International Journal of Bio-Inspired Computation (32 papers in press)

Regular Issues

• Pheromone-Inspired Multiple Moving Targets Search Method for Swarm Unmanned Aerial Vehicles in Environments with Unknown Obstacles  
  by Mao Wang, Shaowu Zhou, Hongqiang Zhang, Lianghong Wu 
  Abstract: A multiple moving targets search problem for swarm UAVs in environments with unknown obstacles is studied. The search is divided into roaming search and collaborative search; the multitarget search algorithm consists of task allocation, roaming search, collaborative search and obstacle avoidance. To convert between collaborative search and roaming search, a distance-based dynamic task allocation strategy is proposed. A confidence area pheromone for roaming search is proposed to reduce the repeated search times conducted in the same areas. Probabilistic finite PSO is proposed to adapt to search for moving targets in collaborative search. Furthermore, a boundary scanning-based obstacle avoidance strategy is improved to achieve efficient obstacle avoidance for UAVs in a grid environment. Based on the above, a multiple moving-target search algorithm mode is constructed. This mode shows better performance than existing methods as verified through simulation experiments, and provides a helpful alternative in postdisaster search, and other search fields.
  Keywords: swarm unmanned aerial vehicles; multiple moving targets search; confidence area pheromone; probabilistic finite particle swarm optimisation; PFPSO.
  DOI: 10.1504/IJBIC.2024.10065092
   
  
• Double Fuzzy Clustering Driven Context Neural Network Optimised with Chimp Optimisation Algorithm for Movie Rating Recommendation system  
  by K. Krishnaveni, S. Siva Ranjani 
  Abstract: This paper proposes a pioneering approach called double fuzzy clustering driven context neural network optimised by chimp optimisation algorithm for movie rating recommendation (DFCCNN-COA-MRR). Motivated by the need to enhance recommendation accuracy and mitigate cold start issues, this model integrates double fuzzy clustering with context-aware neural network architecture, bolstered by the chimp optimisation algorithm for weight parameter optimisation. Leveraging the MovieLens 100k dataset, feature extraction and clustering are conducted to form contextual clusters, enabling more precise recommendations. The performance of the proposed DFCCNN-COA-MRR algorithm attains 33.01%, 37.82% and 36.73% high accuracy, 1.16%, 5.07% and 2.71% lower error rate and 32.92%, 35.65% and 33.15% better precision comparing to the existing methods like DRPRA-MRR, EGJSM-CgS-MRR and CAR-RA-MRR respectively. Through this work, contribute a novel recommendation model that successfully addresses key challenges in collaborative filtering, thereby advancing state-of-the-art in recommendation system research.
  Keywords: grapheme-based anisotropic polarisation meta-filter; force-invariant improved feature extraction method; double fuzzy clustering driven context neural networks; chimp optimisation algorithm.
  DOI: 10.1504/IJBIC.2024.10066576
   
  
• Attention Segmental Recurrent Neural Network Optimised with Sheep Flock Optimisation based Intrusion Detection Framework for Securing IoT  
  by M. Ramkumar Raja, P.J. Sathish Kumar, Jayaraj V, Krishnan Somasundaram 
  Abstract: This manuscript proposes an Attention Segmental Recurrent Neural Network (ASRNN) optimized with Sheep Flock Optimization based Intrusion Detection Scheme for securing internet of things (IoT) environment. Initially, the data is fed to preprocessing, wherein, the redundancy eradication and missing value replacements are performed by random forest and local least squares (LLS). Afterward, pre-processing data is supplied to the feature selection to select optimal features. The Correlation feature selection based processing of feature selection is done. The selected features are fed to Attention Segmental Recurrent Neural Network, which categorizes the data as normal or anomalies. Finally, Sheep Flock Optimization (SFO) is considered to optimize the ASRNN. The simulation performance of the proposed technique attains better accuracy 20.56%, 18.67%, 23.77%, 38.45%, 22.75%, 36.45%, higher precision 42.36%, 22.15%, 56.45%, 22.03%, 28.63%, and 21.36% compared with the existing methods.
  Keywords: Wireless networks; Feature selection; Sheep Flock Optimization; Attention Segmental Recurrent Neural Network; Intrusion detection systems.
  DOI: 10.1504/IJBIC.2024.10066580
   
