bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessment of Satisfaction of MSME at the Pre-Stage of Loan Sanction by Public Sector Banks in Odisha, India
Authors: Niladri Bihari Dash
Published: February 2025

Micro, Small, and Medium Enterprises (MSMEs) are integral to the economic framework, significantly contributing to job creation, promoting equitable income distribution, and mitigating regional economic disparities. The success of MSME entrepreneurs is heavily reliant on the support they receive from financial institutions, particularly in terms of credit access and banking services. This research aims to investigate the determinants of satisfaction among MSME borrowers and assess their overall contentment with public sector banking services. Recommendations are provided to enhance borrower satisfaction, thereby facilitating accelerated growth within the MSME sector. The study employs rigorous scientific methodologies to analyze MSME's satisfaction with commercial banks, revealing that the relationships between various factors and customer satisfaction regarding bank policies and procedures are predominantly linear, with high correlation coefficients indicating a strong connection between these elements.
Battle of Sentiment Lexicons: Wordnet, Sentiwordnet, Textblob and Vader in Web Forum Analysis
Authors: Ahmad Taufik Nursal
Published: February 2025

Sentiment analysis plays a vital role in assessing public perception across different sectors, including the realm of high-rise properties. This study investigates the applicability and effectiveness of four lexicon-based sentiment analysis tools—WordNet, Textblob, SentiWordNet, and Valence Aware Dictionary and Sentiment Reasoner (VADER)—in measuring public sentiment towards high-rise properties in Malaysia. Utilizing a real-world case study, the classification performance and accuracy of these lexicon dictionaries were evaluated. The findings reveal that all four dictionaries predicted a higher proportion of positive reviews in comparison to negative and neutral reviews. WordNet recorded the highest number of positive reviews, closely followed by SentiWordNet and VADER. In contrast, VADER identified the most sentences with negative polarity, followed by WordNet and SentiWordNet. Regarding neutral polarity, VADER showed the highest count of neutral reviews, while SentiWordNet and WordNet had fewer. Given the overall performance, VADER proved to be the more effective lexicon compare to the other three. Misclassification cases by VADER exposed limitations such as restricted vocabulary, difficulties in detecting sarcasm, ambiguity, and issues with misspelled or short terms. This study offers valuable insights for researchers, practitioners, and policymakers in analyzing public sentiment toward high-rise properties, there by facilitating informed decision-making based on precise sentiment classification.
Multi-Layered Model Approach (MLMA) For Heart Vein Blockage Detection Using Gated Recurrent Unit (GRU) and Denoising Autoencoders (DAES)
Authors: G.V. Rajya Lakshmi
Published: February 2025

Cardiovascular diseases (CVD) continue to be the leading cause of death worldwide, and sophisticated diagnostic techniques must be developed to detect and treat them early. This study analyzed vein blockages in the heart and proposed an ensemble model for the early diagnosis of heart problems. Venous blockages can be found using non-invasive imaging techniques, frequently suggesting cardiovascular abnormalities. Using a Coronary Computed Tomography Angiography (CCTA) dataset that includes normal and abnormal cardiac cases, the pre-trained 3D convolutional neural network (3D-CNN) model can learn complex patterns linked to early-stage cardiac problems. To improve the accuracy and dependability of the detection process, the proposed ensemble model incorporates several algorithms, including the segmentation-based threshold method, feature extraction-based form descriptors, and the vessel tracking algorithm (VTA). The ensemble model consisted of several base models, each focusing on a distinct area of vein blockage investigation. Feature extraction techniques extract pertinent information from medical imaging data, such as angiograms and vascular scans. The ensemble model, which uses the combined decision-making ability of several algorithms, including shape descriptors, is fed to the retrieved features. The proposed ensemble model performed better than the individual models and conventional diagnostic techniques regarding early detection accuracy. Moreover, analysis of the feature importance scores improves the interpretability of the model and sheds light on the crucial markers of cardiac anomalies. The suggested ensemble model has a significant potential clinical impact because early identification of cardiac problems enables prompt intervention and better patient outcomes. Finally, the proposed method combines the multi-layer approach with gated recurrent units (GRU) and a Denoising Autoencoder (DAE) to find abnormalities in angiography images. The effectiveness of the suggested method demonstrates a high rate of disease diagnosis and accuracy for specified angiography images.
An Effective Epileptic Seizure Detection Using Graph Neural Networks
Authors: K. Dileep Kumar
Published: February 2025

