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 (14 papers in press)

Regular Issues

  • 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
     
  • Intelligent MPPT for Photovoltaic Panels on Grid-connected Inverter System using Hybrid Meta-Heuristic Algorithm   Order a copy of this article
    by Sebi N.P.  
    Abstract: This research work presents a novel hybrid meta-heuristic algorithm-based MPPT controller for efficiently tracking the maximum power under all weather conditions. The experimentation was conducted with the connection of direct current (DC) to alternating current (AC) single-phase full bridge inverter and optimised fractional order proportional integral derivative (FoPID) controller. The integrated algorithm named crow electric fish search optimisation (CEFSO) is utilised for optimising the global maximum power point (GMPP) of the MPPT. The major scope of optimising this constraint is to attain higher energy from the PV system. The proposed CEFSO-MPPT shows superior efficiency regarding faster convergence at the GMPP and MPPT. The suggested MPPT technique has shown its performance concerning higher efficiency, lower computational burden, and faster MPP tracking while analysing the existing models.
    Keywords: photovoltaic panels on grid-connected inverter system; DC-AC single-phase full-bridge inverter; maximum power point tracking; MPPT; optimised fractional proportional integral derivative controller.
    DOI: 10.1504/IJBIC.2023.10062802
     
  • Determinate Node Selection for Semi-supervised Classification Oriented Graph Convolutional Networks   Order a copy of this article
    by Yao Xiao, Ji Xu, Yang Jing, Li Shaobo, Guoying Wang 
    Abstract: Graph convolutional networks (GCNs) have been proved successful in the field of semi-supervised node classification by extracting structural information from graph data. However, the random selection of labelled nodes used in GCNs may lead to unstable generalisation performance of GCNs. In this paper, we propose an efficient method for the deterministic selection of labelled nodes: the determinate node selection (DNS) algorithm. The DNS algorithm identifies two categories of representative nodes in the graph through structural analysis of the leading tree information granules: typical nodes and divergent nodes. These labelled nodes are selected by exploring the structure of the graph and determining the ability of the nodes to represent the distribution of data within the graph. The DNS algorithm can be applied quite simply on GCNs, and a wide range of semi-supervised graph neural network models for node classification tasks. Through extensive experimentation, we have demonstrated that the incorporation of the DNS algorithm leads to a remarkable improvement in the average accuracy of the model and a significant decrease in the standard deviation simultaneously, as compared to the vanilla method without a DNS module.
    Keywords: graph convolutional networks; granular computing; semi-supervised learning; node classification.
    DOI: 10.1504/IJBIC.2024.10062817
     
  • A Holistic Approach to Early Congenital Heart Disease Detection in Rural Neonates: Bridging the Gap in Postnatal Care   Order a copy of this article
    by Rishika Anand, S.R.N. Reddy, Dinesh Kumar Yadav 
    Abstract: This research seeks to combat the alarming neonatal mortality rates attributed to undiagnosed congenital heart disease (CHD) in underserved rural regions, which often lack the necessary medical equipment and treatments. To address this pressing issue, we propose the development of an early detection system capable of identifying CHD in infants, thus facilitating timely intervention and enhancing postnatal care. Our study advocates an integrated approach that comprehensively analyses various vital parameters, including blood pressure, ECG, SpO2, body temperature, and heart rate in newborns, ensuring the accurate detection of CHD. The primary objective is to enhance the support and guidance provided to families in rural areas concerning postnatal care, prognosis, and treatment strategies. To gauge the effectiveness of our approach, we will compare it with existing techniques and evaluate its precision. The outcomes of this research hold the potential to significantly diminish neonatal mortality rates in these underserved regions.
    Keywords: machine learning; deep learning; congenital heart disease; CHD; early detection.
    DOI: 10.1504/IJBIC.2024.10062935
     
  • Fitness Function with Two Rankings Embedded in a Push and Pull Search Framework for Constrained Multi-objective Optimisation Problems   Order a copy of this article
    by Kangshun Li, Jiaxin Xu, Shumin Xie, Hui Wang 
    Abstract: Optimizing objectives and satisfying constraints present significant challenges in solving constrained multi-objective optimisation problems. In this paper, we propose an algorithm that incorporates the push-and-pull search framework and a two-ranking fitness function named ToR-PPS. The algorithm is divided into three stages: the push stage, transitional stage, and pull stage. In the push stage, the population is directed toward the unconstrained Pareto front, without consideration of constraints. In the transitional stage, a diversity expansion strategy is proposed to optimise the diversity of the population. In the pull stage, the fitness function with two rankings is utilised to pull the population toward the constrained Pareto front. Experiments are conducted to compare the algorithm with five state-of-the-art constrained multi-objective optimisation evolutionary algorithms on three benchmark suites. The results clearly illustrate the superiority and efficiency of the algorithm.
    Keywords: constrained multi-objective optimization problems; evolutionary algorithms; push and pull search framework; transitional stage; diversity.
    DOI: 10.1504/IJBIC.2024.10062987
     
