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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Chandrashekaraiah, Yogeesh Ambalagere
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Secure clustering and routing – based adaptive – bald eagle search for wireless sensor networks Ranganathasharma, Roopashree Hejjaji; Chandrashekaraiah, Yogeesh Ambalagere
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3824-3832

Abstract

Wireless sensor networks (WSNs) are self-regulating networks consisting of several tiny sensor nodes for monitoring and tracking applications over extensive areas. Energy consumption and security are the two significant challenges in these networks due to their limited resources and open nature. To address these challenges and optimize energy consumption while ensuring security, this research proposes an adaptive – bald eagle search (A-BES) optimization algorithm enabled secure clustering and routing for WSNs. The A-BES algorithm selects secure cluster heads (SCHs) through several fitness functions, thereby reducing energy consumption across the nodes. Next, secure and optimal routes are chosen using A-BES to prevent malicious nodes from interfering with the communication paths and to enhance the overall network lifetime. The proposed algorithm shows significantly lower energy consumption, with values of 0.27, 0.81, 1.38, 2.27, and 3.01 J as the number of nodes increases from 100 to 300. This demonstrates a clear improvement over the existing residual energy-based data availability approach (REDAA).
Interrogative insights into depression detection via social networks and machine learning techniques Venkateshagowda, Chaithra Indavara; Ranganathasharma, Roopashree Hejjajji; Chandrashekaraiah, Yogeesh Ambalagere
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 1: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i1.pp388-396

Abstract

As users on social networks (SNs) interact with one another by exchanging information, giving feedback, finding new content, and participating in discussions; thus, generating large volumes of data each day. This data includes images, texts, videos and can be used to help the user find out how they have been doing, when they were depressed, how not to be depressed, and other similar insights. Depression is one of the most common chronic illnesses and it has emerged as a global mental health problem. But the lack of these data is incomplete, sparse and sometimes inaccurate, and so the task of diagnosing depression using automated systems is still proving a challenge. Various techniques have been used to detect depression through the years however, machine learning (ML) and deep learning (DL) techniques offer better ways. In the context of that, this study reviews state-of-the-art ML and DL approaches for the detection of depression using systematic literature review (SLR) method as well as highlight fundamental challenges in literature, which future works can focus on. We hope that this survey will provide a better understanding of these strategies for the readers and researchers in the ML and DL fields, when it comes to diagnosis of depression.
Detection and forecasting of mental health disorders using machine learning models on social media data Venkateshagowda, Chaithra Indavara; Ranganathasharma, Roopashree Hejjajji; Chandrashekaraiah, Yogeesh Ambalagere; Taranath, Narve Lakshminarayan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp672-680

Abstract

The detection and classification of depression and other mental disorders have become crucial in the modern era, particularly with the growing reliance on social media for self-expression. Existing systems often face challenges like limited prediction accuracy, difficulty forecasting future mental illnesses, and handling both clinical and non-clinical data. This study proposes a novel analytical model that not only screens individuals' current mental health status from social media content but also predicts the likelihood of future mental health issues. The proposed methodology integrates classical machine learning (ML) models, ensemble learning approaches, and pretrained models for enhanced detection and forecasting accuracy. The outcome shows that pre-trained language models accomplished maximized F1-score and overall performance significantly better than conventional ML and ensemble models. The system outperforms existing methods with a significant accuracy improvement, achieving 90.9% overall accuracy, a 7.2% improvement over traditional ML classifiers, 5.8% over ensemble models, and 11.3% over language models.
An interpretable deep learning framework for early detection of depression using hybrid architectures Venkateshagowda, Chaithra Indavara; Ranganathasharma, Roopashree Hejjajji; Chandrashekaraiah, Yogeesh Ambalagere
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp895-904

Abstract

Current techniques for detecting depression are labor-intensive and subjective, depending on clinical interviews or self-reports. There is a growing adoption of machine learning (ML) and natural language processing (NLP) to automatically identify depression in textual data. The lack of interpretability, which is essential for healthcare applications, is still a major obstacle, though. By combining convolution neural network (CNN) for feature extraction, bidirectional long short-term memory (BiLSTM) for capturing sequential dependencies, and transformer-based pre-trained language model (PTLM) for contextual understanding, this study offers an interpretable framework for early depression identification. Additionally, the system uses a novel interpretability method to guarantee transparent decision-making. The outcome of the proposed system is found to achieve 96.2% accuracy, 94.5% precision, 95.1% recall, and 94.8% F1-score, which is a significant improvement over current method. This framework acts as a useful tool for early mental health intervention.
Co-Authors Ranganathasharma, Roopashree Hejjaji Ranganathasharma, Roopashree Hejjajji Taranath, Narve Lakshminarayan Venkateshagowda, Chaithra Indavara