguard. The fabrication of composite material is made up of Epoxy resin with banana and jute reinforced polymer is carried out and weight of the mudguard will reduce. In this work, fabrication of Banana and jute fiber composite material is completed and its mechanical properties like hardness, tensile strength etc., are calculated and analyzed. Experimental testings are conducted before and after curing with four different sample ratios. The maximum values are obtained for the sample ratios (40:60, 50:50)

PERFORMANCE OF FOUR BLADE ARCHIMEDES SCREW TURBINE

2024-10-07T07:38:57+00:00

Hydraulic energy is one of the most important sources of renewable energy today.it is also a complementary source to other renewable energy sources, being the only one that offers an important nonpolluting storage capacity. The geometrical shapes are four blades, Screw angel of 10⁰ to 50⁰. The Archimedes screw turbine is being explored all around the world as one of the best candidates for efficient electricity generation at low head and low flow rate sites. The experimental work conducted for the screw turbine angels ranges from 10⁰ to 50⁰ with different flow rates. The efficiency of the Archimedes screw turbine was calculated for the experimental results. The experimental work is completed by using drainage water for the different range angles and different flow rate to produce the higher electricity. Theoritical and experimental values are compared. The maximum power of 12W is generated at the 30⁰ screw angle and 7 rpm rotar.

Impact of Dynamic Technological Advancement on the SaaS-Based Solution Delivery Process in Software Industry: A survey

2024-10-07T07:51:23+00:00

To keep up with rapid technological advancements and evolving business needs, the enterprise software industry prioritizes customer-centricity and adopts Industry 5.0 principles. In this article, we have reviewed the available research literature and interviewed stakeholders associated with the SaaS-based supply chain solution industry, emphasizing the practical challenges technical solution providers face when transitioning from on-premises solutions to cloud-based infrastructure while aligning with industry revolutions. One of the primary challenges we uncovered is effectively integrating these principles into software solutions and implementing Industry 5.0 approaches in the SaaS software delivery process. Addressing customers' dynamic requirements requires a workforce that continually learns and creates a knowledge base that aligns with new demands, for which a lack of suitable end-user technical documentation related to SaaS customization acts as a bottleneck. Furthermore, customers often face challenges when adopting innovative solutions inspired by the dynamic industry revolution. Building consistent customer engagement and establishing open communication channels for future solution roadmaps pose ongoing challenges for SaaS technology providers. This article discusses the end-to-end transformation required to enhance an organization's capabilities. The study findings can benefit critical organizational stakeholders, including change management leadership, policymakers, and decision-makers, in making informed decisions.

The Role of Emotional Intelligence on the Performance of Public Relations Employees Jobs

2024-10-07T08:13:12+00:00

Emotional intelligence is a more significant concept that contributes to identifying the feelings and emotions of the individual,understanding the feelings and emotions of others, and building good relations, whether in social life or working life. Emotional intelligence skillsmust be provided to all employees in organizations, specifically those working in management and those with direct communication and interaction with internal and external audiences. Therefore, this study aimed to identify the impact of emotional intelligence and its relationship with public relations employees' performance. The study sample is a questionnaire that numbered (173) public relations employees in the Kingdom of Saudi Arabia. The researcher used the descriptive approach as it is appropriate for the nature of the study.

The study showed that the level of emotional intelligence of public relations employees in Saudi Arabia was high by an average of (4.11). The results showed that in public relations, employees in Saudi Arabia praise others when they do a good job. They also have awareness and knowledge of their feelings and contribute to helping others when frustrated. The results also showed that the level of the job performance of public relations employees is very high (4.45), as the respondents strongly agreed to the majority of the levels of job performance, the most important of them are responding to their co-workers and working hard for the tasks assigned to them and look for solutions to problems that may appear in their work. The results found a relationship between emotional intelligence and employee performance. The higher the emotional intelligence, the higher the job performance. One of the most important recommendations is to prepare training programs for different departments in the field of emotional intelligence, Specifically public relations management, as it aims to establish good relations with others, which requires understanding their feelings and emotions, attention to the emotional intelligence field through the preparation of future studies and researches that include emotional intelligence and its impact on other field, conducting research in the field of emotional intelligence in the Middle East countries, including the focus of emotional intelligence in the criteria for evaluating employee performance.

An efficient hybrid software-defined networking (HSDN) approach is proposed to optimise the distribution of network traffic in traffic engineering

2024-10-07T08:14:43+00:00

Traffic engineering (TE) is a very efficient technique for optimizing the distribution of network traffic, leading to improved performance of a hybrid software-defined network (SDN). Traditionally, TE systems have mostly used heuristic methods to centrally optimize the setting of link weights or traffic splitting ratios while dealing with static traffic demand. It is important to realise that as the network grows and management gets trickier, centralised traffic engineering (TE) methods have a hard time keeping up with the huge amount of data they have to process and take a long time to find the best way to route traffic when there are problems or changes in the demand for traffic on the network. Aim to improve the implementation of dynamic and efficient routing in traffic engineering (TE). ring (TE). Efficient hybrid SDN (hSDN) schemes are crucial for the preservation of global information and resource allocation to several applications running in the network. These schemes specifically focus on topology identification, traffic categorization, energy management, and load balancing algorithms. Therefore, in order to enhance network performance by enhancing traffic engineering (TE), it becomes crucial to provide appropriate resources to applications in the network. To accomplish a worldwide optimization goal, An interactive setting for training routing agents with access to partial link use data. To improve the distribution of credit in a multi-agent system, A differential reward assignment method. The purpose of this mechanism is to motivate agents to make choices that are more optimal. The comprehensive simulations conducted on real traffic traces demonstrate the superiority of improving traffic engineering (TE) performance, especially in scenarios when traffic demands vary or network outages happen.

