Forthcoming and Online First Articles

International Journal of Bio-Inspired Computation

International Journal of Bio-Inspired Computation (IJBIC)

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International Journal of Bio-Inspired Computation (5 papers in press)

Regular Issues

  • DAIS: Deep Artificial Immune System for Intrusion Detection in IoT Ecosystem   Order a copy of this article
    by Vineeta Soni, Devershi Pallavi Bhatt, Narendra Singh Yadav, Siddhant Saxena 
    Abstract: IoT has risen rapidly over the past decade. Massive data flow in a dynamic, decentralised environment threatens data security. This study addresses machine learning issues in IoT intrusion detection. DAIS is a bio-inspired artificial immune system architecture. The DAIS technique replicates the innate immunity and self-adaptive immune processes, which secures the dynamic IoT environment from existing and novel zero-day assaults. The proposed DAIS architecture outperforms existing data-centric intrusion detection approaches and achieves benchmark accuracy of 99.87% on the MQTTset dataset and 87.64% on the imbalanced KDD-CUP-99 dataset. This means the proposed architecture is more robust to real-world attack scenarios and provides an end-to-end pipeline to secure the dynamic and evolving IoT network ecosystem.
    Keywords: artificial immune systems; AIS; machine learning; intrusion detection; IoT networks; data security; statistics; neural networks.
    DOI: 10.1504/IJBIC.2023.10059828
     
  • Dual Interactive Wasserstein Generative Adversarial Network optimized with Remora optimization algorithm based Lung Disease Detection using Chest X-Ray Images   Order a copy of this article
    by Beaulah David, Mohamed Shameem P, K. Ravikumar, G. Simi Margarat 
    Abstract: Numerous prevailing approaches on lung disease identification are exploited with deep learning, but it does not precisely categorise the lung disease and correspondingly it takes high computation time. To engulf these complications, dual interactive Wasserstein generative adversarial network optimised with remora optimisation algorithm-based lung disease detection with chest X-ray images (DIWGAN-ROA-LDD-CXRI) is proposed for classifying COVID-19, normal and pneumonia lung diseases. Initially, the chest X-ray images are gathered via the dataset of chest X-ray (COVID-19 and pneumonia). The extracted features are given to DIWGAN-ROA for effectively categorise the chest X-ray image from COVID-19, normal and pneumonia. The proposed DIWGAN-ROA-LDD-CXRI approach is activated in Python. The performance of the proposed DIWGAN-ROA-LDD-CXRI approach attains 14.54%, 21.56%, 23.15% and 15.45% higher accuracy, 27.33%, 17.71%, 22.22% and 23.37% lower computation time and 21.11%, 28.89%, 29.95% and 28.14% higher AUC value compared with existing methods.
    Keywords: chest X-ray images; term frequency-inverse document frequency; dual interactive Wasserstein generative adversarial network; remora optimisation algorithm; ROA.
    DOI: 10.1504/IJBIC.2023.10061111
     
  • Enhanced Placement and Migration of Virtual Machines in Heterogeneous Cloud Data Center   Order a copy of this article
    by Amarendhar Reddy, Ravindranath K 
    Abstract: Data centres have become an indispensable part of modern computing infrastructures. It became necessary to manage cloud resources efficiently to reduce those ever increasing power demands of data centres. Dynamic consolidation of virtual machines (VMs) in a data centre is an effective way to map workloads on to servers in a way that they require the least resources and reduces the energy consumption. In this paper, we propose a novel host overload detection algorithm using known CPU utilization and a given state configuration. We propose to use Z-Score for host overload detection which gives an assessment of the degree to which a processor is operating off-target and it is a way to compare the current CPU load to a 'normal' usage. We also design a novel VM selection policy, considering various resource utilization factors to select the VMs. In addition, we propose an improved version of JAYA approach by incorporating levy flights for VM placement that improves the exploration capabilities and minimize the energy consumption of a data centre. We conducted simulations using CloudSim and observed that our approach reduces power consumption by 10% compared to state-of-the-art approaches.
    Keywords: Data Center; Virtual Machine; Placement; Overload Detection; Selection; Optimization; Energy Efficiency; Cloud Computing; Resource Management;.
    DOI: 10.1504/IJBIC.2023.10061221
     
  • Swap Operator based Grey Wolf Optimizer for Materialized View Selection Problem   Order a copy of this article
    by Amit Kumar 
    Abstract: Grey wolf optimiser (GWO) is a novel biological intelligence algorithm that primarily simulates the natural leadership structure and hunting behaviour of grey wolves. It has the ability to decrease the operating time for higher dimensional data by partitioning the large, complicated problems into smaller sub problems and distributing the subsets of operations to each agent. Therefore, it is widely employed in a variety of time-consuming tasks, including NP-hard problems. Materialised view selection (MVS) is an NP-hard discrete optimisation problem in the design process of a data warehouse that significantly speeds up query processing. Therefore, in this paper, GWO has been discretised using swap operators and swap sequence operators to address the MVS problem. Experimentally, it is observed that the proposed swap operator-based grey wolf optimiser (SOGWO) algorithm selects better quality views for materialisation than those selected using well-known metaheuristic algorithms over a number of view selection problem instances.
    Keywords: grey wolf optimiser; GWO; data warehouse; materialised view; view selection; swap operator.
    DOI: 10.1504/IJBIC.2023.10062102
     
  • Integrating Convolution and Transformer for Enhanced Diabetic Retinopathy Detection   Order a copy of this article
    by Xinrong Cao, Jie Lin, Xiao-Zhi Gao, Zuoyong Li 
    Abstract: Diabetic retinopathy (DR) is a common diabetes complication that can cause irreversible blindness. Deep learning models have been developed to automatically classify the severity of retinopathy. However, these methods face challenges like a lack of long-range connections, weak interactions between images, and mismatches between lesion details and receptive fields, leading to accuracy issues. In our research, we propose a deep learning model with three main aspects. Firstly, a transformer structure is incorporated into a convolutional neural network to effectively utilise both local and long-range information. Secondly, the disease details are aggregated from multiple images before applying self-attention to improve inter-image interactions and reduce overfitting. Lastly, an attention-based approach is proposed to filter information from different stages of feature maps and adaptively capture lesion-related details. Our experiments achieved a 5-class accuracy of 85.96% on the APTOS dataset and a 2-class accuracy of 95.33% on the Messidor dataset, surpassing recent methods.
    Keywords: diabetic retinopathy; DR; convolutional neural network; transformer; cross attention; deep feature aggregation.
    DOI: 10.1504/IJBIC.2023.10062454