Epilepsy is a tough neurological condition, characterized by seizures that can be difficult to accurately and quickly diagnose. Existing methods that use brain activity readings (EEG) to detect seizures struggle to capture all the complex connections within the brain. This research proposes a brand new approach to improve seizure detection: using special networks called Graph Neural Networks (GNNs) to analyze EEG signals. Imagine these networks as detectives, following the intricate connections within the brain activity data like clues, leading them to the "culprit" - the seizure. The goal is to build a GNN model that can effectively analyze these connections, leading to much better seizure detection. The model treats the EEG signals like maps, with the connections between brain regions represented as lines. By incorporating special "layers" that understand these maps, the model can uncover hidden patterns that signal an oncoming seizure. This research will test the model on various EEG datasets to ensure it works for different people and in different situations. If successful, this approach could lead to more accurate seizure detection, faster processing for real-time applications, and better results for all kinds of patients. Ultimately, this research aims to push the boundaries of traditional seizure detection methods and harness the power of GNNs to improve epilepsy diagnosis and treatment, making a real difference in the lives of people with this condition.
Advancements in Early Detection of Cotton Leaf Diseases in Plants Using Multimodal Deep Learning Techniques
Authors: M. Dhana Lakshmi
Published: February 2025

Due to the vital importance of promptly controlling plant diseases and the challenges facing today's agricultural disease management techniques, a novel framework has been introduced for detecting crop disease. This paper proposes a new methodology for detecting cotton leaf disease using multi-modal deep learning approaches. Since the early control of plant diseases is paramount and commonly utilized methods for managing agricultural diseases suffer in practice, a new framework for disease detection has been proposed. Inspecting modern deep learning algorithms concerning the analysis of images and processing for pattern recognition, we design a system utilizing visual indicators of diseases from multiple modalities to improve the detection of diseases in cotton leaves. We propose a new methodology utilizing deep learning algorithms that utilize spectral imagery, thermal imagery, and hyper-spectral images to merge their features and thus accurately identify plant diseases. This system allows measuring the plant’s general health to determine the type of disease symptoms occurring on the leaves of various cotton leaf trees in many environments. Hence, the outcomes of extensive experiments using multi-modal images of plants of various kinds – healthy and diseased – show that quantitative tests confirm the ability of the proposed system to detect different types of early cotton leaf diseases with an accuracy, sensitivity, and specificity equal to or better the results published in the literature, which allows the critical impact of the proposed multi-modal deep learning technique to give an early warning against plant diseases before it spreads in the fields, as well as the other numerous uses of its implementation and availability in agricultural papers and applications while aiding the reaching of the goals for further research and developments on the areas to be used by the governing territories where better scalable and accurate disease detection systems would be necessary for such large-scale agricultural needs and real-time surveillance applications.
An Enhanced Model for Mammogram Image Denoising Using a CNN Autoencoder with Residual Connections
Authors: Priyanka Gupta
Published: February 2025

Image denoising has a wide range of applications in obtaining better-quality noisy data, such as medical imaging and low-light photography. This work proposes a new CNN autoencoder architecture also equipped with a residual connection that advances state-of-the-art performance on this denoising benchmark. The proposed model is compared with state-of-the-art methods, including conditional GAN, CNNs with advanced loss functions, and self-supervised learning models in terms of PSNR, SSIM, and computational loss. It achieves the best results on metrics of PSNR at 44.82, SSIM at 0.970, and a loss value as low as 0.015 on a custom dataset of medical images. These results reveal the efficiency of the architecture in noise reduction while retaining the critical information about the structure and, hence, have many promises in real-world applications. The emphasis here is on an application of residual connections fused with deep learning models having superior state-of-the-art performance in image denoising.
A Code-Driven Approach Design with Help of Artificial Intelligence
Authors: Hasan Hashim
Published: February 2025

This paper investigates the creation of real-time experimentation in Artificial Intelligence (AI) by means of a code-driven approach, which addresses the dynamic nature of AI applications in contemporary contexts. The complexities of real-world scenarios are frequently not captured by traditional AI experimentation, which heavily depends on static datasets and preset criteria. This study illustrates the process of adapting and enhancing AI models in live environments by combining real-time data processing with continuous method optimization. The methodology entails the establishment of real-time data channels, the execution of AI models in dynamic conditions, and the application of numerical analysis to quantify performance enhancements. The primary findings. indicate that real-time experimentation substantially enhances the accuracy, productivity, and flexibility of models in comparison to conventional methods. The results are corroborated by meticulous numerical experiments, which encompass metrics such as precision, recall, accuracy, and processing times. This research contributes to the expanding field of AI by illustrating the efficacy of real-time, code-driven testing and offering practical insights. This work has a wide-ranging impact on a variety of industries, as the demand for real-time, adaptive AI solutions becomes more urgent. These methods could be further refined and additional applications across various AI domains could be explored in future research.
Financial Risk Management in Global Supply Chains: Strategies for Resilience and Profitability
Authors: Ajatashatru Samal
Published: February 2025

