bluuebunny/crossref_metadata_2025 huggingface dataset:
Deciphering Human Emotions in Audio with Deep Learning Networks and MFCC Spectrogram Features
Authors: Ritika Bidlaan
Published: February 2025

Introduction: Emotions play a fundamental role in human communication and interaction. In this paper, an effective and highly accurate sequential Deep Learning (DL) based emotion recognition model is proposed.
Objectives: The main contribution of this work is to use effective Feature Extraction, Feature Selection (FS) and classification techniques so that accuracy of the model is increased while its computational complexity is reduced.
Methods: The research utilizes a Surrey Audio-Visual expressed Emotion (SAVEE) dataset of audio recordings capturing emotional states. Feature extraction techniques are applied to obtain representative features from the audio signals. Specifically, Mel Spectrograms are computed and transformed into the decibel scale to capture the frequency content of the signals. These features are then fed into DL architecture for extracting relevant and important features. Moreover, Infinite Feature Selection (IFS) and Fuzzy based techniques are implemented for selecting only important and crucial features from the given feature set. For classification phase, a sequential DL based layered architecture is implemented for detecting and classifying emotions in audio signals.
Results: The inclusion of convolutional layer with larger filter sizes and application of feature selection techniques improved the performance and interpretability of the approach.
Conclusions: Through extensive experiments, an accuracy of 90.27% is achieved by proposed model on test dataset, outperforming traditional QPSO, WQPSO and PQPSO models by 33.027%, 31.817% and 30.897% respectively, and showcasing the potential of proposed model.
Factors Influencing Chinese Postgraduates’ Movement Intention for Employment Opportunities in the Yangtze River Delta Area, China
Authors: Shibo Fu
Published: February 2025

Graduate students' mobility and options for careers, which are an important aspect of developing young knowledge skills, have an impact on the region's economic structure and innovation ecology. The purpose of this study is to investigate on the factors that influence Chinese postgraduates' intentions of relocating and seeking employment in the Yangtze River Delta region of China. This study takes a quantitative method, collecting data from a 446 gradutes who have intention to move for employment opportunities in the Yangtze River Delta Area with a purposive sampling method. The findings show that attitude, subjective norms, perceived behavioral control, and self-efficacy all have a significant beneficial impact on graduate students intends to pursue employment opportunities in the Yangtze River Delta region.
Social Media and Knowledge Sharing: Impact on Academic Development in Public Universities of Kanpur
Authors: Sanjeev Kumar Singh
Published: February 2025

Background ContextEducation have been profoundly impacted by social networking, which has completely changed the way we acquire and exchange information.  Since social media is interactive, it makes learning more effective by giving people a forum for conversations and information sharing. Social media platforms have significantly influenced how individuals interact and exchange information, impacting educational practices by enabling students to share and access knowledge more effectively. This shift is particularly relevant in academic environments where collaborative learning and resource accessibility are crucial.
Knowledge GapDespite extensive social media usage among students, limited research addresses its specific impacts on academic growth in public universities, especially in regional contexts like Kanpur, India. This study seeks to fill this gap by exploring how social media supports academic development, focusing on factors like trust, collaboration, communication, and perceived benefits.
MethodsUsing a cross-sectional survey design, data were collected through structured questionnaires from students across three major public universities in Kanpur. Structural Equation Modeling (SEM) was applied to examine the relationships between social media use and academic outcomes, emphasizing mediating factors such as family and technological support.
Key FindingsThe analysis shows that social media interactions notably enhance academic development by building trust, fostering collaboration, and promoting effective knowledge sharing. Support from family and access to technology further strengthen these benefits, positively influencing students’ academic engagement and performance.
ImplicationsThese findings suggest that educational institutions should consider integrating digital collaboration tools into their academic frameworks and support policies that enhance family and technological access. Such initiatives can help students leverage social media effectively for academic growth, making it a valuable educational resource. Future research might expand on these insights through longitudinal studies to capture evolving academic-social media dynamics.
The Role of Information Systems in Enhancing Entrepreneurial Outcomes: Evidence from Hyderabad's IT Sector
Authors: J V Gopinath
Published: February 2025

An examination of how information systems (IS) contribute to the perception of entrepreneurial success in Hyderabad's information technology (IT) industry is the focus of this research. Business processes, decisions, and entrepreneurial results are being increasingly shaped by the integration and use of IS in technology-driven sectors. Access to real-time data, effective communication systems, and the strategic deployment of IS technologies are some of the important antecedents that this study seeks to identify as influencing entrepreneurial success. The researchers used a mixed-methods strategy, analysing both quantitative data from a poll of 200 IT entrepreneurs and qualitative information gleaned from in-depth interviews. The results show that operational efficiency, innovation, and market competitiveness are all greatly enhanced by IS when used effectively. In addition, having a solid foundation for information systems improves the quality of decisions made and encourages entrepreneurial agility, which in turn increases the likelihood of success. Policymakers and practitioners alike may learn from these findings on the importance of IS in fostering entrepreneurial endeavours in the IT sector.
A Perspective Study on the Challenges Faced by the Small-Scale Industries during COVID-19
Authors: Hai-ja Kim
Published: February 2025

