bluuebunny/crossref_metadata_2025 huggingface dataset:
Literature Review of Earth Fault Self _Extinguished Control System using Petersen _Coil
Authors: Nada Ali Mohammed Al-Rubaie
Published: February 2025

This study analyzes and classifies a set of research related to Petersen's file and control techniques in electrical distribution systems. It is a coil with an iron core installed between the transformer neutral and the ground and is used to limit the value of the ground fault current that flows when a ground fault occurs in the lines. The coil is designed with tapping to adjust the coil reactance with the system capacity. These researches aim to improve the performance and stability of electrical networks by developing new techniques for measuring insulation parameters, controlling ground resistance, and using numerical simulation to analyze the behavior of grounding systems. The focus was on fault detection techniques, ground fault protection, and reducing harmful arc currents. The recent developments in the use of smart and flexible technology to improve the response of networks to faults and increase the reliability and safety of electrical systems were also reviewed. The study concluded that advanced technologies and comprehensive analysis of these researches contribute to enhancing the performance and efficiency of electrical networks, reducing risks and improving the sustainability of energy supplies.
A Study on Smart Home Appliances Based on Artificial Intelligence System
Authors: Ok Hue Cho
Published: February 2025

Objective: The present study was based on the smart home appliances works based on the artificial intelligence system that are the recent evolution of emerging technologies in the deep learning and machine learning technologies.
Methods: Smart home appliances are developed using the system of artificial intelligence. The home automation control, features, techniques used in the smart home appliances construction, its review and analysis are clearly examined.
Results: The analysis of different types of smart home appliance and the comparative analysis of the usage and acceptance by the people in the current scenario are discussed. The performance and features of the smart home appliances and traditional appliances are compared. Thus, proving that the proposed study is human friendly and the features are highly supportive.
Conclusion: The proposed study focuses on the different types of smart home appliances constructed based on the artificial intelligence system is highly affordable, convenient to use, easy operation and energy efficient.
Rookie Tourism- Impact of Risk variables on the Propensity to Return of Tourists
Authors: Anika Nashat Matin
Published: February 2025

Established tourism attractions encounter numerous challenges as they strive to draw repeat visitors and eventually turn into undiscovered gems. Various obstacles faced by destination marketing organizations give rise to typical and frequently encountered "rookie mistakes" in susceptible destinations. This study sought to examine the relationship between destination marketing elements (risk variables) and revisit intention in Sundarban, the biggest mangrove forest in the world, which is a relatively unknown yet popular tourist destination. While there are studies that identify developing destinations as rookie destinations, the focus of this study is on how a trending destination can gradually transition into a rookie destination with significant DM characteristics, which represents a research gap.
Exploring Depression and Suicidal Ideation Among Science and Social Science College Students
Authors: Chae-Yeong Lee
Published: February 2025

Students nowadays have been suffering from various psychological problems due to unfavourable outcomes of life such as insecurity, lack of affection, rejection, loneliness, depression, stress, aggression and suicidal ideation. The purpose of this study was to explore the relationship between depression and suicidal ideation among college students. They ensured accurate and meaningful results by measuring the relationship between variables separately in each group. For this purpose, 200 college students from different colleges in Bihar were selected. Out of which 100 were science students and 100 were social science students. The Beck depression inventory measured depression of college students and the Suicidal Ideation Scale measured suicidal ideation in college students. The t-test and Pearson correlation were applied to find out the relationship between variables. The results indicated that the science college students had significantly greater mean scores than social science students on the Beck depression inventory. Similarly, science college students had significantly greater suicidal ideation than social science college students. A significant positive relationship between depression and suicidal ideation of college students was obtained. The review concludes with a summary of major research findings, as well as a consideration of future directions and implications for practice and policy.
The Impact of International Migration on Inclusive Growth and Human Development: An Empirical Analysis
Authors: Kang-Won Cho
Published: February 2025

This report explains the connections between international migration and inclusive growth and provides direction to judges on how to investigate these connections using the data at hand. The authors provide a useful framework for evaluating migratory miracle in an examination of inclusive growth after reviewing the theoretical foundations of labour mobility and its role in participative growth. The framework aims to give interpreters direction on how migration affects growth and how it is distributed, as well as the essential research to determine the ultimate impact of current and future migration trends on inclusive and sustainable growth. It also highlights the tools that are widely used in inclusive growth diagnostics that is susceptible to migration as well as the strategies economists use to address data constraints and difficulties. The study concludes with a discussion of the policy issues and potential solutions revealed by the migration-enhanced inclusive growth analysis.
Management of Factors Associated with Job Satisfaction of Healthcare Workers in Obstetrics and Gynecology Services at Private Hospitals: A Cross-Sectional Study
Authors: Ju Hee Shon
Published: February 2025