  
• Identification of Primary Central Nervous System Lymphoma from High-Grade Glioma based on a 18F-FDG PET/CT Radiomics Nomogram Compared with Deep Learning: a Multicentre Study  
  by Xin Jin, Yujing Zhou, Lili Qu, Jingtao Wang, Shoumei Yan, Kaiyue Li, Hang Zhou, Li Ma, Xin Li 
  Abstract: Diagnosing central nervous system space-occupying lesions still depend on stereotactic biopsy. Therefore, a non-invasive imaging method is urgently required to distinguish between the two. This retrospective study enrolled 66 patients (38 with PCNSL and 28 with HGG) who underwent 18F-fluorodeoxyglucose-positron emission tomography/computed tomography (18F-FDG PET/CT) between July 2017 and July 2022 to investigate the ability of 18F-FDG PET/CT -based radiomics features in differentially diagnosing PCNSL and HGG. A group of 40 patients was assigned as the training cohort, while another group of 26 patients as the validation cohort. A total of 788 radiomics features were extracted from 18F-FDG PET/CT images in the training cohort. Two features were selected by the LASSO method from 788 features to build the logistic model and radiomics nomogram. The AUC of the radiomics nomogram for discriminating PCNSL from HGG was 0.960 [95% confidence interval (CI): 0.9091] and 0.920 (95% CI: 0.7941) in the training and validation cohorts, respectively. The training and validation revealed that the established radiomics nomogram model of 18F-FDG PET/CT displayed excellent discrimination capabilities in PCNSL and HGG, and may have the potential to improve diagnostic accuracy and patient outcomes.
  Keywords: radiomics; deep learning; 18F-FDG PET/CT; high-grade glioma; HGG; primary central nervous system lymphoma; PCNSL.
  DOI: 10.1504/IJBIC.2024.10066760
   
  
• Multi-Objective Cigarette Production Scheduling Problem  
  by Weidong Lou, Yong Jin, Hailong Lu, Yanghua Gao, Xue Xu 
  Abstract: Cigarette production scheduling in a tobacco enterprise is an optimisation problem that can directly reflect the revenue and production status of the enterprise, so it is necessary to propose a more accurate scheduling model based on the real tobacco enterprise Firstly, this paper proposes a high-dimensional multi-objective cigarette production scheduling model considering time, cost, energy consumption, number of card changes, and load balancing Secondly, a self-adaptive NSGA-III algorithm (SA_NSGA-III) based on indicator guidance is proposed to better solve the modified model and generate a scheduling scheme that can effectively improve the production performance, SA_NSGA-III introduces PD diversity evaluation indicators to guide the population evolution and solve the problem of poor diversity in the late convergence stage of the algorithm Finally, the proposed algorithm is experimentally verified by real data examples from enterprises, and the results show that compared with other comparative algorithms, the SA_NSGA-III algorithm achieves optimal results.
  Keywords: cigarette production; scheduling; optimisation; evolutionary algorithm.
  DOI: 10.1504/IJBIC.2024.10066764
   
  
• Synthesis of Unequally Spaced Linear Antenna Array for Subsidiary Maxima Elimination Using LGChOA Algorithm  
  by Simrandeep Singh, Harbinder Singh, Amit Gupta, Ahmed Jamal Abdullah Al-Gburi 
  Abstract: The chimp optimisation algorithm (ChOA) draws inspiration from the individual intellect of chimps during their collective hunting, setting them apart from other social predators. Despite its potential, ChOA often encounters premature convergence to local optima during the search phase, limiting its effectiveness in balancing exploitation and exploration and remains susceptible to stagnation. In this research, an enhanced modified version of ChOA is proposed, integrating Levy flight and greedy selection procedures to address these shortcomings. The efficacy of the proposed algorithm is evaluated in the context of antenna array synthesis. Linear antenna arrays find extensive use in wireless communication applications, yet achieving the dual objectives of suppressing subsidiary maxima while maintaining sufficient spacing and side lobes poses a significant challenge. The proposed approach is evaluated across various linear array communication requirements, and the results are compared with those obtained using other well-known strategies.
  Keywords: chimp optimisation algorithm; grating lobe; linear antenna arrays; side lobe level; subsidiary maxima.
  DOI: 10.1504/IJBIC.2024.10066765
   
  
• Segmentation and Classification of Brain Tumour with Optimisation Enabled Deep Learning Using MRI Images  
  by Sajeev Ram Arumugam, Sakthi Ulaganathan, Rajeshkannan Regunathan, Vimala S. 
  Abstract: The detection of brain tumour at final stage is difficult to heal and the diagnosis of brain tumour from large image database is difficult. Due to the various sizes, shapes, and locations of tumours in the brain, the present techniques are insufficient to give precise classification. Hence, an effective model for classifying and segmenting brain tumours is developed in this study using the circle inspired teaching learning optimisation method (CITLO). During the first step, an input MRI image from a dataset is obtained, and the obtained image is supplied into the pre-processing module. Following that, SegNet, which is trained using CITLO, is employed for tumour segmentation. The brain tumour classification process employs deep convolutional neural network (DCNN), with classifier hyper parameters learned using CITLO. The CITLO_DCNN attained a maximum accuracy of 95.8%, a sensitivity of 96.9%, a specificity of 96.6%, a maximum segmentation accuracy of 95.7%, and ROC of 93.1%.
  Keywords: SegNet; MRI image; circle inspired teacher learning optimisation; deep learning; tumour segmentation; deep convolutional neural network; DCNN.
  DOI: 10.1504/IJBIC.2024.10066793
   
