Journal of Global Research in Multidisciplinary Studies(JGRMS)
The Journal of Global Research in Multidisciplinary Studies (JGRMS) is a peer-reviewed, national journal committed to advancing knowledge across a wide spectrum of academic disciplines. Our mission is to provide a platform for researchers, academics, and practitioners to disseminate their work and contribute to the global exchange of innovative ideas and solutions. Scope JGRMS welcomes original research articles, review papers, and case studies in diverse fields, including but not limited to: Science and Technology Social Sciences and Humanities Medicine and Health Sciences Business and Economics Engineering and Applied Sciences Environmental Studies Arts and Literature Key Features Interdisciplinary Focus: Bridging gaps between various fields of study. Open Access: Promoting free and global access to research. Rapid Peer Review: Ensuring timely publication of high-quality work. Global Perspective: Encouraging submissions from diverse cultural and geographic contexts. Audience Our readership includes researchers, educators, policymakers, and professionals from academia and industry seeking the latest advancements and insights across multiple disciplines. JOURNAL PARTICULARS Title Journal of Global Research in Multidisciplinary Studies(JGRMS) Frequency Monthly E- ISSN 3049-2963 P-ISSN - DOI https://doi.org/10.5281/zenodo.14640835 Publisher Sandeep Gupta, Ward No. 5, Mathur Vaishy Colony,Ambah,Morena, Madhya Pradesh-476111, India Chief Editor Mr. Sandeep Gupta, Starting Year 2025 Subject Science and Technology, Social Sciences and Humanities, Medicine and Health Sciences, Business and Economics, Engineering and Applied Sciences, Environmental Studies, Arts and Literature Language English Language Publication Format Online Email Id editor@saanvipublications.com, editor.jgrms@saanvipublications.com Mobile No. +91-8305233223 Website https://saanvipublications.com/journals/index.php/jgrms/index Address Sandeep Gupta, Ward No. 5, Mathur Vaishy Colony,Ambah,Morena, Madhya Pradesh-476111, India
Biomarkers in Early Detection of Heart Disease with Special Reference to Troponin – A Critical Review
Heart diseases should be diagnosed in time to minimize complications and enhance survival. The biomarkers currently have been extremely important in identifying damage to the heart before symptoms are felt. The most effective of them is cardiac troponins, particularly high-sensitivity cardiac troponin I (hs-cTnI) and cardiac troponin T (hs-cTnT) since they are very specific to injury of heart muscle. Accuracy can be enhanced by the use of other markers like CK-MB, myoglobin, BNP/NT-proBNP and inflammatory markers like hs-CRP together. This review explains the functioning of such biomarkers, their strengths, and limitations, as well as the clinical issues. It also points out international norms, diagnostic models and access issues. Imaging, expert judgment, clinical scoring, and biomarkers are essential to effective cardiac diagnosis.
Copyright (c) 2025 Vanshika Solanki, Neelu Prasad, Dr. Himadri Shekhar Dey, Dr. Sachin Datt (Author) https://creativecommons.org/licenses/by-nc/4.0
Securing Unified Payments Interface: A Deep Learning Approach for Fraudulent Transaction Detection
Unified Payments Interface (UPI) is a system that integrates multiple bank accounts into a single mobile application, enabling seamless fund transfers and business payments. The proposed study presents a deep learning-based approach for detecting fraudulent UPI transactions using a large online payments fraud detection dataset containing over 6.3 million transaction records. The dataset is highly imbalanced, with fraud cases forming only a small fraction, making it a realistic yet challenging test for fraud detection methodologies. To address this, a novel model combining BiLSTM and Transformer-based encoders was developed to capture both temporal dependencies and contextual relationships in transaction sequences. The performance of the models was evaluated using accuracy, precision, recall, F1-score, and AUC-ROC metrics. Experimental results demonstrated that the BiLSTM model significantly outperformed conventional machine learning methods such as Logistic Regression, Random Forest, and Decision Tree. The BiLSTM achieved an accuracy of 99.90%, precision of 99.99%, recall of 99.81%, and F1-score of 99.91%. Visualization through accuracy/loss curves, confusion matrix, and ROC analysis further validated the model’s robustness and stability. These findings confirm BiLSTM as a reliable and effective real-time fraud detection system for digital payments, enhancing the security and performance of financial transactions compared to traditional approaches.
Copyright (c) 2025 Sandeep Gupta (Author) https://creativecommons.org/licenses/by-nc/4.0
Performance Evaluation of Machine LearningModels Via Mobile Payment for FraudIdentification
Digital and mobile payments have not only led to a surge in fraudulent activities detected in financial systems but also made detecting fraud more difficult. The conventional rule-based methodology frequently lacks the ability to rely on complex patterns of fraud, and thus results in high FP and FN. Using the extremely skewed PaySim dataset, this research presents a machine learning model for detecting mobile money transfer fraud and laundering. A trained XGBoost classifier was then used to learn complex transactional relationships, and overfitting was checked by using regularization built in. The model was tested with accuracy (ACC), precision (PRE), recall (REC) and F1-score (F1) and ROC-AUC metrics all reaching 99.6%, 99.8%, 98.7% and 0.991%, respectively. The ROC curve and confusion matrix prove that there is a high ability to discriminate and low levels of false alarms. As compared with the recent methods like Bert and DenseNet, it is evident that XGBoost can perform significantly better. These findings indicate that the suggested XGBoost-based model is a highly scalable, dependable, and efficient model to detect real-time fraud in the mobile payment system and help a financial institution decrease losses and improve its anti-money laundering compliance.