THE EFFECTIVENESS OF LEADERSHIP IN IMPROVING THE QUALITY OF HIGHER EDUCATION

2024-10-07T08:27:43+00:00

This research aims to realize effective leadership in higher education, presented here is one of the investigations to explore the extent of impact of work planning, providing direction, supervisory activities and the relationship between leaders and subordinates to achieve performance. Data for this study were collected using the qualitative research approach, where it first looked into the general overviews collected through document analysis gathered from reports, brochures, journal articles, newspapers, the Internet, websites and mass media. This research uses a qualitative type, primary data uses interviews, competent informants, namely lecturers, staff and students, the data analysis used is interactive qualitative analysis. The results obtained were significant, effective leadership in higher education, namely the preparation of work programs that involve subordinates, bring benefits to identifying the problems faced. Providing direction that is appropriate to the main tasks and functions has the impact of increasing the professionalism of subordinates. Scheduled and stress-free management activities bring a pleasant work atmosphere. A good relationship between leaders and subordinates can actually produce strength in achieving the vision and mission of higher education. To achieve the vision, mission and goals of higher education institutions, if leaders do not involve subordinates, it is difficult to find out what is hidden. If the direction is not appropriate, it also becomes a problem for subordinates to carry out their tasks well and correctly. Then, supervision is necessary as a step to assess the ability to carry out the duties and work of subordinates. To further support the goals of the institution, a harmonious relationship is needed.

TRANSFORMATION BUMN SUGAR : A STUDY OF PARADIGM AND ECOSYSTEM CHANGES TOWARDS SELF-SUFFICIENCY Review Sociology Institutional Karl Polanyi's

2024-10-07T08:29:14+00:00

The rapid decline in domestic sugarcane productivity has for decades thrown Indonesia into relatively absolute dependence on sugar imports as it continuously weakens productive capacity of both the sugarcane farmers on-farm and sugar mills off farm. The issuance of Presidential Decree 40/2023 on national sugar self-sufficiency then provides policy framework and governance guideline upon which Ministry of Badan Usaha Milik Negara-BUMN (State Owned Enterprise-SOE) is authorised to execute the policy. Central to this initiative is the transformation of both BUMN ministry and sugar SOEs into PTPN III Holding and its subsidiary PT SGN. Againts the backdrop, this study addresses three key questions: (1) How is the transformation of sugar SOEs advancing toward the national sugar self-sufficiency targets set for 2028-2030? (2) What are the normative prerequisites and institutional actions that affect the success of this transformation in driving national sugarcane-sugar production?, and (3) in what ways has such transformation deeply impacted the socio-economic relationships between PT Sinergi Sugar Nusantara’s (PT SGN) sugar mills and their partner farmer groups as the primary suppliers of raw sugarcane? By deploying Karl Polanyi’s Institutional Sociology approach (1957, 1944, 1968) this study dissects the transformation in PTPN III Holding and its subsidiary PT SGN within an intricate context of nationwide sugar industry led by oligarchic force and institutional restructuring on the part of the SOE. Key concepts are embedding, dis-embedding, and re-embedding used to analyze how far and deeply ingrained the three pillars of economic reintegration (redistribution, reciprocity, and householding) have been applied in the whole process of institutional, managerial and operational transformation. Specific attention given to the governance of sugarcane production upstream in two practical breakthroughs namely regionalization of sugar mills and the implementation of a profit-sharing system (SBH). Drawn upon qualitative case study at Gempolkrep Sugar Factory in Mojokerto, Pesantren Baru Sugar Factory in Kediri, and Meritjan Sugar Factory in Kediri, this eight months inquiry (August 2023-April 2024) results in three core findings relating to paradigm, ecosystem and partnership. First, there has been norms-compatibility between productivity paradigm applied in PTPN III Holding since 2021 and eternity paradigm as working norms for institutional transformation in the BUMN ministry since 2019. Second, PTPN III Holding through regionalization of the sugar mills undertaken by PT SGN has succeeded in boosting up productivity of the consolidated sugar mills within collaborative management. Third, productive partnership between the sugar mills and sugarcane farmers, on farm and off farm, has gradually been reinvigorated with the sole mechanism of SBH which proves effective in disentangling them from rent-seeking ecosystem called Pok-Pokan benefiting only the local big capitalists and private sugar mills. This study offers both theoritical and practical policy significances. It sheds more light on the strenghts of Polanyi’s institutional approach in addressing root causes of social- economic disintegration between BUMN and the farmer community in current economic neoliberalization. While in terms of policy significance, this study endorses the collaborative role of BUMN realigning with the farmer community for national sugar self-sufficiency 2028-2030.