Supply chains of the global economy are wedded to financial risks such as exchange risk, political risk, risk of variation in regulation, and risk of economic cycles. Mitigating of these risks is very crucial so as to guarantee the organization’s stability and its ability of making profits. This research paper analyses the measures that organizations have taken towards buffer financial threats in international supply systems. It explores the risk identification, risk evaluation and risk mitigation measures; that include, financial risk management, supplier diversification, application of IT and jointly managed risk-bearing structures. Importantly, the study also includes the discussion of the use of innovations, including blockchain and predictive analytics, in increasing positive financial reporting and better predicting necessary decisions. Examples of supply chain risk management solutions are discussed through various company examples and best practices. The case study lends credence to managing the financial risks in supply chain planning and realistic, responsive approaches to perpetuating profit margin in the unstable global economy. To the best of the author’s knowledge, this paper adds value to existing literature by offering specific recommendations that can be implemented by practitioners and policymakers to enhance supply chain manageability and affordability.
A Study on Consumer Perception towards use of E-Wallet in Nagpur City
Authors: Madhuri Barua
Published: February 2025

E-wallet as part of a rapidly integrating financial technologies has become the key enabler of change across many sectors and industries, impacting business processes and consumer buying habits. The present work aims to analyze the attitudes towards the e-wallets in Nagpur city with specific regard to their effects on business facilitation and the consumers’ convenience. The paper looks into variables that facilitate the use of e-wallet, ease of use, security, convenience, speed and how these characteristics improves customers’ satisfaction and profitability of organisations. A survey in both structured quantitative form and follow up interviews were conducted among consumers and prospective business owners in Nagpur. The study shows that convenience, ubiquity and automation aspects are key drivers to the adoption of e-wallet, while the factors such as security concern and technological glitches act as inhibitors. The study also explores e-wallets systems and their impact of cashless society, effectiveness in business transactions as well as the convenience to the buyer. The findings in this paper are useful to policymakers, e-wallet service providers and merchants intending to encourage the use of digital payments in urban areas.
Enhancing Cybersecurity in Wireless Sensor Networks: AI Solutions to Simulated Attacks
Authors: Mohit Angurala
Published: February 2025

Owing to the real time applications of Wireless Sensor Networks (WSNs) including: industrial automation and remote environment monitoring, WSNs have revolutionized today’s infrastructure. While implementing WSNs in strategic areas, security threats have become increasingly prevalent. Security enhancement in WSN by adopting advanced techniques in machine learning is the major focus of this research work. In an effort to discover possible use of Random Forest and Isolation Forest algorithms on them to detect and prevent the attacks, we look into depth of the attack. In this paper, the dataset is pulled from different repositories that are freely available to the public as an initial step followed by various preprocessing techniques. Data cleaning, feature selection, normalization, and categorical variable encoding have been applied as a part of preprocessing. We then observed a general increase in the detection of malicious flows together with the improvement of the tolerance to the simulated attacks. Moreover, we observed how ML enhances the security of WSNs with the combined use of ensemble learning and anomaly detections showing promising approaches and foundations for theoretical and experimental studies. The carried-out experiment proves the efficacy of the Random Forest Classifier (RFC), while maintaining a high level of accuracy, which is 99. 86% compared to 99. 72% before the attack.
Modeling and Control of Electric Vehicle Powertrains for Enhanced Efficiency
Authors: Shrikant Shantaram Mopari
Published: February 2025

Driven by the growing need for sustainable transportation solutions over the last decade, significant progress has been made in electric vehicle (EV) powertrain technologies. In this study, a holistic model and a control strategy for EV powertrain efficiency optimization are introduced. The development of a dynamic mathematical model of the EV powertrain, comprising of the motor, inverter and energy storage systems, to simulate performance for different driving cycles. More sophisticated supervisory control techniques, like model predictive control (MPC) and adaptive fuzzy logic, has been applied for the improvement of energy management and operational performance. Simulation and experimental results show that the energy-aware control approach has resulted in improvements up to 15% in energy utilization when compared to classical control methods. The suggested power arbitrating technique gives unequalled power dispersion among elements, cuts down vitality loses, and broaden battery lifespan while not relinquishing vehicle execution and driving comfort. It also assesses the role of regenerative braking and thermal management on overall efficiency. The results indicate it is possible to optimize for both performance and efficiency using intelligent control systems, which could enhance the viability and sustainability of EVs in the future mobility. Our research offers a scalable framework for the design and control of next-generation EV powertrains that can be beneficial for both automakers and researchers.
Effective Machine Learning-Based Strategies for Enhancing the Functionality of Hyper Spectral Imaging
Authors: Ok-Kyoon Ha
Published: February 2025

