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
Survey on Integration of IoT with Artificial Intelligence (AI) Technologies for Smart Manufacturing Operations
The IoT and AI are assisting in the development of smart, interconnected, and flexible industrial systems, which in turn are facilitating the evolution of manufacturing. Through its network of interconnected sensors and devices, the Internet of Things (IoT) facilitates the automation of various stages of the manufacturing process. AI adds value by providing detailed analysis, making predictions, and helping with decisions that make workflows smoother, cut down on stops in production, and improve product quality. Together, these technologies form the base of Industry 4.0, which supports eco-friendly practices, better use of resources, and gives companies an edge in the global market. Recent studies show that combining IoT data with AI insights is used in areas like predicting equipment failures, making processes more efficient, managing energy use, building reliable supply chains, and using smart robots. All these improvements help manufacturers be more adaptable, lower the chances of problems, and make products that meet customer needs. This overview shows how combining IoT and AI is making manufacturing more self-sufficient, driven by data, and stronger, which greatly helps in the ongoing development of modern industrial practices.
Copyright (c) 2026 Dr. Prithviraj Singh Rathore (Author) https://creativecommons.org/licenses/by-nc/4.0
Machine Learning-Based Default Loan Prediction for Financial Risk Assessment in Digital Lending
In recent decades, the global financial system has undergone tremendous change. While capital markets play a crucial role in some situations and traditional banks and financial institutions continue to be the main source of funding for businesses and people in the majority of nations, new digital lending platforms have lately emerged. In this paper, the author introduces a viable machine learning-based approach to the estimation of financial risk in online lending on the Lending Club dataset. The suggested solution starts with an extensive data collection and exploratory analysis, followed by intensive preprocessing of data that includes outlier treatment, feature engineering, one-hot encoding, and normalization, as well as the class imbalance mitigation method of SMOTE. A systematic hyperparameter optimization is applied to creating an optimized Extreme Gradient Boosting (XGBoost) model that is used to ensure that nonlinear relationships between borrower and loan traits are learned. There are various performance measures that are used to evaluate the model, some of them include accuracy, precision, recall, F1-score, ROC curve, and confusion matrix analysis. The experimental outcomes are highly predictive with an accuracy of 97.70, precision of 94.78, recall of 93.30 and a near perfection of the AUC of 0.9991. The fact that the proposed model is generalizable compared to the traditional machine learning models also justifies the superiority of the given approach and shows that it has power, can be scaled, and placed in the context of applying it into practice to evaluate financial risks in the digital lending model.
Copyright (c) 2026 Mrs. Neha Upadhyay (Author) https://creativecommons.org/licenses/by-nc/4.0
Engineering Strategies for High-Availability Mission-Critical Facilities: A Survey
Mission-Critical Facilities in Diverse Domains investigates the key features, engineering requirements, as well as design provisions that allows continuous operation in high-consequence environments. The paper identifies that mission-critical facilities are becoming more important in various sectors, including data infrastructure, healthcare, defense, and manufacturing, and that they are sensitive to reliability, security, and resilience. It scans classification structures in terms of operational urgency and defines performance expectations which stimulate redundancy, real-time monitoring and disaster preparedness. Contributions of mechanical engineering are examined in reference to the redundancy strategy (including N+1, 2N strategy) and the task of adaptive controls in alleviating dynamic uncertainty. Structural and civil engagements are analyzed using modular construction and structural optimization practices that increase performance in terms of efficiency, safety and lifecycle. The research provides a multidisciplinary basis to the understanding and enhancement of mission-critical systems and balances design practices with current requirements of fault tolerance and always-on operation.
Copyright (c) 2026 Dr. Nilesh Jain (Author) https://creativecommons.org/licenses/by-nc/4.0
Leveraging Artificial Intelligence Algorithms for Retirement Income Optimization in Financial Planning
Responsibility for one's own financial future is required in people's financialized everyday lives. However, as a larger proportion of the population ages, more people are concerned about their financial stability due to factors such as fluctuating retirement and pension systems, uncertain government funding, and market and other crises. The present study addresses the urgent issue of low uptake of pension schemes by developing and testing high-performance predictive models based on data from FinAccess. To address the extreme imbalance among the classes, the more extensive AdaSyn resampling method was used. A sophisticated combination of 22 sociodemographic predictors was specified to train and optimize Multi-Layer Perceptron (MLP) and Decision Tree (DT) models systematically. The obtained results show outstanding predictive power, as the MLP model has an accuracy of 95.74%, precision of 96.26%, recall of 95.60%, F1-score of 95.76%, and a near-perfect AUC of 0.9968, while the DT model yielded closely competitive metrics of 95.25% accuracy, 95.65% precision, 95.03% recall, 95.26% F1-score, and a 0.9950 AUC. A comparative study proves that both of the proposed models are far much better than modern standards, such as RNN-LSTM, LightGBM, and Random Forest. The study develops a new analytical pipeline that offers practical, quantitative information of creating specific financial inclusion initiatives to enhance security in changing retirement environments.