Small and Medium Enterprises with various ventures, such as equipment and machinery manufacturing, metal and electrical products, cement, food and drinks, construction and construction supplies, rubber and plastic goods, and automation technologies, are seeing considerable expansion in the Indian market. The Covid-19 pandemic has impacted all economic sectors, but SMEs in India have been particularly hard hit. The current effect of coronavirus on SME's in India is investigated in this study. So that after smoke clears, SME's discover that they must grow and change over time to stay profitable. Safeguard processes, hygiene and disinfection techniques, a revised procurement strategy, the integration of affiliate marketers, standardising their product range, a much more receptive assessment of supply chain management, and an assessment of catastrophe or disaster response plans are some of the answers to this problem. This study author is trying to analyse the challenges faced by small scale industries during lockdown period and the government initiatives in this regard.
Application of Cryptocurrencies using Blockchain for E-Commerce Online Payment
Authors: Dong-il Kim
Published: February 2025

This article discusses the potential benefits of using blockchain technology in e-commerce, including payment processing, data privacy, and increased security. Blockchain-based cryptocurrencies are becoming increasingly popular for e-commerce businesses due to their ability to securely and quickly transfer money between parties Cryptocurrency is an online payment method that is used as a virtual currency. This payment is using the blockchain method that is used to secure the information generated in the process. It is also mentioned that this process of payment also required data. This cryptocurrency also has the process of investment. There is network security that is required for making the payment faster.
Mitigation and Detection of Faulty Nodes in Multinode Hadoop Cluster using Hybrid Machine Learning Techniques
Authors: Atul V. Dusane
Published: February 2025

In today's modern significant computational systems, jobs are broken down into several smaller processes that run simultaneously to increase the rate at which jobs are completed and lower the amount of energy that is consumed. However, dealing with straggler processes, which are sluggish running processes that raise the total response time, is a typical performance challenge in these kinds of systems. These kinds of jobs have the potential to have a substantial effect on the Quality of Service (QoS) provided by the system. It is necessary to have automatic straggler identification and mitigation systems that can complete jobs in a shorter amount of time in order to address this problem. Previous work often constructs reactive frameworks, the central emphasis of which is, in order, the identification, followed by the mitigation, of straggler tasks, that ultimately results in delays. Other research make use of prediction-based proactive systems, however they disregard the peculiarities of heterogeneous hosts or dynamic tasks. In this article, Hybrid Machine Learning (HML) is offered as a method that may determine which jobs are likely to be behind schedule and dynamically adjust scheduling in order to obtain faster response times. The method that has been suggested examines all tasks as well as hosts on the basis of the use of compute and network resources, and it is also able to predict and mitigate the effects of expected straggler activities. This speeds up the execution without lowering the quality of service. The proposed HML is evaluated in terms of quality of service factors such as energy usage, processing time, resource contention, and CPU utilisation in comparison to other machine learning methods that already exist, including Support Vector Machine (SVM), ADABOOST, Artificial Neural Network (ANN), Naive Bayes (NB), Decision Tree (DT), and Random Forest (RF). According to the results of several evaluations, the proposed HML cuts down on processing time, resource contention, and energy usage by 13.5%, 11.25%, and 16.75%, correspondingly, when compared to standard machine learning methodologies. The proposed HML has a performance accuracy of 98.1%, making it superior to those other conventional ML methods.
The Influence of Employee Engagement on Employee Performance is Mediated by Organizational Commitment
Authors: Janah Sojanah
Published: February 2025

Introduction: Human resources are one of the important factors in an organization or company, besides other factors such as assets and capital. Every organization and company will always try to improve its employee performance to achieve company goals. Performance is often a problem that arises among employees.
Objectives: The purpose of this study is to show the effect of Employee Engagement and employee organizational commitment on Employee Performance.
Methods: The design used in this study is the cross-sectional method. This type of research uses a quantitative method with an explanatory survey. The sampling technique used is the probability sampling technique using a sampling of 100 respondents through the distribution of questionnaires. The data analysis technique used is Partial Least Squares (PLS) with the help of the smartPLS version 4.0 computer software program.
Results: Based on the results of the study, it can be seen that there is an influence of Employee Engagement and employee organizational commitment on Employee Performance. Organizational Commitment is able to mediate the influence of employee organizational commitment on employee performance.
Conclusions: Based on the results of the research that has been conducted using verification analysis, it can be concluded that the influence of Employee Engagement on Employee Performance is mediated by Organizational Commitment. This shows that the higher the Employee Engagement, the higher the Employee Performance mediated by Organizational Commitment. The existence of this research is expected to help subsequent researchers in conducting research on job involvement and Organizational Commitment using different indicators from more diverse theoretical sources, and on different objects, because there are still many limitations in this research, especially those related to research methods and data collection techniques.
Advancing Book Recommendation Systems: A Comparative Analysis of Collaborative Filtering and Matrix Factorization Algorithms
Authors: Prasert Luekhong
Published: February 2025