Copyright (c) 2025 Dr. Pradeep Laxkar (Author) https://creativecommons.org/licenses/by-nc/4.0
Internet of Things (IoT) Systems: A Survey ofPlatforms, Protocols, and Application Frameworks
A new paradigm in information and communication technology, the Internet of Things (IoT) allows for the interconnection of previously siloed systems in many different fields, including transportation, healthcare, industry, and even the environment. Despite its potential, IoT development faces significant challenges, including architectural heterogeneity, interoperability constraints, data management complexity, and security vulnerabilities. This survey presents a structured overview of IoT architectures, focusing on layered system models that incorporate edge, fog, and cloud computing to optimize scalability, energy efficiency, and Quality of Service (QoS). It examines communication protocols and IoT platforms, emphasizing their role in achieving device interoperability and robust data exchange. Middleware and application frameworks are highlighted as critical enablers, bridging device-level interactions with application-layer functionalities while ensuring data processing, security, and privacy. A comprehensive literature review synthesizes recent advancements in IoT middleware design, platform evaluation, multi-protocol integration, and lightweight cryptography, identifying prevailing gaps such as the absence of standardized frameworks and persistent security concerns. The survey underscores the necessity for unified middleware standards, standardized platform evaluation methodologies, and resourceefficient security mechanisms. Future research directions include real-world implementation of sustainable architectures, enhanced protocol flexibility, and advanced privacy-preserving solutions to enable resilient, scalable, and secure IoT ecosystems.
Copyright (c) 2025 Dr. Bal Krishna Sharma (Author) https://creativecommons.org/licenses/by-nc/4.0
Automated Image Preprocessing Pipelines forLarge-Scale Datasets Based on Deep LearningModels
Improving the effectiveness of deep learning (DL) for image classification on large datasets requires not only data preprocessing but also the right network designs. In this research, a new image classification system is introduced, which applies a modified Inception model to a subset of the ImageNet dataset containing 10 classes. Preparation of the data includes cleaning, augmentation, normalization, and feature extraction. These activities not only preserve the quality of data but also provide efficient input to the model. The modified Inception architectural changes enhance the original Inception network idea, thus increasing the power of the network for feature extraction and data classification. To evaluate the results, besides the modified Inception model, several well-known DL architectures like DenseNet, ResNet50, and VGG16 have been employed and compared using common performance metrics. The modified Inception model achieves better performance in the classification tasks than the baseline models. The accuracy is at 98.7%, the precision at 98.5%, the recall at 99%, and the F1-score at 98.6%. The results reported here have highlighted the role of architectural modifications and the impact of the thorough preprocessing pipeline in boosting the performance of image classification tasks on large datasets. Such a system offers a viable means to address challenging visual recognition problems.
Copyright (c) 2025 Dr. Jvalantkumar Kanaiyalal Patel (Author) https://creativecommons.org/licenses/by-nc/4.0
Predicting Customer Churn in Telecom: A Review of Advanced ML-Based Methods and Applications
The telecoms industry is developing rapidly, which has increased competition. Retaining customers is now a highly significant strategic objective. This article discusses the latest findings from studies that employ machine learning (ML) and deep learning (DL) to forecast the number of telecom customers who would switch. Based on the study, telecom operators are able to determine if customers are going to cancel their subscriptions based on their demographic, service usage, and behaviour information. This sequence deals with decision tree models, logistic regression, RNNs, support vector machines, and artificial neural networks (ANNs). The study also explores the application of churn prediction in real-life scenarios to improve customer retention, enable targeted marketing, and enhance service quality. The largest issues, such as data imbalance, explainability, and scalability, and directions for future research that encompass hybrid and explainable AI approaches. This study highlights the essential role of MLdriven churn prediction in ensuring customer loyalty and maximizing the performance of telecom businesses.
Copyright (c) 2025 Dr. Prithviraj Singh Rathore (Author) https://creativecommons.org/licenses/by-nc/4.0
A Review of Machine Learning Techniques for RiskEvaluation in Healthcare and Insurance Systems
Financial institutions require an accurate estimation of the risk of loan default in order to reduce losses incurred by credit and sustain lending. This study proposes a robust stacking-based machine learning framework that integrates Knowledge Graph Embedding (KGE) for semantic feature enrichment with XGBoost as the final predictive model. The approach is evaluated on the Home Credit Default Risk (HCDR) dataset, comprising diverse financial, demographic, and behavioral attributes of loan applicants. A comprehensive preprocessing pipeline, including imputation, normalization, one-hot encoding, and correlation-based feature selection, ensures data quality and model generalizability. The proposed KGE-XGBoost model captures both structured tabular and relational semantics by transforming borrower-entity relationships into dense embeddings, which are concatenated with original features to form a unified representation. Experimental results demonstrate superior performance with 96.79% accuracy (ACC), 80.83% precision (PRE), 78.75% recall (REC), and an F1-score (F1) of 79.00%. The proposed model exhibits a strong ability to outperform the baseline models (Random Forest achieved ACC 94.20%, NN achieved ACC 89%, and DT achieved ACC 73%), particularly in scenarios with class imbalances. The KGE integration has been found to greatly contribute to feature expressiveness and it presents a scalable and promising credit risk assessment solution to real-life financial applications.