Purpose: The job satisfaction of employees is a critical factor in determining the level of health care quality. This study aimed to find out the factors affecting the job satisfaction level of healthcare workers working in obstetrics and gynecology services. Further, to evaluate the job satisfaction level of these healthcare workers on the basis of various dimensions of job satisfaction by using different statistical analysis tools.Design, methodology, and approach: Healthcare workers in obstetrics and gynecology services were measured on the basis of their job satisfaction level by using cross-sectional and descriptive research methods. Survey responses of 220 health care workers were collected from private hospitals in Delhi and NCR India. The collected data was coded and incorporated into SPSS version 20.0 for further evaluation. Descriptive statistical analysis and multi-level logistic regression were employed to summarize the data, and frequency tables and percentages were used to present the results. Findings: A self-administered survey was conducted with a sample of 220 health workers. Overall, 65 (29.54%) of health workers reported satisfaction with their job, and 155 (70.45%) were not satisfied, with neutral responses being assumed. The study also concluded that pharmacists were less satisfied with their jobs, while laboratory professionals, junior resident doctors, and senior resident doctors were more satisfied with their jobs than nurses. Additionally, criteria for performance evaluation and appraisal were found to be the most influential factors in job satisfaction. Practical implications: The findings of this article are useful for hospital management, government, the healthcare sector, and policymakers. All these stakeholders can perform better and work for the betterment of healthcare workers and society by implementing appropriate service policies. Originality/value: The findings of this study reinforce current knowledge on the causes and consequences of dissatisfaction among healthcare workers.
Impact of Social Capital on the Organizational Commitment at Workplace
Authors: Sanjoy Roy
Published: February 2025

There has been a surge in concern regarding the social capital of organizations. Social capital is receiving increasing attention from industries as communication methods continue to evolve. Information technology and social media have created platforms where not only are internal employees connected with one another, but they are also well connected with external stakeholders. This phenomenon has heightened employers' concerns about encouraging both personal and professional bonding. As a result, it has become essential for employers to foster an environment of trust among employees. Organizational commitment is closely tied to the social capital of the firm, and this commitment can be achieved through clear and well-defined communication pathways. Employees’ commitment is influenced by the trust and communication they experience with both their employers and fellow employees.This study analyzes the relationship between social capital and employee commitment to the organization. The authors examine the connection between social capital and various forms of employee commitment, including continuance, emotional, and normative commitment. The results of the study reveal that trust, communication, and an employee-centered focus significantly influence organizational commitment, both directly and to a moderate extent. Similarly, affective commitment has a strong impact on workplace belongingness. Affective commitment plays a substantial mediating role in the relationship between job satisfaction, leadership, and workplace belongingness, with partial mediation.
Transforming the Business Landscape: Applications and Impacts of Blockchain Technology
Authors: Sameer Shekhar
Published: February 2025

Blockchain technology has emerged as a transformational force, revolutionizing business procedures across industries by providing transparency, security, and efficiency. This study examines the applications and effects of blockchain technology on organizational functions and stakeholder confidence. This study utilizes a sample of 113 respondents and using statistical methods to examine data gathered from surveys and interviews. Research indicates that blockchain plays a crucial role in improving corporate efficiency and trust. The research emphasizes blockchain's capacity to diminish operational redundancies and promote innovation across multiple sectors. Furthermore, it emphasizes the increasing necessity for regulatory certainty to promote broader adoption. The study finishes with recommendations for the effective integration of blockchain and identifies areas for further research, including the exploration of blockchain's potential in upcoming technologies such as AI and IoT.
Deep learning Models for Multi-class Pneumonia Detection with emphasis on Covid-19
Authors: Apurva Mali
Published: February 2025

Pneumonia is a dangerous condition that can lead to death. Bacteria, fungi, or viruses are common causes of this condition, which can affect one or both lungs. The x-ray scans can be used to identify this lung ailment. Pneumonia is a dangerous condition that can lead to death. Bacteria, fungi, or viruses are common causes of this condition, which can affect one or both lungs. The x-ray scans can be used to identify this lung ailment. This illness usually presents itself with a cloudy white structure on the chest x-rays. The Kaggle datasets used for chest X-rays contains a variety of images that are divided into 4 categories – “Normal”, “Viral Pneumonia”, “Bacterial Pneumoia”, “Covid-19”. A deep learning model will be developed that can truly tell us whether or not a person has pneumonia disease. The model will be able to accurately assess the illness severity by detecting the degree of this cloudiness with an automated pneumonia detection system. This project is based purely on ‘Deep Learning’ concepts and will be scalable in types of complications covered, model accuracy and utility convenience analysis/application. Statistical results obtained demonstrate that CNN models can be trained to analyze chest X-ray images, specifically to detect Pneumonia and even to differentiate between Covid-19 and Pneumonia. The attempt to differentiate between viral and bacterial pneumonia has been partially successful, and suggests that it is difficult to distinguish between them due to similar patterns on the X-rays. With the help of MobileNet Architecture is possible to scale this model, the results also suggest that it is also possible to differentiate between pneumonia and COVID-19, which present similarly on the x-rays, which most models fail to undertake. It is thus deduced that the lung damage is most similar in viral and bacterial pneumonia, but the pattern is different for lungs affected with Covid-19.
Image Features Hiding by Permutation of Pixels Locations
Authors: Mohammed Abdul-Hameed
Published: February 2025

