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
Lightweight Deep Learning Models for Intrusion Detection in Resource-Constrained Cyber-Physical Devices
Important infrastructures like water treatment and smart grids rely heavily on cyber-physical systems (CPS), which are increasingly vulnerable to new and developing threats. The advent of Intrusion Detection Systems (IDS) tailored to CPS design has become one of the essential tactics for safeguarding them, despite the fact that traditional security measures like firewalls and encryption do not function well enough with CPS architecture. The study article presents a deep learning (DL) approach to an IDS to identify security attacks in a network of resource-constrained CPS using the CICIDS2017 database. Some preprocessing operations, such as cleaning, transformation, one-hot encoding, outliers removal using the Z-score method, and class balancing using SMOTE, were performed on the dataset. The tasks of dimensionality reduction and feature selection were performed with the help of Principal Component Analysis (PCA). The binary and multi-class intrusion detection was done with a built and tested six-layer Deep Neural Network (DNN) and a Convolutional Neural Network (CNN). The DNN could obtain 99.7% and the CNN 99.5%. The two models were good generalizers, with low overfitting, and high precision (PRE), recall (REC), and F1-Score (F1). Comparative performance analysis of the existing methods and the suggested models proved that the suggested models possess a higher detection accuracy and can be utilized much faster, hence, they may be an appropriate option when it comes to the deployment of a strong and scalable IDS in a cyber-physical environment.
Machine learning (ML) and Deep Learning (DL) Approaches
Copyright (c) 2026 Dr. Prithviraj Singh Rathore (Author) https://creativecommons.org/licenses/by-nc/4.0
Survey on AI-Based Predictive Cooling in Data Centers and Edge Devices
Increasing energy consumption and thermal concentration in current data centers and thermal power systems necessitates a high level of cooling technologies and intelligent control measures in these systems to attain efficiency, reliability and sustainability. Modern data center cooling systems are covered in this paper. These systems include liquid, air, immersion, spray, and hybrid options. Optimization techniques offered include PID control, model predictive control, and reinforcement learning. Plus, it delves into how ML, DL, and RL (reinforcement learning) may revolutionize cooling prediction, real-time adaptability, and energy optimization. The article also delves into the topic of parameter control in thermochemical treatment processes, including gasification, combustion, and pyrolysis, covering topics like the impact of pressure, heating rate, residence time, and temperature on performance and energy output. The paper illuminates both AI-based and data-based solutions to the better thermal management, emissions cuts, stronger robustness, and sustainable operation.
Copyright (c) 2026 Dr. Nilesh Jain (Author) https://creativecommons.org/licenses/by-nc/4.0
Survey of Vibration Damping and Isolation Techniques for Piping Support Systems
Controlling vibration on piping support systems is a decisive consideration in enforcing security, dependability, as well as durability of industrial, commercial and residential piping networks. There are various dynamic loads experienced by piping systems due to the flow of fluids, mechanical equipment, thermal expansion, seismic phenomenon and other environmental disturbances. Uncontrolled vibrations may result in unnecessary noise, permanent damages, fatigue failures, leakages and higher maintenance expenses, so vibration reduction is crucial in any design. In this survey, the author includes a detailed review of the vibration damping and isolation methods used in piping support systems, passive, active, and semi-active, and special purpose isolation devices. Passive designs like material-based damping, viscoelastic dampers, tuned mass dampers and friction devices use vibrational energy to convert into heat or dissipation via mechanical means, whereas active designs use sensors, actuators and smart materials to suppress vibrational energy in real time. Semi-active methods, such as magnetorheological and electrorheological dampers, provide adaptive damping and need little power. Vibration isolation devices including elastomeric and spring-based isolators, flexible supports, and base isolation systems are used to reduce the transfer of dynamic loads. Industrial applications in power plants, nuclear plants, oil and gas pipelines, chemical industries, and the HVAC are discussed in the paper as well and provide practical ways of improving structural integrity, operational reliability and safety.
