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Evaluate Machine Learning Techniques for Early Disease of Cardiovascular Disease
Cardiovascular diseases are one of the major causes of death around the world, and their early detection is critical for effective intervention. The paper presents a systematic review of machine learning techniques used for the early prediction of cardiovascular diseases, focusing on studies carried out between 2019 and 2024. Widely used models considered in the review include Logistic Regression, Support Vector Machines, Random Forest, K-Nearest Neighbors, Gradient Boosting, and hybrid ensemble methods with the aim of ascertaining predictive accuracy, interpretability, and clinical relevance. In most of the reviewed studies, ensemble and Random Forest models attained the highest accuracies of 90% - 98%, while Gradient Boosting and SVMs were mostly above 90% in balanced datasets. Logistic Regression had a moderate accuracy of 85%-91% but remained the most interpretable, while KNN established the lowest performance of 80%-86%. Despite the promising strides, there are a number of limitations, such as imbalance in datasets, limited external validation, and small benchmark datasets, that are limiting general application in health. This systematic review highlights strengths and weaknesses of the contemporary machine learning approaches and makes it evident that clinically validated, interpretable, and generalizable models should be developed in order to assist real-world medical decision-making. 2025 IEEE. -
Large Language Models in Economic Forecasting: A Comprehensive Analysis of Predictive Performance and Benchmarking Against Traditional Methods for India FY 2025-26
This study presents a comprehensive systematic evaluation of the performance of Large Language Models (LLMs) in economic forecasting, specifically examining their ability to predict key Indian macroeconomic indicators for the fiscal year 2025-26. Through a comparative analysis of ten prominent LLMs against traditional econometric models and expert forecasts from leading institutions, we assess the forecasting accuracy, reliability, and practical limitations of these models using a rigorous multistage validation framework. We validate predictions using actual quarterly data from Q1 and Q2 of FY 2025-26, providing a real-time assessment of forecasting capabilities with bootstrap confidence intervals and time series cross-validation techniques. Results reveal significant variations in LLM performance, with validation against Q1 2025-26 actual GDP growth of 6.7 per cent showing that several LLMs achieved superior accuracy (MAPE less than 3 per cent) compared to traditional ARIMA models (MAPE 13.58 per cent). Top-performing LLMs demonstrate forecasting capabilities that approach expert-level accuracy while maintaining computational efficiency and scalability. Statistical significance tests using the Diebold-Mariano framework confirm the superiority of ensemble LLM approaches over individual traditional methods. The findings demonstrate that leading LLMs can serve as valuable supplementary forecasting tools, positioning between conventional statistical methods and expert analysis in terms of accuracy, while offering advantages in processing qualitative information and adaptation to structural changes. 2025 IEEE. -
The Efficacy of Augmented and Virtual Reality Exposure in Reducing Diverse Phobias: A Comparative Study
Anxiety disorders represent one of the most prevalent mental health challenges worldwide, affecting over 300 million individuals across all age groups. Among these, specific phobias constitute a large subset, often leading to avoidance behaviors, functional impairment, and reduced quality of life. Traditional in vivo exposure therapy (IVET) has long been considered the gold standard for treating such disorders, yet its practical limitations-such as patient noncompliance, difficulty in replicating real-life stimuli, and logistical challenges-have necessitated the exploration of digital therapeutic alternatives. This study presents a comprehensive comparative analysis of Augmented Reality (AR) and Virtual Reality (VR) exposure interventions, examining their therapeutic efficacy, engagement mechanisms, and clinical applicability relative to IVET. Through a systematic review of high-quality Randomized Controlled Trials (RCTs), meta-analyses, and empirical studies published between 2010 and 2025, this research synthesizes evidence regarding symptom reduction, physiological response modulation, and patient adherence across age groups and phobia categories. Findings demonstrate that both AR and VR interventions achieve symptom reductions equivalent to IVET, while offering additional benefits in accessibility, immersion, and personalization. VR shows superior outcomes in complex environmental and situational phobias through enhanced sensory engagement, whereas AR demonstrates strong ecological validity and user comfort in natural settings, particularly for pediatric and home-based use. The paper concludes by identifying current challenges such as cybersickness, ethical considerations in data use, and the necessity for longitudinal studies to assess sustained therapeutic impact. 2025 IEEE. -
Metamaterial Integrated Patch Antenna Design with Enhanced Gain for WLAN and WIMAX Applications