  
• Multi-Objective Jaya War Strategy Optimisation Enabled Cloud Storage in Blockchain Network for Internet of Medical Things Applications  
  by Kavita Shelke, Subhash Shinde 
  Abstract: The internet of medical things (IoMT) includes accessibility and is adequate for medical areas. Diverse techniques are developed based on cloud storage for IoMT applications, but these methods failed to obtain sufficient security in less time. In this research, a Jaya war strategy optimisation (Jaya WSO)-based cloud storage in a blockchain network is introduced for IoMT. Firstly, transactions are generated in IoMT and fed to the base station (BS). Then, it is passed to peers in the blockchain and the ledger is stored in respective peers. For each peer, blocks are selected optimally by Jaya WSO, which is an integration of the Jaya algorithm with war strategy optimisation (WSO) and the multi-objectives. The Jaya WSO attained minimal query probability, storage cost and local space occupancy of 0.428, 20.766 and 52.7 respectively and maximal values of trust level and sensitivity level of 0.849 and 0.916 respectively for block size = 2.
  Keywords: internet of medical things; IoMT; blockchain; Jaya algorithm; war strategy optimisation; WSO; cloud storage.
  DOI: 10.1504/IJBIC.2024.10066976
   
  
• Design of Logic Circuit for All Optical Sampling Gate Based on FIBER-FWM Nonlinear Fiber  
  by Yunhu Wu, Yarang Yang, Wei Zheng, Saidiwaerdi Maimaiti, Hui Peng 
  Abstract: To solve the problems of limited sampling bandwidth and low sampling rate in existing optical sampling techniques, an all optical sampling gate logic circuit design based on FIBRE-FHM nonlinear fibre is adopted to measure high-speed optical signals, improving the bandwidth of traditional sampling methods. This design addresses significant limitations posed by electronic bottlenecks in high-speed optical signal measurement. Utilising the four-wave mixing (FWM) model of a semiconductor optical amplifier (SOA), a sampling gate is realised via a logic circuit leveraging nonlinear effects. These results confirm that after nonlinear processing, the pulse width increases by 80% to 0.1
  Keywords: light sampling; nonlinear fibre; logic circuit; semiconductor optical amplifier; SOA; four-wave mixing; FWM.
  DOI: 10.1504/IJBIC.2024.10067779
   
  
• Attention Mechanism-Based Facial Age Estimation  
  by Zhang Huiying, Lin Jiayan, Sheng WenShun, Dong Jiangwei, Zhang Yu, Geng Xin, Deyin Zhang, Jin Xin 
  Abstract: With the development of the deep learning (DL) technique, especially Long Short-Term Memory (LSTM) for personal aging patterns, the accuracy of facial age estimation has been significantly improved. However, in traditional DL framework, the interdependence between individual facial images has not been fully exploited. To improve the estimation accuracy further, we propose an attention mechanism-based face aging estimation (AM-FAE) to characterize such meaningful interdependence. The proposed AM-FAE is able to select the most relevant parts of the input and assigns different weights to different contextual face information, thereby can achieve high value of information. Compared to state-of-the-art facial age estimation methods, AM-FAE improves the accuracy of age estimation on two public datasets.
  Keywords: mechanism of attention; Convolutional Neural Networks; Label Distribution Learning; facial age estimation.
  DOI: 10.1504/IJBIC.2024.10067926
   
  
• ST Bilateral-Deep filter: Shearlet Transform based Bilateral Filter and deep Learning Approach for Noise Reduction in CT Images  
  by Rashmita Sehgal, Vandana Dixit Kaushik 
  Abstract: This paper develops a ST-BF+Taylor-ACVO for effective denoising. The results from ST and deep learning are combined using the fusion-based quality measure to compute the restored image. Input noisy image is passed to CNN, where noise pixels are identified and the pixel restoration is done by proposed Taylor-ACVO. To produce a denoised image, the pixel enhancement is done using the vectorial total variation norm. On the other hand, input noisy image is applied to ST and the resulted image is fed to bilateral filter to generate noise free image, which is applied to Inverse Shearlet transform to reconstruct back the original image. Finally, the image obtained from VTV norm and IST is fused by the quality metrics to compute restored image. The proposed method obtained higher efficiency in terms of PSNR, SDME, and SSIM with values of 27.85 dB, 40.27 dB, and 0.87 using Gaussian noise.
  Keywords: image denoising; CT image; deep learning; bilateral filter; BF; Shearlet transform; ST.
  DOI: 10.1504/IJBIC.2024.10068092
   
  
• Prediction of International Shipping Container Throughput Based on Particle Swarm Optimisation and Grey Wolf Optimisation  
  by Xia Zhao 
  Abstract: In order to improve the prediction accuracy for port container throughput in international shipping, PCA is first used to reduce the dimension of the input SVR indicators, thereby reducing the dimension of the SVR input; Secondly, GWO is used to improve PSO, and a port container throughput prediction model based on GWO-PSO-SVR is constructed, thereby improving the prediction accuracy of SVR for port container throughput. Results show that the improved PSO performs well in the test function; Based on data from Tianjin Port, the SVR prediction results indicate that its MAPE index is the lowest, at 12.96, which is closest to the true value.
  Keywords: Particle Swarm Optimization; Grey Wolf Optimization Algorithm; SVR prediction model; MAPE indicators; PCA.
  DOI: 10.1504/IJBIC.2024.10068325
   