Hyperspectral Imaging (HSI) is a powerful technology for capturing high-dimensional spectral information across a wide range of applications, including remote sensing, medical diagnostics, and material analysis. However, extracting meaningful insights from HSI data presents unique challenges due to its complex nature and high dimensionality. This study explores innovative machine learning-based strategies aimed at enhancing the functionality of HSI in various applications. One key approach investigated in this research is the implementation of Deep Learning-based frameworks, with a particular focus on Generative Adversarial Networks (GANs). GANs, equipped with Convolutional Neural Networks (CNNs) as generators, which are utilized to create data points that are not only statistically accurate but also semantically meaningful in relation to the underlying distribution. Through an adversarial objective function, the generator is trained to predict the distribution, while the discriminator network distinguishes between real and generated data. This adversarial game between the discriminator and generator has shown promise in improving HSI classification efficiency. Furthermore, the proposed GAN framework incorporates a classifier component capable of categorizing HSI samples based on their spectral characteristics, using a novel algorithm called the Generative Model-based Hybrid Approach (GMHA-HSIC). This approach adds a valuable dimension to HSI data analysis by enabling accurate classification based on spectral features.
Management of Construction, Stability, and Performance in Solar-Powered Metal Halide Photo-Catalysis Systems
Authors: Deageon Kim
Published: February 2025

The formation of harmless to the ecosystem, monetarily reasonable, and chemically efficient cycles is the present key chemistry challenge. Presently, semiconductor heterogeneous photocatalysis is utilized in many cycles that affect society, from those that produce hydrogen and convert CO2 to those that transform natural unrefined substances into compounds with added esteem. Metal halide perovskites have recently emerged as a possible inspirational class of unassuming and simple to create photocatalytic semiconductors, despite their precariously abruptly supported gem structure. However, up until now, their widespread use has been required. We examine the challenges MHP-based photocatalysis encounters in this Review and emphasize the numerous frameworks being employed to create circumstances that are both favourable and stable for photocatalytic reaction. For hydrogen development responses, we explicitly talk about the utilization of (1) halogen corrosive arrangements (HX; X = I or Br), (2) extremely low-extremity solvents for natural changes and CO2 photograph decrease, and (3) the embodiment of perovskites for CO2 decrease and water parting. The means used to make innately stable photocatalytic materials are additionally definite, eliminating the interest for uncommon conditions. We close by introducing possibly alluring open doors and approaches for metal halide perovskite-based photocatalysis research going ahead, with every innovation offering particular arrangements of benefits and challenges.
Leveraging Artificial Intelligence for Talent Acquisition and Employee Retention in Human Resources
Authors: S. Jyothirmaye Reddy
Published: February 2025

This study examines the transformative effects of Artificial Intelligence (AI) on talent acquisition and employee retention in human resources (HR). This research, using a sample of 173 HR professionals and employees from various industries, examines how AI-driven solutions optimize recruitment procedures and improve long-term engagement tactics. The research investigates the impact of predictive analytics, chatbots, and machine learning algorithms on enhancing candidate screening, minimizing time-to-hire, and cultivating a tailored recruitment experience. Additionally, it assesses the efficacy of AI in forecasting staff turnover risks and facilitating proactive retention strategies. Advanced statistical techniques, like as factor analysis and Mann-Whitney U tests, are employed to evaluate the hypotheses and reveal actionable insights. The results indicate substantial enhancements in HR operational efficiency, applicant satisfaction, and workforce stability due to AI. This research highlights the capacity of AI to transform HR practices while confronting issues such as prejudice and ethical considerations in its use. The research offers actionable suggestions for firms seeking to utilize AI in developing competitive, future-oriented HR systems.
Navigating Crises: The Strategic Role of CSR in Crisis Management and Mitigation
Authors: Aarti Chopra
Published: February 2025