Introduction: Recommendation systems (RS) are pivotal in enhancing personalized user experiences, particularly in the educational domain, where tailored book recommendation systems (BRS) support individualized learning. Despite their importance, selecting an optimal recommendation algorithm remains challenging, especially in scenarios involving large and sparse datasets.
Objectives: This study aims to evaluate the performance of four recommendation algorithms—Random Baseline, Popular, User-Based Collaborative Filtering (UBCF), and Singular Value Decomposition (SVD)—to identify their strengths and tradeoffs regarding accuracy and computational efficiency. The goal is to provide actionable insights for selecting appropriate algorithms for educational platforms and similar applications.
Methods: The evaluation employed a dataset containing over 10,000 books, 53,424 users, and 981,756 ratings. The algorithms were assessed using Root Mean Square Error (RMSE) to measure predictive accuracy and training time to indicate computational efficiency. The analysis addressed challenges such as data sparsity and rating biases.
Results: The results demonstrated that SVD achieved the highest accuracy (RMSE: 0.950), effectively uncovering latent relationships in sparse datasets. UBCF, with a slightly lower accuracy (RMSE: 1.020), offered a balance between accuracy and computational efficiency, making it suitable for real-time applications. Conversely, simpler algorithms like Random Baseline and Popular exhibited faster training times but significantly lower predictive accuracy, highlighting the limitations of non-personalized methods.
Conclusions: This study underscores the tradeoffs between accuracy and efficiency among recommendation algorithms. While SVD is ideal for accuracy-driven applications, UBCF provides a practical alternative for scenarios requiring computational efficiency. The findings have substantial implications for educational platforms and e-commerce, where personalized recommendations enhance user satisfaction and engagement. Future research should focus on integrating deep learning models and expanding evaluation criteria, including user satisfaction and diversity, to further improve the performance of recommendation systems.
From Pixels to Precision: Techniques for Image Dataset Refinement
Authors: Raji.N
Published: February 2025

Preparing and processing image datasets is a crucial step in deep learning and computer vision applications. However, many challenges arise in creating high-quality image datasets that can make deep learning models more accurate and generic. We provide a thorough review of best practices in this document for preparing and processing image datasets, from raw data collection to final dataset creation. We discuss various techniques and tools that can be used to enhance image quality, reduce noise, and address issues such as class imbalance and bias. Additionally, we provide practical tips for data cleaning, normalization, and augmentation, which are essential for improving the accuracy and robustness of machine learning procedures. Consequently, we demonstrate the effectiveness of our approach by applying it to real-time images and converting them to datasets suitable for several computer vision applications, such as object detection and classification.
Artificial Intelligence for Environmental Protection: Opportunities and Challenges
Authors: Shashikant Tripathi
Published: February 2025

Artificial Intelligence (AI) is emerging as a revolutionary force that can alter environmental protection efforts in the face of growing environmental issues. This study explores the profound potential of AI technology to safeguard our planet. Important ideas covered in the study are summed up in the abstract, including the use of AI in sustainable resource management, biodiversity preservation, and climate change mitigation. In order to fully utilize AI's potential for the good of our global environment, it also discusses ethical issues and the significance of interdisciplinary cooperation. The significance of AI solutions as vital instruments in the toolbox of politicians and environmentalists attempting to tackle the most pressing environmental issues of our day is emphasized in this paper.
Quality of Work Life and its Impact on Employee Engagement
Authors: Ekos Albar
Published: February 2025

Employee engagement plays a pivotal role in achieving organizational success, particularly in dynamic business environments. This study explores the influence of Quality of Work Life (QWL) on Employee Engagement by focusing on key dimensions such as work-life balance, job security, and career development. The research addresses challenges faced by organizations in maintaining engagement levels, particularly due to workplace dissatisfaction and reduced employee retention. Utilizing a quantitative approach, the study employed Structural Equation Modeling (SEM) to analyze the relationships between QWL and engagement. Findings reveal that improving QWL fosters a more engaged workforce by enhancing employees’ emotional, cognitive, and behavioral commitment to their roles. Among the QWL dimensions, work-life balance emerged as a primary driver of engagement, followed by career development opportunities and job security. This research contributes to the literature by emphasizing the strategic role of QWL in enhancing engagement and offers practical recommendations for organizations. These include adopting flexible work policies, designing robust career development frameworks, and ensuring job stability. By integrating these strategies, organizations can create a supportive work environment that nurtures employee satisfaction and long-term productivity. The study underscores the importance of aligning QWL initiatives with organizational goals to address current workforce challenges effectively.
Analyzing the Impact of Digital Information Communication Technologies (DICT) on Literacy Development in Third Grade Primary School: A Case Study on Education
Authors: Natalina Purba
Published: February 2025