In this paper a dual-band high gain metamaterial antenna is designed using RT Duroid 5880 with overall size of 50 60 mm2 and thickness of 1.64 mm. A rectangular patch antenna with two slots is designed to operate at 3.5 and 5.8GHz for Worldwide Interoperability for Microwave Access (WiMAX) and Wireless local area network (WLAN) applications. Further, to enhance the gain characteristics of patch antenna, a reflective metasurface (RMS) is designed on FR-4 substrate, which is placed as a superstrate above the antenna. The metasurface used as superstrate is placed at the air gap of 15 mm to achieve optimum gain and bandwidth. It is observed that the proposed metamaterial integrated antenna shows enhanced gain of 6.5 and 7.73 dBi at both the resonant frequencies. The designed antenna is a good candidate for various WLAN and WiMAX applications. 2025 IEEE. -
Integrating LLMs into Smart Home Architecture: Design, Implementation, and Experimental Insights
Smart home systems are a key application of the Internet of Things (IoT) paradigm. These devices are either directly or indirectly connected to a network in order to perceive actions based on user willingness or sensing. As Information and Communication Technology (ICT) advances, Large Language Models (LLMs) are growing increasingly potent. Agents with LLM capabilities could be very supportive for smart home systems because of their natural language comprehension. In this paper, we introduce an LLM-powered smart home agent with real-time connectivity to smart home devices. This proposal has been experimented with and evaluated with typical smart home use cases. This experimental architecture illustrates a realtime scenario of a standard smart home, where sensors, lights, and switches are interconnected in a multi-tiered environment. The sensor, gateway, and switch modules are connected to the smart home edge server via a Web of Things (WoT) interface. The experiment has been conducted with various types of smart home use case prompts, including status requests, control requests, automation requests, and reasoning requests. The outcome of the experiment indicates that the addition of LLM to smart homes excels in natural conversational patterns compared to keywordbased agents. The prompt response time, which is unsuitable for time-sensitive tasks like anomaly detection, is a drawback, and edge LLMs could be a solution. 2025 IEEE. -
Low-Profile Metasurface-Integrated Ultra-Wideband Antenna with Enhanced Gain
In this paper a metamaterial-integrated compact antenna is proposed, and the design, simulation, and implementation are presented which works in Ultra-Wideband frequency (UWB) range. The FR4 substrate has been used to design a compact, flexible, wearable antenna. Metamaterial structure comprises of periodic arrangement of unit cells termed as metasurface, to achieve higher gain. The proposed integrated antenna exhibits maximum gain of 8.1 dBi with overall dimensions of 50 mm 40 mm. Also, the gain enhancement of 3.2 dBi along with 0.2 GHz increment in bandwidth is observed after adding the metamaterial array. Thus, the proposed antenna is suitable for wearable applications. 2025 IEEE. -
Integrating Explainable Machine Learning (XAI) in Stroke Medicine: Opportunities and Challenges for Early Diagnosis and Prevention
Stroke is a leading cause of mortality and disability worldwide, emphasizing the critical need for early diagnosis and prevention. Machine learning (ML) has demonstrated significant potential in improving stroke prediction and management by analysing complex datasets for risk stratification, diagnosis, and treatment planning. However, the adoption of ML in stroke medicine is limited by the opacity of these models, which can hinder clinical trust and decision-making. Explainable Artificial Intelligence (XAI) addresses this challenge by making ML models more interpretable and transparent, enabling healthcare professionals to understand, validate, and trust their outputs. This research work explores the integration of XAI in stroke medicine, highlighting its potential to enhance early diagnosis, personalized prevention strategies, and treatment planning. We discuss the opportunities XAI provides in identifying high-risk patients, uncovering critical predictors, and enabling informed clinical decisions. Furthermore, we examine challenges such as ensuring model reliability, addressing biases in stroke datasets, and navigating ethical considerations related to patient data privacy and algorithmic accountability. 2025 IEEE. -
Cross Domain Lexicon Transfer -A Case Specific to Application in Banking Domain
This study demonstrates the development of the financial domain lexicon and the implementation of the same in the banking sector. The study compared the working of Financial Nae Bayes Lexicon (FNB Lex) developed with 533 quarterly Earnings Call Transcript (ECT) of 16 software companies, with standard available dictionaries like VADER and Loughran-McDonald. The study showed VADERs poor discriminatory power and Loughran-McDonald with satisfactory performance. FNB Lex lexicon performed better and provided better lift over VADER and Loughran-McDonald with improved precision, recall and F1-score. 2025 IEEE. -
Self-Organizing Micro Service Composition for IoT Ecosystem
