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p-Index From 2021 - 2026
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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control)
Anandaraj Shanthi Pichandi
Presidency University
Computer Science & IT Engineering

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Biomedical-named entity recognition using CUDA accelerated KNN algorithm Manish Bali; Anandaraj Shanthi Pichandi; Jude Hemanth Duraisamy
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 4: August 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i4.24065

Abstract

Biomedical named entity recognition (Bio-NER) is a highly complex and time-consuming research domain using natural language processing (NLP). It’s widely used in information retrieval, knowledge summarization, biomolecular event extraction, and discovery applications. This paper proposes a method for the recognition and classification of named entities in the biomedical domain using machine learning (ML) techniques. Support vector machine (SVM), decision trees (DT), K-nearest neighbor (KNN), and its kernel versions are used. However, recent advancements in programmable, massively parallel graphics processing units (GPU) hold promise in terms of increased computational capacity at a lower cost to address multi-dimensional data and time complexity. We implement a novel parallel version of KNN by porting the distance computation step on GPU using the compute unified device architecture (CUDA) and compare the performance of all the algorithms using the BioNLP/NLPBA 2004 corpus. Results demonstrate that CUDA-KNN takes full advantage of the GPU’s computational capacity and multi-leveled memory architecture, resulting in a 35× performance enhancement over the central processing unit (CPU). In a comparative study with existing research, the proposed model provides an option for a faster NER system for higher dimensionality and larger datasets as it offers balanced performance in terms of accuracy and speed-up, thus providing critical design insights into developing a robust BioNLP system.
Hybrid feature selection strategies for strong network intrusion detection Josephine Rathinam; Anandaraj Shanthi Pichandi
IAES International Journal of Artificial Intelligence (IJ-AI) 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/ijai.v15.i5.pp4366-4380

Abstract

High dimensional network traffic datasets pose great challenges to network intrusion detection systems (NIDS) due to redundant and irrelevant features, which decrease detection accuracy and increase the computational cost. Traditional feature selection methods often cannot achieve a balance between classification performance and processing efficiency. This paper proposes hybrid feature selection for network intrusion detection systems (HybFS-NIDS), a hybrid feature selection framework using filter- and wrapper-based optimization methods to detect the most significant intrusion detection features to do this. The proposed model was evaluated on the CIC-IDS2017 dataset using performance metrics such as accuracy, precision, recall, F1-score, and Matthew’s correlation coefficient (MCC). The experimental results selected 18 optimal features from the initial 78 features of the dataset and achieved 90.68% accuracy, 91.74% precision, 90.64% recall, 91.19% F1-score, and an MCC of 0.9148. The proposed method also achieved a 41.5% reduction in training time compared with the full-feature baseline model while improving the overall classification performance. The results show that HybFS-NIDS can successfully improve the efficiency of intrusion detection and fit for real-time cybersecurity applications with a large amount of network traffic data.
Optimizing cloud energy with deep reinforcement learning: an artificial bee colony-Q-learning hybrid approach Mohammed Ziaur Rahman; Anandaraj Shanthi Pichandi
IAES International Journal of Artificial Intelligence (IJ-AI) 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/ijai.v15.i5.pp4645-4655

Abstract

Load balancing in the cloud is a crucial characteristic for distributing resources from data centers. However, load balancing in a cloud environment remains a challenging issue due to the influx of numerous incoming tasks, particularly in an infrastructure as a service (IaaS) cloud environment. To address the complexity and high dimensionality of the process, machine learning (ML) techniques combined with optimization algorithms are used, which fasten the process and minimize execution time. The deep reinforcement learning (DRL) – artificial bee colony with Q-learning (ABCQ) approach is proposed to solve load balancing in a cloud environment. The agent in DRL makes intelligent decisions to handle a large number of tasks efficiently at lesser time and increasing speed. The artificial bee colony (ABC) algorithm uses a global guiding factor to initialize position updates, enhancing the searchability and performance of the algorithm. The proposed DRL-ABCQ approach optimizes scheduling and resource utilization, increases the throughput of virtual machines (VMs), and balances the load between VMs. The DRL-ABCQ approach achieves a makespan of 125.52 s, energy utilization of 99.81 kJ, and a throughput of 2.39 jobs/s, compared to existing approaches.
Energy efficient resource allocation using deep reinforcement learning with starling murmuration optimization in cloud Yogeetha Bangalore Rajanna; Anandaraj Shanthi Pichandi
IAES International Journal of Artificial Intelligence (IJ-AI) 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/ijai.v15.i5.pp4592-4601

Abstract

Cloud computing is one of the widely used technologies due to its advanced features like pay -per-use, scalability, flexibility, and many more. It initially aims at fulfilling users’ needs for accessing computing resources and purchasing cloud services as necessary. This is all done within the framework of on-demand resource sharing, which is facilitated by internet-based applications. Efficient resource allocation (RA) in the cloud includes challenges of high-dimensional state spaces and scalability, as the tasks keep increasing. To solve this problem, deep reinforcement learning (DRL) with Taylor-based optimization neighborhood starling murmuration optimization (TBONSMO) algorithm model is used for RA according to the tasks efficiently. The integration of DRL with deep neural networks enhances task scheduling by enabling adaptive and efficient RA. This approach improves the decision-making processes, optimizes energy usage as well as system performance in response to varying workloads and operational demands. The experimental results of the proposed TBONSMO method were calculated as follows: energy consumption of 30.58 kilo watt per hour and resource utilization of 99.54%, which is lesser when compared to the existing deep Q-network (DQN) approach.
Co-Authors Josephine Rathinam Jude Hemanth Duraisamy Manish Bali Mohammed Ziaur Rahman Yogeetha Bangalore Rajanna