International Journal of Bio-Inspired Computation (43 papers in press)

Regular Issues

• 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 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
   
  
• 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
   
  
• Spatial and Temporal Distribution of Ecosystem Service Values in the Northeast Tiger and Leopard National Park and Analysis of Driving Factors based on the Geographical Detector and Remote Sensing Classification  
  by Zhihan Wan, Hongxun Li 
  Abstract: This study provides a basis for research on the value of ecosystem services of Northeast Tiger and Leopard National Park (NTLNP) after the implementation of the natural protection project by analyzing the change of land use, temporal and spatial changes of ecosystem service value, and driving factors from 2000 to 2022. The results showed that the area of broad-leaved forest in NTLNP was the largest and the land use types changed from broad-leaved forest to mixed coniferous and broad-leaved forest and coniferous forest from 2000 to 2022; the ecosystem service value of NTLNP showed a trend of first decreasing and then increasing, with a total increase of 464.99 million yuan. The interaction between elevation and climate factors has a great influence on the value of ecosystem services, and natural factors have a binding effect on the spatial differentiation of ecosystem service value.
  Keywords: Northeast Tiger and Leopard National Park; ecosystem services value; driving factors; geographical detector.
  DOI: 10.1504/IJBIC.2025.10071729
   
  
• Research on Regional Low-Carbon Economy Analysis Model based on Improved SVM  
  by Na Xiao, Yan Liu 
  Abstract: In order to further grasp the level of low-carbon economic development in the region and provide feasible suggestions for regional development, this paper proposes a regional low-carbon economy analysis and prediction model based on multiple strategies improved sparrow search algorithm optimised SVM (ISSA-SVM). Where, support vector machine (SVM) is used as the basic prediction method, and sparrow search optimisation algorithm improved based on multi strategy and combine with PLS component extraction is introduced to optimise the parameters of SVM and further enhance its predictive performance. Simulation results show that the designed prediction model based on ISSA-SVM has good prediction performance. Furthermore, the prediction error rate of this model has always been stable below 2.5%, and its precision is high. Therefore, the proposed model can be used to analyse and predict the development level of low-carbon economy in practice, which has certain feasibility.
  Keywords: low-carbon economy; support vector machine; sparrow search algorithm; prediction model.
  DOI: 10.1504/IJBIC.2025.10072382
   
  
• Classification of Malaria Disease using Optimal Fire Hawks based Capsule Convolutional Vision Transformer Network  
  by Pallavi Bhanudas Salunkhe, Pravin Sahebrao Patil 
  Abstract: Malaria is a widespread harmful disease caused by parasites transmitted through infected mosquitoes. The primary challenge addressed in this paper is the low performance, high error rates, and poor segmentation accuracy in malaria disease classification using existing machine learning (ML) and deep learning (DL) techniques. To overcome these limitations, this study proposes an advanced malaria classification model using the optimal fire Hawks-based capsule convolutional vision transformer network (OFH_C2ViTNet). The input image was segmented using feature concatenation-based U-Net (FC-UNet) based on red blood cells, white blood cells, and malaria parasites. Finally, the suggested optimal fire Hawks-based capsule convolutional vision transformer network (OFH_C2ViTNet) was used to categorise malaria into difficult, trophozoite, gametocyte, ring, and schizont kinds. From the input images, the proposed model provides an efficient result for identifying the disease at an early stage. Experimental evaluation demonstrates that the proposed model achieves superior performance, with an accuracy of 97.42% and a precision of 98.68%, outperforming existing state-of-the-art methods in malaria detection.
  Keywords: Median Filtering; Contrast Enhancement; Feature Concatenation; Hawks Optimizer; Vision Transformer Network; Malaria Parasites.
  DOI: 10.1504/IJBIC.2025.10072835
   
  
• Self-Improved Optimisation Model On Energy-Aware Resource Deployment Algorithm for Cloud Data Centres  
  by Prabha B, Kalangi Ruth Ramya, Charanjeet Singh 
  Abstract: The high processing demands of business, social, web, and scientific applications are driving a sharp rise in the demand for cloud computing. Most of the current cloud data centre resource management algorithms take CPU utilisation as the main consideration and increase CPU utilisation through virtual machine integration to reduce the energy consumption of cloud data centres. This work intends to propose a new cloud resource deployment model that includes three major aspects. Clustering is the initial phase, where clustering takes place by the Improved FCM-based clustering. Here, it is used to group the physical machines under the consideration of Energy and Distance. The process of deployment of the virtual machine is handled by Optimisation assisted Bi-GRU. Thereby, the proposed optimisation problem will be solved by Updated SSA with Baker Map Evaluation. The improved FCM with enhanced Jensen-Shannon distance has achieved a high accuracy value of 0.944.
  Keywords: Cloud Computing; Cloud Data Centers; Improved Fuzzy C Means based clustering; Resource Deployment; Updated Squirrel Search Algorithm with Baker Map Evaluation Algorithm.
  DOI: 10.1504/IJBIC.2025.10073095
   