The Internet of Things (IoT) has become the central focus in many computing applications, with smart devices seamlessly integrated to meet user needs by providing services that reflect their functionalities. Service composition, the process of integrating multiple services to deliver unified functionality, is crucial in this context. However, traditional service composition techniques fall short in highly dynamic and open environments such as the IoT ecosystem, necessitating decentralized models that can effectively support service composition in such settings. The self-organizing microservice composition model for IoT addresses this need by leveraging decentralized, localized interactions that utilize bio-inspired mechanisms. These mechanisms enable the system to autonomously form complex service compositions with minimal human intervention through emergent behaviour, enhancing the systems flexibility, adaptability, and overall performance. This paper presents a model specifically designed for the IoT ecosystem, focusing on healthcare applications. The model dynamically responds to changing conditions, such as varying patient needs, device availability, and network conditions, making it highly suitable for critical healthcare environments. By providing a robust framework for managing the complexities inherent in healthcare IoT, this model has the potential to revolutionize the delivery and management of healthcare services. 2025 IEEE. -
Collaborative Model for Sustainable Energy Utilization in Cloud Infrastructure
As the infrastructures of cloud computing provides paramount services to worldwide users, persistent applications are congregated using large scale data centres at the customer sides. For such wide platforms, virtualization technique has been incorporated for multiplexing the essential sources available. Due to the extensive application variations in the workloads, it is significant to handle the resource allocation methodologies of the virtual machines (VM) for assuring the Quality of Service (QoS) of cloud. On concentrating this, the paper proposed a Decentralized Energy-Aware Collaborative Model (DEACM) for effectively managing the data centres in cloud infrastructures. Initially, the optimal model for system management and power management are declared. Then, functions of workload vectors and data collection about workloads has been carried out for optimal selection of virtual machines to migrate for balancing loads efficiently. This can be further applied for Target-based VM Migration Algorithm for determining the migrating target for VM. Moreover, the algorithm involved in energy utilization with managed QoS. The developed DEACM is evaluated using CloudSim platform and the results are discussed. The results exemplify that the DEACM can balance the workload across variety of machines optimally and provide reduced energy consumption to the complete system efficiently. 2025 IEEE. -
Enhanced Image Classification using Transfer Learning with ResNet50-V2: A Case Study on Wildlife Recognition
This study explores the application of transfer learning using the ResNet50-V2 architecture for accurate classification of Arctic wildlife species, including Arctic foxes, polar bears, and walruses. Transfer learning leverages pretrained networks to enhance performance in new tasks with limited labeled data, reducing the need for extensive data collection and computational resources. In this work, we utilized a dataset of 1000 labeled images across the three species and applied ResNet50-V2, pre-trained on ImageNet, as a feature extractor. The model achieved high accuracy, with training and validation accuracies nearing 99% and 95-97%, respectively, though minor overfitting was observed. This indicates the model's strong ability to generalize across the dataset while benefiting from pre-trained weights on diverse, non-related images. Additionally it compares with models like SSD and CycleGAN, emphasizing its capability to generalize well, handle small datasets, and mitigate overfitting. We discuss model architecture, data preprocessing, and the experimental results, focusing on improvements achievable through regularization techniques to counteract overfitting. This study demonstrates the effectiveness of transfer learning for wildlife classification, providing insights into optimizing CNNs for ecological and conservation applications. 2025 IEEE. -
Electricity Demand Prediction: An Analytical Comparison of ARIMA and Artificial Neural Network
Electricity plays a dominant role globally, especially in the economies of India. Accurately projecting its consumption is crucial for energy planning. This study focuses on forecasting electricity consumption across distinct sectors using Autoregressive Integrate Moving Average (ARIMA) and Artificial Neural Network (ANN). The efficacy of the models is evaluated via various error metrics and compared, demonstrating the superior performance of the ANN model over ARIMA model. 2025 IEEE. -
Label Informativeness-Based Minority Oversampling in Graphs (LIMO)