Copyright (c) 2025 Mrs. Neha Upadhyay (Author) https://creativecommons.org/licenses/by-nc/4.0
Influence Of Die Geometry And Friction On Springback During Cold Drawing Of Tubes: An Fea Investigation
The cold drawing of seamless tubes can cause unwanted geometric deviations in the form of undesired springbuck, where die geometry, frictional conditions, and material properties are the main factors of influence. The AISI 304 stainless steel tubes were simulated using both experimental and finite element (FE) models in this paper in order to discuss how die semi-angle, bearing length, and friction coefficient influence the springback of the tubes. The numerical model has been constructed based on ABAQUS/ Explicit and checked against laboratory-scale experiments of tube drawing. The Coulomb law was used to model the frictional contact with a coefficient between 0.05 and 0.15. Findings have shown that the smaller die angles and reduced friction coefficients are useful in reducing the elastic recovery and dimensional deviations. The springback values predicted by the FEA were close to experimental values, with the highest deviation of 6.8, which proved that the model was accurate. These results can be used in the future to design processes and the selection of dimension control parameters in the production of seamless tubes.
Copyright (c) 2026 Mr. Jayesh Nagraj Chaudhari, Dr. Kamal Kumar Ojha, Dr. Kasi Raja Rao (Author) https://creativecommons.org/licenses/by-nc/4.0
Addressing The Idiom Challenge in Machine Translation: A Review Focused on Low-Resource Languages
Machine translation (MT) has made significant progress for high-resource languages, yet idiomatic translation remains a persistent challenge, especially for low-resource languages like Punjabi. Idioms are non-compositional with respect to cultural values, which makes literal translations insufficient. This paper presents a systematic review of idiom translation in pairs of low-resource-to-high-resource languages, focusing on Punjabi-English as a case study. We analyze key challenges—including dataset limitations, figurative-literal ambiguity, structural complexity, and evaluation limitations—and examine existing approaches, including rule-based, statistical, neural, and large language model (LLM)-based methods. We identify gaps in idiom-specific datasets, evaluation frameworks, and multilingual transfer techniques. Finally, we provide some guidance for future research, highlighting hybrid models, community-driven datasets, multimodal translation, and idiom-aware evaluation metrics. This review aims to guide the development of more accurate and culturally aware MT systems for low-resource languages.
Copyright (c) 2025 Manjot Kaur, Jasvir Kaur, Jasmin Kaur Gahlot, Prof. Palak Sood (Author) https://creativecommons.org/licenses/by-nc/4.0
Facial Emotion Detection in Low Resolution Image: Review and Challenges
Face emotion is a form of non-verbal communication extensively used by human beings. Detecting emotion to ascertain a human's mental state is a vital research area. Emotion detection for images with low resolution is an area where research is going as practical real-world images will be mostly low resolution. Real-world applications like surveillance, forensic and drone imagery capture low-resolution images due to camera resolution, distance of face, and motion of the camera as well as face and may contain noise. Most of time, researchers consider image less than 32* 32 pixels as low-resolution image as there is no fixed definition for categorizing low-resolution images. Human face emotion detection performance depends on factors such as lighting conditions, facial occlusion, and facial movements. The loss of resolution will reduce the image's vital facial details. Performance on facial emotion recognition is improving with newer deep learning models, but model accuracy remains a major issue. This paper will elaborate methods and algorithms used in face emotion detection in low resolution conditions. The paper will summarize different datasets, methodology and performance parameters and challenges.