After completing a scientific research project on image encryption using third-order differential equations, we will focus on designing a new algorithm to conceal the features of the image by exchanging pixel positions in a calculated, precise, and reversible mathematical manner. The exchange will occur in five stages, where the first four stages involve flipping the pixels in a way that resembles paper flipping, causing the pixels to interleave, making their features almost 90% disappear. It is quite natural to significantly increase the disappearance ratio of the image features, reaching up to 99%, by increasing the stages of pixel flipping in different and interleaved positions. However, this is not necessary as we will make the fifth stage of pixel position swapping using a chaotic map, which has the great ability to completely obscure the image features to a ratio of 100%. We applied this algorithm to several images, and the results were excellent. It is worth mentioning that, despite completely hiding the image features, this algorithm is not considered an encryption algorithm because a crucial aspect of encryption, namely the encryption key, is not present. However, it serves as a prelude to the encryption process, we have accomplished through our previous research, where the encryption here is more secure from a security perspective. Additionally, we note that it is possible to enhance this algorithm by adding a key, making it a key-based encryption algorithm, thus obtaining a new encryption algorithm
Analytical Examination of Commonly Utilized Biometric Equipment in Educational Institutions: Features, Applications, Practical Usage, and Role in Fostering Organizational Discipline
Authors: Shweta Vaibhav Patil
Published: February 2025

Biometric technology has become a vital component in modern educational institutions, facilitating the management of attendance, security, and overall discipline. This study explores the features, utilities, and effectiveness of commonly used biometric equipment in educational settings, with a focus on how these systems contribute to building a disciplined and organized institution. The analysis primarily focuses on three types of biometric technologies: fingerprint scanners, facial recognition systems, and iris recognition devices. Each technology is evaluated based on key features such as accuracy, speed, cost-effectiveness, and user-friendliness. For instance, fingerprint scanners are widely used for their affordability and ease of use, though they may suffer from wear and tear over time. Facial recognition systems, while offering contactless use and greater convenience, raise privacy concerns. Iris recognition is highly accurate but expensive, limiting its widespread adoption in educational institutions. The utilities of biometric systems are diverse, extending beyond simple attendance tracking. They are instrumental in restricting unauthorized access to sensitive areas, monitoring punctuality, and even controlling student movement within the campus. These systems ensure real-time data collection and can be integrated with existing management software for seamless reporting and analysis. The actual use of these technologies varies between institutions, with some using them merely for attendance purposes and others incorporating them into broader security protocols. However, their common purpose remains the same: to promote accountability and enhance the discipline within the institution. By minimizing human error and ensuring consistent monitoring, biometric systems play a pivotal role in fostering a disciplined environment. This study concludes that while biometric technology offers significant advantages in terms of efficiency and security, institutions must balance these benefits with considerations of cost, privacy, and ethical concerns to maximize their utility in promoting a disciplined educational environment.
Ensemble DL Techniques for EEG Epilepsy Classification: Utilizing HRPCS for Enhanced Feature Selection and Improved Accuracy in Seizure Detection and Diagnosis
Authors: Jothi. B
Published: February 2025

Recurrent seizures are an indicator of epilepsy, a complicated neurological illness that requires prompt and precise diagnosis for successful treatment. Electroencephalogram (EEG) signals are critical in identifying seizure types and patterns, yet the analysis of these signals poses significant challenges. The variability in brain activity, the presence of noise and artifacts, and the complexity of differentiating between epileptic and non-epileptic seizures complicate accurate classification. Conventional methods often fall short, leading to misdiagnoses and inadequate treatment plans. To address these challenges, this study proposes a robust classification model for detecting and classifying EEG epilepsy data. The methodology begins with rigorous data preprocessing, which includes data cleaning and normalization to enhance the signal quality and ensure consistency across the dataset. Next, use dynamic and statistical feature extraction techniques to obtain key EEG signal parameters, which are necessary for accurately differentiating between seizure types. Furthermore, implement a Hybrid Red Piranha Cuckoo Search (HRPCS) algorithm for feature selection, allowing us to identify the most relevant features while reducing dimensionality. Finally, hybrid deep learning techniques HCRNN are utilized, incorporating Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) for classification and prediction tasks. Blockchain technology is used to secure EEG data, ensuring integrity and patient privacy. After classification, the model also incorporates predictive analytics to forecast potential future seizures, enhancing patient management strategies.
Evaluation of Internal Management Information System Success at Indonesia’s National Zakat Agency: A DeLone and McLean Model Approach
Authors: Umi Masithoh
Published: February 2025