Copyright (c) 2025 Dr. Pradeep Laxkar (Author) https://creativecommons.org/licenses/by-nc/4.0
A Review of Finite Element Methods in Pressure Vessel and Piping Stress Analysis
The building blocks of the industrial process systems are pressure vessels and piping networks, in which a leak or a loss of integrity may have disastrous safety, environmental, and economic impacts. With the ever-growing operating pressures, temperatures and the complexity of the systems, stable stress analysis has emerged as one of the major requirements in system design and evaluation. The present paper is a review of traditional and new methods of stress analysis of pressure vessels and piping with emphasis on the Finite Element Method (FEM). The restrictions of traditional analytical and code-based methods of managing complex geometries, combinations of loads, and local stress impacts are addressed. Special focus is laid on the behavior of stress in pipelines, study of nozzle in pressure vessels, classification of stresses and failure mode. Recent research advancements, such as advanced numerical modeling, coupled thermal structural analysis, experimental stress monitoring, and data-based prediction methods, are also surveyed in the paper. This review offers a clear vision of the emerging role of FEM and related methods in the design of safe, efficient, and reliable pressure vessel and piping systems by bringing together the existing methodologies and identifying the available research gaps.
Copyright (c) 2025 Bal Krishna Sharma (Author) https://creativecommons.org/licenses/by-nc/4.0
Smart Fraud Detection in Online Retail: Leveraging Classification Algorithms for E-Commerce Transactions
The quick increase in e-commerce transactions through the internet has turned fraud detection into a major obstacle to e-commerce operations' security and reliability. This paper suggests a good DNN-based framework for intelligent fraud detection, which is built on the analysis of a very imbalanced credit card fraud dataset. This process consists of numerous data preprocessing steps, which involve, but are not limited to, how the missing values are treated and how duplicates and outliers are eliminated, as well as how z-score normalization can be used to obtain a homogeneous scale. To address the problem of class imbalance, SMOTE is applied, and a minority fraud is oversampled, and Principal Component Analysis (PCA) is applied, and the most beneficial attributes are obtained. The improved data is divided into training and testing (80:20) before training the DNN model. The results of the experiment indicate that the given DNN is more advantageous compared to traditional machine learning algorithms, including ANN, XGBoost, and Decision Tree in terms of accuracy, precision, recall, and F1-score of 97.10%. Overall, the proposed DNN-based system is effective in understanding the complex fraudulent activities in online shopping, it is also highly accurate in detection and offers a scalable solution that can be used to manage the contemporary e-commerce fraud prevention.
Copyright (c) 2025 Mr. Himanshu Barhaiya (Author) https://creativecommons.org/licenses/by-nc/4.0
A Review of Energy-Efficient HVAC (Heating, Ventilation, and Air Conditioning) Systems for Smart Buildings
Effective HVAC (heating, ventilation, and air conditioning) systems management in buildings is necessary to attain energy saving and comfort. Adequate sophisticated control techniques that can adjust to changing environmental circumstances and occupant preferences are crucial for effectively balancing these goals. This paper discusses passive, active, and intelligent HVAC technologies, their advantages in energy conservation, indoor air quality, sustainability, and system reliability. A discussion on HVAC operations and popular systems type is provided in order to have background knowledge. An in-depth review of the literature will be done concerning recent machine learning and predictive control methods in optimization of HVAC, such as occupancy-based control, model predictive control, and gradient-based learning with a particular interest in energy savings and comfort of the occupants. Although progress is being made, the available literature tends to work with small scale or linear models and this restricts their usability to complicated and dynamic settings. The novelty of the research is that the multi-factor considerations, including the occupancy patterns, environmental uncertainties, renewable energy integration, and real-time adaptive control are put in a single framework. The primary contribution is that it suggests scalable and smart HVAC optimization that improves the energy use, cuts expenses, and guarantees the occupants comfort, which could not be done by linear or small-scale optimizations.