Copyright (c) 2025 Neha Upadhyay (Author) https://creativecommons.org/licenses/by-nc/4.0
Deep Learning for Integrative Healthcare Analytics:A Focus on Multimodal Brain Imaging Data
Medical imaging faces several significant challenges, including image segmentation, cross-modal translation, and real-value prediction. Link CT and MRI scans using a well-liked method. Image quality and diagnostic efficacy can be enhanced with this technique. One new area of data science is brain imaging genetics, which aims to better understand the brain's normal and abnormal phenotypic, molecular, and genetic features and how they influence its function and behaviour. Enhancing diagnostic accuracy in neurological diseases, this work proposes a multimodal approach to analyzing brain tumor imaging data for healthcare analytics. It leverages modern predictive models. Multiple models were tested using extensive data, such as CNN, Report Guided Net, MLP, and ResNet18. With an F1-score of 99.12%, recall of 99.67%, precision of 99%, and accuracy of 99.28%, the Convolutional Neural Network (CNN) model stood out from the contest. These results prove that deep learning techniques, such as CNNs, can understand complex multimodal images and extract valuable information from them. The results provide credence to the idea that these models can be useful in automating the identification of brain disorders by incorporating them into clinical procedures.
Copyright (c) 2025 Vivek Sharma (Author) https://creativecommons.org/licenses/by-nc/4.0
Comparative Study on Deep Learning Approachesfor Fraudulent Transaction Detection
Credit card theft has become a major problem in the financial world because of the huge rise in online shopping and the complexity of fraud cases. A convolutional neural network (CNN) 1D could be useful in spotting fraudulent transactions, according to recent research. As part of the preparation, the data will be standardised and validated. The Synthetic Minority Over-Sampling Technique (SMOTE) will be used to balance the classes. Next, datasets are created for training, validation, and testing purposes. Impressive results in domains such as recall, accuracy, and precision (including a 99.7 F1-score) are achieved by the proposed CNN 1D model, surpassing more traditional ML models like Support Vector Machine, Naive Bayes, and K-Nearest Neighbour. Furthermore, the model's robustness and generalisability were demonstrated using learning rate optimization, ROC-AUC, and confusion matrices. The results indicate that CNN 1D model is a highly predictable and scalable credit card fraud detection model with significant better performance compared to the traditional methods in accuracy, sensitivity and scalability.
Copyright (c) 2025 Dr. Manish Saraswat (Author) https://creativecommons.org/licenses/by-nc/4.0
Survey of Data-Driven Approaches for Credit Risk Evaluation in Banking Using AI Techniques
Credit risk measurement is one of the most prominent problems in modern banking and financial services that has a direct impact on the lending operations, profitability, and stability. Conventionally, qualitative, experience-based methods have been applied in assessing credit risk based on transactional jeopardy, inherent peril and concentration risk in loan portfolios. Nonlinear patterns and dynamic risk behavior are however usually not well captured using the traditional methods, particularly due to increasing complexity of the financial markets, as well as due to the profile of diverse borrowers, and the presence of large volumes of data. Credit risk assessment has been transformed by recent advancements in AI and ML, which have made it data-intensive, adaptable, and scalable. When dealing with high-dimensional, unbalanced, and heterogeneous financial data, the approaches that have proven to be effective include logistic regression, random forests, gradient boosting machines, and deep neural networks. Additionally, alternative sources of data, such as transactional, demographic, behavioral, and social data can be utilized to predict credit scoring with increased accuracy and reduced bias with the aid of advanced preprocessing and feature engineering. In addition to risk evaluation, AI is also used to identify fraud, predictive analytics, regulatory compliance, and chatbots. Although the above advantages are there, there are still issues of data privacy, ethics and model interpretability. The paper indicates traditional and AI-based methods of credit risk assessment.
Forecasting Employee Attrition in HRM Based on Advanced Supervised Machine Learning Models
Human resources are a company's most valuable asset, so losing employees is a big deal for the business. Human resources managers and department heads have a number of challenges when trying to predict when employees may start to leave their positions. Consequently, this research provides a machine learning-based methodology for HR planning-related employee turnover prediction utilizing the IBM HR dataset. Data cleaning, label encoding, min-max scaling, and class balancing with SMOTE were all part of the preprocessing stages to ensure reliability and reduce bias. Including 35 demographic, job-related, and satisfaction-related factors, the dataset comprised 1,470 employee records. Decision Tree, AdaBoost, and Support Vector Machine were some of the more conventional models tested with two more modern ensemble models, XGBoost and Random Forest (RF). As shown in the testing findings, XGBoost achieved a 96.95% accuracy rate and RF a 93.90% accuracy rate, both of which were better than the benchmark models. With its outstanding generalizability and predictive capacity, XGBoost stood out as the most effective of the bunch. The results show that the suggested models can handle complicated, high-dimensional HR data and provide dependable predictions of employee turnover, demonstrating their resilience and scalability. By allowing firms to detect people at risk early and put effective retention initiatives in place, this adds to proactive HR decision-making.
