bluuebunny/crossref_metadata_2025 huggingface dataset:
Validity of Mathematics Learning Devices with a Sedekah Context to Enhance Mathematical Communication Skills and Awareness of No Poverty
Authors: Nurniqta
Published: February 2025

Education plays a crucial role in achieving the Sustainable Development Goals (SDGs), particularly through Education for Sustainable Development (ESD). Implementing ESD requires higher-order thinking skills, which are currently still low among Indonesian students. One important aspect of higher-order thinking is mathematical communication skills, which can be enhanced using the Problem-Based Learning (PBL) model. Sedekah is a part of charity in the Islamic perspective and is relevant to SDG Goal 1 on No Poverty. By implementing a PBL device about sedekah, it is hoped that both mathematical communication skills and awareness of No Poverty can be improved. This study aims to describe the validity of a mathematics learning device with the context of sedekah in enhancing students' awareness of No Poverty and their mathematical communication skills. The learning device includes teaching modules, student worksheets, and learning outcome tests. This research is a developmental study, especially validation test. The research instrument is a validation sheet for the learning device. The learning device was validated by seven experts. The results showed that the validated learning device achieved a valid category, with an average score of 4.5 for the teaching module, 4.4 for the student worksheets, and 4.3 for the mathematical communication tests. Based on these results, it can be concluded that the mathematics learning device with the context of sedekah for enhancing students' mathematical communication skills can be used for the next development stage.
Implementation of Business Intelligence Technologies of Course Learning Outcomes based on Multidimensional Model Approaches
Authors: Azwa Abdul Aziz
Published: February 2025

Business Intelligence (BI) technologies offer intuitive insights and advanced analytical capabilities by transforming raw data from integrated sources into actionable knowledge. In the context of higher education, where institutions face heightened competition, dwindling public funding, and increased accountability, BI serves as a pivotal tool for data-driven decision-making. This paper addresses the problem of optimizing Course Learning Outcomes (CLOs) by implementing BI technologies. Our objectives are to integrate CLO data into a multidimensional model, apply BI tools for CLO analysis, and evaluate how BI can enhance CLO performance in higher education institutions (HEIs). We propose the Intelligence Course Learning Outcomes Platform (ICLOP) as a solution, utilizing BI technologies to generate efficient student data and performance metrics. The case study involving students from Universiti Sultan Zainal Abidin (UniSZA), Malaysia, demonstrates that BI can significantly improve enrolment management, resource allocation, and student success. The findings conclude that BI tools provide an effective means to enhance CLOs, offering a robust framework for improving educational outcomes and institutional efficiency, highlighting the transformative potential of BI in the academic sector.
Prototype of Web-Based Application as Information Media for SLB and Therapy Services in Malang Raya
Authors: Siti Annoora Niroha Pulungan
Published: February 2025

The study aims to investigate the development and impact of a web-based application that seeks to centralize and streamline access to information about special education schools (SLB) and treatment facilities in the Malang Raya area. The rapid progress of information and communication technology (ICT) presents a huge chance to improve the delivery of services and the spread of information in the domains of education and healthcare, specifically for children with special needs. This research utilizes many data collection techniques, such as observations, interviews, and document analyses, to identify the precise requirements and difficulties encountered by parents and caregivers when seeking pertinent information. The program employs digital technology to centralize fragmented data, providing a user-friendly interface that enhances the effectiveness of data retrieval and facilitates streamlined decision-making for parents. The program offers several important features, such as interactive maps, comprehensive service descriptions, and a community forum. These features work together to promote stronger community support and improve communication between service providers and users. The study finds that the application effectively helps to decrease the digital gap, empowering parents and caregivers by offering timely and accurate information that promotes the optimal development of children with special needs. This research highlights the significant impact that ICT may have in developing educational and therapeutic environments that are more inclusive and responsive.
Per Diem Management and Internal Control in the Modernization of the State in a Public Organization, 2024
Authors: José Ramón Panduro-Ríos
Published: February 2025

Public organizations in Europe, Latin America and Peru face various problems that affect their efficiency, transparency, and management quality. According to the OECD (2023), 41% of European citizens do not trust their governments and 56% consider corruption to be widespread in the public sector. In Latin America, the situation is even more critical, with 79% of the population perceiving corruption as a serious problem and 53% being dissatisfied with the public services they receive. In Peru, corruption costs the country 10% of its annual GDP and that 87% of Peruvians disapprove of government management; as of May 2018, it had a corruption index of 53.9% and was considered a major problem in the country. This study has methodological justification, given that the instrument has been created to measure per diem administration, which will serve the institution to evaluate the per diem process that begins in the certification and concludes in the rendering; in addition, it will be important for future researchers to use this instrument or take it as a reference in their studies; likewise, the instrument has been created to measure the modernization of the State. As a result of the research, it was found that per diem administration and internal control had a significant impact on the modernization of the State in a public organization (Significance=0.000; chi*squared=39.701; Nagelkerke coefficient=0.268).
Laying the Groundwork: Establishing Machine Learning Foundations in LGU Educational Policymaking
Authors: Rowell John B. Artiaga
Published: February 2025