  • Research on micro defect recognition based on deep learning   Order a copy of this article
    by Donghong Yang, Yude He 
    Abstract: To improve the overall recognition accuracy for micro defects, magnetic tile defect images are taken as the research object, and a defect image recognition method based on deep learning is proposed. Using MobileNetV3 network as the basic model, the number of deep convolution and the number of channels are trimmed. Then, mish function is used to replace h-swish function as the activation function, and the training speed and recognition accuracy of the model are improved, thus realising efficient and accurate recognition of micro defect images. The simulation results show that compared with the recognition methods standard MobileNetV3 network and other classification models faster R-CNN and EfficientNet, the proposed method performs better in terms of accuracy and F1 value, reaching 99.59% and 98.75%, respectively. The proposed method can recognise the defect image more accurately, and has the advantages of high inference speed, low parameter quantity and low computational cost.
    Keywords: mage recognition; image segmentation; deep learning; MobileNetV3 network.
    DOI: 10.1504/IJBIC.2024.10063158
     
  • Improved Harmonic Spectral Envelop Extraction for Singer Classification with Hybridised Model   Order a copy of this article
    by Balachandra Kumaraswamy  
    Abstract: The singing voice has an effect on humans with the addition of expressions, lyrics, and instruments. It is easier for human beings to distinguish the singing tone of voice from a specified auditory clip owing to an individual's perceptual tools and audible physiology. On the other, without human intervention, it is not simpler to identify non-vocal portions, vocal portions, feelings, and singers from the related signal owing to intrinsic complications. This proposed a new singer classification mechanism with four stages: pre-processing, vocal segmentation, feature extraction, and classification. Initially, first stage, an improved convolutional neural network (CNN) is deployed for the segmentation of the vocal part. Further, features like zero crossing rate (ZCR), Mel-frequency cepstral coefficients (MFCCs), vibration estimation and improved harmonic spectral envelope are derived to bidirectional gated recurrent unit (BI-GRU) and long short-term memory (LSTM). The results from LSTM and BI-GRU are median and the final result is attained.
    Keywords: singer classification; zero crossing rate; ZCR; convolutional neural network; improved CNN; bidirectional gated recurrent unit; BI-GRU; long short-term memory; LSTM.
    DOI: 10.1504/IJBIC.2023.10063534
     
  • EEG-based emotion recognition via Improved Evolutionary Convolutional Neural Network   Order a copy of this article
    by Lexiang Guo, Nan Li, Tian Zhang 
    Abstract: Deep learning has emerged in many practical applications, such as vascular segmentation, fault diagnosis, and human detection. More recently, convolutional neural networks (CNNs), representative techniques of deep learning, have been used to solve emotion recognition. However, the current design of CNNs for emotion recognition is highly dependent on domain knowledge and needs a large amount of trial and error. For this reason, an evolutionary CNN framework is developed to automatically find network architecture for EEG-based emotion recognition. Specifically, we firstly design a search space based on three advanced network basic units. Based on this, a flexible variable-length encoding is proposed and the corresponding reproduction operators (i.e., crossover and mutation) are designed. To reduce search overhead, this paper proposes an acceleration strategy based on the similarity metric for population memory. A series of experimental results show that the architecture by ECNN-ER method achieves higher accuracy (96.47%) compared to the state-of-the-art results (i.e., DARTS-PV) on the DEAP dataset, as well as competitive results (accuracy = 95.78%) on the DREAMER dataset.
    Keywords: neural architecture search; evolutionary computation; population memory; emotion recognition; convolutional neural network; CNN.
    DOI: 10.1504/IJBIC.2024.10064033
     
  • Bio-inspired Optimization Algorithms in Medical Image Segmentation: A Review   Order a copy of this article
    by Tian Zhang, Ping Zhou, Shenghan Zhang, Shi Cheng, Lianbo Ma, Huiyan Jiang, Yu-Dong Yao 
    Abstract: Medical image segmentation (MIS) is a primary task in medical image processing, with a great application prospect in medical image analysis and clinical diagnosis and treatment. However, MIS becomes a challenge due to the noisy imaging process of medical imaging devices and the complexity of medical images. Against this backdrop, the broad success of bio-inspired optimisation algorithms (BIOAs) has prompted the development of new MIS approaches leveraging BIOAs. As the first review of BIOAs for MIS applications, we present a comprehensive review of this recent literature, including genetic algorithm, particle swarm optimisation, ant colony optimisation, and artificial bee colony for blood vessel, organ, and tumour segmentation. We investigate the image modality and datasets that are used, discuss the application status of the four algorithms in MIS and address further research directions considering the advantages and disadvantages of each algorithm.
    Keywords: bio-inspired optimisation; genetic algorithm; particle swarm optimisation; PSO; ant colony optimisation; ACO; artificial bee colony; ABC; medical image segmentation; MIS.
    DOI: 10.1504/IJBIC.2024.10064036
     