Copyright (c) 2025 Dr. Parth Gautam (Author) https://creativecommons.org/licenses/by-nc/4.0
Artificial Intelligence Approaches for Diagnosis and Continuous Monitoring of Chronic Liver Disorders
Chronic liver disease, or CLD, is becoming a bigger problem in the world's health care system because it is hard to notice when it starts and is fatal in its later stages. It is difficult to detect the disease at an early stage since conventional diagnostic procedures including imaging, biopsies, and liver function tests are intrusive, expensive, or not widely available. This highlights the need for accurate, non-invasive, and data-driven approaches to support timely intervention and reduce clinical risks. Machine learning (ML) has shown promise in processing large, heterogeneous datasets to uncover hidden patterns and enhance predictive performance. Using the Liver Disease Patient Dataset from the UCI Repository, comprising 30,691 records with 11 attributes, this study applied extensive preprocessing, including missing value imputation, outlier removal with the IQR method, Min–Max normalization, and SMOTE for class balancing. Feature selection was employed to improve interpretability and efficiency. A model called Gradient Boosting (GB) was created and tested against many other methods, including SVM, Random Forest, MLP, and XGBoost. Surpassing baseline models, GB attained the best performance with a 98.60% ROC-AUC, 98.50% recall, precision, and F1-score. Based on these findings, ensemble approaches are reliable for predicting early CLD. Improving practicality will be the goal of future studies that investigate clinical validation and the integration of multimodal data
Copyright (c) 2025 Mr. Sachin Manekar (Author) https://creativecommons.org/licenses/by-nc/4.0
Enhancing Employee Performance in Dynamic Business Environments Through Machine Learning Approaches
Employee performance prediction is among the most significant undertakings within dynamic business environment to enable the organization to optimize the management of its human resources and improve its productivity. This paper presents an Extreme Gradient Boosting (XGBoost)-based predictive model on the Kaggle Human Resource dataset, which includes 35 variables in 1,470 participants. Data pretreatment procedures contain handling of outliers, missing, or negative values, one-hot encoding of categorical data, normalization to minmax, and feature selection using Principal Component Analysis (PCA). The data was split into two sets: training and testing, after the Synthetic Minority Oversampling Technique (SMOTE) was used to fix the problem of class imbalance. They used standard performance metrics including F1-score, recall, accuracy, precision, and ROC-AUC to train and evaluate the proposed XGBoost model. With an F1-score of 98.66, an accuracy of 97.87, and a precision and recall of 99.9%, the model achieved impressive results in the testing. This means it can outperform other predictive models. These results indicate the strength, scalability, and reliability of the suggested strategy in predicting employee performance, and providing meaningful actionable data to support data-driven human resource planning and organizational development.
Copyright (c) 2025 Dr Manish Saraswat (Author) https://creativecommons.org/licenses/by-nc/4.0
Improvising Cybersecurity in IoT Networks Using Machine Learning for Intrusion Identification
Smart cities, industrial automation, healthcare, and the Internet of Things have all been profoundly affected by the exponential expansion of the IoT, which has enabled massive connection and intelligent decision-making. The ever-changing, varied, and sometimes under-resourced nature of IoT devices makes intrusion detection all the more important in protecting these networks from cyberattacks. The drawbacks of classic intrusion detection systems (IDS) include issues with scalability, high false positive rates, and the inability to identify sophisticated attacks such as zero-day vulnerabilities. To get over these problems, this study presents a robust intrusion detection model that optimizes features and leverages deep learning. To ensure high-quality input utilizing the UNSW-NB15 dataset, preprocessing procedures for data were utilized, such as normalization, resolving missing values, and balancing with SMOTE. Employed the Butterfly Optimization Algorithm (BOA) to boost computing efficiency and cut down on redundant features. Then, a Deep Neural Network (DNN) was trained with F1-score, recall, accuracy, and precision exceeding 98.04%. The suggested framework's better performance and scalability are brought to light through comparative study with existing frameworks. Lightweight adaptations for IoT devices with limited resources, real-time deployment, and evaluation across datasets the main areas of future development.