This study investigates the integration of Machine Learning (ML) into a Local Government Unit (LGU) educational policymaking to enhance educational outcomes, streamline administrative processes, and enable data-driven decision-making. It identifies challenges including lack of technical expertise, inadequate infrastructure, funding limitation, and ethical concerns such as data privacy and bias mitigation. A literature review identifies ML algorithms applicable to education policymaking, including decision trees, neural networks, clustering, and random forest algorithm. The proposed framework emphasizes the need for high-quality datasets, robust data management systems, computational resources, and trained expertise. Stakeholder engagement, involving educators, policymakers, and community representatives, ensures alignment of ML solutions with educational goals. Upgrading data infrastructure storage solutions and comprehensive data governance frameworks is essential for efficient ML deployment. Additionally, establishing ethical guidelines for responsible data handling and bias mitigation, as well as regular performance evaluations, will ensure the fair and effective use of ML. By leveraging ML, LGUs can enhance educational outcomes, streamline administrative processes, and create data-driven, equitable educational policies. Further research is encouraged to expand data collection, apply the proposed ML system, conduct longitudinal studies, and continuously refine the framework, empowering LGUs to harness the transformative potential of ML in education policymaking.
Analysis of the Entrepreneurial Orientation Impacts of Food and Beverages SMEs in Jabodetabek
Authors: Student Celly Widjaja
Published: February 2025

This research shows how entrepreneurial orientation, dynamic capability, and innovation capability impact the performance of food and beverage SMEs in the Jabodetabek area, with competitive advantage acting as a mediating factor. SMEs in this sector face growing challenges in improving performance and maintaining a competitive edge in an increasingly dynamic business landscape. The objective of this study is to show that enhancing entrepreneurial orientation, along with developing dynamic and innovation capabilities, can significantly influence both firm performance and competitive standing. Data was collected through structured questionnaires distributed to SME owners in Jabodetabek, and the analysis involved regression and mediation techniques. The results demonstrate that entrepreneurial orientation, dynamic capability, and innovation capability strongly influence both firm performance and competitive advantage. Furthermore, competitive advantage serves as an important mediator in the relationship between these factors and performance, emphasizing its role in optimizing business capabilities for better outcomes. The study suggests that for SMEs to succeed in a competitive market, they need to foster an entrepreneurial approach while continuously adapting and innovating. It contributes to the existing literature by providing empirical evidence on how these capabilities can enhance performance and competitiveness in SMEs. For SME owners and managers, the findings highlight the need to build dynamic and innovative capabilities to remain competitive and ensure sustainable business success. Future studies could explore these dynamics across different industries and regions.
Implementation of Audio MOOC Book on Flipped Classroom to Enhance English Learning
Authors: Haris
Published: February 2025

Traditionally learning English has limited learning time and learning media. So a Flipped Classroom model is needed that allows students to study anytime and anywhere before learning activities in face-to-face classes. To implement the Flipped Classroom, interactive learning media is needed, therefore it is proposed to integrate the Audio Massive Open Online Course (MOOC) Book in the Flipped Classroom to improve student learning outcomes in English. The integration of audio and quiz-based content in the Flipped Classroom MOOC aims to provide a dynamic and accessible learning experience, encouraging increased language proficiency and increased student engagement. This article describes the design, implementation, and assessment of this innovative pedagogical approach. Preliminary findings indicate a positive correlation between the use of Audio MOOC Books and improved English learning outcomes, thus highlighting the potential benefits of combining online learning resources with the Flipped Classroom model in the context of English education. This research is expected to improve students' ability to pass the TOEFL test with a high score, so that they can qualify for higher education scholarships.
Exploring the Impact of Work from Home on Sustainable Transport and Car Ownership: A Leeds Case Study
Authors: Sekar Warangi Nurcahyati
Published: February 2025