This study aims to answer the question of how digital information communication is linked to students’ literacy development during third-grade primary education. This study is significant considering the ongoing debate on how to deal with digital media in schools. Many schools are asked to include digital tools in their education. Literacy is one of the main aims of early education, with children starting to learn to read and write at school. Third-grade primary education was chosen in this study of decision-making alongside other years in primary education, as reading comprehension has become a new focus this year. The focus shifts from learning to reading. The teachers made use of digital tools to manifest the connection between new pieces of information and stored relevant background knowledge when stimulating their reading comprehension. This study directly examined the potentially significant influence of digital communication on literacy development. It is crucial to ensure that all elementary school educators receive training to responsibly and effectively incorporate technological advancements into the primary education curriculum. This study reveals how various digital platforms and tools may impact different aspects of literacy, such as reading comprehension, writing skills, and vocabulary acquisition. By investigating these relationships, educators can develop targeted strategies to leverage digital communication and enhance learning outcomes. Furthermore, the findings from this research could inform policy decisions regarding technology integration in elementary education, ensuring that digital resources are used to complement rather than replace traditional literacy instruction.
Moving Object Detection in Aerial Images using DeepSORT with Faster R-CNN
Authors: T. Sanjeeva Kumar
Published: February 2025

Aerial imagery is increasingly utilized in various applications, including surveillance, disaster management, agriculture, and urban planning. Detecting and tracking moving objects within aerial images is a crucial task for these applications. This paper presents a novel approach to moving object detection in aerial images, combining the Faster R-CNN (Region-based Convolutional Neural Network) for object detection and the DeepSORT (Deep Simple Online and Realtime Tracking) algorithm for object tracking. The proposed method leverages the strengths of both techniques, enabling accurate and efficient detection and tracking of moving objects in aerial imagery. First the Faster R-CNN model is employed to detect objects in each frame of the aerial image sequence. The model has been pre-trained on a diverse dataset, making it capable of detecting a wide range of objects. Post-detection, the DeepSORT algorithm is applied to track the detected objects across frames. DeepSORT utilizes deep learning for object appearance and Kalman filtering for state estimation, resulting in robust tracking even in challenging scenarios. The proposed model obtained an overall accuracy of around 86%.
Enhanced Brain Tumor Detection using Adaptive Preprocessing and Hybrid Deep Learning Techniques
Authors: Suresh Kumar N
Published: February 2025

Neurological and brain cancers represent major global health issues, with MRI serving as a vital diagnostic tool. However, interpreting MRI images manually is often slow and prone to inconsistency. This study presents a sophisticated framework for detecting brain tumors using Dynamic Contrast-Enhanced MRI (DCE-MRI). The framework integrates advanced preprocessing and hybrid deep learning techniques to enhance performance. Adaptive multi-scale Gaussian filtering is used initially to reduce noise and enhance picture quality. Following the identification of the most relevant features using Recursive Feature Elimination with Cross-Validation (RFECV), Levy Flight Particle Swarm Optimization (LFPSO) is used to optimize feature extraction. Convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) are combined in a hybrid deep learning model to accomplish the classification, which successfully captures both temporal and spatial patterns. This methodology offers notable improvements in detection accuracy and processing speed, showcasing its potential for clinical use and better patient outcomes.
Optimized Cluster-Based Routing Protocol for Improved Bandwidth and Reduced Delay in Vehicular Ad-Hoc Networks
Authors: G. Karthikeyan
Published: February 2025