  • Research on estimation of permeability coefficients in microbial geotechnical soils based on data-driven models   Order a copy of this article
    by Mayao Cheng, Linsheng Chen 
    Abstract: Microbial geotechnical soil permeability coefficient estimation prediction is extremely valuable for the development of soil engineering. The study proposes an integrated data-driven model combining three base learners, RVM, ANFIS and iTLBO-ELM, assigning corresponding weights to each base learner through the PLS integrated model combination model, and applying the model to the prediction of microbial geotechnical soil permeability coefficient estimation. The MAE of the proposed integrated model has lower values compared to the single model, but the two metrics MAPE and RMSE are not the lowest; however, the integrated model outperforms both iTLBO-ELM and RVM in terms of MAPE, and the estimated predictions of permeability coefficients for November data are better than those for May. For iTLBO-ELM and RVM, the MAPE of PLS decreased by 5.51% and 1.56% respectively in May and 3.46% and 1.24% respectively in October. The integrated data-driven model proposed in the study can effectively achieve the estimated prediction of microbial geotechnical soil permeability coefficients and facilitate the intelligent acquisition of engineering permeability coefficients.
    Keywords: data-driven models; microorganisms; geotechnical land; permeability coefficients.
    DOI: 10.1504/IJBIC.2024.10064267
     
  • Dual Model Surrogate-assist Evolutionary Algorithm for Expensive Multi-Objective Optimisation   Order a copy of this article
    by Songyi Xiao, Wenjun Wang 
    Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) efficiently solve expensive optimisation problems by employing surrogate models to identify promising solutions. However, the surrogate model's weak performance in a limited sample prevents accurate prediction of solution fitness and the identification of promising solutions. To overcome these limitations, we propose a dual model-based surrogate-assisted evolutionary algorithm (DM-SAEA). The dual model consists of a Gaussian model used as the global model and a pairwise ranker employed as the local model. Meanwhile, an offspring selection strategy is proposed to select promising solutions based on the cooperation of dual models. Moreover, a dynamic fitness function is developed based on the Pareto rank and penalty boundary intersection to enhance the discriminatory quality of the solutions. The experimental results demonstrate that the proposed DA-SAEA outperforms current evolutionary algorithms in addressing various expensive multi-objective test problems.
    Keywords: dual surrogate model; expensive multi-objective problem; pairwise ranker; offspring selection.
    DOI: 10.1504/IJBIC.2024.10064327
     
  • Adaptive bald eagle search algorithm with elite swarm guiding and population memory crossover   Order a copy of this article
    by Dongmei Zhang, Xiaolei Liang, Yulian Cao, Mengdi Zhang, Di Xiao 
    Abstract: Bald eagle search (BES) algorithm is easy to fall into local optimums and the population diversity declines rapidly. To address the problems, an adaptive bald eagle search (ABES) algorithm with elite swarm guiding and population memory crossover is proposed. At the selecting stage, an elite population information is provided to replace a single information to guide location updating. At the searching stage, a population memory crossover strategy is proposed. The swooping stage is deleted in ABES due to search redundancy. Furthermore, the key parameters of ABES are designed to adjust adaptively to improve the population search ability. Finally, the test suite of CEC-2013 and three real-world engineering design problems are taken to test the performance of ABES, compared with seven state-of-art algorithms. The experimental results show that ABES has a much better comprehensive performance on convergence and local search capability than other selected algorithms.
    Keywords: bald eagle search algorithm; elite swarm guidance; population memory; crossover recombination; adaptive.
    DOI: 10.1504/IJBIC.2024.10064329
     
  • Multispectral and Hyperspectral Image Fusion: A Systematic Analysis and Review with the State of Art Techniques   Order a copy of this article
    by Balajee Maram, Suresh Kumar K., Veerraju Gampala 
    Abstract: The fusion of multispectral images (MSI) and hyperspectral images (HSI) has been acknowledged as a promising method for performing HSI-MSI fusion, which is also an essential part of the precise recognition and cataloguing of the underlying materials. However, the HSI-MSI fusion needs high resolution images to perform precise analysis and decision-making. Numerous techniques were devised in the prior works that employed image fusion using HSI and MSI. This paper presents a complete survey of 80 papers using HSI-MSI fusion methodologies, which involve pan sharpening, subspace, artificial intelligence, deep learning, and hybrid models. In addition, thorough investigations are performed based on the year of publication, adapted methodology, datasets used, implementation tool, evaluation metrics, and values of evaluation metrics. Finally, the issues of existing methods and the research gaps considering conventional HSI-MSI fusion schemes are elaborated to obtain improved contributions for devising significant HSI-MSI fusion techniques.
    Keywords: image fusion; deep learning; multispectral images; MSI; hyperspectral images; HSI; pansharpening.
    DOI: 10.1504/IJBIC.2024.10064388