  
• Research on Philosophy Sentence Segmentation Algorithm Based on BERT Pre-training and Deep Learning Network  
  by Dan Cheng 
  Abstract: To improve the accuracy of sentence breaking in ancient philosophy texts, based on BERT pre-training and deep learning network, an ancient philosophy sentence segmentation algorithm with high accuracy is proposed. Based on Lattice model, BERT model is used to encode ancient philosophical texts into word vectors, and Flat-Lattice Transformer (FLT) position coding method is adopted to determine the interaction relationship between vectors. Then, sentence segmentation situation of ancient philosophical texts is predicted. Finally, sentence segmentation accuracy of ancient philosophical texts is improved. The simulation results show that compared with Lattice-LAN, BERT-Bi-LSTM-CRF and Bi-LSTM-CRF models, the proposed algorithm has obvious advantages in the accuracy of ancient philosophy sentence segmentation. Moreover, accuracy, recall, and F-value of the proposed algorithm are 82.82%, 70.71%, and 76.29%, respectively, which improves the sentence segmentation accuracy of ancient philosophical texts and has certain reference value.
  Keywords: philosophy sentence segmentation; BERT pre-training; deep learning; LSTM model; FLT position coding.
  DOI: 10.1504/IJBIC.2024.10068326
   
  
• Research on Bi-Objective Charge Batch Planning Optimisation Method based on Improved -Constraint Framework  
  by Congxin Li, Liangliang Sun 
  Abstract: Charge batch planning (CHBP) is the basis of steelmaking-continuous casting section batch planning (SCCSBP). With the rapid development of the market-oriented demand of steel enterprises in the direction of multi-species, small batch, and just-in-time delivery, the integrated production process of SCCSBP dramatically increases the functional requirements of flexibility, material yield, and time-dynamic balancing of the CHBP. Therefore, the preparation of a high-quality CHBP is of great significance to improve the efficiency of steelmaking production and reduce material and energy consumption. A bi-objective mathematical model is established, and a cooperative optimisation framework combining an improved -constraint method (IECM) with branch-and-cut (B&C) is developed. Employing a bisection method-based heuristic, can rapidly detect the valid number of sub-problems, thus avoiding the computational burden of redundant sub-problems in traditional -constraint method (ECM). Meanwhile, this method can obtain multiple Pareto non-dominated solutions, providing more schemes for synergistic optimisation at each stage. The B&C can acquire high-quality solutions for sub-problems. Finally, simulation experiments with actual production data validate that the proposed method reduces the objective function values by 7.19%, 7.22% and 0.16% compared to the linear weighting method, traditional ECM and multi-objective optimisation method, respectively; and reduces the CPU time by 46%, 80.7% and 73.64%, respectively.
  Keywords: charge batch planning; steelmaking-continuous casting; improved ε-constraint; Pareto.
  DOI: 10.1504/IJBIC.2024.10068460
   
  
• A Fractional Order Proportional Controller for Control Liquid Level in Nonlinear Spherical Tank System  
  by S.P. Selvaraj, R. Thiyagarajan, Mohanraj Kalidass 
  Abstract: In order to keep the process level at the specified operational parameters and attain peak performance, the Fractional Order Proportional-Resonant (FOPR) Controller was proposed for a spherical tank. All of the parameters in the operating circumstances are tuned by the proposed controller. The FOPR approach is less reactive and is used to manage liquid levels. It is implemented in real time, has cost effective, and has good time domain features. The FOPR controller's servo responses for various operating regions are recorded. The findings are contrasted to methods: glow swarm optimisation based proportional integral (GSO-PI) controllers, Bacterial Foraging Optimisation, and Particle Swarm Optimization. Compared to existing approaches, the proposed FOPR controller can give little overshoot, minimal settling time, faster rise time, and minimal integral standard error (ISE) and integral absolute error (IAE) values. It also produces a highly stable output over the tank's full span at various operating positions.
  Keywords: Fractional Order Proportional-Resonant (FOPR); Bacterial Foraging Optimization (BFO)Glow Swarm Optimization based controller; Particle Swarm Optimization (PSO); Integral Standard Error (ISE); IAE.
  DOI: 10.1504/IJBIC.2024.10068480
   
  
• Efficient VM Selection from Over-utilised PMs: a Grasshopper-Based Approach for Cloud Resource Optimisation  
  by Jaspreet Singh, Navpreet Kaur Walia 
  Abstract: Efficient resource allocation is critical for optimising performance and controlling operational costs in cloud computing environments. In this proposed work, a comprehensive two-segment study is presented, in which the first segment relies on grasshopper based optimisation while the second segment relies on conditional-MBFD. The grasshopper algorithm effectively addresses the intricate challenge of selecting virtual machines (VMs) from overutilised physical machines (PMs) in dynamic cloud environments, thus reshaping resource allocation paradigms. The contributions outlined in this proposed work are rigorously examined through comprehensive comparative evaluations. These evaluations compare grasshopper based approach against well-established optimisation algorithms such as artificial bee colony (ABC), particle swarm optimisation (PSO), and firefly. Additionally, the proposed work strategies are validated against state-of-the-art algorithms. These meticulous comparisons highlight the resounding superiority of proposed strategies in minimising power consumption, reducing SLA violations, and optimising resource utilisation in cloud computing environments. Meanwhile, the conditional-MBFD algorithm astutely fine tunes resource allocation.
  Keywords: allocation and migration; cloud computing; grasshopper optimisation algorithm; GOA; swarm intelligence; SI.
  DOI: 10.1504/IJBIC.2024.10068593
   