Vehicular Ad Hoc Network (VANET) is a cutting-edge technology that enables communication between vehicles and the surrounding road infrastructure, paving the way for intelligent transportation systems. Ensuring stable connections in VANETs requires a robust and reliable routing protocol, as these networks lack central coordination, exhibit high node mobility, and possess highly dynamic topologies, making routing a significant challenge. Many existing mobility-based routing protocols fall short in addressing Quality of Service (QoS) requirements, as their performance is heavily influenced by vehicle speed and driving conditions. On the other hand, QoS-based approaches often fail to account for the challenges posed by high-speed mobility, leading to frequent connection failures due to the increasing mobility of nodes within a given area. To overcome these challenges, this paper introduces a novel Cluster-Based Congestion Control Routing (CBCCR) protocol designed to enhance overall network efficiency by improving route throughput, optimizing bandwidth utilization, and minimizing end-to-end latency. The CBCCR protocol addresses the limitations of repetitive route detection and frequent Cluster Head (CH) reelection processes, thereby enhancing route stability and network performance.The proposed approach involves several key steps. First, the network is divided into stationary clusters. Next, a new distributed CH selection method is introduced, leveraging specific parameters to optimize the selection process. To further improve efficiency, an Enhanced BAT Algorithm (EBA) is employed for CH selection, and a novel routing method is developed to identify the most suitable CH based on the destination's position and the locations of neighboring CHs.Simulation results highlight the effectiveness of the CBCCR protocol, showcasing significant improvements in bandwidth utilization, increased throughput, and reduced transmission delays. These findings demonstrate that the CBCCR protocol offers a robust and efficient solution to the routing challenges in VANETs, making it a promising approach for advancing intelligent transportation systems.
Investing in ELSS Funds: Redeem or Stay Invested?
Authors: Ram Sapkota
Published: February 2025

With the evolution of capital market, investors prefer investing in mutual funds as they are relatively less risky compared to equity share investments. ELSS is an excellent avenue for regular small investments to earn attractive return over long period and it is the only category of mutual fund which give tax benefit under section 80C of Income Tax Act 1961. The study evaluates the performance of ELSS funds in lock-in period and open-ended ELSS funds after lock-in period using risk and return measures. Multiple measures used are 3 years Average return (%), Standard Deviation (%), Sharpe Ratio, Sortino Ratio, Beta, Alpha, R Square, Expense Ratio, Net Assets (Cr), Equity Asset (%), Debt Asset (%) and Cash and cash equivalent asset (%). The duration of the study was three years, from 16 June 2021 to 15 June 2024. The study incorporates 119 Equity ELSS funds of which 84 were open ended and 35 were in Lock-in period. The study reported that Funds in Lock-in period have recorded more returns than the ELSS open ended funds. Also, the funds in Lock-in period have recorded more variance in returns than the ELSS open ended funds which explain their higher return. Hence investors can redeem the ELSS units and reinvest the amount in another ELSS fund once lock-in period is over. The schemes which fall under growth investment style and medium capitalization are a good choice.
Secure and Energy Efficient Routing Approach for Wireless Sensor Networks to Counter Flooding Attack
Authors: Vivek Sharma
Published: February 2025

Wireless Sensor Networks (WSNs) are used in many applications owing to their scalability and economic efficiency, but their adhoc characteristics makes them vulnerable to security attacks such as RREQ flooding attacks. RREQ flooding attack overload the network with excessive RREQ messages resulting in network congestion and power exhaustion and interruption of standard operations. This paper presents a secure and energy-efficient routing approach that focuses on effectively mitigating the effects of flooding attacks. The proposed approach combines sophisticated algorithms into the Ad hoc On-Demand Distance Vector (AODV) routing protocol to detect and isolate malicious nodes by analyzing route request (RREQ) frequency. Nodes demonstrating abnormal behavior are identified and removed from network, significantly minimizing the effects of attacks. An energy-aware clustering technique is also included for improving energy efficiency throughout the network. This approach dynamically identifies cluster heads depending on remaining energy and distance, hence minimizing energy expenditure and extending the network's lifespan. Results from the simulation demonstrate the effectiveness of the suggested method in enhancing packet delivery ratio, decreasing energy consumption, and prolonging network lifespan, thereby attaining an optimal equilibrium between security and efficiency.
China’s Provinces Artificial Intelligence as a Catalyst for Green Horizons: A Provincial Analysis of China's Stride Towards Sustainable Development Goals
Authors: Yufeng Wang
Published: February 2025

Next-generation information technology (IT) and artificial intelligence (AI) can support environmental improvement, climate change response, and the conservation and efficient use of resources for green transformation, potentially impacting the Sustainable Development Goals (SDGs). This study estimates the impact of AI on five SDGs using provincial panel data from 2006 to 2018. The estimation results indicate that AI makes a significant contribution to sustainable development, a finding that has been confirmed through a series of robustness tests. Furthermore, the mechanistic analysis demonstrates that AI primarily promotes sustainable development by enhancing energy structure and technological innovation. The greater the reduction in dependence on fossil fuels and the higher the degree of technological innovation, tthe greater the effectiveness of AI in promoting sustainable development. Furthermore, the regional heterogeneity test revealed that the effect  in enhancing sustainability achieved by AI is most effective in the central and western regions, with the eastern region following closely behind.
Developing an Assessment Model of the Independent Campus Internship Program Based on an Expert System
Authors: Fahmi Rizal
Published: February 2025

