Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
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This Author published in this journals
All Journal International Journal of Informatics and Communication Technology (IJ-ICT) International Journal of Advances in Applied Sciences Indonesian Journal of Electrical Engineering and Computer Science
Selvaraj Rajalakshmi
Botswana International University of Science and Technology
Computer Science & IT Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Authentication schemes in wireless internet of things sensor networks: a survey and comparison Pendukeni Phalaagae; Adamu Murtala Zungeru; Boyce Sigweni; Selvaraj Rajalakshmi
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1876-1888

Abstract

The proliferation of wireless sensor networks (WSNs) fuels internet of things (IoT's) rapid global development, connecting diverse devices. IoT transforms devices into intelligent entities delivering exceptional services. This work addresses IoT authentication gaps through a comprehensive survey, analyzing recent works and exploring techniques in various applications. It includes a comparative analysis of authentication schemes, evaluating Bi-Phase authentication scheme (BAS) in WSNs. BAS outperforms sensor protocol for information via negotiation (SPIN), broadcast session key protocol (BROSK), and localized encryption and authentication protocol (LEAP), resulting in lower energy consumption and higher efficiency. With energy efficiency at 60 Kb/J for 25 nodes, BAS focuses on power optimization and lightweight security measures, reducing energy consumption, maximizing efficiency, and extending WSN lifespan. The evaluation, conducted using MATLAB/Simulink, demonstrates BAS's superiority, achieving 10 J, 12 J, 14 J, and 15 J energy consumption for 25 nodes during simulation, showcasing its effectiveness and future potential in advancing IoT authentication.
Machine learning centered energy optimization in mobile edge computing: a review Chandapiwa Mokgethi; Tshiamo Sigwele; Kabo Clifford Bhende; Aone Maenge; Selvaraj Rajalakshmi
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp465-476

Abstract

Current literature reviews on machine learning-based approaches for mobile edge computing (MEC) energy optimization often lack in-depth gap analysis and fail to identify trends or offer actionable insights. Most focus narrowly on comparing MEC frameworks without critically evaluating or benchmarking prior research. This review contributes by addressings these gaps via analysis of existing reviews and related studies, with a focus on ML models, research objectives, evaluation metrics, datasets, tools, and gap identification. The review method follows a systematic literature review (SLR) using the PRISMA framework for transparency and reproducibility. Key findings reveal persistent challenges in energy consumption, computational overhead, cost, and poor performance in accuracy, QoS, latency, scalability, and carbon footprint. Deep reinforcement learning (DRL) emerges as the most commonly used model (55%), while TensorFlow (35%) is the most adopted tool, valued for its flexibility and robust community support. The AudioSet dataset is frequently used (28%) due to its compatibility. However, methodology limitations include dependency on study quality and exclusion of grey literature, context sensitivity. The review concludes by recommending advanced solutions such as serverless computing, liquid cooling, containerization, software-defined power, quantum computing, and blockchain to drive future MEC energy optimization.
Adaptive sentiment analysis for stock markets using deep learning Talent Mawere; Selvaraj Rajalakshmi; Venu Madhav Kuthadi; Othlapile Dinekanyane
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp416-426

Abstract

Stock markets are highly volatile, making price prediction very difficult. One of the factors influencing the volatility of financial markets is rapidly changing news sentiment. This study presents a novel adaptive deep learning (DL) framework for sentiment analysis with concept drift capabilities. The proposed model combines convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and attention mechanisms in its processing architecture. The model inputs preprocessed news headlines into both the CNN and BiLSTM-Attention networks to extract local features, model contextual dependencies, and prioritizes important sentiment cues in its prediction mechanism. We use FastText and Word2Vec for word embeddings, while incremental learning is used to manage concept drift. One key advantage of handling concept drift is that the model can continuously learn new patterns in data streams without needing to fully retrain the model. The model is validated on a curated dataset from various sources with superior performance across all metrics, like accuracy (0.9753) and an F1-score (0.98). It significantly outperforms benchmarks like distilled bidirectional encoder representations from transformers (DistilBERT), LSTM, and valence aware dictionary and sentiment reasoner (VADER). A run of ten iterations validated that the real-time pipeline did not exceed 200 ms in processing and classifying headlines. This signifies the practical viability of our model in fintech applications such as algorithmic trading and risk management.
Co-Authors Adamu Murtala Zungeru Aone Maenge Boyce Sigweni Chandapiwa Mokgethi Kabo Clifford Bhende Othlapile Dinekanyane Pendukeni Phalaagae Talent Mawere Tshiamo Sigwele Venu Madhav Kuthadi