Implementing Internal Management Information Systems (Intranets) varies in effectiveness across different organizations and is often influenced by system and information quality and utilization. This study evaluates the effectiveness of the Intranet system at Indonesia’s National Zakat Collection Agency, employing the Information System Success Model developed by  DeLone and McLean. A quantitative approach was employed using survey data from 206 employees, with 67 valid responses (response rate 32.5%). Structural Equation Modelling-Partial Least Squares (SEM-PLS) was used in this study to examine nine hypothesized relationships among system quality, information quality, service quality, use, user satisfaction, and net benefits. The analysis revealed that five hypotheses were supported: (1) information quality significantly influences user satisfaction (β=0.335, p<0.005), (2) service quality positively affects user satisfaction (β=0.540, p<0.001), (3) use significantly impacts net benefits (β=0.326, p<0.005), (4) use positively influences user satisfaction (β=0.762, p<0.001), and (5) user satisfaction significantly affects net benefits (β=0.538, p<0.001). Four hypotheses were rejected, indicating non-significant relationships between information quality-use, service quality-use, system quality-use, and system quality-user satisfaction. The findings suggest that the Intranet’s success is primarily driven by user satisfaction and system usage rather than technical aspects. Management should improve user experience and promote system utilization to enhance organizational benefits.
An Enhanced Two-Way Unet Approach for Copy Move Image Forgery Detection
Authors: M. Samel
Published: February 2025

The paper explores a deep learning-based approach to semantic classification, emphasizing its utility in complex real-world situations. The main aim is to engineer a model that can recognize features in images and distinguish them accurately and efficiently. Leveraging advanced architectures, including convolutional neural networks (CNNs) and their variants, the research combines complex training methods with advanced datasets to achieve the state-of-the-art Includes conceptual techniques and data enhancement methods to the model's capability to generalize to diverse has greatly impressive images. The report describes typical improvements in accuracy and loss coefficients at various stages, and highlights the importance of fine-tuning hyperparameters Analytical metrics such as accuracy, accuracy, loss, and validation loss reveal high model performance displayed, balanced in terms of computational efficiency and classification quality Alongside envisioning predictive coverage, the report offers qualitative and quantitative evidence for on how effective the model is This approach holds particular promise for applications such as autonomous driving, surveillance, and medical imaging. The findings also highlight the importance of continuous innovation in model construction and training techniques to push the limits of logical classification.
Enhancing E-Learning Quality in Business Schools: Leveraging Communication Technologies in the Online Education Era
Authors: Sameer Mohammed Majed Dandan
Published: February 2025

Introduction: This study examines the influence of Quality Management Systems (QMS), such as ISO 9001 and EFQM, on e-learning environments in business and science schools.
Objectives: It highlights how combining QMS with modern e-learning standards, including SCORM, Learning Object Metadata (LOM), and tools like wikis and blogs, enhances the scalability, consistency, and compatibility of online learning systems.
Methods: The research applies a systematic review methodology (2020–2025) using a logical filtering framework.
Results: The findings show that implementing QMS frameworks leads to improved student satisfaction and better learning outcomes by supporting well-organized, accessible online platforms. The study also identifies challenges in faculty training and infrastructure, emphasizing the need for more in-depth research. These results underline the importance of QMS in transforming educational platforms to meet the needs of digital-native students and improving the overall quality of online education.
Conclusions: implementing Quality Management Systems (QMS) in e-learning is not just an operational task but a strategic priority for improving the overall quality of education.
Medical Data Security using Deep Learning based Key Generation, Quantum Key Exchange and Modified AES
Authors: Konka Kishan
Published: February 2025

Medical data security refers to the protection and safeguarding of sensitive patient data in the healthcare domain. It encompasses various measures and protocols designed to ensure the confidentiality, integrity, and availability of medical information. This includes not only medical imaging data but also electronic health records (EHRs), medical test results, patient demographics, and other personally identifiable information (PII). Medical data security is of utmost importance due to several reasons like patient privacy, preventing unauthorized access or disclosure of personal health information, risk of data breaches and identity theft. This paper presents a medical data security framework using deep learning based key generation, quantum key exchange and modified Advanced Encryption Standard (AES). Deep learning-based random number generation for encryption key is an approach that utilizes deep learning models to generate secure and unpredictable sequences of numbers that can be used as encryption keys. Traditional random number generators rely on algorithms or physical processes to generate randomness. Deep learning, on the other hand, offers an alternative approach by leveraging the power of neural networks to learn patterns and generate seemingly random sequences.  This generated random number is used as key for quantum key exchange. The proposed framework uses BB84 protocol the utilizes principles of quantum mechanics to achieve secure key exchange. This ensures the confidentiality and integrity of the transmitted data. The AES algorithm is a widely used symmetric encryption algorithm that is known for its high level of security and efficiency. The conventional mix column operation is replaced with low complex algorithm for better performance.
Enhancing Meteorological Pattern Recognition Utilizing U-Net and Bidirectional LSTM Networks
Authors: Z. S. Khaleel
Published: February 2025

Classification of weather images is very significant in climate monitoring, disaster management, and weather forecasting. This work is an innovation on the architecture that combines U-Net with a BiLSTM network for classification accuracy improvement. U-Net is one of the most advanced convolutional neural networks, and it is characterized by  fine-grained feature extraction and spatial information preservation in images. The model combines it with BiLSTM, which focuses on detecting temporal and contextual patterns, thereby enabling a combined understanding of the spatial and temporal dimensions of weather phenomena.
It strengthens all aspects: in addition to how the spatial U-Net's capabilities build strong arguments and why the recognition of correlations with a model such as BiLSTM significantly improves this method against more common, traditionally used, models of the convolutional neural networks family. Tests, also carried out, showed excellent and improved parameters during evaluation like precision, recall, and balance measures, when evaluating results of similar common datasets commonly used within weather forecast systems analysis.
It provides a strong structure for enhancing the classification of weather images and supporting the study of meteorology by bringing in the strengths of convolutional networks and recurrent networks.
A Novel Hybrid Algorithm for Enhanced Low-Conductivity Material Imaging in Magnetic Induction Tomography
Authors: A.J. Lubis
Published: February 2025

Background: Magnetic Induction Tomography (MIT) faces significant challenges in imaging low-conductivity materials, particularly in optimizing multi-frequency excitation parameters for enhanced detection sensitivity. Conventional approaches face challenges in processing weak electromagnetic responses from low-conductivity materials (10^-18 to 10^-12 S/m). This limitation results in poor image quality and reduced detection capabilities.
Purpose: This study introduces an innovative adaptive multi-frequency optimization framework integrated with deep learning for MIT, specifically designed to enhance the detection and characterization of low-conductivity materials. The framework introduces a novel HBDL-TVR-MF-ACC-MIT algorithm that dynamically optimizes excitation frequencies while leveraging deep learning for improved signal processing and image reconstruction.
Method: We developed an integrated approach combining adaptive frequency optimization (1 kHz - 10 MHz) with deep learning architectures. The system employs frequency-hopping techniques and custom-designed CNN for optimization and reconstruction. The framework was validated through comprehensive COMSOL Multiphysics simulations and experimental testing using standardized phantoms.
Results: The framework demonstrated substantial improvements in MIT imaging performance, including enhanced detection sensitivity for ultra-low conductivity materials, significant reduction in reconstruction time, and improved spatial resolution. The system achieved consistent performance across diverse material types, with notable improvements in image quality metrics and system stability. Key achievements include a 45% reduction in reconstruction time and 40% improvement in spatial resolution compared to conventional methods.
Conclusion: This adaptive multi-frequency optimization approach represents a significant advancement in low-conductivity MIT imaging, enabling accurate and efficient detection of previously challenging materials. The integration of deep learning with optimized frequency selection establishes a robust framework for non-invasive imaging applications in medical diagnostics and industrial monitoring, with potential for substantial cost reduction and efficiency improvements in both sectors.
An Optimization Study of Absorption Rate of NPK Fertilization under Multi-Depth of Black Loamy Sandy Soil
Authors: Nikhil Bhende
Published: February 2025

Sustainability is an empirical practice that focuses on social, economic, and environmental situations even for Cotton farming like systems to cater its agro-ecological problems. A suitable fertilization approach is worth implementing in cotton crop farming to get better absorbability of nutrients. However, when fertilization depth is altered to different depths its effect on absorption rate on cotton crop is unclear. An experimentation was performed on cotton crop using Black-Loamy Sandy soil of Vidarbha region of India by applying Carbamide (H2NCONH2) and Diammonium-Phosphate (NH4H2PO4) fertilization under multiple depth to determine the effect of NPK absorption rate. Three fertilizer placement depths comprising of 2 inches (FD2), 4 inches (FD4) and 6 inches (FD6) were tested. This experiment utilizes advanced IOT based sensors for measuring parameters and Lab-View framework to monitor the readings. This paper examines the effect of these parameters using Grey-based Taguchi analysis process. The scrutiny focuses on multi-response optimization of the NPK absorption rate using mini-tab software. Compared to various combinations, the optimum combination for low value of N, P & K was found as FDP = 6 inches, FQ = 40 gm-kg, SM = 70% (FDP-3-FQ-3-SM-3) gives the optimum NPK absorption rate. The projected, grey-based Taguchi method showed effective in resolving multi-response problem and such conclusions provide a practical recommendation for boosting the fertilizing policy in this region.
An Enhanced Oppositional Crow Search Optimization Algorithm-based Colour Edge Segmentation and Modified Resnet-39 Architecture for Prediction of Crop Disease
Authors: Rahul Kumar
Published: February 2025

Accurate and timely prediction of crop diseases is vital for improving agricultural productivity and ensuring food security. This research work proposes new framework that integrates an improved swarm-based color edge segmentation method with a modified ResNet-39 architecture to efficiently detect and categorize crop diseases. The suggested segmentation technique utilizes Crow Search optimization algorithm combined with oppositional learning to improve edge recognition accuracy using color segmentation. This allows precise localization of disease-affected areas even under difficult situations such as poor contrast and noise. The segmented outputs are further processed using a tailored ResNet-39 architecture, optimized for computational efficiency and prediction accuracy. The proposed Enhanced edge segmentation Crow Search Optimization with ResNet-39 (EesCSO-ResNet-39) methodology is tested using publically accessible crop disease datasets, showing substantial improvements in critical metrics such as accuracy, precision, and recall relative to conventional segmentation techniques and current deep learning architectures, including ResNet-50 and VGG-16.
Evaluating the Effect of Marketing Ethics on Customer Satisfaction: A Descriptive Analytical Approach
Authors: Moneer Hossen Alzalook
Published: February 2025

This study aims to know the impact of marketing ethics on customer satisfaction in light of the mediation of compliance with ethical rules as an interactive mediating variable by applying it to the customers of the two telecommunications companies, Libyana and Al-Madar, by relying on the descriptive analytical methodology. The questionnaire was relied upon to collect data and distributed to the research sample of users of the two companies. A statement within the municipality of Misrata covered all the factors that the researcher assumed in the relationship between marketing ethics and customer satisfaction, with compliance with the rules of Sharia as a mediating variable. The number of questionnaires reached 279, and after processing them, the data was analyzed using a statistical analysis program SPSS, Smart PLS. The study found a positive, statistically significant effect between marketing ethics and customer satisfaction, except for pricing ethics. The results also found no statistically significant effect for compliance with ethical rules as an interactive mediating variable between marketing ethics and customer satisfaction because the values of Sharia law are inherent. We have had an environment of compliance and fear of God Almighty since childhood, which makes most of the sample members apply ethical standards in their policy, and this is also reflected in the surrounding environment and customer satisfaction and gain.
Social Marketing Mix : A Framework for Promoting Mental Health Services
Authors: Mahesh Luthia
Published: February 2025

Aim/Purpose: The study aims to apply the social marketing concepts and framework and develop a marketing mix for reducing social stigma associated with help-seeking behaviour among individuals dealing with mental illness. The study examines perception of practitioners involved in mental well-being services on factors that influence selection of mental health services. This paper highlights the role of social marketing in promoting behavioural change and improving help-seeking behaviour to address mental illness.
Background: The discipline of social marketing can become an important driver for change in terms of brining behavioural change. Social marketing involves adapting commercial marketing theories and practices for supporting campaigns for social change. There is a need to examine how contemporary marketing practices can help to promote mental health service.
Methodology: The study involved review of existing literature on social marketing and its role in social change. A cross-sectional study was conducted among psychologist, social workers, mental health counsellors and HR professionals      using online questionnaire based on stratified sampling. The findings of the survey were integrated with the literature using to adopt a mixed method research and propose a framework for social marketing. Descriptive analysis was carried out based on the data collected. 
Contribution: The study adds to the body of knowledge of social marketing by of proposing a social marketing framework that can enable service providers to create awareness and reduce the stigma associated with seeking help for mental illness.  The study could help community-based services in mental to Promote help-seeking behaviour.
Findings: The findings indicate that adapting the marketing mix for social marketing can creative positive attitude towards mental health and its services. Emotional or interpersonal problems carries social stigma and Confidentiality and safe space for counselling can create a conducive environment for help-seeking. Social media and influencers can contribute to create awareness on the benefits of mental well-being will bring in a social change to accept counselling services.
Optimizing Smart Grids for Distributed Energy Resource Integration and Management
Authors: Neeraj Shrivastava
Published: February 2025

Incorporating Distributed Energy Resources, which comprise solar, wind energy, and batteries, in traditional energy supply represents a substantial opportunity and challenge regarding the maximization of efficient management of energy along with its proper stability at a grid level. Hence, rising penetrations call for advanced smart grids offering real-time monitoring, analytics based on forecasting capabilities, and control at local decentralised nodes due to high variation and uncertainties brought about by renewables. This study aims to optimize smart grids for seamless integration of DERs by harnessing advanced technologies such as the Internet of Things, machine learning algorithms, and blockchain-enabled energy transaction systems. The methodology includes data collection from a distributed grid network through IoT-enabled sensors, predictive modelling of energy demand and supply through machine learning, and implementation of a decentralized trading mechanism using blockchain technology. Prototyping a smart grid system and testing its efficiency, reliability, and scalability were simulated-based evaluations. Results include a 25% improvement in energy utilization efficiency, a 30% reduction in grid balancing costs, and improving system resilience to such power fluctuations. The study also opens a way to demonstrate the possibility of blockchain usage while allowing energy transactions to be transparent and secure to reduce administrative overhead. Findings on these issues highlight how intelligent technologies play an important role in the transition toward more sustainable and resilient energy systems. This research contributes to the broader effort of creating adaptive smart grids capable of accommodating the growing complexity of modern energy networks.
Enhancing Communication in English for Business and Management through Collaborative Learning: A Mixed-Method Study with Syrian Learners
Authors: Moustafa Armnazi
Published: February 2025

This study investigates the effectiveness of collaborative learning in improving communication skills among English for Specific Purposes learners, who voluntarily enrolled in English for Business and Management Language Training course. This mixed-methods study investigated the effectiveness of a collaborative learning approach in enhancing communication skills among Syrian learners in English for Business and Management course. Quantitative data from a 12-item questionnaire administered to 22 participants revealed consistently high mean scores, indicating strong agreement regarding the positive impact of collaborative learning on communicative strategies such as active listening, negotiation, and handling disagreements. Qualitative data from semi-structured interviews, analysed thematically, further supported these findings, highlighting key themes of enhanced communication skills, increased engagement and motivation, and a supportive learning environment. Participants reported increased confidence and fluency in business-related communication, attributing this improvement to the interactive nature of collaborative activities and the supportive peer interactions. The study concludes that collaborative learning is an effective pedagogical approach for developing communicative competence among Syrian learners in English for Business and Management contexts.
The Future of Retail Pricing: AI-powered Optimization and Customization
Authors: M. Prithi
Published: February 2025

Determining the dynamic properties of objects is crucial in autonomous environments. Event cameras that can move freely are used in outdoor security systems. Frames are not static, though, because to environmental factors, which results in higher energy and time usage. The deep learning-based approach to retail experience and consumer personalized discounts is reviewed in this poll. Here, deep learning techniques have been used to analyze the current retailing and customer personalization survey. The issues with current methods have then been enumerated. To test the theoretical approach, a structural equation modeling technique is employed. Although the full model of the total retail experience is not tested, this study demonstrated the human relationship between a retail staff and a client. On the other hand, there are physical clues that have a significant impact on consumer satisfaction.
An Online Alumni Tracer System for the Graduates of Cavite State University
Authors: Rosalina D. Lacuesta
Published: February 2025

Technology implementation can be seen in a variety of professional disciplines, industries, and even the average home. Different sorts of technology are now being used to improve efficiency, increase output, and bring answers to present concerns and problems. Graduates is one of the important supports in the development of a university. Graduates have a role to assist universities in developing the university into the community through collaboration in academic terms. Employability of graduates is one of the indicators being assessed by Higher Education Institutions (HEIs) to find out whether the quality of education they provide is suitable to the needs of the industry. The researchers developed an Online Alumni Tracer System for the Graduates of Cavite State University Imus Campus to allow the university to trace the CvSU-Imus alumni employment status, work alignment, and job whereabouts. The web-based application system can be used as a tool in finding out important information about them and it aims to track the professional development of graduates with regards to the relevance of their skills and training acquired from the university. The information in return can help the administration determine the improvement that they need to do with the existing curriculum. It can also generate comprehensive reports which are necessary for planning, program implementation and any decision-making purposes. The Agile Methodology was adopted by the researchers during the system development of the Alumni Tracing system. The software used for developing a system are PHP, CSS, Javasript and MYSQL as a database. The evaluation of the CvSU-Imus campus Alumni Tracing system revealed that the developed system met its requirements by utilizing a modern method of recording information and reports generation.
FinAI: Gamified Finance Learning Platform
Authors: Asmita Deshmukh
Published: February 2025