  
• Leveraging Similarity and Structural Correlation for Attention-Based Graph Embedding  
  by Jing-Hong Wang, Chang-Xin Li, Jia-Teng Yang 
  Abstract: Graph Attention Networks (GATs) are a prevalent method for graph embedding, utilising attention mechanisms to aggregate first-order neighbourhood node information. However, their inability to adequately consider high-order neighbourhood nodes and structural information poses limitations. To address this gap, we propose a novel joint attention graph embedding model integrating similar networks and structural correlation. By introducing the concept of structural correlation, our model comprehensively incorporates both node content features and joint node topological structure features when computing attention scores. Experimental results showcase the efficacy of our approach, yielding significant accuracy improvements of 2.70%, 3.94%, and 2.60% on the Cora, Citeseer, and Pubmed datasets, respectively, compared to traditional GATs. Our proposed method significantly enhances node embedding representation, underscoring its importance in improving performance in node classification tasks.
  Keywords: Graph embedding; Graph attention network; Node similarity; Similar network; Node classification.
  DOI: 10.1504/IJBIC.2024.10068642
   
  
• Research on Sports Image Restoration Algorithm based on Deep Learning  
  by Xinjiao Zhang, Xuefei Tao 
  Abstract: To improve the sharpness and realism of sports image restoration, an image restoration method based on deep learning is proposed. The improved GAN (Generative Adversarial Network) is adopted for image denoising, and the improved RAiA-Net is used for image rain removal. The simulation results show that the peak signal-to-noise ratio and structural similarity of the improved GAN network reach 32.56 and 0.91, respectively, which are 3.45 and 0.04 higher than those of GAN network. The peak signal-to-noise ratio and structural similarity of RAiA-Net are 36.87 and 0.98 respectively, and the time consumption is shorter (0.43s), which has certain advantages and practicability. Compared with the standard GAN network, CBM3D network and CNN network, the designed network has better image denoising performance and better performance in peak signal-to-noise ratio and structural similarity indexes.
  Keywords: deep learning; image restoration; image denoising; image rain removal; generative adversarial network.
  DOI: 10.1504/IJBIC.2024.10068652
   
  
• A Ranking-Based Firefly Algorithm with Adaptive Parameter Control and Modified Attraction  
  by Zhaoxing Xu, Peng Liu, Qinqin Wu, Wangping Xiong 
  Abstract: In this paper, an improved firefly algorithm (FA) is proposed for numerical optimization. The new approach, termed RBFA, employs three strategies. First, each firefly is assigned a probability based on a ranking method. The probability determines whether a firefly moves to a new position. Then, a novel parameter method is designed to dynamically adjust the step size. Furthermore, a modified attraction strategy is used to strengthen the search efficiency. To verify the performance of RBFA, a set of famous benchmark problems are tested. Computational results demonstrate the proposed RBFA can obtain better performance than several other FA variants.
  Keywords: firefly algorithm; ranking method; adaptive parameter; dynamical adjustment; modified attraction; numerical optimization.
  DOI: 10.1504/IJBIC.2024.10068761
   
  
• DafVBM: Multimodal Deep Learning for Predicting Efficacy of Salvage Chemoradiotherapy  
  by Han Zhang, Xinwei Guo, Liang Gu, Zhenyu Lei, Masaaki Omura, Shangce Gao 
  Abstract: This study leverages multimodal deep learning to predict the efficacy of salvage chemoradiotherapy for aesophageal squamous cell carcinoma. The target patients are those with regional lymph node recurrence after curative resection. Predicting efficacy is challenging due to limited data and the reliance on manual feature selection. Integrating multimodal data and optimising its utilisation can significantly support treatment prediction. However, there is little research on fully integrating multimodal data. To address this, we conduct a retrospective study and design a multimodal architecture to process patient data, including images, text, and tabular features. The model employs attention mechanisms and Fourier transform paths for seamless feature fusion. Our comparative analysis shows significant performance improvements with multimodal data, achieving an average precision, recall, and F1-score of 91.96%, 91.18%, and 90.86%, with best scores of 97.20%, 97.10%, and 97.00%.
  Keywords: Multimodal model; esophageal squamous carcinoma; efficacy prediction.
  DOI: 10.1504/IJBIC.2024.10068835
   