Copyright (c) 2025 Mr. Deepak Mehta (Author) https://creativecommons.org/licenses/by-nc/4.0
Anomaly Detection in Smart Grids Using Artificial Intelligence A Survey of Techniques and Tools
The rapid advancement of smart grids has revolutionized modern energy distribution by integrating renewable energy resources, distributed generation, and advanced communication technologies. However, the growing complexity, interconnectivity, and cyber-physical integration have made smart grids increasingly vulnerable to various anomalies, including equipment failures, cyber-attacks, load forecasting errors, and sensor malfunctions. This paper presents a comprehensive survey of state-of-the-art techniques for anomaly detection in smart grids, with a focus on artificial intelligence (AI), hybrid frameworks, and semantic modeling approaches. A wide spectrum of AI-driven methodologies—such as cross-modal collaborative learning, hierarchical semantic representations, physics-informed hybrid algorithms, and deep learning architectures—are systematically reviewed to highlight their roles in improving cybersecurity, fault diagnosis, energy theft prevention, and real-time monitoring. The study also examines the inherent challenges posed by heterogeneous grid infrastructures, diverse data sources, and evolving threat landscapes. Furthermore, the survey identifies emerging directions for designing scalable, explainable, and adaptive anomaly detection frameworks integrated with real-time analytics, privacy-preserving mechanisms, and standardized benchmarking datasets. By synthesizing current advancements and open challenges, this work provides a structured foundation to guide future innovations in secure, resilient, and intelligent smart grid ecosystems.
Copyright (c) 2025 Mr.Ram Pratap Singh (Author) https://creativecommons.org/licenses/by-nc/4.0
Efficient ML-Based Identification Models of Network Intrusion in Internet of Things (IoT) Systems
The wide adoption of IoT devices has brought about increased connectedness and convenience, but it has also revealed new security concerns. Protecting IoT systems from malicious hackers is crucial as they become more important in various areas. Using the Random Forest (RF) method for machine learning, this study presents an efficient and effective Intrusion Detection System (IDS) tailored to Internet of Things (IoT) devices. The research employs a wide variety of data processing approaches, including data cleaning, normalization, and one-hot encoding, to get the NSL-KDD dataset ready for model training and testing. With a remarkable 99.9 % accuracy, precision, recall, and F1-score, the RF model surpassed other conventional modelling approaches, such as Decision Tree (DT), Support Vector Machine (SVM), and Conv1d networks. Using ensemble learning effectively is where this approach really shines. Interpretability was also facilitated by visualization (including correlation heatmaps and feature importance plots), which helped in selecting features. Effectiveness of the system under verification was confirmed with consistent evaluation metrics and confusion matrices analysis, and RF in the ability to reliably differentiate between malicious and normal traffic
Copyright (c) 2025 Dr.Nilesh Jain (Author) https://creativecommons.org/licenses/by-nc/4.0
Machine Learning Applications and Techniques for Predictive Maintenance in Industrial Operations: A Review
The rapid development of Industry 4.0 has transformed industrial systems because it has allowed the use of data-driven solutions to monitor equipment and prevent faults. Predictive maintenance (Pd.M.), which utilizes advanced analytics and uses artificial intelligence to predict when a breakdown is likely to happen, is becoming more and more prevalent in the industry, in addition to more traditional methods like reactive and preventative maintenance. Pd.M. can utilize event logs, control systems, and real-time sensor data streams to enhance equipment availability, minimize downtime, and allocate resources as efficiently as possible. Strong anomaly detectors, defect classifiers, and Remaining Useful Life (RUL) predictions may be obtained using machine learning (ML) and deep learning (DL) models, which are regarded as crucial tools in Pd.M. Across a range of industrial situations, Random Forest (RF) methods, Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) may be applied with great predictive flexibility. Additionally, scalability, efficiency, and sustainability are improved in contemporary operations through integration with digital twins, the Industrial Internet of Things (IIoT), and quantum-enhanced techniques. These developments notwithstanding, there are still challenges, including low data quality, heavy computing requirements, and barriers to adoption by organizations. However, with the further implementation of smart PdM systems, operational efficiency can be enhanced, safety can increase, and sustainable industrial growth can be achieved, marking a crucial step toward smarter and healthier industrial ecosystems.
Copyright (c) 2025 Dr. Sunita Dixit (Author) https://creativecommons.org/licenses/by-nc/4.0
Optimization in Industrial IoT Networks Based on AI-Technologies: A Review of Various Frameworks and Applications
Artificial Intelligence (AI) and the Industrial Internet of Things (IIoT) integration is now a pivotal force of digital transformation in contemporary industries. AI enables smart decision-making, predictive control, and automation where IIoT makes scale connectively and real-time data acquisition possible. These technologies combined transform the way industries operate, making them less complex, more efficient, less expensive and with reduced downtime. This review explores the AI-based optimization solutions in IIoT networks with specific focus on benefits of use of machine learning frameworks and their applicability in industries. Some of the methods of optimization including predictive maintenance, anomaly detection, load balancing, and energy efficiency are explained illustrating how they are applicable in industries like manufacturing, logistics, and management of energy use. Open-source machine learning frameworks such as TensorFlow, Porch, and H2O are discussed and their benefits on scalability, flexibility, and efficient deployment of the model pivot around intelligent IIoT solutions. The fact that they have the capacity to support deep learning, reinforcement learning, and real-time analytics highlights their usefulness in undertaking multifaceted industrial work. Communication between variously distributed assets is identified as a key issue and interoperability, cybersecurity risk, and computation limitation are viewed as principal challenges. The paper concludes by providing future implications of knowledge on emerging technologies and future areas of research that can enhance the strength of AI-powered IIoT ecosystems.