Leeds City Council (LCC) aims to reduce car dependency by decreasing annual car travel by 30% (approximately 900 miles per person) by 2030. Heavy reliance on private vehicles is unsustainable, contributing to pollution, traffic congestion, and increased traffic accidents—issues that disproportionately impact vulnerable communities and raise social equity concerns. This study investigates how Working from Home (WFH) and non-WFH groups influence car ownership reduction and the willingness to adopt sustainable transportation options. An online survey was conducted in Leeds, gathering data on socio-demographics, travel behaviours, and perceptions of public transport. The survey data was analysed using Chi-Square and Binary Logistic Regression to assess the relationship between WFH and travel demand management, focusing on shared mobility schemes and public transport adoption as ways to discourage car ownership. Results indicate that WFH can reduce car ownership and promote sustainable transport usage. These findings suggest that encouraging WFH could help boost participation in shared mobility schemes, enhance public transport utilisation, and ultimately lower car ownership.
Enhancing Stunting Detection Accuracy in Children Using SVM with Advanced Data Balancing Techniques
Authors: Joko Minardi
Published: February 2025

Stunting is a significant public health concern with enduring effects on children's physical and cognitive development. Traditional stunting classification methods often fail due to the complexity and imbalance of health data. This study proposes a novel technique combining Support Vector Machines (SVM) with Synthetic Minority Oversampling Technique (SMOTE) and Tomek Links to address these challenges. The proposed method was evaluated on a dataset from Kecapi Jepara, focusing on children's nutritional status before and after vitamin intervention. The results showed a significant improvement in classification accuracy, with an F1-score improvement of 12% and a 10% increase in overall accuracy compared to conventional methods. Specifically, the use of SMOTE and Tomek Links corrected the data imbalance, reducing the misclassification of stunted children by 15%. By incorporating these advanced machine learning techniques, the study offers a robust framework for early stunting detection, providing valuable insights for targeted public health interventions and contributing to global efforts to reduce stunting prevalence.
Financial Well-Being in the Covid-19 Period: How Better Than Average Bias Shapes the Perception of Future Economic Conditions
Authors: Kris Brantas Abiprayu
Published: February 2025

This study aims to discover individual perceptions or views of future economic conditions at the global household, national, and economic levels during the COVID-19 pandemic. We also tested the relationship between variable financial ignorance, financial literacy, and financial well-being during the pandemic. Broadly speaking, the findings of this study show that individuals have an overview of future economic conditions during the pandemic.  What makes this interesting is that it turns out that individual expectations of future economic conditions during the pandemic will be different at the household, national, and global levels.  The test results concluded that individual optimism about future economic conditions is stronger if the talk is about the economy at the private or household level. In contrast, this optimism will fade if individuals are asked to assess the future economic prospects during the pandemic at the national and global levels, where they are relatively more pessimistic.  This finding once again confirms the existence of better-than-average behavior in which individuals will evaluate themselves better than when they evaluate other entities. Better-than-average behavior is quite like optimism bias that, if not managed properly, can cause toxic positivity due to an irrational sense of optimism.
Serial Fusion based Hybrid Features Extraction Method for Tomato Plant Leaf Disease Detection
Authors: Puja Dipak Saraf
Published: February 2025

Detecting and classifying leaf diseases at an early stage is crucial. However, many current approaches have struggled to achieve the necessary accuracy due to several challenges. These challenges include dealing with input images that have low contrast, irregularities in leaf spots, minimal color differences between foreground and background, and the need to handle a large number of extracted features. To address these issues, we have explored feature extraction techniques based on color, shape, and texture, each with its own advantages and disadvantages. We have conducted experiments on color histograms, color means, color DHV, dominant colors, and obtained results. In shape feature extraction, we have experimented with existing methods such as linear, UNL, Gaussian, grayscale Fourier, area, perimeter, roundness, compactness, and convex hull parameters. The results obtained from color and shape feature extraction methods show that combining multiple features of the image to detect leaf infections provides better accuracy compared to using single feature types. To extract features from the images, we have proposed a hybrid (serial fusion) strategy for color, texture, and shape feature extraction, which achieves the desired accuracy.
An Implementation and Analysis of Modified Approach for Mobile Apps Review Mining using Scrapper Package
Authors: Makarand Lotan Mali
Published: February 2025