Copyright (c) 2025 Sumeet Mathur (Author) https://creativecommons.org/licenses/by-nc/4.0
Integration of Edge AI and Digital Twins in Industrial Systems: A Review of Trends and Architectures
The amalgamation of the Edge Artificial Intelligence (Edge AI) and Digital Twin (DT) technologies is changing the industrial systems by facilitating faster intelligence, real-time monitoring, and more dependable decision-making. The edge AI performs local processing of data with low latency, whereas digital twins generate virtual models of machines and processes to assist in the analysis, prediction, and optimization. They both improve the efficiency and reliability of operations and automation in Industry 4.0 and IIoT. The current review intends to draw attention to modern trends, architectures, and applications, which combine Edge AI with digital twin systems in manufacturing, energy, logistics, and smart infrastructure. Some of the main research trends are on how to enhance scalability, security, interoperability and data management in interrelated industrial ecosystems. Notwithstanding this, there are still challenges which include fragmented architectures, lack of resources, privacy issues and lack of common standards to large scale deployment. It should focus on lightweight AI models and standard frameworks and federated methods of learning in future work. Comprehensively, this review offers a brief insight into the ability of Edge AI and Digital Twins to work together to create more intelligent, autonomous, and sustainable industrial systems.
Copyright (c) 2025 Sandeep Gupta (Author) https://creativecommons.org/licenses/by-nc/4.0
Employee Attrition Prediction through Machine Learning: Advancing Human Resource Analytics and Workforce Management
The term "employee attrition" describes a certain reduction in a company's workforce due to a wide variety of factors. Employee attrition may also be caused by various factors in companies. Nevertheless, human resource managers must be able to identify signs of employee attraction at the initial stages. Organizational losses may be caused by employee attrition due to numerous reasons that include disruption of work and activities required to be done, re-employment and re-training cost, and loss of information. This study examines the accuracy of machine learning and deep learning models in forecasting employee turnover using the IBM HR Employee Attrition dataset. A number of models, including CNNs, were trained and evaluated against other methods, including DT, SVM, and LR. After comparing CNN model to more conventional machine learning algorithms, the experimental findings show that CNN model outperformed them with respect to accuracy (ACC) (92), precision (PRE) (96.67), recall (REC) (87), and F1-score (F1) (91.58). These findings indicate the efficiency of CNN to identify multifaceted trends in staff data, and it is a prospective HR analytics tool. These results demonstrate that predictive models based on deep learning have the potential to deliver actionable information to an organization to lower attrition and reinforce strategic human resource management.
Copyright (c) 2025 Dr. Nilesh Jain (Author) https://creativecommons.org/licenses/by-nc/4.0
An Exploratory Survey on the Synergy Between Agile Development and Quantum Computing in Engineering Applications
Agile development and quantum computing still are very distant entities that have not yet come together, but their convergence, should it be utilized correctly, can deliver not only the fruits of interaction but also the rewards of innovation, performance, and computing capabilities. Scrum, Kanban, and XP are examples of agile methods that emphasize team development iteratively through collaboration and continuous improvement. This helps teams handle complex and dynamic projects. The quantum computing system finds these criteria to be absolutely perfect, as the whole field revolves around exploring and calculating the likelihood of superposition, entanglement, and quantum interference, which are physical phenomena that are foreign to the micro-world. Qiskit, Cirq, and cloud services like IBM Quantum Experience are only a few of the tools that offer various platforms for creators of quantum algorithms, as quantum technology advances. Agile, combined with quantum software engineering, can not only be disaggregated into smaller pieces that can be more easily handled by available staff, but also raise issues such as insufficiently skilled staff, inefficient testing, and hardware constraints. In recap, the two dimensions of up-to-scale and versatile engineering practices are united by this synergy, making them predictable and future-ready.
Copyright (c) 2025 Prof. (Dr.) Abid Hussain (Author) https://creativecommons.org/licenses/by-nc/4.0
Optimization of Ensemble ML Models for Cybersecurity Analytics Using CICIDS 2017
Intrusion detection is one of the most significant components of modern cybersecurity systems due to the increase in the complexity of cyberattacks. Conventional detection approaches can be inefficient in handling the scale, imbalance, and complexity of network traffic, resulting in lower accuracy and an increase in false positives. This paper offers a hybrid ensemble model for intrusion detection using the CICIDS 2017 dataset, which combines Random Forest (RF) and Light Gradient Boosting Machine (LGBM). The methodology includes intensive data preprocessing, such as stratified random sampling, elimination of irrelevant features, aggregation of attack classes, and balancing of the distributions of classes using the SMOTE Tomek technique. The most important predictors are chosen using Pearson correlation and mutual information. The hybrid ensemble model capitalizes on the strengths that RF and LGBM have in complement and thus it can generalize better, minimize variance, and be more resistant to noisy and skewed data. The experimental findings indicate that the proposed method successfully attains 99.90% accuracy (ACC), 99.80% precision (PRE), 99.92% recall (REC) and 99.90% F1-score (F1), and can be used in comparison with standard ML models, including SVM and Naive Bayes. The results confirm the efficiency of ensemble learning in practice of intrusion detection and thus provide a scalable, precise, and dependable method of cloud-based cybersecurity detection.