Machine learning (ML) and Deep Learning (DL) Approaches
Copyright (c) 2025 A.R. Landge, Manish Jain (Author) https://creativecommons.org/licenses/by-nc/4.0
Artificial Intelligence as a Catalyst for Sustainable Development: Addressing Environmental, Healthcare, and Urban Infrastructure Challenges
The escalating environmental degradation, healthcare disparities, and urban infrastructure challenges demand transformative solutions that transcend conventional approaches. This paper investigates the role of artificial intelligence as a catalyst for sustainable development across three critical domains: environmental conservation, healthcare delivery, and urban infrastructure management. Through a comprehensive analysis of recent implementations and empirical evidence, we demonstrate how machine learning algorithms, neural networks, and intelligent systems are reshaping sustainability practices. The study examines specific case studies in which AI has achieved measurable improvements in carbon footprint reduction, disease outbreak management, and smart city operations. We present quantitative evidence showing that AI-enhanced systems can reduce energy consumption by 30-50%, improve diagnostic accuracy by up to 95%, and decrease urban traffic congestion by approximately 60%. The paper also addresses implementation challenges, including algorithmic transparency, data governance, and the digital divide. Our findings suggest that successful AI integration requires collaborative frameworks involving policymakers, technologists, and community stakeholders. This research contributes to the growing body of knowledge on AI-driven sustainability by providing a holistic framework that bridges technological innovation with practical implementation strategies.
Copyright (c) 2026 Srinivas Reddy Kosna (Author) https://creativecommons.org/licenses/by-nc/4.0
Ensuring fruit quality through automation is becoming increasingly important in agriculture to maintain food safety and minimize post-harvest waste. This study proposes an automated system for identifying fresh and spoiled fruits by integrating Convolutional Neural Networks (CNN) and Random Forest Classifiers (RFC). The experiments were carried out using a publicly available Kaggle dataset containing 11,257 images of apples, bananas, and oranges. Image preprocessing steps such as resizing, normalization, and data augmentation were performed to enhance model robustness. The CNN model achieved a validation accuracy of 96.9% with an F1-score of 0.97, outperforming the RFC, which recorded 94.7% accuracy and an F1-score of 0.95. The CNN demonstrated superior feature extraction from spatial patterns, whereas RFC performance was constrained by its reliance on flattened image data. Furthermore, a web-based interface was implemented to allow users to upload fruit images and obtain classification results in real time. This research highlights the potential of combining traditional and deep learning techniques for intelligent fruit grading and real-time quality assessment in agricultural systems.
Copyright (c) 2026 Rachana Nayak, Vachana, Yuktha Krishna , Vikas Bhat D, Sandeep R. (Author) https://creativecommons.org/licenses/by-nc/4.0
Self-Supervised Learning in Generative AI: Enhancing Model Efficiency and Adaptability for Limited Data Scenarios
Recent advancements in Generative AI and deep learning have demonstrated remarkable capabilities in producing high-quality, human-like outputs across domains such as text, image, and audio generation. However, these models often rely on extensive labeled datasets, which can be resource-intensive to curate. This research focuses on the integration of self-supervised learning (SSL) paradigms into Generative AI to address the challenges of limited data availability, domain adaptation, and computational efficiency. The proposed study explores novel SSL techniques tailored for generative tasks, including contrastive learning, masked prediction, and clustering-based approaches. It examines their impact on enhancing model generalization, reducing overfitting, and improving robustness against adversarial attacks. Additionally, it investigates hybrid architectures combining SSL with transformer-based and diffusion models to optimize performance across diverse generative applications. By leveraging SSL, this research aims to bridge the gap between data efficiency and generative quality, paving the way for more accessible and adaptable AI systems capable of thriving in real-world, data-sparse environments.
Copyright (c) 2026 Arvind Kumar, Basudeo Mahato, Anamika Kumari (Author) https://creativecommons.org/licenses/by-nc/4.0
Ransomware Attack Detection using Intelligent Machine Learning Algorithms: A Systematic Review of Challenges and Future Directions
Cybersecurity is a major concern in the IT sector these days. As a direct result of the increasing number of ransomware attacks and other types of cyberattacks, the protection of user data has become a primary focus. This systematic review explores the use of smart machine learning (ML) algorithms to identify ransomware. It demonstrates how these algorithms, towards their flexibility and precision, can be used for malware detection. A thorough survey of 30 primary studies from 2018 to 2025 reveals that advanced machine learning models like Random Forest, SVM, CNN, LSTM, XGBoost, and ensemble methods are the main contributors to achieving high detection accuracy results, very often beyond 99%, particularly when ransomware datasets are used. These models are able to detect the presence of ransomware in various platforms by utilizing static, dynamic, and hybrid features sets. The review also takes into account issues such as dataset imbalance, limitations of generalization, and detection strategies. The next goals are to create models that are resistant to adversarial attacks, employing generative augmentation (GANs), developing light machine learning architectures for real-time edge deployment, and using Explainable AI (XAI) to make AI more transparent. The findings demonstrate that ML models with high accuracy that are well-optimized and contextualized can significantly enhance the efficacy of cutting-edge ransomware defense systems. In sum, this study serves as a detailed and organized guide from the perspective of researchers and practitioners for the production of high-precision, robust, and scalable ransomware detection systems.