Design and Analysis of Advanced Machine Learning Methods for Financial Fraud Identification in Credit Card Activities
Credit card theft is simple and easy to do. There are more ways to pay for things online, thanks to e-commerce and many other websites. This makes online scams more likely. The frequency of online transaction fraud prompted specialists to employ a toolbox of machine learning methods in their quest to uncover and analyze the problem. The risk of credit card scams has gone up a lot because of the fast growth of digital financial services like online shopping, online banking, and mobile payments. When it comes to complex fraud, traditional security measures like encryption and tokenization often fall short. Credit card theft can be better detected by comparing CNN and K-Nearest Neighbors (KNN), two machine learning models that were recommended in the study. The research employs the SMOTE-ENN resampling method to rectify the data discrepancy in a freely accessible dataset of extremely unequal cardholder transactions throughout Europe. Ensuring data quality is achieved through comprehensive preparation that incorporates Min Max scaling, categorical encoding, and imputation for missing values. Measures such as accuracy, precision, recall, F1-score, and AUPRC mostly concentrate on the model's ability to deal with class imbalance. On the basis of the lab data, the CNN model outperforms the KNN and baseline models with respect to accuracy rate (99.68%) and F1-score (99.60%). My last remarks: Through the application of deep learning techniques, develop scalable fraud detection systems capable of identifying tiny indicators of fraud in real-time financial transactions.
Low-Power Design Techniques for Embedded Processors in Portable Devices: A Review of Methods and Applications
Processor and system designers now priorities power consumption minimization for embedded systems that run on batteries or other portable power sources. Designing low-power embedded processors has become more important due to the increasing need for portable electronics and Internet of Things (IoT) devices. Optimization of performance and area are just as important as minimizing power consumption. As CMOS scaling advances, power consumption—both dynamic and static—poses substantial challenges in extending battery life, managing thermal constraints, and maintaining reliable operation in resource-constrained environments. Power gating, clock gating, adaptive body biasing, energy-aware software optimization, Multi-Threshold CMOS, Dynamic Voltage and Frequency Scaling (DVFS), and other low-power design strategies are reviewed in this work. It further discusses the power-performance trade-offs and emerging trends in embedded processor design, while highlighting practical applications in healthcare, automotive, consumer electronics, and industrial domains. Detailed comparisons of static versus dynamic power, processor- versus system-level consumption, and recent innovations are presented to offer holistic insight into efficient embedded system design for portable applications.
Copyright (c) 2025 Prof. (Dr.) Abid Hussain (Author) https://creativecommons.org/licenses/by-nc/4.0
A Review of Machine Learning Techniques for Risk Evaluation in Healthcare and Insurance Systems
As chronic diseases surge globally and insurance markets grow increasingly complex, traditional risk assessment frameworks are becoming obsolete. Static evaluations based on historical data and rigid actuarial models can no longer keep pace with the dynamic nature of individual health profiles or the rising volume of real-time behavioral and biomedical data. Machine learning (ML), with its capacity to analyze vast, high-dimensional, and heterogeneous datasets, is revolutionizing risk modeling in both healthcare and insurance sectors. This survey highlights the critical role of ML in accurately and proactively identifying, predicting, and managing risks. In healthcare, ML models enable disease prediction, patient risk stratification, early warning systems, and mortality forecasting by processing data from electronic health records, medical imaging, wearable devices, and genomics. In parallel, the insurance domain benefits from ML-driven claims prediction, fraud detection, customer segmentation, and dynamic underwriting using behavioral, demographic, and historical claims data. Techniques such as Random Forest, Support Vector Machines (SVM), Neural Networks, and Gradient Boosting (GB) are evaluated for their performance, interpretability, and flexibility. Key issues, including data fragmentation, model bias, and interpretability in high-stakes scenarios, are addressed. Grounded in state-of-the-art algorithms and real-world applications, this research demonstrates ML’s transformative potential for individualizing, resilient, and data-driven healthcare and insurance systems.
Emerging Trends and Tools in the Modern Software Development Life Cycle: A Survey of Strategies for Modern Engineering
The Software Development Life Cycle (SDLC) has undergone dramatic change that has seen the end of rigid and linear approaches to software development and has replaced them with dynamic and iterative approaches to software development, heavily dependent on technology. The paper is a review of thought processes and tools that define new-fangled SDLC practice. It starts by looking at the conventional models, including the Waterfall and V-Model, that focus on sequential development and voluminous documentation. As the requirements of agility, speed/ agility, and customer focus have increased, approaches to meet these demands have become more focused on Cross-functional integration, iterative delivery, and continuous integration/continuous deployment (CI/CD), leading to the increase in the popularity of methodologies such as Agile, Scrum, and DevOps. Adoption of new technologies, such as machine learning (ML) and artificial intelligence (AI), cloud computing, adoption, and containerization, is also discussed in the paper and redefining the design, development, testing, and maintenance of software. There is also a focus on modern approaches of testing, such as shift-left and shift-right testing, which improve the quality of software and increase delivery. The paper also indicates the environmental and ethical factors that shape the sustainable practices in software engineering. This review can be of interest to researchers, developers, and industry professionals who have to look into the future of software engineering and are interested in receiving a complete picture of modern SDLC strategies and tools.