Organizations face a wide range of crises in the unstable business climate of today, from public relations scandals to natural calamities. Despite these difficulties, companies can use “corporate social responsibility (CSR)” as a strategy to manage and lessen the effects of crises. Thus, the study examined the strategic role of CSR in crisis management and mitigation. The primary data was acquired through a structured questionnaire from 200 respondents (executives). Respondents were selected by utilizing a purposive sampling technique and Excel and SPSS 26 were utilized for data analysis. Statistical tools such as paired sample regression and correlation were used. The results demonstrated that CSR engagement and long-term organizational sustainability following crisis events have a substantial correlation. Moreover, it has been found that CSR initiatives positively influence crisis management and mitigation strategies in organizations, and Community support significantly enhances CSR-driven crisis management strategies. Finally, it has been concluded that by integrating CSR strategies with crisis management tactics, businesses may show their dedication to moral behavior, social responsibility, and sustainability, which eventually helps organizations mitigate and manage crises more successfully. The study could motivate organizations, policymakers, and management to include CSR in their business practices and company culture to promote flexibility and resilience, which can assist them in dealing with crises more skillfully.
Embracing Data Mesh and Zero ETL: Revolutionizing Intra-Company Data Exchange and Collaboration
Authors: Rajesh Remala
Published: February 2025

New methods of sharing and collaborating on data within companies are required due to the fast development of data ecosystems. In order to respond to changing business demands, organisations typically struggle with agility, scalability, and real-time insights caused by traditional centralised architectures and Extract, Transform, Load (ETL) procedures. In order to overcome these obstacles, this research investigates the revolutionary possibilities of the Data Mesh and Zero ETL paradigms. Data Mesh allows cross-functional teams to manage and distribute data as a product, decentralising ownership by aligning it with business domains. By doing away with tedious data pipelines, Zero ETL allows for frictionless, real-time data transfer across platforms and systems, significantly improving efficiency. Taken as a whole, these methods improve decision-making, streamline operations, and promote self-service analytics, all while redefining intra-company data cooperation. This study proves that data accessibility, governance, and time-to-insight all saw considerable improvements after analysing case studies and performance indicators in depth. Data Mesh and Zero ETL have the ability to transform the way organisations use data, encouraging a spirit of creativity, cooperation, and agility, according to the results. Businesses may use this research as a guide to adopt these paradigms and thrive in the ever-changing digital world. Innovative solutions for intra-company data interchange and cooperation are required due to the increasing complexity and volume of current data ecosystems.When it comes to agility, scalability, and real-time, traditional data architectures and Extract, Transform, and Load (ETL) procedures can't cut it. In order to tackle these problems head-on, this research looks at how the Data Mesh and Zero ETL paradigms may work together. By distributing ownership of data and tying it to certain business domains, Data Mesh allows teams to manage data as a product and guarantees interoperability. Zero ETL reduces the time and resources needed for data transformation and transfer while eliminating the need for conventional data pipelines. It enables real-time data exchange. A data-driven decision-making environment that is agile and collaborative is fostered by these paradigms when they are used together. Organisations that use these strategies increase data accessibility, governance, and operational efficiency, as shown by empirical assessments and real-world case studies. Data Mesh and Zero ETL have the ability to transform how companies handle data internally. This might lead to more innovation, less complexity, and a faster return on investment. Results from this research may help businesses update their data strategy and adopt a more collaborative, domain-oriented approach to data management.
Fatty Liver Disease Stage Classification Using Ultra Sound Images by ResNet Deep Learning Model
Authors: M. Swapna
Published: February 2025

Fatty liver disease (FLD) is a growing global health concern, with its prevalence on the rise due to factors such as sedentary lifestyles and poor dietary choices. The non-invasive classification of FLD stages using ultrasound images is a vital diagnostic endeavor that aids in early detection and intervention, thus mitigating the potential for severe liver-related complications. In this research, a ResNet-based deep learning framework is proposed for ultrasound images based automatic staging of FLD. The model uses features obtained through ultrasound scans to classify the stage of FLD, i.e Grade 1, Grade 2 and Grade 3. They are trained, validated and tested on a dataset of standardized ultrasound images. This indicates the model's ability to classify FLD at a stage correctly and proposes a potential avenue for improved efficacy in diagnostics in hepatology.
Employee Performance Evaluation and Succession Planning through the Impact of Predictive Analytics in Jordanian Institutions
Authors: Suad Abdalkareem Alwaely
Published: February 2025