Improving the quality of vocational education is a critical priority to ensure that graduates possess the cognitive abilities, soft skills, and hard skills required by the modern workforce. However, significant challenges remain, such as the inability of graduates to meet workplace demands, outdated and conventional assessment methods, and the absence of expert system-based tools to evaluate the effectiveness of the Independent Learning Campus (MBKM) program. Additionally, the lack of comprehensive assessment guidelines provided by universities has further hindered the optimal implementation of this program. These issues underline the need for an innovative approach to address the gaps in assessment practices for the MBKM internship program. The objective of this research is to develop an expert system-based assessment model for the MBKM internship program that is valid, practical, and effective. The research leverages the Borg and Gall development model, focusing on creating a framework that integrates higher education assessments with industry-specific requirements. The resulting model facilitates theoretical and practical evaluations, using a website-based expert system that incorporates artificial intelligence to monitor and assess learning outcomes in real-time. The novelty of this study lies in its ability to bridge the gap between academic assessments and industry needs through an innovative, expert system-driven approach. This model provides a comprehensive assessment framework, including preparation guidelines, implementation strategies, assessment rubrics, and reporting mechanisms for lecturers, instructors, and students. By enhancing the alignment between vocational education and industry demands, this research offers a transformative contribution to the MBKM program and sets a foundation for further advancements in assessment technology.
Empowering Lives: Unveiling the Impact of LZNK Business Capital Assistance through the MAQASID SHARIAH Lens in Alignment with the Sustainable Development Goals (SDG)
Authors: Aryati Juliana Sulaiman
Published: February 2025

Objective: The objective of this study is to investigate the effectiveness of business capital assistance provided by the Kedah State Zakat Board (LZNK) in fulfilling the basic needs of Al-Daruriyyat, with the aim of understanding its impact on recipients' religious, personal, intellectual, progeny, and financial well-being.
Theoretical Framework: The main concepts and theories that underpin the research are presented through the lens of Maqasid Shariah. This framework provides a solid basis for understanding how zakat distribution aligns with Islamic principles and supports the United Nations Sustainable Development Goals (SDGs), particularly Goals 1 and 8.
Method: The methodology adopted for this research comprises a qualitative study design, involving in-depth interviews with recipients of the Self-Reliance Assistance Scheme. Data collection was carried out through structured interviews to gather detailed insights into the experiences and outcomes of the participants.
Results and Discussion: The results obtained revealed that LZNK's capital aid successfully meets the basic needs of Al-Daruriyyat, with recipients reporting positive impacts across various aspects of their lives. In the discussion section, these results are contextualized in light of the theoretical framework, highlighting the alignment with Maqasid Shariah and the SDGs. Possible discrepancies and limitations of the study are also considered in this section.
Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the field of zakat distribution and socioeconomic development. These implications could encompass poverty alleviation strategies and sustainable economic growth.
Originality/Value: This study contributes to the literature by offering a comprehensive analysis of zakat distribution through the Maqasid Shariah perspective, highlighting its potential as a tool for sustainable development. The relevance and value of this research are evidenced by its potential impact on poverty reduction and economic development practices.
The Power of Knowledge in Shaping Retirement Planning in Jordan: Trust and Advice as Key Factors
Authors: Talal Tayel Alabdallat
Published: February 2025

The purpose of this study was to identify the association between financial advice, trust in financial advisors, financial knowledge, and retirement planning behavior and to determine the role of financial knowledge on the association between financial advice and trust in financial advisors as well as between trust in financial advisors and retirement planning behavior. A quantitative research design was used for the study and data were collected from 412 working Jordanian adults aged 25-60 through the use of an online questionnaire. The results demonstrate that financial advice and trust to financial advisors both directly and indirectly through facilitating financial knowledge impacts retirement planning behaviour positively. The aforementioned findings lend more insight to the literature while confirming the complexity of such relationships in the context of Jordan and offering practical implications for financial service providers, policymakers, and individuals. Nonetheless, its strengths include the use of self-reported data and lack of a longitudinal design. The main contribution of this study is its theoretical and empirical contextualization of the application of the TTM to the study of the impact of financial advice on trust, financial knowledge, and retirement planning behaviour in the Middle East.
Entrepreneurship, Market Orientation and Marketing Performance in Culinary Business: Mediating Effects of Innovation and Competitive Advantage
Authors: Westim Ratang
Published: February 2025