Copyright (c) 2025 Dr Chintal Kumar Patel (Author) https://creativecommons.org/licenses/by-nc/4.0
AI-Based Personalized Approaches to Postoperative Pain and Anxiety: Opportunities and Challenges
Anesthesia, Intraoperative and Persistent Postoperative Pain and Anxiety pose considerable and multifaceted challenges in present-day surgical practice, which negatively influence recovery, satisfaction, and the long-term health outcomes. Conventional pain management guidelines are not always flexible enough to handle the personal differences and comorbid psychological problems. In this review, discuss how the sphere of perioperative care is being transformed by artificial intelligence (AI), in particular, it allows for the real-time monitoring of the patient, accurate pain detection, and individual approach to treatment. AI allows using dynamic and data-driven methods of pain and anxiety management by incorporating technologies, including machine learning (ML), natural language processing, and predictive analytics. The examples of applications mentioned are AI-assisted chatbots to support patients, voice pain measurements, and virtual reality-based interventions to minimize psychological distress. Also, the importance of AI in the promotion of personalized medicine and addressing multimorbidity is outlined. Ethical consideration, data privacy, and system scalability are two other concepts that the paper highlighted in regard to the use of AI tools in the clinical environment. It ends with discussing the existing drawbacks and possible ways of perspective development of intelligent and patient-centered methods of enhancing postoperative outcomes.
Design-for-Test Optimization for Low-Power Semiconductor Devices: A Survey
A big design challenge for semiconductor devices is power consumption during testing, since these devices are getting smaller while getting more complicated and having more integration density. If the tests are to be economical, scalable, and dependable without sacrificing performance or adding extra system costs, it need to optimize the Design-for-Test for low power consumption. This survey provides an in-depth analysis of important low-power DFT approaches, such as clock gating, scan chain reordering, Built-In Self-Test (BIST), and test data compression, with the goal of minimizing static and dynamic power consumption during testing. In order to manage switching activity, leakage currents, and enable complicated System-on-Chip (SoC) architectures, power-aware DFT is crucial. The evolution of BIST architectures and the use of low-power test controllers in portable and high-performance systems are discussed in detail. Additionally, the impact of semiconductor scaling, multi-voltage domains, and advanced packaging technologies on test strategies is explored. Emerging trends such as AI-driven test generation and adaptive power control offer promising directions for sustainable and intelligent test solutions. These strategies not only enhance test efficiency and coverage but also contribute to reducing thermal stress, extending device lifespan, and supporting green design initiatives in modern semiconductor development.
Copyright (c) 2025 Dr. Nilesh Jain (Author) https://creativecommons.org/licenses/by-nc/4.0
Sensor Data Acquisition Techniques for Fault Detection in Mechanical Systems: A Review of Accuracy and Timeliness
The reliability of mechanical systems in industrial environments relies heavily on accurate and timely fault detection, which is made possible through efficient sensor data acquisition techniques. This review investigates the role of various sensors and acquisition methods in detecting faults in mechanical components, highlighting recent advances and applications. The study categorizes key sensor types such as temperature, motion, proximity, and chemical sensors and explores their roles in real-time monitoring and diagnostics. Further emphasis is placed on modern data acquisition techniques, including synchronization methods, signal preprocessing, and intelligent systems that enhance decision-making accuracy. It also explores the integration of machine learning (ML) and deep learning (DL) models in fault detection frameworks, which improve diagnostic efficiency and reduce dependency on manual inspection. Additionally, the review presents comparative findings from recent open-access studies to evaluate strengths, challenges, and future directions. The importance of accuracy and timeliness is discussed, emphasizing how delayed or incorrect detection can affect industrial productivity and safety. Overall, this review serves as a foundation for researchers and engineers seeking to develop or improve sensor-based fault detection systems in mechanical applications.
Copyright (c) 2025 Mrs. Neha Upadhyay (Author) https://creativecommons.org/licenses/by-nc/4.0
Machine Learning for Medical Billing Fraud and Insurance Risk Detection: Trends and Challenges in the US Healthcare System
The monetary costs of healthcare fraud in the United States surpass billions of dollars annually, and to address and prevent fraud and insurance risk loss, effective data-driven methods of fraud detection and risk mitigation are required. The paper examines how Machine Learning (ML) and Artificial Intelligence (AI) hold the potential to transform current challenges. Marketable fraudulent billing practices and using ML models to improve premium risk scoring through the discovery of non-intuitive patterns in large-scale healthcare data is achievable by the means of employing supervised, unsupervised, and reinforcement learning. Recent developments are discussed and compared in various detection frameworks, and specifically, the involvement in the new technologies, Blockchain to secure data sharing and biometrics to identify. The researchers stress that scalable and explainable ML models that are consistent with the changing trends of frauds and healthcare demands are vital. In addition, it recommends cooperation between technology developers, insurers, and policymakers in order to gain ethical use of AI and adherence to data privacy policies. The results demonstrate the importance of AI as a way of improving the efficiency of the operations, financial stability, and individual care in terms of service delivery to healthcare insurance systems.
