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    Volume 2, Issue 1 (Online First) - Table of Contents
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    Cover Story: Forthcoming
    Citations: 0, 0,  0   |   Viewed: 1147, Download: 116
    Free Access | Review Article | 16 January 2025
    A Comprehensive Survey on UAV-based Data Gathering Techniques in Wireless Sensor Networks
    by
    Muhammad Aamir Khan
    ,
    Fahad Farooq
    IECE Transactions on Intelligent Systematics | Volume 2, Issue 1: 66-75, 2025 | DOI:10.62762/TIS.2025.790920
    Abstract
    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 breadth and range 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 integrat... More >
    Graphical Abstract
    A Comprehensive Survey on UAV-based Data Gathering Techniques in Wireless Sensor Networks
    Free Access | Review Article | 04 January 2025
    Futuristic Metaverse: Security and Counter Measures
    by
    Nidhi Ramolia
    ,
    Pranav Piyushbhai Tank
    ,
    Ravikumar R N
    ,
    Babar Zeb
    ,
    Manish Kumar
    ,
    Sushil Kumar Singh
    IECE Transactions on Intelligent Systematics | Volume 2, Issue 1: 49-65, 2025 | DOI:10.62762/TIS.2024.194631
    Abstract
    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 >
    Graphical Abstract
    Futuristic Metaverse: Security and Counter Measures
    Free Access | Review Article | 04 January 2025
    A Machine Learning-Based Scientometric Evaluation for Fake News Detection
    by
    Hafiz Muhammad Zeeshan
    ,
    Inam Ullah
    ,
    Furkan Yousaf
    ,
    Amin Sharafian
    ,
    Md Belal Bin Heyat
    ,
    Shazia Saqib
    ,
    Atta Ur Rahman
    IECE Transactions on Intelligent Systematics | Volume 2, Issue 1: 38-48, 2025 | DOI:10.62762/TIS.2024.564569
    Abstract
    In the modern world, disseminating false information is a problem that must be addressed, and algorithms based on machine learning are used to spot and stop the spread of incorrect information. Due to the current unregulated development of false news fabrication and dissemination, democracy is continuously under threat. Fake news may mislead individuals while influencing them because of its persuasiveness and life sciences. Using data from the Web of Science, this study undertakes a bibliometric analysis of research on the application of machine learning for fake news identification. The research underscores the need for a streamlined approach to analyze data exclusively from the Web of Scie... More >
    Graphical Abstract
    A Machine Learning-Based Scientometric Evaluation for Fake News Detection
    Free Access | Research Article | 31 December 2024
    Feature Fusion for Performance Enhancement of Text Independent Speaker Identification
    by
    Zahra Shah
    ,
    Giljin Jang
    ,
    Adil Farooq
    IECE Transactions on Intelligent Systematics | Volume 2, Issue 1: 27-37, 2024 | DOI:10.62762/TIS.2024.649374
    Abstract
    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. Specifically, four key features—pitch, intensity, spectral flux, and spectral slope—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 experimenta... More >
    Graphical Abstract
    Feature Fusion for Performance Enhancement of Text Independent Speaker Identification
    Free Access | Research Article | 27 December 2024
    Advancing Robotic Automation with Custom Sequential Deep CNN-Based Indoor Scene Recognition
    by
    Fida Hussain Dahri
    ,
    Ghulam E Mustafa Abro
    ,
    Nisar Ahmed Dahri
    ,
    Asif Ali Laghari
    ,
    Zain Anwar Ali
    IECE Transactions on Intelligent Systematics | Volume 2, Issue 1: 14-26, 2024 | DOI:10.62762/TIS.2025.613103
    Abstract
    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 picture 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 t... More >
    Graphical Abstract
    Advancing Robotic Automation with Custom Sequential Deep CNN-Based Indoor Scene Recognition
    Free Access | Research Article | 22 December 2024
    Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis
    by
    Huadong Pang
    ,
    Li Zhou
    ,
    Yiping Dong
    ,
    Peiyuan Chen
    ,
    Dian Gu
    ,
    Tianyi Lyu
    ,
    Hansong Zhang
    IECE Transactions on Intelligent Systematics | Volume 2, Issue 1: 1-13, 2024 | DOI:10.62762/TIS.2025.367320
    Abstract
    In the healthcare sector, the application of deep learning technologies has revolutionized data analysis and disease forecasting. This is particularly evident in the field of diabetes, where the deep 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 fir... More >
    Graphical Abstract
    Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis