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All Journal Bulletin of Electrical Engineering and Informatics
D. Vetrithangam
Chandigarh University
Electrical & Electronics Engineering

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Machine learning for adaptive intrusion detection system towards secure internet of things enabled healthcare systems Omar Isam Al Mrayat; Malik Jawarneh; D. Vetrithangam; Pallavi Sunil Bangare; Khaled Abdulwahab Zaid Alyamani; Reema Goyal; Sugumar Rajendran; Shubham Kumar; Yuvaraj Kunnathur Periyasamy; Abhishek Raghuvanshi
Bulletin of Electrical Engineering and Informatics Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i5.10512

Abstract

Internet of medical things (IoMT) ties medical sensors, actuators, wearable, and communication technologies to provide an improved healthcare delivery process by means of constant patient monitoring and decision-making. The trend has resulted in an increase in its use because of the increasing prevalence of chronic diseases and aging, and the need to have cost-effective healthcare services. Nonetheless, the networks provided by the IoMT are prone to cyberattacks under the considerable threat as both process huge volumes of sensitive health data, and do not have the resources to do it. In this paper, the author suggests a new adaptive intrusion detection system (IDS) as a way to enhance the security of IoMT-enabled healthcare environments. The proposed model the pre-treats NSL-KDD dataset, filling gaps and transmitting nominal values. Particle swarm optimization (PSO) is used in the optimal selection of features and artificial neural network (ANN), K-nearest neighbors (KNN), and random forest (RF) classifiers are used in intrusion detection. Experimental performance proves that the PSO-ANN model can perform better with a high accuracy of 99.54%, sensitivity of 99.31% and specificity of 97.32% on average with high protection of intelligent healthcare systems.
TF-IDF, chi-square, and ANN machine learning techniques for fake news anomaly detection in online social networks Venkata Ashok Kumar Boyina; N. Sabiyath Fatima; Omar Isam Al Mrayat; Pallavi S. Bangare; Kasula Venkata Durga Kiran; D. Vetrithangam; Sunil L. Bangare; Mohan Awasthy; Bhaskar Roy; Batyrkhan Omarov; Guna Sekhar Sajja
Bulletin of Electrical Engineering and Informatics Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i5.10532

Abstract

Platforms such as Facebook, WhatsApp, Twitter, and Telegram have a substantial influence on the dissemination of information in modern society. Numerous individuals rely on them without verifying the veracity of their information or the sources of their knowledge. False information is referred to as "Fake News" and is disseminated through both traditional and non-traditional media channels including social media. Contemporary media consumers uncritically embrace the news they read online, rather than taking the time to critically assess it. This method has led to a heightened trust in internet content, which has facilitated the spread of misinformation. This article describes a machine learning (ML)-based approach to classifying and detecting fake news anomalies in online social media data. This framework accepts social media data as input. Features are chosen using the term frequency identification term frequency–inverse document frequency (TF-IDF) and chi-square tests. The categorization model is then built utilizing approaches such as artificial neural network (ANN), support vector machine (SVM), and random forest (RF). The classification model is trained and tested on a preprocessed data set. Finally, fake news is discovered, and the suggested framework is evaluated using measures such as accuracy, precision, recall, and F1-score. ANN is performing better for fake news classification and detection.
Co-Authors Abhishek Raghuvanshi Batyrkhan Omarov Bhaskar Roy Guna Sekhar Sajja Kasula Venkata Durga Kiran Khaled Abdulwahab Zaid Alyamani Malik Jawarneh Mohan Awasthy N. Sabiyath Fatima Omar Isam Al Mrayat Pallavi S. Bangare Pallavi Sunil Bangare Reema Goyal Shubham Kumar Sugumar Rajendran Sunil L. Bangare Venkata Ashok Kumar Boyina Yuvaraj Kunnathur Periyasamy