  
• Optimized Dual Temporal Gated Multi-Graph Convolution Network Based Distributed Denial of Service Attack Detection in Cloud Computing  
  by Ramesh Babu Putchanuthala, Gopisetty Naga Rama Devi, Prabha Murugesan, Muniyandy Elangovan, Radhika Rathanasalam, Ramasamy SenthamilSelvan 
  Abstract: Distributed denial of service (DDoS) attacks are growing threat to network security, and existing methods attains higher false positive and false negatives when classifying attack and legitimate data, resulting in reduced accuracy. To overcome this, optimised dual temporal gated multi-graph convolution network based fennec fox optimisation for distributed denial of service attack detection in cloud computing (DTGMGCN-DoS-ADD-CC) is proposed. Initially, data adaptive Gaussian average filtering (DAGAF) pre-processes the CICIDS2017 dataset to correct mismatched values. Then swarm optimisation algorithm (DSOA) selects the transformed features, its used by multiple-graph convolution network with dual temporal gates (DTGMGCN) for precise detection of normal and attacked packet of information (API). The Fennec fox optimisation (FFO) fine-tunes DTGMGCNs weight parameters, further boosting performance. Experimental results show that DTGMGCN-DoS-ADD-CC achieves 99.37% accuracy, 98.9% sensitivity, and 98.95% specificity, outperforming existing methods. The improvement highlights robustness and efficacy of the DTGMGCN-DoS-ADD-CC approach for DDoS attack detection in cloud computing.
  Keywords: Dual Temporal Gated Multi-Graph Convolution Network; Fennec Fox Optimization; Cloud Computing; Distributed Denial of Service Attack Detection; Machine Learning.
  DOI: 10.1504/IJBIC.2024.10069266
   
  
• Optimal Path Selection for Mobile Sink Using Henry Gas Particle Swarm Optimisation and Energy Prediction Based on Deep Residual Network  
  by Aparna Ashok Kamble, Patil B.M 
  Abstract: The advantage of Wireless Sensor Network (WSN) anywhere at any time makes it one of the popular technologies. The most prominent issues faced by the WSN are energy availability and reliability. The energy prediction among the nodes acts as a significant factor to achieve reliability. Hence, this research proposes a mechanism for optimal path selection of MS nodes using a proposed approach named the Henry Gas Particle Swarm Optimization (HGPSO) algorithm. Here, the hybrid Harris Hawk Salp Swarm optimization (HHSSO) model along with the MS policy is employed for performing the CH selection effectively and is referred as EE-hHHSS. The Deep Residual Network (DRN) is exploited to predict energy. The proposed HGPSO-based DRN has achieved a maximum Packet Delivery Ratio (PDR) of 42.121%, maximum residual energy of 0.083J, minimum delay of 0.0000027089sec, minimum path distance of 13.357, and maximum number of alive nodes is 48 while analysing 50 nodes.
  Keywords: Wireless Sensor Network (WSN); Henry gas solubility optimization (HGSO); Particle Swarm Optimization (PSO); mobility model; Cluster Head (CH) selection.
  DOI: 10.1504/IJBIC.2024.10069450
   
  
• Contrastive Hypergraph Attention Reinforcement Learning for Asset Portfolio Management in Digital Auditing  
  by Xuan Liu, Xu Cao, Xinying Fang, Nan Gao 
  Abstract: Asset portfolio management (APM) is a critical task within digital auditing, aimed at continuously reallocating funds across a set of assets with the aim of maximising investment returns or suppressing risks. Recent APM methods have learned a policy function to generate appropriate portfolios in a reinforcement learning framework and have achieved encouraging progress. However, these methods ignore the fact that assets in portfolios may be broadly interrelated and affected by each other, and the asset relationship information is highly valuable in improving APM. In this paper, we introduce group-wise relationship information as additional environmental cues to improve portfolio policy learning for APM. We propose a dual-channel hypergraph attention network to jointly capture the heterogeneous fund-holding and industry-belonging relationships via attention-based information aggregation among assets. In addition, we introduce contrastive learning to provide extra supervision signals for higher modelling capacity. Experimental results demonstrate that this method yields significantly greater benefits compared to state-of-the-art methods for APM.
  Keywords: Asset portfolio management; Hypergraph learning; Contrastive learning; Attention;Reinforcement learning.
  DOI: 10.1504/IJBIC.2024.10069679
   
  
• Financial Fraud Detection Based on Distributed L  
  by Hongwei Chen, Jianpeng Wang, Lun Chen, Zexi Chen, Rong Gao, Xia Li 
  Abstract: With the growth of online payments, financial fraud has led to significant economic losses. Accurate fraud detection is therefore crucial for protecting consumer rights and ensuring the security of payment platforms. Current detection methods mainly rely on machine learning algorithms, but their performance is often limited by hyperparameter tuning constraints. To address this, we propose a Spark-based distributed L
  Keywords: fraud detection; sailfish optimiser; Lévy flight; Spark.
  DOI: 10.1504/IJBIC.2025.10069838
   
  
• MOWOA-D based Feature Selection Study for Pneumonia Detection  
  by Leyi Xiao, Yixuan Su, Xia Xie, Chaodong Fan 
  Abstract: AI's application in medical image analysis presents challenges in balancing solution diversity and convergence in high-dimensional datasets for accurate feature selection. To this end, we propose an improved multi-objective evolutionary algorithm (MOWOA-D) based on decomposition and Whale Optimization Algorithm (WOA) learning strategy. This algorithm integrates the WOA learning strategy into the original MOEA/D to optimize the search mechanism, ensuring accuracy and uniformity. Then we design a dynamic neighborhood reference strategy to select reference points based on the population's real-time state, guiding the generation of offspring and enhancing solution quality. We also use an adaptive binary mutation operator that introduce the Hamming distance and adjust mutation probability during iteration to balance exploration and exploitation. Experimental results indicate that MOWOA-D outperforms five multi-objective evolutionary algorithms in efficiency and uniformity. Feature selection experiments on the UCI database show improved classification accuracy and reduced data redundancy. Tests on pneumonia data demonstrate the method's practicability.
  Keywords: Multi-objective optimization; Feature selection; MOEA/D; Pneumonia; Whale optimization; Heuristic method; Dynamic reference strategy; Binary mutation operator; Hamming distance; Machine learning.
  DOI: 10.1504/IJBIC.2025.10070157
   