Financial illiteracy in India is pervasive, leading to detrimental financial decisions and outcomes. In response, we developed FinAI, a software platform designed to simplify complex financial concepts through gamification. By offering accessible courses and clear explanations, FinAI empowers users, particularly beginners, to make informed financial decisions and capitalize on market opportunities. Our study addresses a pressing need for accessible and engaging financial education, aiming to mitigate the negative effects of financial illiteracy. Through FinAI’s gamified approach, users can navigate courses and grasp complex financial terms, promoting financial literacy and enabling users to navigate the market effectively. Utilizing React.js and Node.js for frontend and backend development, Neon DB for database management, and hosting on Vercel, FinAI incorporates a chatbot named ”Finbot” powered by Bard API (Application Programming Interface) and Finhub, providing comprehensive responses to user queries. A stock pre- diction model leveraging LSTM (Long Short-Term Memory) and ARIMA (Auto-Regressive Integrated Moving Average) machine learning models, along with the yfinance live data, offers insights into future stock prices. The platform also includes a news feed feature to enhance users’ understanding of financial news and events. Through the implementation of energy points and experience points, users progress through course levels, with the option to purchase additional energy points contributing to the platform’s revenue stream. Our study revealed significant interest among young students in learning financial concepts through FinAI, with ARIMA demonstrating superior accuracy in stock prediction. FinAI represents a crucial step towards addressing financial illiteracy in India, offering an accessible, engaging, and effective means of financial education.
A Comprehensive Deep Learning Framework for Breast Cancer Detection and Classification Using Multiple Convolutional Neural Network Architectures
Authors: Satish L Yedage
Published: February 2025

Improving rates of survival of patient for breast cancer (BC) requires early identification and precise categorization. Using the DDSM mammography dataset , this work The goal is to enhance the detection and classification of breast cancer through the application of deep learning techniques. The suggested framework uses a method based on C.N.N. 55,890 pre-processed mammography pictures, divide into train and test sets, make up the dataset. Both positive and negative instances were included in the data, which was further divided into benign masses, benign calcifications, malignant masses, and benign calcifications. The images were scaled to 299x299 resolution. Accuracy, sensitivity, specificity, and other pertinent measures were used to train and assess four models. The classification accuracies of C.N.N, VGG16, ResNet-50, DenseNet121, and Efficient Net were 90.83%, 87.00%, 91.35%, 92.40%, and 94.97%, respectively. Efficient Net achieved the highest performance, demonstrating superior generalizability across diverse imaging modalities and demographic variations. The proposed frameworks, supported by pre-trained models, demonstrates significant potential for improving Early recognition and identification of breast cancer as well. The integration of ethical considerations, interpretability, and a focus on clinical impact ensures its relevance for real-world applications.
Development of Security Aware Content Based Image Retrieval System using Lightweight Trapdoor Verification in Cloud Environment
Authors: Pushpanjali Munnalal Chouragade
Published: February 2025

Demand for personal privacy protection, safe cloud storage, and search over encrypted information have all grown to be critical issues due to the rapid development of cloud services. The movement towards safe computation has received a lot of attention, particularly asymmetric scalar-product-preserving encryption (ASPE) and homomorphic encryption (HE). Although ASPE has the capacity to effectively encrypt and compare cipher texts, its reliance on the assumption that users can be completely trusted in the actual world and potential key leakage issues make it an impractical technique. In this research develop a content-based image retrieval system model that is security conscious and utilize the lightweight trapdoor model for verification, initially the input are collected from the standard repository and saved in an encrypted format. The image is encrypted using optimal chaotic map-based encryption, and the keys are produced on the user side to provide the image privacy and security. The image obtained after encryption is called as ciphered image. Hence for the purpose of image retrieval, the ciphered image is kept in the cloud. The relevant ciphered images are provided to the user whenever a search request is made by the query user using the trapdoors. Using an efficient hybrid distance-based clustering method, the cloud's most pertinent ciphered images are chosen, and the user who requests them is given access to the relevant image clusters. The proposed hedge learner optimization is used to optimize the hybrid distance based clustering of images. Finally the cloud image that the user currently has been ciphered is decrypted using the owner's key.Based on the achievements at TP 80, the HLO Optimized Chaotic Encryption model achieve the MSE, PSNR, RMSE and SSIM values of 0.04, 61.67, 0.21 and 93.97 respectively. Similarly HLO Optimized Hybrid Distance based Clustering model achieves the F1 measure, precision and recall values during NOR 500 is 94.73%, 95.20% and 95.60% respectively.