Copyright (c) 2025 Dr Chintal Kumar Patel (Author) https://creativecommons.org/licenses/by-nc/4.0
The Next Generation of Atherectomy: Recent Progress, Challenges, Strategies, Relationships, and Future Pathways
Atherectomy has become a vital complementary technology in the treatment of complex arterial disease especially in lesions with heavy calcification such that their treatment with balloon angioplasty and stenting is limited. New technologies have made atherectomy no longer a mere instrument of plaque removal in practice, but instead extremely sophisticated systems that can achieve specific and vessel-conforming manipulation of the plaque. This review discusses the recent generation atherectomy equipment directional atherectomy equipment, rotational atherectomy equipment, orbital atherectomy equipment, and laser atherectomy equipment and the clinical associations they have with lesion morphology, imaging guidance, and adjunctive therapies. It also dwells on the next-generation innovation such as AI-guided navigation, new energy-powered solutions, micro-atherectomy of distal vessels, and drug-based platforms that can enhance vessel healing and minimize restenosis. The most important issues including device-vessel interaction, embolic risk, operator learning requirements, economic limitations and regulatory complexity are critically evaluated. New direction of future development is determined, and the focus is placed upon smarter sensors, real-time imaging integration, nanotechnology, and predictive modelling of personalised treatment planning. Case studies show the performance in real life and reveal discrepancies between the theoretical performance and the actual results. All in all, next-generation atherectomy has tremendous potential to enhance the safety of implementation of the procedure, its efficacy, and its long-term vascular outcomes, when technological innovation proceeds with resolving the existing limitations in a systematic and evidence-based way.
Copyright (c) 2025 Neelu Prasad, Vanshika Solanki, Dr. Himadri Shekhar Dey, Dr. Sachin Datt (Author) https://creativecommons.org/licenses/by-nc/4.0
Pharmacogenomics and Precision Dosing A Modern Approach to Reducing Adverse Drug Reactions
The research of the genetic variation in the individual responses to drugs has become one of the pillars of personalised medicine and this field has been labelled as pharmacogenomics. Pharmacogenomics facilitates the clinical practitioner to individualize therapy by pinpointing any genomic variations that influence drug absorption, distribution, metabolism, and activity. It is the reason why a personalised approach is especially significant since adverse drug reactions (ADRs) are a significant clinical and public-health issue of concern on an international scale, as they cause significant morbidity, mortality, non-compliance, and increased healthcare expenses. A combination of pharmacogenomic understanding and precision dosing, which involves incorporation of genetic information in conjunction with clinical factors including age, comorbidities, organ functioning and concomitant treatment, provides a more precise approach to optimisation of drug choice and dose as well as minimising unnecessary toxicity. The paper focuses on the principles of pharmacogenomics, genetic predictors of drug reactions, and the most important gene-drug interactions that are linked to the risk of ADR. It also discusses the technological advances that allow the use of a specific dose, clinical case studies in real-life practice, the existing barriers to its implementation, and the worldwide policy that promotes the use of personalised therapeutics. In general, the integrative application of pharmacogenomics and precision dosing is a radical move towards safer, predictive and patient-intelligent pharmacotherapy.