This study examines how companies can find talent, assess performance, and plan for future leadership needs by leveraging predictive analytics to evaluate employee performance and plan for leadership succession. Predictive analytics uses data, statistical techniques, and machine learning to identify patterns and make educated guesses about the future. It has evolved into a critical tool for enhancing decision-making in HR management in Jordan. By examining its impact on succession planning and employee performance reviews, this study focuses on how predictive analytics can enhance accuracy, speed, and fairness in organizational processes. Traditional methods of evaluating an employee’s work, such as annual reviews and self-evaluations, may not necessarily provide a complete and unbiased picture of their abilities. Predictive analytics, a more data-driven approach, uses vast amounts of data from multiple sources, including individual workflows, peer and manager feedback, and past performance records. This gives companies additional insight into their employees’ performance and helps them make decisions about training needs, rewards, and raises. Since predictive analytics identifies an organization’s future leaders, it is also essential for succession planning. Models can predict future employee performance and readiness for leadership roles by leveraging performance data, skill sets, behavioral patterns, and even external factors such as market trends and industry changes. By doing so, companies can reduce risk, fill skill gaps, and prepare future leaders. With changeable leadership comes predictive analytics. Applying predictive analytics also improves the fairness and clarity of decision-making. Companies can ensure that succession planning and performance evaluations are fairer by eliminating human biases and relying on data-driven insights.
Reduce Cooling Load using AI-based KNN Enhanced Roof System (KNN-ERS): CASE STUDY (TRIPOLI- LIBYA)
Authors: Khayri M. M. Mousay
Published: February 2025

Energy depletion is considered one of the greatest challenges facing the planet. One way towards solving this challenge involves architectural adaptations to the local climate to decrease energy use. This study looks at the city of Tripoli located in northern of Libya. People depend extensively on air conditioning systems that result in higher energy consumption. This study proposes implementing AI-based KNN Enhanced Roof System (KNN-ERS) in buildings to decrease cooling energy consumption. The focus is on the importance of insulating the external walls of the building, by adding 50.8 mm thick board insulation to the wall components and use of double glazing techniques. Our methodology consisted of an energy simulation using a hourly analysis program (HAP). Using this simulation, we assessed the effects of KNN-ERS on the reduction rate of cooling loads in library building at the Higher Institute for Science and Technology Qasr Bin Ghashir, Tripoli, Libya. Simulation results show that the proposed AI-based KNN Enhanced Roof System (KNN-ERS) reduce the cooling load significantly, from 32,242 kW to approximately 30,580 kW during peak cooling load in July. This represents a 5.15% reduction in the total cooling load. Insulating the external walls reduces the cooling load through the walls by 32%, and using double glazing technology reduces the thermal load through the windows by 33.7%. The significance of this impact suggests that architects should be more mindful about utilizing passive cooling methods in buildings, reducing the consumption of energy for residents and prompt accomplishing environmental friendly buildings.
The Impact of Work Environment Efficiency in Employee Organizational Commitment in Saudi Arabia
Authors: Abdulrahman AlMubireek
Published: February 2025

Purpose – This paper analyses the impact of work environment efficiency in employee organizational commitment in Saudi Arabia, including the relationship between Job satisfaction, job security, employees’ participation, work conditions with organizational commitment.
Research Design– Data for this study was collected from 176 participants working in Saudi Arabia.
Findings – The research findings have validated the constructive influence of Job satisfaction, work conditions, job security, and employee participation on organizational commitment. This underscores the importance of cultivating a favourable work milieu, guaranteeing job security, and engaging employees in decision-making procedures.
Originality/value - This study’s originality stems from extensive research on the dynamics surrounding work environment in Saudi Arabia. Its value also arises from the development of a conceptual framework that explains work motivation and its efficiency on organizational commitment of employees. As a result, the outcomes provide the impact of different factors which enhance loyalty with more job satisfaction and minimize job turnover.
Assessing the Impact of Social and Environmental Sustainability on Financial Performance: An Empirical Study on Private Sector Banks in India
Authors: Soheli Ghose
Published: February 2025

The study's goal was to determine how financial performance is affected by social and environmental sustainability. In this work, a valid technique was employed to examine existing theories, resulting in quantitative analysis. Carbon emissions and climate action, environmental management practice and policies, and green economics and investment were the determinants of independent variables for environmental sustainability, while gender diversity and welfare Innovation in green technology and corporate social responsibility were the determinants of social sustainability. The study's dependent variables include environmental sustainability, social sustainability, and financial performance. 393 bank employees make up the sample, which was chosen using a basic random sampling technique. To gather the primary data, a systematic questionnaire was also given out. To investigate the hypothesis, regression analysis and correlation were employed. Results indicated that Social and Environmental sustainability have a statistically noteworthy effect on Financial performance at 0.05 significant level, while correlation analysis indicated a constructive relationship between financial performance and social and environmental sustainability. The enhancement of the efficacy of organizations is essential to mitigate adverse impacts on the environment and secure a viable future for them. In particular, the banking sector is essential to this effort since it fosters the development of a strong, prosperous low-carbon economy. Furthermore, financial organizations should use non-financial facts while making decisions. greater account when deciding which loans and investments to make in order to enhance their performance and promote sustainable corporate growth.
A Security Control Strategy Based on Blockchain for Attack Detection in the Health Care Environment
Authors: M.Sandhya
Published: February 2025