This study introduces a new method for mining mobile app reviews using the "scrapper" package and sophisticated ML algorithms. The goal was to improve app marketing and development by gleaning useful information from a massive database of user reviews. This was accomplished by classifying the retrieved reviews as either favorable, negative, or neutral using a thorough sentiment analysis. The outcome of this study laid the groundwork for future research on user preferences and perceptions. After that, we used a number of ML algorithms to refine the sentiment analysis and make the classifications more accurate. We used metrics like accuracy, recall, precision, F1-score, and confusion matrices are used for assessment of the performance of logistic regression, support vector machines (SVMs), neural networks, and Naive Bayes. Results showed that the suggested method worked, outperforming baseline methods in sentiment categorization by a wide margin. Another piece of evidence that the model could distinguish between good and bad feelings came from the ROC curve analysis. Finally, by presenting a strong and effective methodology, this research makes a significant contribution to the area of mobile app review mining. Researchers, app marketers, and developers all stand to benefit from a better understanding of user input and how to optimize app performance as a result of these discoveries.
Challenges to Ukraine's Demographics: Analysing the Exodus of Economically Active Citizens
Authors: Vitalii Turenko
Published: February 2025

Introduction: The relevance of this established research topic relies on the increasing dangers of the population dynamics owing to the end of the Russia – Ukraine conflict and the movement of working-age citizens.
Objective: The research intends to evaluate the effects of migration on the rate of Ukraine's population and suggest favourable demography. Factors considered include assessment of the existing population situation, movement trends, forecasting and possible interventions to maintain stability.
Methods: The methodologies for evaluating the decade risks of demographic transition impact on Ukraine's economy consist of stages of justification of measures: comprising the collection and estimation of demographic statistics; analysing migration trends; developing scenarios.
Results: The study results show the patterns and trends in population increase attributed to the displacement of people. A key aspect of the research is evaluating how population movements and displacement affect the Ukrainian economy, society and international relations. The tasks include getting acquainted with the structure of the Taras Shevchenko National University of Kyiv and formulating recommendations to the government on demographic matters during the war and post-war reconstruction periods. The study seeks to evaluate measures aimed at encouraging the return of migrants, encouraging birth rates and enhancing social amenities. The supportive results will enable a strategic plan to restore the Ukrainian demographic potential.
Conclusions: Given the currently prevailing circumstances, specific measures are suggested to reinforce demographic resilience and the effectiveness of such measures. Further directions for enhancing the state policy to attain sustainable population growth and the state's economic advancement are given.
Management Strategies to Enhance Media and Information Literacy and Critical Thinking in Modern Society
Authors: Valerii Novitskyi
Published: February 2025

Introduction: The relevance of the topic lies in the fact that in the context of the active development of digital technologies and social media, it is necessary to develop critical thinking today, and media literacy needs to be improved among the general population in the modern information society. The growing volume of disinformation and media manipulation in the media space requires the search for new approaches to improving the level of media literacy of the Ukrainian population.
Objectives: The purpose of the study is to identify effective strategies for developing media literacy and critical thinking in the modern world.
Methods: The research methodology includes analysis and synthesis of scientific approaches to media literacy and inductive and deductive methods to identify trends in its development.
Results and conclusions: The work is of practical importance for the adaptation of European media education strategies to Ukrainian realities and their implementation in the educational system in order to develop critical thinking of pupils and students. Another important result of the study is that it emphasizes how much media education can offer to the curricula of different courses at different educational levels. This underscores the critical role of educational institutions and policymakers in fostering an informed and critically thinking society. Additionally, integrating media literacy into everyday learning can significantly reduce the impact of false information on public opinion.
Mining Resettlements and Their Repercussions on the Affected Communities: A Qualitative Study
Authors: James Nguah Acheampong
Published: February 2025

The contributions from mineral exploitations to the Gross Domestic Product of many countries in the world cannot be overemphasized. However, it comes at a great cost. The study examines the repercussions on mining resettled communities. The focus is on the unintended consequences confronting the people in a mining resettlement communities. It focused on the interaction with the people living in the communities, involving 54 qualitative interviews and focus group discussions with household heads, chiefs, the youth, opinion leaders and market women. The research indicates that most people in the communities were experiencing a downturn in terms of income to take care of their families as well as difficulty to get employment in the mining companies that displaced them.
Structural Integrity and Durability: A Comparative Study of Steel Arched Trusses with Conventional vs. Slurry Infiltrated Fiber Concrete Deck
Authors: Mohammed Hadi Jebur
Published: February 2025

