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 (23 papers in press)

Regular Issues

  • Pheromone-Inspired Multiple Moving Targets Search Method for Swarm Unmanned Aerial Vehicles in Environments with Unknown Obstacles   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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
     
  • A Chaotic Simulated Annealing Genetic Algorithm with Asymmetric Time for Offshore Wind Farm Inspection Path Planning   Order a copy of this article
    by Lei Kou, Yukuan Wang, Fangfang Zhang, Quande Yuan, Zhen Wang, Jingya Wen, Wende Ke 
    Abstract: The harsh environment of offshore wind farms causes wind turbines to be easily broken down. To ensure the normal operation of wind turbines, it is necessary to carry out inspections of offshore wind farms. Path planning is an important step to complete the inspection. The unmanned surface vessel (USV) is subject to the water current, leading to deceleration and acceleration, which makes the round-trip travelling time of the USV between two wind turbines asymmetric. To sum up, it belongs to asymmetric travelling salesman problem. To address this problem, a chaotic simulated annealing genetic algorithm (CSAGA) considering asymmetric time is proposed in this paper. Firstly, the initial sequence with high quality, as the initial solution of the simulated annealing algorithm, is generated by logistic-tent chaotic mapping. Then, effective solutions are obtained by a series of operations of the simulating annealing algorithm and is used to replace the worst fitness individuals in the initial population of the genetic algorithm. Finally, genetic operations such as selection, crossover, mutation, and reversion are applied to the population to obtain the optimal solution. The feasibility of the algorithm is verified by simulation experiments. The results display that CSAGA has better performances compared to other algorithms.
    Keywords: travelling salesman problem; TSP; inspection path planning; simulated annealing algorithm; genetic algorithm; offshore wind farm.
    DOI: 10.1504/IJBIC.2024.10066897
     
  • Multi-Objective Jaya War Strategy Optimisation Enabled Cloud Storage in Blockchain Network for Internet of Medical Things Applications   Order a copy of this article
    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
     
  • Natural Nearest and Shared Nearest Neighbors Density Peaks Clustering Algorithm for Manifold Data   Order a copy of this article
    by Lu Li, Zhigang Li, Shenyu Qiu, Zhaoxiu Nie, Han Longzhe 
    Abstract: Addressing the challenges faced by the density peaks clustering (DPC) algorithm in precisely identifying cluster centres when dealing with manifold data and its tendency to misclassify samples distant from cluster centres, this paper proposes a natural nearest and shared nearest neighbours' density peaks clustering algorithm for manifold data. The local density is redefined by natural nearest and shared nearest neighbours, which highlights the difference between the cluster centre and other samples, and makes the identification of the cluster centre more accurate; the similarity matrix is constructed according to the similarity between the samples defined in the process of defining the local density to complete the allocation of the remaining samples, which effectively improves the problem of incorrectly allocating samples far away from the centre of the clusters in the manifold clusters. Upon comparing the algorithm described in this paper with DPC and other refined algorithms, the experimental outcomes stemming from the manifold datasets unambiguously demonstrate its proficiency in accurately pinpointing the centroid of each cluster, thus effectively carrying out the clustering task. At the same time, on the UCI datasets and Coil20 dataset, this paper's algorithm can get an ideal clustering effect.
    Keywords: density peaks clustering; DPC; manifold data; natural nearest neighbours; NNN; shared nearest neighbours; SNN.
    DOI: 10.1504/IJBIC.2024.10067699
     
  • Solving Bank Debt Problems based on Parallel NSGA-II Algorithm   Order a copy of this article
    by Xuezhi Yue, Teng Xiong, Wenxing Zhu 
    Abstract: In order to alleviate the balance of bank liquidity, profitability, and safety, this paper regards bank liability management as a multi-objective optimisation problem, establishes a multi-objective bank liability model with solvency, liquidity risk, and net interest income as the goals, and proposes an improved adaptation value method and environment selection method to improve the NSGA-II algorithm (PNSGA-II) to realise the optimisation of bank asset management. Compared with the NSGA-II algorithm, the PNSGA-II algorithm has better convergence and diversity, so as to better solve the problem of bank liability management. Compared with the NSGA-II algorithm, SPEA2 algorithm, and improved algorithm, the Pareto frontier distribution and IGD index of the PNSGA-II algorithm have better performance, indicating that the proposed algorithm has better convergence and diversity, and better comprehensive performance. The experimental results show that by using the parallel NSGA-II algorithm to solve the bank liability problem, banks can select a realistic set of optimal solutions according to the actual situation among the six sets of Pareto optimal solutions, so as to more conveniently and objectively predict the liability management, asset allocation, and macro-control in the next few years.
    Keywords: asset liability management; multi-objective optimisation; PNSGA-II; prioritisation.
    DOI: 10.1504/IJBIC.2024.10067776
     
  • Design of Logic Circuit for All Optical Sampling Gate Based on FIBER-FWM Nonlinear Fiber   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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