Copyright (c) 2026 Himani Pandya, Snehal Sathwara (Author) https://creativecommons.org/licenses/by-nc/4.0
Smart Test Automation: Integrating AI and ML for Continuous Digital Transformation
The high rate of digital transformation and the rising complexity of software developed a necessity to explore how Smart Test Automation combined with AI and ML would enhance continuous delivery pipelines. This paper explores the transition of manual testing to intelligent automation to gain insight into the efficiency and reliability of tests through AI-driven capabilities. The main aspects, including autonomous test generation, self-healing scripts, intelligent test prioritization, and defect prediction, contribute to stability, accuracy, and maintainability in testing ecosystems to a great degree. Other important issues in digital transformation identified in the research are cultural resistance, lack of digital skills, infrastructural constraints, and data privacy issues. The analysis of the recent literature shows how the innovations in the field of AI-based automation, Visual AI, script generation with the help of NLP, and simulation tools redefine contemporary testing processes in different industries. In general, the research highlights the fact that AI-enhanced smart automation is the key to facilitating continuous integration, continuous testing, and continuous deployment via human intervention minimization, faster feedback cycles, and scalable and reliable software delivery.
Copyright (c) 2026 Ram Pratap Singh (Author) https://creativecommons.org/licenses/by-nc/4.0
Performance Evaluation of Artificial Intelligence-Based Ticket Demand Forecasting
Complex policies are generally employed by corporations to fluctuate the price of the products. The airline industry is amongst the most advanced in the application of the revenue management in an effort to optimize their revenue. Air ticket market is not uniform in all countries and it depends on the supply volume and pattern as well as demand. The paper explores the issue of ticket demand prediction and customer support behavior along with real-life Customer Support Ticket Dataset of Kaggle (8,469 records with rich metadata). Once the exploratory analysis of the ticket priorities and word cloud visualization of the resolved and unresolved issues were completed, the text preprocessing with tokenizing, normalizing of cases, stopwords elimination, and TF-IDF features extraction was conducted to prepare the textual fields to be modeled. This was followed by the division of the data into training (80) and testing (20) set and the application of a Gradient Boosting model, which predicts the demand of tickets based on its high ensemble learning properties to transform a number of weak learners into a powerful predictor. The assessment of the performance according to R2 RMSE MSE MAE indicated that the model has outstanding predictive power with R2 value 95.0 percent and low error measures of RMSE 0.47, MSE 0.22, and MAE 0.30 being obtained and with the close clustering of the actual and predicted plots and also close-to-the-data residual values. It was demonstrated that Gradient Boosting was more effective than the traditional and deep learning baselines such as Linear Regression (72.3%), LSTM (70.0%), and KNN (65.0%). Findings support the validity of enhancing-based ensemble techniques in demand prediction in customer support settings, which is important in terms of resource allocation, workload, and service optimization.
Copyright (c) 2026 Mr. Deepak Mehta (Author) https://creativecommons.org/licenses/by-nc/4.0
Dynamics of Rotating Machinery: Modeling, Fault Diagnosis, and Control Strategies
The rotating machinery forms the foundation of modern industrial systems, and major industries, including power generation, petrochemicals, aviation and aerospace, are greatly reliant on rotating machinery. Due to continuous service under high mechanical, thermal, and aerodynamic loads, such machines are very vulnerable to failures such as imbalance, misalignment, wear of the bearings, cracks along the shaft, lubrication damage and defective gears. The complicated rotor-stator interactions in the form of fluid-structure interactions, thermal stress interaction, as well as vibration phenomena are necessary in understanding the reliability, operational safety, and cost-effective maintenance. In this paper, the modeling, fault diagnosis and control methods employed in rotating machinery have been surveyed in detail. The advanced analytical and numerical modeling methodology, e.g., the finite element method, transfer matrix method, and modal analysis are discussed to model the dynamic behavior in various working conditions. Furthermore, the work delves into the most recent advancements in defect diagnostic approaches that include signal processing, vibration analysis, and artificial intelligence. It highlights the growing trend of information-based predictive maintenance. Also, recent control schemes, such as passive and active vibration control schemes, smart and fault-tolerant and resilient control structures are discussed in terms of their contribution to stability in the system and failure prevention.