The usage of blockchain in healthcare spans a wide range of applications, including secure patient identification, clinical research, medication management, insurance, and the detection of medical fraud. In the hospital system, attack detection is also the most challenging responsibility. As a result, the data will be protected in this research by a revolutionary Blockchain-based African Buffalo Identity-Based Encryption (BABIBE) system. The system that is built in the Python program also collects and trains Electronic Health Records (EHR). Update the blockchain security to detect and ignore threats while also continuously monitoring them. The IBE approach, which encrypts data using the public key and decrypts it by private key, is used to secure data. As a result, blocks receive encrypted data that is then added to the blockchain using the private key. Additionally, depending on the threshold value, identify attacks, and compare the created technique's performance results with those of other traditional models, like energy consumption, throughput, latency, computation time, and detection rate.
Utilization of Blockchain Technology in Talent Management: Increasing Transparency and Security of the Employee Recruitment Process
Authors: Idris Idris
Published: February 2025

Blockchain technology has become a promising innovation that improves transparency and security in talent management, especially in recruitment and human resource management, to find, develop, and retain employees. This article explores how blockchain can address key challenges such as data manipulation, time efficiency, and document authenticity in recruitment. Using the Systematic Literature Review (SLR) approach, the study analyzed 50 indexed scientific articles from high-reputation journals published in the last decade. The results of the review show that blockchain can provide solutions for verifying candidate certification, simplifying the selection process, and maintaining the integrity of employee data. In addition, implementing this technology can also speed up decision-making time and reduce operational costs. However, there are obstacles, such as limited infrastructure, high initial implementation costs, and the need for better regulation. This article provides recommendations for further research related to blockchain adoption in various industry contexts. With blockchain's great potential, this technology is hoped to become a new standard in a more efficient and secure employee recruitment system.
Enhancing Image Quality in Crop Disease Diagnosis: A Comparative Evaluation of Laplacian and Average Filtering with Established Techniques
Authors: Smita Desai
Published: February 2025

The agricultural sector is vital to ensuring global food security, yet plant diseases caused by various environmental factors lead to significant reductions in crop yields. Early detection of such diseases is critical, as crop health directly affects both yield and quality. This research introduces a novel pre-processing algorithm designed to enhance the accuracy of crop disease detection from leaf images. The proposed algorithm is compared against three widely used filtering techniques: Median filtering, Wiener filtering, and Gaussian filtering. Before implementing the proposed approach, a detailed assessment of these conventional methods is conducted.The algorithm integrates Laplacian filtering and average filtering to optimize image pre-processing. The effectiveness of this approach is evaluated using performance metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Mutual Information (MI). Results indicate that the proposed method achieves a 0.34% increase in PSNR, a 0.92% reduction in MSE, and a 0.48% improvement in MI compared to existing techniques. These improvements underscore the algorithm's ability to enhance image quality and outperform traditional methods.The findings suggest that incorporating Laplacian and average filtering into the pre-processing pipeline introduces a more efficient methodology for image enhancement. This approach holds potential for advancing image analysis and interpretation in agriculture and other fields, providing a foundation for further innovation in image processing technologies.
Understanding the Role of Employee Empowerment in Shaping Workplace Relationships. “A Study on NTPC Odisha
Authors: Ipsita Mohapatra
Published: February 2025

Employee empowerment plays a crucial role in fostering positive employee relations within an organization. It involves giving employees the authority, confidence, and resources they need to make decisions and take ownership of their work. This approach can lead to numerous benefits, both for the employees and the organization as a whole. One significant impact of employee empowerment is increased job satisfaction. When employees feel that their opinions are valued and that they can contribute meaningfully to their roles, they tend to be more motivated and engaged. This satisfaction often translates to better performance and a stronger commitment to the organization. Moreover, empowered employees are more likely to develop strong relationships with their colleagues and supervisors. This sense of autonomy and trust fosters a collaborative work environment where individuals feel comfortable sharing ideas and feedback. As a result, conflict resolution becomes more effective, and communication flows more freely, all of which contribute to healthier employee relations The population of this study was employees of NTPC Talcher-Kaniha Plant, a power-generating organization in Odisha, India. A structured questionnaire was administered to collect data from the employees. During the study, 311 valid responses were processed for data analysis. The results of the analysis were presented through structural equation modeling (SEM). The findings revealed six major factors such as self-managed teams, interpersonal trust, work culture, job design, information sharing, and employee involvement are the drivers of employee empowerment which significantly influences employee relations in the organization. Further, the findings reflected the fact that empowered employees tend to display stronger interpersonal relationships, increased trust, and increased levels of job satisfaction, which in turn, positively impacts improved employee relations, thereby contributing to overall organizational effectiveness.
Unified Institute Management System for Efficient Administra-tion and Accurate Data Management
Authors: Ketki Kshirsagar
Published: February 2025