Copyright (c) 2025 Devangi Solanki, Dr. Sachin Datt, Dr. Himadri Dey (Author) https://creativecommons.org/licenses/by-nc/4.0
Comparative Performance Analysis of Active vs Passive Mutual Funds
This review paper will discuss the relative performance of both the active and passive mutual funds based on their performance in terms of returns, risk assumed, cost effectiveness and market environment on which their performance relies. Active funds seek to beat benchmarks by identifying the best securities and the best time to buy and sell securities, although their performance is frequently limited by larger expense ratios and lack of reliability in generating alpha. The passive funds which are used to track market indices enjoy the advantage of low costs, reduced turnover and the long-term growth of the market meaning that most investors have a stable investment. This paper summarizes empirical research in the world and in India to define tendencies in the performance of funds in different markets. The results show that passive strategies tend to perform better in efficient and stable markets whereas active funds can be beneficial during volatile markets or in less efficient markets. The review also identifies several critical determinants that influence investor decision-making and gives regions that need additional research in the emerging markets.
Copyright (c) 2025 Harshad Shyamkarn Patil, Dr. Sachin Datt, Dr. Himdari Shekhar Dey (Author) https://creativecommons.org/licenses/by-nc/4.0
Study of Investor Attitude Towards Mutual Funds and Increasing Penetration of Mutual Funds in India
Indian mutual fund (MF) industry has grown tremendously within the last twenty years with the help of regulatory reformation, the development of technology and the increased culture of systematic retail investments. The insight of the attitude of the investor has gained more significance as financial literacy levels and risk preferences as well as the behavioural tendencies have a strong influence on investment choices. This review focuses on the development of mutual fund industry in India and how these aspects of investor psychology, demographic factors, and digital money affect the MF penetration. The paper draws on the wide-ranging survey of recent scholarly works to identify the aspects that promote or obstruct participation, such as awareness, financial inclusion, an adoption of fintech and socio-economic factors. As can be seen in the analysis, despite the very high rate of assets under management growth as well as SIP participation, penetration of the investment business, particularly in rural and semi-urban areas, has been very low, given poor financial literacy, risk aversion, and lack of trust. Digital channels and ICT-based financial services, in their turn, become powerful facilitators of a wider access, enhancing convenience, lowering transaction costs and otherwise helping the fintech-mediated advisory systems. The essential results imply that the behavioral biases and socio-economic limitations are still limiting the participation of retail investors, yet digital finance and awareness campaigns could also contribute to a great improvement in the attitudes towards MF-investment. The review reveals lapses in literature on the topic of rural adoption trends, long-term behavioural trends and the impact of sustainability-oriented investment preferences. The paper points out the necessity of combined policy responses and investor-focused projects by AMCs and fintech companies to enhance MF penetration rates in India.
Copyright (c) 2025 Hitesh Aggarwal, Dr. Himdari Shekhar Dey, Dr. Sachin Datt (Author) https://creativecommons.org/licenses/by-nc/4.0
Smart Health Monitoring System with Deep Learning Models for Automated Heart Disease Detection
Heart disease has remained a major cause of mortality globally and therefore there is the important necessity of early diagnosis and follow-up health studies. Conventional diagnostic methods are also time-consuming, costly and require the interpretation of the expert, and thus they are not suitable in mass screening. In this study, a Smart Health Monitoring System is presented to overcome such problems. This is by incorporating data collection which is enabled by Internet of Things (IoT) and a CNN model to conduct automated detection of cardiac illness. Before the model is trained, the Heart Disease Dataset that is composed of thirteen clinical attributes was preprocessed by data cleansing, normalization, feature extraction, and balancing. As an insistence of its resilience and usefulness in diagnosing heart disease, the proposed CNN model showed good prediction accuracy (ACC) of 99.9, 99.5, 98.4, and F1-score (F1) of 99.4. The CNN model was much better than the more traditional models such as Naive Bayes (NB), Random Forest (RF) and Logistic Regression (LR) in all measures of assessment. All things considered, findings demonstrate that the suggested Smart Health Monitoring System based on CNN has great promise as an intelligent, dependable, and real-time answer to the problems of early cardiac risk assessment and preventative healthcare.
