In the recent era of communication, wireless sensor networks (WSNs) emerged as a demanding area of study due to their communication capacity especially in the application of Internet of things (IoT). As the scale and coverage of networks expand quickly, it becomes necessary to sense, transmit, and interpret the massive amount of data in IoT devices. WSN becomes even more beneficial and popular among the researchers when it integrates with unmanned aerial vehicles (UAVs) to increase the life span and establish a reliable communication between itself and Network Control Centre in an efficient way. Memory problems and network data transmission processing times are also addressed by this integra... More >

This paper presents a comprehensive analysis of the security and privacy challenges in the Metaverse, introducing a novel framework for evaluating and addressing these emerging threats. Our research makes three key contributions: (1) a systematic classification of Metaverse-specific security vulnerabilities across interconnected virtual and physical environments, (2) a framework for assessing privacy risks in AR/VR-enabled social interactions, and (3) targeted solutions for securing blockchain-based digital assets and identity management in the Metaverse. Our analysis highlights how traditional cybersecurity approaches must evolve to address the unique challenges posed by the fusion of physi... More >

Fake news detection has emerged as a critical challenge in the modern information ecosystem, where the rapid proliferation of misinformation threatens democratic processes, public health, and societal stability. Machine learning (ML)-based approaches have demonstrated significant promise in automatically identifying and classifying misleading information across diverse platforms. This study presents a comprehensive scientometric and systematic review of ML-based fake news detection research, drawing on 649 peer-reviewed articles indexed in the Web of Science database (1991--2023). Using bibliometric tools including R-Bibliometrix and VOSviewer, we systematically evaluate publication trends,... More >

Speaker identification systems have gained significant attention due to their potential applications in security and personalized systems. This study evaluates the performance of various time- and frequency-domain physical features for text-independent speaker identification. Four key features—pitch (P), intensity (I), spectral flux (SF), and spectral slope (SS)—were examined along with their statistical variations (minimum, maximum, and average values). These features were fused with log power spectral features and trained using a Convolutional Neural Network (CNN). The goal was to identify the most effective feature combinations for improving speaker identification accuracy. The experi... More >

Indoor scene recognition poses considerable hurdles, especially in cluttered and visually analogous settings. Although several current recognition systems perform well in outside settings, there is a distinct necessity for enhanced precision in inside scene detection, particularly for robotics and automation applications. This research presents a revolutionary deep Convolutional Neural Network (CNN) model tailored with bespoke parameters to improve indoor image comprehension. Our proprietary dataset consists of seven unique interior scene types, and our deep CNN model is trained to attain excellent accuracy in classification tasks. The model exhibited exceptional performance, achieving a tra... More >

In the healthcare sector, the application of deep learning technologies has revolutionized data analysis and disease forecasting. This is particularly evident in diabetes research, where in-depth analysis of Electronic Health Records (EHR) has unlocked new opportunities for early detection and effective intervention strategies. Our research presents an innovative model that synergizes the capabilities of Bidirectional Long Short-Term Memory Networks-Conditional Random Field (BiLSTM-CRF) with a fusion of XGBoost and Logistic Regression. This model is designed to enhance the accuracy of diabetes risk prediction by conducting an in-depth analysis of electronic medical records data. The first p... More >

The increasing prevalence of fake news on social media has become a significant challenge in today’s digital landscape. This paper proposes a hybrid framework for fake news detection, combining Natural Language Processing (NLP) techniques and machine learning algorithms. Using Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction, and classifiers such as Logistic Regression (LR), Naïve Bayes (NB), and Support Vector Machines (SVM), the model integrates Maximum Likelihood Estimation (MLE) with Logistic Regression to achieve 95% accuracy and 92% precision on a Kaggle dataset. The results highlight the potential of combining statistical and NLP approaches to improve fake... More >

Intelligent project management systems rely on high-quality historical data for accurate automated decision-making, yet missing data in software project repositories remains a persistent challenge that degrades intelligent estimation performance. This study proposes an Intelligent Decision Support Framework (IDSF) for software development effort estimation (SDEE) that integrates Multiple Imputation (MI) as a critical data quality enhancement layer within the Analogy-Based Effort Estimation (ABEE) model. The framework is evaluated on the ISBSG dataset by systematically comparing six imputation strategies. Results demonstrate that the MI-enhanced framework achieves competitive and more stable... More >