This research addresses the pressing need for a cohesive and efficient Institute Management System (IMS) within educational institutions. Presently, institutions contend with fragmented systems, relying on disparate software solutions and manual processes for student management. This fragmentation hampers data handling and administrative tasks, leading to inefficiencies in enrollment, course registration, grading, and communication. Moreover, the lack of integration exacerbates data inaccuracy, as manual entry and disparate systems introduce errors into student records. This research proposes a comprehensive IMS solution to streamline administrative processes, enhance data accuracy, and improve communication among students, faculty, and administration. By integrating functionalities such as enrollment, course registration, grading, and communication into a unified platform, this solution aims to mitigate the challenges posed by fragmented systems and inefficient processes, ultimately facilitating more effective decision-making and reporting within educational institutions.
Enhanced Heart Disease Prediction Using Advanced Machine Learning and Deep Learning Models
Authors: Yerraginnela Shravani
Published: February 2025

Heart disease prediction is a critical area in healthcare, where accurate and timely diagnosis can lead to better patient outcomes and reduced mortality rates. This study compares the performance of various machine learning models, including Logistic Regression, Random Forest, Gradient Boosting, and Neural Networks, alongside advanced deep learning models such as Convolutional Neural Networks (CNN) and VGG16, a pre-trained deep learning architecture. The models are evaluated using precision, recall, F1-score, and accuracy, with accuracy being the primary metric for comparison. Experimental results demonstrate that while traditional machine learning models like Random Forest and Logistic Regression perform adequately, deep learning models, particularly CNN and VGG16, excel in predictive accuracy and other performance metrics. Among all models, CNN and VGG16 deliver superior results, with VGG16 slightly outperforming CNN in terms of precision and recall due to its ability to leverage pre-trained features and deeper architecture.The findings highlight the efficacy of deep learning techniques, especially VGG16, in heart disease prediction, emphasizing their ability to capture complex patterns and improve diagnostic accuracy. This study provides valuable insights into the potential of leveraging state-of-the-art deep learning architectures for enhancing predictive models in healthcare applications, setting the stage for future real-time diagnostic tools.
Generalized Intuitionistic Trapezoidal Fuzzy Numbers in Transportation Problems: An Optimizational Method
Authors: S. Jyothi
Published: February 2025

In this study, we propose an optimization method for transportation problems utilizing Generalized Intuitionistic Trapezoidal Fuzzy Numbers (GITFNs). This method effectively handles uncertainty and imprecision in transportation systems, enabling more realistic and reliable decision-making. A GITFN-based transportation model is developed, incorporating fuzzy costs, capacities, and demands. An optimization algorithm is designed to minimize transportation costs while satisfying supply and demand constraints. The method is applied to a numerical example, demonstrating its efficiency in solving complex transportation problems. The results show significant improvements in transportation effectiveness and cost reduction.
Mentoring Program on Shipboard Training, Navigation and Seamanship: PMMA’s Supplementary Learning Support amid Learning Crisis due to Covid-19
Authors: Manny I Ching
Published: February 2025

The onset of the COVID-19 pandemic disrupts not only the delivery and implementation of the maritime curriculum but also training preparations before apprenticeships of higher education institution students. While the Philippine Merchant Marine Academy adapts the Alternative Learning Delivery mode during the quarantine and restriction period, the College of Marine Transportation led the implementation of two supplementary learning support (SLS) programs to utilize time and availability and therefore maximize the learning of in-house scholars. The implementation of the Peer Mentoring Program in preparation for the Shipboard Training Program (STP) and Collaborative Learning in Navigation and Seamanship subjects urged this program evaluation research. Using the Context, Input, Process, and Product (CIPP) model for the implementation of SLS, the study shows that both mentoring and collaborative learning programs intensify the knowledge, understanding, and know-how skills of the beneficiaries in terms of shipboard training, navigation, and seamanship by providing the simplest approach to learning. The programs promoted camaraderie and established interactive-academic relationships between the senior and junior cadets/cadettes. Through the SLS programs, senior mentors and peers increased the awareness, readiness, and confidence of the mentees or the juniors despite the academic interruption brought by the pandemic. However, the study identifies a gap in the curriculum particularly in the shipboard training program. The results of this program evaluation research shall be considered before the institutionalization of these SLSs.