Class imbalance is a pervasive issue in many realworld datasets, particularly in graph-structured data, where certain classes are significantly underrepresented. This imbalance can severely impact the performance of Graph Neural Networks (GNNs), leading to biased learning or over-fitting. The existing oversampling techniques often overlook the intrinsic properties of graphs, such as Label Informativeness (LI), which measures the amount of information a neighbor's label provides about a node's label. To address this, we propose Label Informativenessbased Minority Oversampling (LIMO), a novel algorithm that strategically oversamples minority class nodes by augmenting edges to maximize LI. This technique generates a balanced, synthetic graph that enhances GNN performance without significantly increasing data volume. Our theoretical analysis shows that the effectiveness of GNNs is directly proportional to label informativeness, with mutual information as a mediator. Additionally, we provide insights into how variations in the number of inter-class edges influence the LI by analyzing its derivative. Experimental results on various homophilous and heterophilous benchmark datasets demonstrate the effectiveness of LIMO in improving the performance of node classification for different imbalance ratios, with particularly significant improvements observed in heterophilous graph datasets. Our code is available at https://github.com/smlab-niser/limo. 2025 IEEE. -
A Pipeline for Speech-to-Text Summarization and Question Identification for Enhanced Chatbot Interactions
The rapid advancements in natural language processing provide strong support for the new potential application of integrating Google Speech Recognition API, BART, and BERT to create a full pipeline for speech recognition, text summarization and question answering without breaking human interaction. The research aims to develop such a holistic pipeline involves integrating the Google Speech Recognition API to perform speech-to-text, BART for text summarization, and finally BERT for question answering based on both the summary and original transcript. The system was tested under various criteria such as testing accuracy, real-time processing performance, and stress tests for scalability where the findings include an average of 60% text compression with BART, an 88% accuracy in BERT-based question answering, and scores indicating high user satisfaction (4.3/5). Real-time processing latency can be able to cater to interaction within 2-3 seconds and the capacity of the system has proven without performance loss during simultaneous users. The research done can practically find applications in areas like intelligent virtual assistants, customer service automation and e-learning applications that improve accessibility and user experience. 2025 IEEE. -
Reinforcement Learning-Driven Innovation Clusters: Strategic Planning for Sustainable Corporate Growth
This paper explores the role of reinforcement learning (RL) in optimizing innovation clusters to foster sustainable corporate growth. We go on to establish how RL allows organizations to optimize core performance metrics (innovation output, profit growth, sustainability impact and resource allocation efficiency), and show in dynamic datasets how a network of simulated strategic decisions were made in an innovation ecosystem. Moreover, it highlights the ability of RL to adapt to ever changing industries and implement long term strategic plans besides traditional strategic practices. The results demonstrate that RL-based methods contribute to unleashing innovation and profitabilising the companies, but also to more sustainable operations, bringing into proportion the growth and social responsibility. These results demonstrate RL as an implication tool with a strong future for optimizing corporate strategies that serves as an incentive for further innovation, translating into long-term viability and success. 2025 IEEE. -
Multilevel CNN Based Hybrid Framework for Adaptive Credit Card Fraud Detection
Credit card fraud presents a substantial problem to financial organizations, as fast changing fraudulent activities necessitate advanced detection techniques. Conventional machine learning methods frequently encounter challenges with adaptability and precision in imbalanced datasets. This study presents a multilevel CNN-based hybrid architecture that combines deep convolutional networks with traditional ensemble classifiers for adaptive credit card fraud detection. The platform includes an adaptive learning module that facilitates ongoing model upgrades, guaranteeing responsiveness to emerging fraud trends. The system, evaluated using a benchmark Kaggle dataset, attained an accuracy of 99.48%, precision of 98.76%, recall of 99.05%, F1-score of 98.90%, and AUC-ROC of 99.91%, outperforming established baseline models such as Logistic Regression, Random Forest, and XGBoost. The suggested system's capacity to integrate deep feature extraction with hybrid classifiers yields enhanced detection efficiency, reduced false positives, and improved generalization. This research enhances fraud detection by overcoming the constraints of static models, rendering it applicable for real-time financial applications and adaptable to emerging threats. 2025 IEEE. -
Fine-Tuned Deep Contextual BERT for Enhanced Aspect-based Sentiment Analysis: A Comparative Study on Laptop Reviews