Survey of Machine Learning and Deep Learning Approaches for Housing Price Forecasting: Datasets, Features, and Challenges
Accurately predicting housing prices is a key part of how buyers, sellers, investors, and lawmakers make decisions about real estate. Although the Hedonic Pricing Model and linear regression have seen extensive usage in the field of statistics, both methods frequently fail to capture the complexities of multidimensional data and intricate nonlinear interactions. Recent years have seen the rise of deep learning (DL) and machine learning (ML) as potent methods for improving predictive performance in housing data models that are multimodal, dynamic, and high-dimensional. Outlining the methodology, applicable situations, benefits, and limits of several ML and DL approaches to housing price forecasting, this paper offers a complete assessment of the field. Advanced DL architectures like LSTM and multimodal neural networks are studied, along with key models like Random Forest, XG Boost, and Support Vector Machines. In addition to popular datasets, feature engineering methods, assessment criteria, and the current trend towards combining visual, textual, and geographic data, the review delves into these topics and more. In addition, it delves into important obstacles such dataset constraints, computing demands, model interpretability, and data heterogeneity. In the last section of the paper, the authors suggest areas for further study, highlighting the importance of standardized multimodal datasets, explainable AI (XAI), domain adaptation, and causal modelling in improving the realism and practicality of models.
A Review on Large Language Models (LLMs) with RAG Research Challenges and Opportunities
Large Language Models (LLMs) are now essential to contemporary applications in natural language processing, and retrieval-augmented generation (RAG) approaches have emerged in response to the need for more factual, context-aware, and trustworthy outputs. In order to overcome constraints like hallucinations and static knowledge bounds, RAG improves generating quality by including external information retrieval into the LLM process. This paper systematically surveys the evolving landscape of RAG systems, highlighting foundational components such as sparse, dense, hybrid, and graph-based retrieval methods, and their impact on semantic richness, scalability, and reasoning capability. Challenges such as hallucination, data privacy, security vulnerabilities, and ethical risks are examined in depth, along with faithfulness issues in output generation. Evaluation frameworks, including both quantitative (e.g., Precision, BLEU, BERT Score) and qualitative metrics, are reviewed alongside emerging benchmarks like RAG-Bench, MIRAGE, and MTRAG. Further, the study identifies key research gaps in adaptive retrieval, sufficient context modeling, and privacy-preserving methods. As RAG continues to evolve, the integration of hybrid neuro-symbolic systems and reinforcement learning-based adaptation presents promising avenues for future research. The survey provides a very detailed insight into the technical progress of RAG, its assessment systems, and deployment issues, and it can be viewed as a guide towards researchers and developers who seek to develop reliable, interpretable, and robust retrieval-augmented generation systems.
Copyright (c) 2025 Gautam Dr. Parth (Author) https://creativecommons.org/licenses/by-nc/4.0
A Review of Deep Learning Approaches Using ECG Signal Analysis
Electrocardiograms (ECGs) are non-stationary signals that are often used to assess heartbeat tuning and rate. In order to monitor cardiovascular health and identify diseases, electrocardiogram (ECG) signals are an essential diagnostic tool. This study offers a thorough examination of the core ideas, preprocessing strategies, and deep learning approaches used in ECG analysis. It begins by detailing the structure and components of ECG signals, common acquisition methods, and the major noise sources that affect signal quality. Preprocessing stages, including denoising, segmentation, feature extraction, and feature selection, are discussed with an emphasis on their role in improving classification accuracy. The paper also examines cutting-edge deep learning architectures that have demonstrated significant promise in automating ECG-based diagnoses, including Transformer models, Autoencoders, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid CNN-RNN models. Practical applications such as arrhythmia detection, rhythm classification, sleep and stress monitoring, noise removal, and real-time analysis in wearable health devices are reviewed. The integration of such models into wearable technologies, exemplified by devices like ECG WATCH, indicates a significant shift towards real-time, personalized cardiac care.
Looking into How Machine Learning is Used for Regression Testing Within the Agile Software Development Context
Regression testing is essential for maintaining software reliability, especially in agile development environments marked by frequent code changes and iterative delivery cycles. Traditional regression testing methods often fall short in keeping up with the speed and complexity of modern development, leading to a growing need for smarter and more efficient solutions. In this context, Machine Learning (ML) has been developed as a transformational method, offering intelligent capabilities such as test case selection, fault prediction, and change impact analysis. These developments ensure that new code modifications do not adversely affect current functionality by automating and optimizing testing. This paper presents a comprehensive review of how ML is being applied to enhance regression testing in agile settings, highlighting current methodologies, categorizing state-of-the-art applications, and outlining the benefits in terms of accuracy, efficiency, and automation. The review aims to support researchers and practitioners in leveraging ML to develop more effective and Agile software development techniques for adaptive regression testing.