  
• Optimised Route Selection for Multipath Routing in MANET using Latent Encoder Coupled Generative Adversarial Network  
  by Muthumarilakshmi S, Hemanand D, Manikandan S 
  Abstract: Mobile Ad hoc Networks (MANETs) enable decentralized communication in environments without traditional infrastructure, such as disaster zones and military settings. However, they face challenges like high latency and energy inefficiency in route selection, affecting overall network performance. To address this, an Optimized Route Selection for Multipath Routing in MANET using Latent Encoder Coupled Generative Adversarial Network (LECGAN-RS-MR-MANET) is proposed. Initially, the Tyrannosaurus Optimization Algorithm (TOA) is employed for efficient route exploration, followed by LECGAN for optimal node selection through latent feature learning. The Improved Sooty Tern Optimisation Algorithm (ISTOA) ensures effective route maintenance, while the Multitask Multi-attention Residual Shrinkage Convolutional Neural Network (MMRSCNN) handles route recovery. The proposed LECGAN-RS-MR-MANET is evaluated and demonstrates 23.92% lower energy consumption, 24.92% lower end-to-end delay, and 22.42% lower routing overhead compared to existing techniques. These findings confirm that the proposed model significantly improves routing performance and reliability in MANETs.
  Keywords: Latent Encoder Coupled Generative Adversarial Network; Tyrannosaurus optimization algorithm; Mobile ad hoc networks; Improved Sooty Tern Optimization algorithm.
  DOI: 10.1504/IJBIC.2025.10070398
   
  
• Enhancing Circuit Adaptability in VLSI using Hybrid Optimization for Functional Unit Selection and Resource Allocation in High-Level Synthesis  
  by M. Thillai Rani, K.P. SaiPradeep, R. Sivakumar, S. Suresh Kumar 
  Abstract: Very-large-scale integration (VLSI) plays a crucial role in integrating transistors into a Single chip, yet variations in process-voltage-temperature (PVT) present challenges for accuracy. In this manuscript, enhancing circuit adaptability in VLSI using Hybrid Optimization for Functional Unit Selection and Resource Allocation in High-Level Synthesis (VEA-Hyb-TSGTO-HLS) is proposed. It begins with Data Flow Analysis (DFA), where the behavioural inputs of the VLSI circuits are analysed to gather crucial information about data flow and operation dependencies. Then Volcano Eruption Algorithm (VEA) is used to determine the optimal functional unit based on variations in PVT, ensuring adaptability to changing conditions. Finally, Hybrid Transient search and Group teaching optimization algorithm (Hyb-TSGTO) is used to estimate the resource allocation. The proposed VEA-Hyb-TSGTO-HLS approach has achieved 24.6%, 21.4%, and 14.5%lesser cost and 15.6%, 18.8%, and 19.3% higher FU selection accuracy for usings38584 circuit when compared with the existing state of art methods.
  Keywords: Group teaching optimization; High Level Synthesis; Transient search optimization; Volcano eruption algorithm; Very-large-scale integration.
  DOI: 10.1504/IJBIC.2024.10070412
   
  
• IoT-Based Plant Disease Detection Using Enhanced Elman Spike Neural Network with Capuchin Search Optimisation Algorithm  
  by D. Karunkuzhali, B. Meenakshi 
  Abstract: In recent years, the Internet of Things (IoT) has gained attention for its transformative role in agriculture. A main challenge in agriculture is early identification of plant disease which is needed to prevent crop loss and ensure food preservative. Typical plant disease detection techniques are often time-consuming and labor-intensive, making it important to replace them with automated systems. Therefore, IoT-Based Plant Disease Detection Using Enhanced Elman Spike Neural Network together with Capuchin Search Optimization Algorithm(IoT-PDD-OEESNN) is proposed in this paper for detecting potato plant. The input data is preprocessed using Altered phase preserving dynamic range compression (APPDRC) filtering model for extracting the leaf region of the image and also eliminates the noise and blur image. The proposed IoT-PDD-OEESNN approach is implemented in Python using certain metrics. The IoT-PDD-OEESNN method attains better accuracy of 30.12%, 26.75% and lower computation time of 27.18%, 26.29%, and 29.56% when analyzed with the existing methods.
  Keywords: Capuchin search optimization algorithm; Enhanced Elman Spike Neural Network; Gray-Level Co-Occurrence Matrix; Internet of Things; Plant village dataset and Variation Density Peaks Clustering.
  DOI: 10.1504/IJBIC.2025.10070546
   