Copyright (c) 2026 Dr Chintal Kumar Patel (Author) https://creativecommons.org/licenses/by-nc/4.0
Few-Shot Question Answering in Low-Resource Languages using Model-Agnostic Meta-Learning (MAML)
Question Thanks to massive datasets like SQuAD, Question Answering (QA) systems have made tremendous strides in languages with abundant resources, such as English. Unfortunately, model performance is severely constrained in low-resource languages due to the lack of annotated data. The Model-Agnostic Meta- Learning (MAML) framework is suggested in this study as a means of few-shot quality assurance in languages with limited resources. With only a small number of annotated question-answer pairs, the method allows for quick domain or language adaptation. With an emphasis on low-resource Indian languages like Telugu, Bengali, and Hindi, we assess the framework using the multilingual QA standards TyDiQA and XQuAD. Our MAML-based technique achieves an 8.5% increase in F1 score and an 8.2% improvement in Exact Match (EM) over the best fine-tuned baselines, according to experimental data. This method considerably outperforms standard fine-tuning and transfer learning approaches in few-shot circumstances. This study demonstrates how meta-learning may be used to create flexible and scalable quality assurance systems for languages that aren't widely used.
Copyright (c) 2026 Tahseen Equbal, Wasim Nehal , Wasim Ahmad Sheikh, Aarif Rasul, Irshad Anwar, Asad Iqbal (Author) https://creativecommons.org/licenses/by-nc/4.0
Multi-Cloud Deployment Strategies for Resilient Enterprise Systems Under Dynamic Workloads
Multi-Cloud Deployment Strategies to Resilient Enterprise Systems under dynamic Workloads investigates the way enterprises utilize the heterogeneous cloud providers to enhance their performance, resilience, and scalability and maintain operational continuity. The multi-cloud strategies of modern organizations are adopted due to the need to avoid vendor lock-in, lower costs, and provide availability; however, platform heterogeneity also presents interoperability, workload orchestration, and resilience challenges. This paper analyzes the theoretical basis, architecture and orchestration systems that facilitate enterprise-scale multi-provider cloud computing. With the growing use of multi-cloud strategies in organizations to provide a more flexible and resilient and lower cost, organizations are confronted with new challenges of interoperability, vendor dependency, and managing the work load in heterogeneous environments. The study initially examines enterprise-focused principles that lie behind multi-provider cloud ecosystems with a focus on service abstraction, risk sharing, and provider lock-in elimination mechanisms. The conceptual framework of resilient multi-cloud networks is subsequently provided, incorporating designs in terms of layers, security, scalability, and reliability as fundamental elements. To resolve the big data requirements of the new businesses, several architectural designs are examined such as Data Lakehouse, Lambda Architecture, Microservices, Data Mesh, and Federated Learning to meet the demands of distributed analytics in cloud vendors. The other gap in the study is the research gaps on the areas of standardization, governance and cost optimization. Finally, the work recommends a multi-cloud Kubernetes-based orchestration model, which comprises smart scheduling, real-time resource optimization and workload distribution according to latency in an attempt to optimize operational performance. The findings focus on highlighting that in order to realize the successful implementation of multi-cloud adoption, one should not confine themselves to the technological integration, but make sure that the strategies are aligned on the level of infrastructure, analytics, and governance to promote the sustainable digital transformation.
Copyright (c) 2026 Dr. Pradeep Laxkar (Author) https://creativecommons.org/licenses/by-nc/4.0
Survey of Mechanical Property Enhancement Techniques for Piping Materials
Failure of pipelines may pose significant hazards leading to detrimental environmental and economic impacts. Since the beginning of this millennium, nearly 600 severe gas pipe incidents were reported with more than 200 fatalities. This paper provides an exhaustive state of art analysis of piping materials, metallurgical enhancement methods, and surface engineering methods and how they are applied to pipeline and mechanical systems in the modern world. Both metallic and non-metallic piping materials are discussed, with emphasis put on their mechanical properties, corrosion, manufacturability, and ability to work in various service environments. Some of the key metallic materials include carbon steels, alloy steels, stainless steels and non-ferrous alloys are discussed together with non-metallic alternatives like polymers and fiber-reinforced composite materials highlighting their pros, cons and their use in industry. The paper also discusses the key mechanical properties that control the performance of pipes, including yield strength, tensile strength, ductility and toughness. Heat treatment, thermomechanical processing and severe plastic deformation are the methods of advanced metallurgical enhancement that are analyzed in terms of microstructure and mechanical performance enhancement. Also, surface engineering techniques, including, but not limited to, coating technologies, surface modification and thermal treatments are considered in terms of their effectiveness to increase wear resistance, corrosion protection and service life.
Copyright (c) 2025 Dr.Jvalantkumar Kanaiyalal Patel (Author) https://creativecommons.org/licenses/by-nc/4.0
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.
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.
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