Steel arched trusses are used most commonly in construction applications because of their capability of distributing weigh. The nature of the material used to construct their decks has a greater impact on their reliability and durability. This paper aims to assess the efficiency of the steel arched trusses formed by standard concrete slabs and surfaces developed with SIFCON, a fiber-reinforced, high tensile strength, and crack-resistant composite material. These performance parameters were mechanistic load-carrying capacity, deflection profiles, and durability under static and cyclic loading regimes, in addition to the numerical modeling.
In the experimental phase, there were 14 specimens subjected to different deck thicknesses and fiber reinforcements. As concerning shown conclusions, SIFCON decks revealed up to 30% of the enhanced load-carrying capability or fresh concrete about its deformation and an approximate three-fold decreased deflection as compared with traditionally used concrete stereotype decks. Moreover, the improvements to SIFCON crack-bridging capability restored suitable performance to its opposites under cyclic loading, suggesting its durability for chronic use. Numerical simulations supplemented these results by showing improved uniform stress distribution and failure modes in trusses reinforced with SIFCON.
Investigating Style Satisfaction in Online Fashion Apparel Shopping Across Generations using Multigroup Structural Equation Modeling: A Comprehensive Study
Authors: Susmi Biswas
Published: February 2025

Online shopping has emerged as a widely adopted activity on the internet, demonstrating substantial growth in recent years, albeit not yet realizing its full potential. The internet has provided new opportunities for organizations to effectively engage with both current and potential customers. Websites play a crucial role in facilitating customers to seamlessly explore, compare, and conduct online purchases of various products and services, with a notable emphasis on fashion apparel.
This study aims to identify the diverse factors influencing customer satisfaction with online shopping for fashion apparel across different age groups. Utilizing a Likert Scale format with five options (SA= strongly agree 5, A= agree 4, U= undecided 3, D= disagree 2, SD= strongly disagree 1), the questionnaire comprises two sections. The first section captures demographic information, while the second delves into four factors through 21 questions. Conducted via Google Forms, distributed through email and WhatsApp, the survey targeted respondents aged 18 and above, mentally sound, engaged in online shopping, and residing in Purba Midnapore. Employing stratified random sampling, the population was segmented into smaller groups. Out of 700 collected responses, 60 were discarded due to incomplete information, resulting in a dataset of 640.
Factor analysis, facilitated by SPSS21 (IBM Corporation), was employed to identify constructs. Confirmatory factor analysis, conducted using AMOS 20 (IBM Corporation), further elucidated the variables and their alignment within specific factors. Four factors—perceived quality, perceived benefit, perceived price, and perceived safety—were identified for each age group. In summary, the model has achieved a commendable level of goodness of fit.
Evaluating Financial Performance: A Comparative Analysis of Earnings and Profitability between LIC and Selected Private Life Insurers in India
Authors: T. Geeta Madhuri Naidu
Published: February 2025

Insurance plays a crucial role in mitigating risks for individuals and businesses. It provides a sense of security and helps them in facing life's uncertainties with confidence. The Life Insurers in India play a vital role as intermediaries in our economy, facilitating risk transfer, stimulating private investment, creating jobs, and supporting development projects. Life insurance has been a vital tool in managing risk for centuries. Even today many generations rely on life insurance products and this will also be the case in the future. The life insurance sector, marked by its diversity of products and services, has witnessed significant growth in recent years. However, these companies still face challenges such as intense competition, customer acquisition and retention, and sustaining profitable growth. The life insurance companies are struggling to sustain the profitable growth. To thrive in a competitive global market, these companies must remain profitable. Profitability is essential to attract external capital and achieve their long-term goals. An understanding of the performance of the life insurers is necessary for different stakeholders for taking key decisions. This study aids marketers, academicians, and policymakers in evaluating the performance of life insurers for taking critical decisions.
This study is primarily focused on measuring the performance in earnings and profitability of the selected private life insurers and the sole public company LIC over a period of ten years, from 2013 to 2022. Key factors considered include operating and commission expenses to net premium ratio, investment income to investment assets ratio, return on equity ratio (ROE), and return on assets ratio (ROA). Simple statistical tools like mean, standard deviation, and Mann Whitney U test were employed. The analysis highlights that the performance of the LIC has been superior to that of private life insurers in the expense-to-net premium ratio and ROE. On the other hand, private life insurers outperform the LIC in the investment income to investment assets ratio and ROA ratio.
Design and Implementation of FourCropNet: A CNN-Based System for Efficient Multi-Crop Disease Detection and Management
Authors: H. P. Khandagale
Published: February 2025