  
• Efficient Data Retrieval Model based on Semantic Similarity Analysis using Chiroptera Buzzard Optimisation Tuned Deep CNN  
  by Ankush Raosaheb Deshmukh, Premchand B. Ambhore 
  Abstract: To extract meaningful insights, integrating the data classification and semantic text summarization is essential, aiding in the identification of contextually significant content. Most of the existing techniques encounter multiple challenges from the perspective of machine understanding, especially for languages with limited resources, and fail to learn the sequence of correlations effectively. Nevertheless, there is still much space for enhancing the speed of data retrieved because current approaches fail to take the spatial and semantic aspects into account. To tackle this issue, this research presents an efficient data retrieval model utilising Chiroptera Buzzard optimization adapted deep Convolutional Neural Network (CBO adapted deep CNN) for semantic similarity analysis. Specifically, the Chiroptera buzzard optimisation is utilised for feature selection and fine-tuning the hyperparameters of DCNN that improves the classification accuracy. Hence, the proposed model reduces the computational complexity and provides remarkable performance in terms of metrics attaining 99.98% accuracy, 99.53% recall, 99.93% precision, 99.84%Fbeta, 99.52%Cohen kappa, and 99.52% F1-score for 90% of training.
  Keywords: Data retrieval model; Convolutional Neural Network; Chiroptera buzzard optimization; Semantic Similarity Analysis; and Text summarization.
  DOI: 10.1504/IJBIC.2025.10070716
   
  
• SenseNet: Satellite Image Enhancement using Optimised Deep Denoiser for Cloud Removal  
  by Renuka Sandeep Gound, Sudeep D. Thepade 
  Abstract: The research focuses on devising a Hybrid CFO deep denoiser model to eliminate the blurring edges of cloud-covered boundaries in the RS image The need for reconstructing the high-quality satellite image is elaborated in this research article for which a proposed Hybrid CFO deep denoiser is developed The optimized learning of deep denoiser increases the reconstruction ability, which is the main focus of the research The satellite images are pre-processed and exposed to the reconstruction in such a way that the proposed Hybrid CFO deep denoiser reconstructs the high-quality satellite image without the influence of cloud The experimental results also demonstrate that the CFO-based deep denoiser exhibits higher performance in terms of PSNR, SSIM, and MSE, while compared with the existing denoiser The performance improvement of 2 165dB, 1 436% and 0 816% is obtained by the Hybrid CFO-deep denoiser concerning existing denoisers in terms of PSNR by maintaining the K-fold at 5.
  Keywords: Image enhancement; Satellite Image; Deep learning; Optimization; Cloud Removal.
  DOI: 10.1504/IJBIC.2025.10070740
   
  
• Constrained Multi-Objective Optimisation with Collaboration-Driven Strategy and Infeasible Knowledge-Based Cooperation  
  by Bin Xu, Haifeng Zhang, Xiaodan Miao, Lili Tao 
  Abstract: Most existing constrained multiobjective evolutionary algorithms adopt unitary environmental selection and reproduction operator, which face the issue of striking a proper balance between objectives and constraints. To this end, we developed a new method with collaboration-driven strategies and infeasible knowledge-based cooperation. The main idea of this approach is using heterogeneous frameworks and operators. Specifically, the main population is maintained using the domination-based framework and constrained-domination principle. An auxiliary population is maintained using the decomposition-based framework and epsilon-constrained method. Meanwhile, the infeasible knowledge-based genetic algorithm and differential evolution are employed to generate offspring. The superior of this algorithm is validated by comparing with some popular methods with distinct characteristics on 4 suites. Experimental results indicated that our method performs best among all competitors on 62.5%, 50%, 50%, and 25.56% for CTPs, MWs, LIRCMOPs, and DOACMOPs, respectively. The effectiveness of collaboration-driven and infeasible knowledge-based cooperation are also verified by some ablation studies.
  Keywords: Constrained multiobjective optimisation; Collaboration-driven; Infeasible knowledge; Cooperation; Reproduction operator.
  DOI: 10.1504/IJBIC.2025.10071002
   
  
• Student Performance Prediction (SPP)Model using HLRO-DMN  
  by L. Srinivasan, D. Kalaivani, C. Nalini, I. Gugan 
  Abstract: This research introduced the proposed hybrid leader remora optimisation algorithm with deep maxout network (HLRO_DMN) for accurately predicting the students performance. Initially, the input data acquired from the dataset is transformed into a suitable format using Yeo-Johnsons transformation. Then, the dice coefficient is employed for selecting optimal features, which combines the feature score obtained from the Fisher score and the Tversky index. In addition, the data augmentation is completed by the bootstrapping method, and the performance prediction is carried out by the DMN, wherein the weight of the DMN is tuned by the HLRO algorithm. Besides, the experimentation of HLRO_DMN attained the best result using certain metrics, like mean square error (MSE), Root mean square error (RMSE), and mean absolute error (MAE), and the accuracy of the corresponding values noted by the devised scheme are 5.4032, 0.175, 0.4444, and 91.314, respectively.
  Keywords: Remora Optimization algorithm; Deep Maxout network; Hybrid leader based optimization; Yeo-Johnson's transformation; Dice coefficient.
  DOI: 10.1504/IJBIC.2025.10071048