Sentiment analysis entails the care full analysis, conduction of interpretation and conclusion of subjective texts even as an evaluation. In the business context, the companies' strategies towards growth makes use of both level of experience of consumers, market reach, social media, opinion and reputation of the brand. The different levels of performing the analysis includes the analysis at the document, phrase, and aspect levels. The sentiment which targets the polarity on some components of texts is often recognized by various Natural language processing (NLP) tasks for example aspect level sentiment analysis. This study presents the fine-tuned deep contextual BERT (FTDC BERT) aiming at improving the accuracy of sentiment polarization prediction. We look at different types of models including the LSTM based and the attention based and the BERT based models and where they performed on the laptop dataset. The fine-tuned and pre-trained BERT model exceeded all benchmarks and gave the most accurate work at 84.48%. This remarkable achievement testifies to the capability of the model in adapting its structure to varying degrees of sentiment contained in laptop reviews. Based on the comparative analysis, different models have different degree of success which indicates that sentiment has to be modelled separately for every set of data. This paper describes interesting areas of the future inline sentiment analysis for researchers and practitioners. 2025 IEEE. -
An Optimized Approach for Spam Message Detection Using C4.5 Classifier with Stochastic Hill Climbing and Genetic Algorithm for Feature Selection
In the mobile industry, text messaging is a popular feature that is mainly intended to make money for service providers. But spam, which is defined as unsolicited bulk messages that contain commercial content, has become a widespread problem. These spam texts are frequently used to spread phishing links or advertise goods and services in order to make money. The phone alerts the user whenever spam text messages arrive in their inbox. When the user discovers that the message is unsolicited, these unsolicited texts not only take up storage space and waste their time, but they also irritate them. Even with the development of numerous sophisticated algorithms to identify spam, users are still impacted by text message spam. Thus, the mobile sector needs to implement efficient filtering methods. The proposed study uses the C4.5 Decision Tree as the classification model and combines a Genetic Algorithm and Stochastic Hill Climbing to find optimal features in order to detect spam in text messages. This method uses metaheuristic techniques to find the best features, which are then categorized using decision trees. This hybrid model performs better than current classification methods. 2026 IEEE. -
Robust Rice Leaf Disease Detection using Advanced Preprocessing and Deep CNNs for Class Imbalance Resolution
This study addresses the growing challenges posed by plant diseases, particularly in the rice industry, which is vital for many communities. The research propose a robust framework that integrates Deep Convolutional Neural Networks (Deep CNN) with advanced preprocessing techniques to identify rice leaf diseases, including Brown Spot, Leaf Blast, Hispa, and healthy leaves. Our approach employs normalization to enhance convergence during training and data augmentation to improve model generalizability. Additionally, implement the Synthetic Minority Over-sampling Technique (SMOTE) to create synthetic samples for under-represented classes, addressing class imbalance within the dataset. Experimental results demonstrate the model's impressive accuracy, achieving 98.2% for Brown Spot, 97.5% for Leaf Blast, 94.3% for Hispa, and 96.8% for healthy leaves. Furthermore, our method outperforms established CNN architectures such as AlexNet, VGG16, and ResNet50, showcasing the effectiveness of sophisticated preprocessing in enhancing plant disease detection systems and supporting food security initiatives. 2025 IEEE. -
Data-Driven Insights into Student Performance: Benchmarking Machine Learning Models for Grade Prediction using Regression and Classification Approaches
This research explores the effectiveness of 17 machine learning models in predicting student performance across Mathematics and Portuguese datasets. The primary goal of this study was to evaluate and compare regression and classification models to identify the most accurate predictors of student grades. A range of algorithms was tested, including linear models (Linear Regression, Elastic Net, Ridge, Lasso), tree-based models (Random Forest, Gradient Boosting, CatBoost, LightGBM), and advanced techniques (Neural Networks, SVM, XGBoost, Naive Bayes, SVR). The methodology involved data preprocessing, feature engineering, and splitting data into training and test sets. Base models were implemented, followed by hyperparameter tuning to optimize performance. Metrics like RMSE, MAE, MSE, R2 (for regression), and accuracy, precision, recall, F1 score (for classification) were used to assess performance. The study found that Gradient Boosting and Elastic Net consistently outperformed other models in regression tasks, achieving the highest R2 scores. For classification, Logistic Regression proved to be the most accurate, followed by Naive Bayes. These findings provide valuable insights for model selection in educational performance prediction, establishing Gradient Boosting and Logistic Regression as benchmark models. 2025 IEEE.