Plant disease detection is a critical task in agriculture, directly impacting crop yield, food security, and sustainable farming practices. This study proposes FourCropNet, a novel deep learning model designed to detect diseases in multiple crops, including CottonLeaf, Grape, Soybean, and Corn. The model leverages an advanced architecture comprising residual blocks for efficient feature extraction, attention mechanisms to enhance focus on disease-relevant regions, and lightweight layers for computational efficiency. These components collectively enable FourCropNet to achieve superior performance across varying datasets and class complexities, from single-crop datasets to combined datasets with 15 classes. The proposed model was evaluated on diverse datasets, demonstrating high accuracy, specificity, sensitivity, and F1 scores. Notably, FourCropNet achieved the highest accuracy of 99.7% for Grape, 99.5% for Corn, and 95.3% for the combined dataset. Its scalability and ability to generalize across datasets underscore its robustness. Comparative analysis shows that FourCropNet consistently outperforms state-of-the-art models, such as MobileNet, VGG16, and EfficientNet, across various metrics. FourCropNet’s innovative design and consistent performance make it a reliable solution for real-time disease detection in agriculture. This model has the potential to assist farmers in timely disease diagnosis, reducing economic losses and promoting sustainable agricultural practices.
Optimizing Plant Disease Detection Models for RISC Architectures with Mini TensorFlow
Authors: Nitin N. Sakhare
Published: February 2025

Global agricultural productivity is a challenge to plant diseases, generating strong demand for rapid and efficient diagnostic tools for early detection and effective management. For plant disease classification, this study evaluates four deep learning models (EfficientNet B3, GoogLeNet, DenseNet, and VGG16). The models are evaluated based on accuracy, precision, inference latency and computational resource usage on a large dataset covering several crops and disease types while consuming CPU and RAM. However, it's found that EfficientNet B3 has better accuracy and computational efficiency compared with the other models, especially in a resource constrained setting. The rest are DenseNet, GoogLeNet and VGG16, both perform well and in DenseNet, it performs well in both accuracy and resource spent too. VGG16 only gives slightly lower accuracy than ResNet50, but it needs more computational resources. The study shows that EfficientNet B3 is a candidate for real time precision agriculture applications. The findings presented in this research offer valuable guidance about model selection for plant disease detection, enabling the development of scalable, low-cost diagnostic systems that can be used by farmers to reduce crop losses and improve yield quality.
A Deep Learning Approach for Predicting Student Academic Performance and Grades Based on C Code Analysis
Authors: Urmila Kadam
Published: February 2025

This research employs deep learning to enhance student assessment by analyzing the quality and structure of programming assignments, focusing on C code submissions. Traditional grading methods often fail to capture the intricate details of a student’s coding abilities, focusing primarily on code functionality over quality and comprehension. To address this limitation, it requires to extracts in-depth metrics from code—such as the total lines of code, use and quality of comments, variable declarations, control structures, and overall readability—and integrates them with traditional academic data like past grades and attendance. Using a Deep Neural network, the model predicts students' grades and academic performance percentages based on these rich, combined inputs, providing a more comprehensive and nuanced evaluation. This innovative approach empowers educators with insights into each student’s overall performance and areas needing improvement, enabling personalized feedback and fostering a balanced, skill-based assessment framework that goes beyond conventional grading systems.
UPACPL: Design of an UML Pattern Analysis Model for Identification of Code Clones via Augmented Deep Learning Process
Authors: Shobha G
Published: February 2025

Detection of code clones is necessary for ensuring high code quality, byte-level security, preserving intellectual property rights, and incorporating various compliance measures. Existing clone detection models moreover showcase higher complexity otherwise has lower efficiency when evaluated on large code bases. Moreover, most of these models only consider syntactical checking, which makes them inapplicable for cross-project analysis. To conquer these matter, this text suggests intend of a competent novel pattern analysis model for identification of code clones via augmented deep learning process that uses UML (Unified Modelling Language) based information sets. The proposed model is trained on different UML class diagram components that include methods, classes, and attributes, relationships between classes, their associations, dependency levels, realizations, multiplicity instances and interface patterns. All these pattern information sets are aggregated, and processed by an Ant Lion Optimizer (ALO), which helps to analyze very different processes. The selected collection is divided into ‘clone’, and ‘original’ classes by modified a one-dimensional Convolutional Neural Network (CNN), which helps to evaluate the degree of cloning probability. Due to evaluation of UML metrics, the proposed model can be scaled to cross project & cross language deployments. The proposed model was tested on GPT-J Code Clone Detection Dataset, Code Glue Dataset, and Smart Embed Code Clone Analysis Datasets. It was anticipated that the suggested model was able to improve precision of code clone detection by 5.9%, precision by 2.3%, and recall by 4.5% when compared with existing methods on similar data samples.
The Effect of Unbalance Supply on the Three- Phase Synchronous Motor Performance in the Laboratory
Authors: Abdulsatar H. Jasim
Published: February 2025