This study examines the mediating effects of innovation and competitive advantage on entrepreneurial orientation, market orientation, and marketing performance in the culinary business sector of Jayapura and Papua. This study uses quantitative exploratory analysis; within the population of 20 business owners who registered with the Tax Service Office in Jayapura, a questionnaire was completed to obtain their perspectives on these variables and their mediating effects. After validity and reliability tests and regression analysis using a structural equation model, this study found significant effects between entrepreneurship and market orientation, product innovation, and competitive advantage on marketing performance, and between entrepreneurship and product innovation, as well as between market orientation and competitive advantage. Product innovation has been proven to mediate entrepreneurship and marketing performance, along with competitive advantage in market orientation and marketing performance. Although this study is limited to culinary business respondents in the region, the findings are crucial in encouraging the development of small businesses, especially those engaged in the culinary sector, despite the amount of production, product variants, and their offered quality. Considering their ability to improve the regional economy, governments should pay more attention to the movement and success of these businesses.
Improving and Protecting the Privacy of Data Security Using Blake2 Algorithm
Authors: Ghsuoon B. Roomi
Published: February 2025

Several characteristics of data open up many new avenues for exploitation, and as such, new privacy and security models are required to address these emerging challenges. The magnitude of datasets generated make it near impossible for data managers to capture all the contextual semantics relevant to a unit of data, and this causes novel difficulties in privacy and security of these data systems using BLAKE2 Algorithm. At the micro-level, a datum in any software system transitions between several phases, from inception to deletion; this is known as the data life cycle, which provides a concrete guide for tracking the many states of a unit of data. The usefulness of such a model only increases with the complexity of the system that it represents, so it is useful to examine privacy and security from this perspective, as it provides a precise framework for discussion. This paper provides practical privacy and security recommendations for every step of the data life cycle, examining prominent infrastructures and their features that relate to their data management policies using BLAKE2 Algorithm with 98.97% secure and reliable all the time. BLAKE2 is a cryptographic hash function faster than MD5, SHA-1, SHA-2, and SHA-256. The most glaring issue with SHA is that its data storage system is completely unencrypted by default. Like MD5, SHA1, and many encryption algorithms were designed to be run within a trusted environment. In the case of unencrypted data, this means that malicious parties that have physical or virtual access to the file system can extract information as they please. Fortunately, BLAKE2 is largely reliant on the client code for encryption of any sensitive information before it saves to the database. Some mitigation techniques for this scheme include implementing proper file system permissions and file system level encryption.
Detection of Knee Osteoarthritis grade using Convolutional Neural Networks
Authors: Annu James
Published: February 2025

Knee OsteoArthritis (KOA) is a disease that affects a person’s quality of life. Early detection and monitoring of KOA progression is essential for effective therapy and quick recovery. A survey of the recent literatures indicates that deep learning methods can effectively assess KOA severity with improved accuracy and efficiency. Convolutional Neural Networks (CNN) help us to classify the levels of severity of Knee Osteoarthritis. The present study proposes a deep learning method in classification of osteoarthritis using Convolution Neural Networks. The study focuses on predicting the grades of input images with KL grades of Knee osteoarthritis. The study employs convolutional neural networks with the Rectified Linear Unit function (ReLU), activation function and Adam optimization algorithm to achieve high performance. The study evaluates 10 performance measures and the results indicate an improvement in performance measures when compared with existing techniques.
A Rule-Based Approach for Identifying Failure Inducing Combinations in Combinatorial Testing
Authors: Rekha Jayaram
Published: February 2025

In complex software systems, identification of parameter combinations that lead to failures is important for effective debugging and robust software quality assurance. Combinatorial testing (CT) can greatly reduce the number of test cases (TC) used for testing a complex system by generating TCs based on various parameter combinations, while still maintaining high fault-detection capabilities. In this paper, a rule-based approach is presented that will aid in identifying Failure Inducing Combinations (FIC) of parameters that caused a fault in CT generated TCs. The approach uses heuristics to identify rules to methodically split parameter combinations into more likely or less probable combinations causing failure, thus reducing the set of failure sources. This approach is tested on two different case studies i) Three factor authentication system and ii) Existing literature-based input. The results obtained show significant accuracy and time saving for program debugging, thereby indicating applicability of the approach to real-world problems. The approach was found to be successful in identifying the pair-wise combinations of parameters that are likely to cause failure.
Influence Stakeholder Communication Towards Sustainability Handling Waste Young Coconuts in West Java
Authors: Indriyati Kamil
Published: February 2025

Research This aiming analyze influence stakeholder communication regarding sustainability Handling waste coconut young people in Bandung, Ciamis, and Tasikmalaya. With approach quantitative and methods survey against 300 respondents, research This identify influence verbal, non-verbal, visual and digital communication towards effectiveness management waste. Analysis using the Partial Least Squares Structural Equation Modeling (PLS-SEM) model shows that all over form communication own influence significant to effectiveness management waste. Visual communication provides contribution highest (44.8%), followed by non-verbal communication (39.2%), verbal (38.6%), and digital (21.4%). The Q-Square result of 0.360 indicates that this model explains 36% of the variance effectiveness management waste, he stressed importance approach holistic communication. Recommendations study This covers integration of communication strategies that include all form communication For strengthen coordination between government, industry, and society, as well as increase awareness and involvement in the management program waste sustainable. With Thus, research This give outlook strategic for policy management effective waste in West Java.
Alleviating Psychological Distress through Green HRM: Fostering a Harmonious and Sustainable Environmental Passion
Authors: Kavita Nitin Khadse
Published: February 2025