Copyright (c) 2025 Mrs. Neha Upadhyay (Author) https://creativecommons.org/licenses/by-nc/4.0
A Unified Learning Strategy for Intrusion Identification in Intelligent Cyber-Physical Systems
There is an increasing requirement to identify attacks against Cyber-Physical Systems (CPSs) due to their increasing application in critical infrastructures. Despite perhaps depending on sophisticated machine learning (ML) and deep learning (DL) models, the majority of intrusion detection techniques do not take practical implementation into account. This study uses the Knowledge Discovery and Data Mining Cup 1999 (KDDCup99) dataset to develop a novel hybrid deep learning (DL) model for the reliable detection of intrusions in Smart Cyber-Physical Systems (CPSs). The Synthetic Minority Oversampling Technique (SMOTE) is used to address the problem of class imbalance after the dataset has been cleaned, noise-reduced, fitted with outliers eliminated, and normalized using the Z-score. The main innovation is that it blends the temporal sequence modeling of bidirectional long short-term memory (BiLSTM) with the spatial feature-originating of convolutional neural networks (CNNs). The model's ability to capture both sequential and structural attack patterns is made possible by its synergy. With a score of 99.9% on every metric, including F1-score (F1), recall (REC), accuracy (ACC), and precision (PRE), the model achieves exceptionally good results. When compared to more conventional machine learning models like Support Vector Machine (SVM), K-Means Clustering (K-Means) + Random Forest (RF), and a simple CNN, the superiority of the suggested hybrid model is demonstrated. Because of its exceptional generalization and adaptability, it is most appropriate for real-time intrusion detection in the intricate and dynamic Cyber-Physical System (CPS) environment. The potential advantages of hybrid DL in improving the security and resilience of vital smart systems are discussed in this study.
Energy-Aware Mobile Processor Architectures: A Systematic Review of Design Principles and Optimization Methods
The increasing need for high-performance and high-energy efficiency in mobile computing has resulted in significant innovations in mobile processor architectures. This review article examines some of the major design concepts and algorithm optimization techniques that can be used to ensure energy efficiency and computational power balance in mobile devices. It explores such architectural innovations as RISC-V, logic-memory co-integration, and system-level technologies as clock gating, power gating, and dynamic voltage and frequency scaling (DVFS). Moreover, the paper also discusses software-hardware co-design options, thermal-aware scheduling, and the new machine learning-based energy prediction and management approaches. This study gives us an idea of the dynamic nature of mobile processor design to enhance performance with longer battery life through synthesis of the existing research and developments. The findings are especially applicable because mobile applications are becoming increasingly more demanding with regard to computation. The mentioned techniques can be considered the basis of future innovation in the cellular energy optimization and chip architecture.
Dynamic Voltage and Frequency Scaling (DVFS)
Copyright (c) 2025 Vivek Sharma (Author) https://creativecommons.org/licenses/by-nc/4.0
Machine Learning Approaches for Early Identifictaions of Heart Disease in Healthcare Applications
Early detection and accurate diagnosis are crucial for improving patient outcomes and prescribing appropriate therapy for heart disease, which is still one of the leading causes of death globally. It can be extremely costly, time-consuming, and intrusive to use conventional diagnostic methods in settings with limited resources. Automated, data-driven prediction of cardiovascular problems has shown promising potential thanks to new ML and DL advances. This research presents a strong artificial neural network (ANN) model for the prediction of cardiovascular disease using a large-scale Kaggle dataset on the subject. The dataset includes more than 300,000 samples with 17 health-related variables. Complete data pre-processing, including missing value filling, duplicate removal, feature selection via the Chi-Square test, and min-max normalization, is required by the method. Two sets of data, one for training and one for testing, are used to teach an artificial neural network (ANN) to correctly identify cases of cardiac diseases. The experimental findings indicate that the proposed ANN is more effective, with an accuracy (ACC) of 98.75%, precision (PRE), recall (REC) and F1-score (F1) of 99, which is better than the baseline models including MLP, DNN, and Random Forest. Remarkably, the model is stable, can be generalized and is reliable, which is evidenced by ROC and loss curve analyses. The results point out the potential of the proposed method as a high-precision, cost-effective and clinically appropriate tool of early heart disease detection, which may help achieve timely interventions and better healthcare decisions.
Copyright (c) 2025 Dr Manish Saraswat (Author) https://creativecommons.org/licenses/by-nc/4.0
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.
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.
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.
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