A detrimental worldwide phenomena that affects the output of three-phase synchronous motors is voltage unbalance. There are several ways that a three-phase source voltage can become unbalanced. A motor may be practically impacted by imbalanced instances that vary in several ranges. The goal of this work is to assess the operations using relevant hypotheses. To understand the degree of such modifications, this paper examines balanced and imbalanced situations practically in relation to their mechanical and electrical performance. This performance entails the currents of stator, total harmonics distortion, the speed of the motor, and the efficiency. The experiment was conduct laboratory on three phase synchronous motor.
Analysis of the Elasticity of Village Funds and Special Autonomy Funds and Additional Infrastructure Funds on Poverty and Human Development Index and Human Development Index in Papua
Authors: Yundy Hafiszrianda
Published: February 2025

This article analyzes the elasticity of village funds, Special Autonomy Funds, and Additional Infrastructure Funds to poverty, Human Development Index (HDI), and Village Development Index (IDM) in Tanah Papua. Regional development in Indonesia, especially in Papua, is a major concern of the government in order to improve people's welfare and reduce development gaps between regions. Village funds, Special Autonomy Funds, and Additional Infrastructure Funds play an important role in accelerating the development of disadvantaged regions, with a focus on improving the economy, quality of life, and poverty reduction. This study explores the relationship between the allocation of these funds and improvements in the HDI and IDM, both of which are important indicators in assessing the success of development. The results of the analysis show that the elasticity of the use of village funds and Special Autonomy Funds has a significant influence on poverty reduction and improving the quality of life of the community, although there are challenges in distribution and management that need to be overcome to maximize its impact. This article also discusses the importance of synergy between local governments, communities, and the private sector to create inclusive and sustainable development in Papua.
Enhancing E-Commerce Recommendations Through Data-Driven Approaches: A Case Study of Amazon Product Reviews
Authors: Enas M. Turki
Published: February 2025

In the dynamic landscape of e-commerce, personalized product recommendation systems are pivotal in enhancing user experiences and driving business success. This study leverages the Amazon Product Reviews dataset, a rich source of user-generated feedback, to design a scalable and effective personalized recommendation system. Adopting a structured methodology encompassing four data analysis phases includes descriptive, diagnostic, predictive, and prescriptive. This research extracts meaningful insights from product reviews and ratings. The study captures user preferences and sentiments using advanced natural language processing (NLP) and machine learning techniques, including sentiment analysis and hybrid recommendation models. Implementing distributed computing frameworks like Apache Spark ensures scalability and operational efficiency. Centered on the electronics category, this research integrates sentiment insights with collaborative and content-based filtering techniques to address challenges like data sparsity and the cold-start problem. The findings contribute to advancing personalized recommendation systems by delivering actionable insights that enhance customer satisfaction, streamline product discovery, and provide significant value to academic research and industry practices.
Sentiment Analysis of Top Stock Market Companies
Authors: Abeer Kheder Alghamdi
Published: February 2025

In the current era, data grew in volume, variety, and velocity; therefore, handing the term big data. While conventional data type consists of formatted data, big data involve structured, semi structured and unstructured data for which enhanced tool is requisite for processing the data to pull out the value-added information. The characteristics of big data are briefly discussed in this paper particularly, the 5 Vs of big data which include Volume, Velocity, Variety, Veracity and Value as features used for the selection of datasets for analysis.A set of 3 million tweets associated with five popular international enterprises, including Amazon, Apple, Google, Microsoft and Tesla was chosen to illustrate the characteristics of big data. This data set is both numeric and non-numeric; numeric data includes only tweet ID and metadata, while the non-numeric data includes the text of the tweet from 2015 to 2020. The study used this dataset to assess its applicability to the 5Vs framework and perform the descriptive statistical analysis to determine broader trends using Python.The result of this study shows that the sentiment of all companies mainly ‎neutral sentiments, ‎followed by positive sentiments; a lesser quantity of tweets expressed negative ‎‎sentiments. Through machine learning approaches, the best result was achieved by the SVM model with the correct rate of sentiment prediction at 96%, which is better than the other models like LR and Decision Tree. This analysis highlights the significance of real-time sentiment monitoring and spotlighting prescriptive recommendations, such as creating content strategies that actually engage audiences by successfully driving their interactions. By implementing these strategies, enterprises can strengthen their positions in the market and control their weaknesses more readily.
Unlocking the Potential of Artificial Intelligence in Human Resources Management: A Review of Applications, Challenges, and Future Directions
Authors: Navaneetha Krishnan Rajagopal
Published: February 2025