This study examines the influence of Green Human Resource Management (Green HRM) strategies on reducing psychological discomfort and promoting environmental passion among employees. Data were gathered from 75 participants across two organizations, utilizing statistical techniques including correlation, regression, and ANOVA. The results indicate a substantial correlation between Green HRM practices and employee psychological well-being. Moreover, Green HRM practices were seen as essential in fostering environmental passion, consequently encouraging sustainable behaviors inside the workplace. These findings emphasize the significance of incorporating Green HRM to improve employee welfare and organizational sustainability.
Comparative Analysis of CNN Architectures for English and Gujarati Speech Recognition Using MFCC Features
Authors: Jasmine J. Karagthala
Published: February 2025

The paper investigates the efficiency of Convolutional Neural Network (CNN) architectures for speech recognition, focusing on the English and Gujarati languages. The study explores the impact of different CNN layer depths, utilizing 2, 3, and 4-layer configurations. Mel-Frequency Cepstral Coefficients (MFCC) is employed for feature extraction before feeding the data into the CNN models. The activation functions Rectified Linear Unit (ReLU) and hyperbolic tangent (tanh) are examined across all architectures. The research uses the Speech Commands dataset for English and a Gujarati digits dataset for analysis. After preprocessing and MFCC feature extraction, CNNs with varying depths and ReLU activation are employed. Training encompasses both languages, exploring parameters for balanced performance and efficiency, emphasizing tailored solutions for diverse linguistic contexts. The results reveal that the ReLU consistently yields superior performance on both the English and Gujarati datasets. In addition, the study found that increasing the depth of the CNN layers does not necessarily lead to improved recognition accuracy. The findings underscore the importance of selecting appropriate activation functions, highlight the nuanced relationship between CNN depth and recognition performance, and contribute to the understanding of CNN architecture optimization for speech recognition tasks in diverse linguistic contexts. The insights gained can inform the design of more effective speech recognition systems for globally recognized languages, such as English, and vernacular languages like Gujarati.
Analysis and Optimization of Process Parameters for Kerf Identification in Abrasive Jet Machining of Hastelloy: A Software-Based Approach Using ANOVA
Authors: P. Chiranjeevi
Published: February 2025

A software-based approach, leveraging Analysis of Variance (ANOVA), is employed to analyze the influence of different process variables on kerf width and depth. AJM (Abrasive jet machining) has been widely utilized to cut diverse materials. It has been utilized for challenging-to-machine materials, primarily metals, employed in diverse industrial procedures within the fabrication sector. An extensive knowledge of the systems governing its process of cutting is necessary for ascertaining its effective application, given its numerous uses. The chemical, marine, and aerospace industries are seeing a sharp rise in demand for micro goods. Here, the super alloys that are strong and resistant to corrosion are crucial. Hastelloy is an alloy that can be machined using AJM but it is hard to do so using regular methods. On the AJM test rig, a Hastelloy C276 sheet having a 1 mm thickness was drilled with the help of various process parameters. This paper presents the RSM methodology for the improvement of the process parameters of Hastelloy C276 abrasive jet machining. The ANOVA(Analysis of Variance) and RSM Analysis were utilized to compare the outcomes. L15 Orthogonal Array for KERF experiments are carried out at various levels.
Reinventing Smart Farming Using Adaptive Quantum Machine Learning Model
Authors: Sharad S. Jagtap
Published: February 2025

This study investigates how precision agriculture and crop production predictions may be improved using Quantum Machine Learning (QML) models, namely the Variational Quantum Circuit (VQC). The VQC outperformed traditional linear regression and other quantum models such as Quantum Neural Networks (QNN) and Quantum Convolutional Neural Networks (QCNN) by using quantum computing's ability to analyze high-dimensional agricultural data. With the lowest Mean Squared Error (MSE: 28.00), Mean Absolute Error (MAE: 3.8), and greatest R-squared (R2: 0.97), the VQC successfully identified intricate relationships between input variables such as acreage, rainfall, and fertilizer usage. Classical models, on the other hand, had more prediction errors and showed serious limits. This work opens the door for further research and the use of quantum technologies in agricultural systems by demonstrating the revolutionary potential of QML, particularly VQC, in tackling issues in agriculture, including food security, resource sustainability, and climate resilience.