This paper comprehensively explores the integration of Artificial Intelligence (AI) in Human Resource Management (HRM), examining its applications, challenges, and future directions. The review encompasses AI's transformative impact on recruitment processes, talent management, employee engagement, and performance management within HRM. Key applications include AI-powered tools for resume screening, candidate matching, chatbots for initial candidate interaction, enhancing recruitment efficiency and candidate experiences. Also, AI facilitates talent management by providing insights into employee performance, potential, and engagement, enabling tailored learning and development programs. Challenges such as ethical considerations, algorithmic bias, and job displacement are discussed, along with future directions emphasizing ongoing innovation, interdisciplinary collaborations, and the need for HR professionals to stay updated with emerging trends. By addressing challenges and embracing innovation, organizations can unlock the full potential of AI to reimagine HR processes and drive sustainable business success.
Artificial Intelligence-Enhanced Green Building Design for Environmental Sustainability
Authors: Maysaa H. Abdulameer
Published: February 2025

Green buildings are those that use sustainable methods of construction to either maintain or improve the local quality of life. Decisions affecting a project's quality, safety, profitability, and timetable are made using Artificial Intelligence (AI) in Green Construction by analyzing data gathered from monitoring the construction site and using predictive analytics. For instance, increased accuracy in weather predictions might lead to more production, less waste, lower costs, and less greenhouse gas emissions. Green building construction is a significant source of carbon dioxide released through the breakdown of carbonates. Researchers have concluded that integrating industrial wastes is crucial in green concrete making due to its benefits, such as reducing the requirement for cement. When planning with concrete, its compressive strength must be taken into account. Due to their high predictive power, AI algorithms may be used to determine the compressive strength of concrete mixtures. Existing artificial intelligence (AI) models may be evaluated for their modeling process and accuracy to inform the creation of new models that more accurately represent the comprehensive evaluation of setting parameters on model performance and boost accuracy. Potential sources of conflict in this anthropocentric future include climate change and the availability of renewable energy sources. Scientists think there is a connection between the increased emission of greenhouse gases like carbon dioxide (Co2) from the combustion of fossil fuels and the acceleration of climate change and global warming. Research has demonstrated that the building sector is a significant source of atmospheric carbon dioxide (Co2). Construction, building activities, and subpar energy sources have all significantly increased atmospheric CO2.The proposed research set out to measure how well AI in Green Building Construction (AI-GBC) might reduce carbon emissions and utility bills. Artificial intelligence uses SVM and GA to reduce energy use and carbon dioxide emissions. Several statistical metrics, such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Root Mean Squared Log Error (RMSLE), are used to evaluate the AI-GBC's precision. Both Machine Learning (ML) models yielded positive results, with prediction accuracies above 95%. Regarding predicting Co_2, GA models were close to the mark, with an R2 of 0.95. Ninety-six percent will complete a performance analysis, and 97% will conduct a k-fold cross-validation analysis. Cross-validation is used to ensure that the findings of the extended modeling technique are accurate and prevent overfitting.
Detection of Postpartum Depression using Machine Learning
Authors: Smita Bhagwat
Published: February 2025

Postpartum depression (PPD) is a prevalent and serious mental health issue that affects a significant number of women after childbirth. Early detection of PPD is crucial for effective intervention, but traditional screening tools often fall short in terms of accuracy and timely prediction. This study focuses on extensive machine learning methods by using psychological and behavioural data from a large survey of postpartum women. Various traditional machine learning algorithms along with CatBoost are implemented in this research. The accuracy of 95.68% demonstrate that the CatBoost-based model outperforms other machine learning models, providing a robust and reliable method for the early detection of postpartum depression.
Conditional Mutual Information Maximization and Restricted Boltzmann Machine for Stock Price Prediction
Authors: P. Deepa
Published: February 2025

Using various machine learning (ML) methodologies in forecasting the stock prices has lately been successful. Nonetheless, most of these models depend on the narrow set of attributes as input and lacks the sufficient information to offer the estimates of stock market. To enhance the stock price prediction models, conditional mutual information maximizing (CMIM) method is used for data preprocessing and feature selection based on restricted boltzmann machine (RBM). By optimizing their conditional mutual information with the target variable, CMIM assists in selecting the most significant characteristics which reduces the dimensionality and duplication. Following that, the picked features are refined using RBM, ML model is capable of detecting the hidden patterns in data. These approaches use selection algorithms to extract the key features. Hence, improves the performance of stock price prediction models. In terms of prediction accuracy, the experiments results indicate the significantly outperforming standard feature selection strategies. This proposed method outperforms with 97.69% accuracy. Thus, the model offers a solid foundation for financial forecasting.