  
• Modified African Vulture Optimisation Algorithm using Inertia Weight and Clerc-Kennedy Formula  
  by Saleh Altbawi, Saifulnizam Bin Abd. Khalid, Rayan Hamza Alsisi, Touqeer Ahmed Juman, Zeeshan Arfeen 
  Abstract: This study introduces the Modified African Vulture Optimization Algorithm (MAVOA) as an enhancement of the existing African Vulture Optimization algorithm (AVOA). MAVOA addresses several limitations of AVOA, including suboptimal solutions and premature convergence due to demographic homogeneity. The MAVOA involve incorporating the Clerc-Kennedy formula and adjusting the inertia weight, which leads to faster convergence and better-quality hyper parameter approximation. One notable feature of MAVOA is its flexibility, allowing it to dynamically regulate its search strategy to balance local exploitation and global exploration. Comparative evaluations against other algorithms demonstrate the superior performance of MAVOA. This study also showcases MAVOA's effectiveness through real-world engineering challenges, highlighting its potential for various optimization applications.
  Keywords: African Vulture Optimisation Algorithm; metaheuristic; Optimization; Benchmark; Economic Load Dispatch.
  DOI: 10.1504/IJBIC.2025.10074201
   
  
• Research on Parallelisation Prediction for the Size of Industrial Economies based on FA-SSA Optimisation SVM  
  by Xiaofang Shi 
  Abstract: Aiming at the problems of slow prediction speed and low accuracy of industrial economy scale, this paper proposes a prediction model based on SVM (Support Vector Machine) and parallel framework. Firstly, SSA (Sparrow Search Algorithm) algorithm improved by elite inverse strategy and firefly disturbance strategy is adopted to optimize the kernel function parameter and penalty factor C of SVM model, and the optimal parameters of SVM model are obtained by using the MapReduce parallel computing framework, which improves the prediction speed and accuracy of industrial economic scale. The results reveal that the mean absolute error, mean square error and explained variance of the proposed method are 0.31, 0.17 and 0.85, respectively. Therefore, the proposed model can be applied to the actual industrial economy scale prediction.
  Keywords: size of industrial economies; SVM model; sparrow search algorithm; parallel framework.
  DOI: 10.1504/IJBIC.2025.10074479
   
  
• A Multisensory Direction Cues Integration Model based on Continuous Attractor Neural Networks  
  by Jinhan Yan, Naigong Yu 
  Abstract: Accurate heading perception is essential for spatial navigation. The mammalian brain integrates self-motion cues from multiple sensory modalities in a Bayesian manner to achieve more reliable spatial localisation, providing important inspiration for robot navigation. This paper proposes a multisensory integration model based on continuous attractor neural networks (CANNs) to fuse directional cues from visual optical flow and vestibular system. Prior to integration, we perform a decoupling analysis of optical flow induced by self-motion and design a simple method to estimate visual angular velocity quickly. Separate CANNs are then constructed to perform angular path integration for both visual and vestibular inputs, followed by multisensory integration. Experiments on simulation and real-world datasets demonstrate that the proposed model achieves accurate and reliable direction estimation while maintaining low computational cost and strong noise resistance, outperforming several advanced methods. This work provides a brain-inspired solution for efficient and reliable navigation.
  Keywords: Bio-inspired navigation; head direction cells; continuous attractor neural network; multisensory integration; angular velocity.
  DOI: 10.1504/IJBIC.2025.10074549
   
  
• Enhanced Water Body Segmentation Using Optimized DeepLabV3+ and YOLOv7 on High-Resolution Satellite Imagery  
  by A. Kalaiselvi, T. Jarin, P. Sreeja, Rajesh V 
  Abstract: Efficient segmentation of water bodies from satellite images is crucial for environmental monitoring, resource management, and flood prediction. Traditional machine learning methods face challenges such as manual feature extraction and complex spectral analysis. This work introduces an advanced model that integrates optimised DeepLabV3+ and YOLOv7 to enhance the segmentation of water bodies using high-resolution satellite imagery. The model employs DeepLabV3+ enhanced with an improved mantis search algorithm (IMWSA) to better delineate water boundaries. Subsequently, YOLOv7 assists in localising and classifying water regions. This combination harnesses the robust segmentation capabilities of DeepLabV3+ with the rapid detection potential of YOLOv7, resulting in improved accuracy and efficiency. Comparative analysis with two benchmark datasets demonstrates the models superiority, achieving accuracies of 99.5% and 99.02%. This optimised approach is pivotal for sustainable water management and disaster mitigation, providing a significant advancement in water body segmentation and monitoring
  Keywords: satellite images; segmentation of water bodies; Optimized DeepLabV3+; YOLOv7.
  DOI: 10.1504/IJBIC.2025.10074607
   
  
• Global-Local Perception GAN For Text-to-Images Synthesis  
  by Li Jianghua, Zhang Shouxin 
  Abstract: Text-to-image synthesis is a challenging cross-modal task with significant applications in multimodal artificial intelligence. While generative adversarial networks (GANs) have advanced text-conditioned image generation, existing methods suffer from two critical limitations: 1) semantic inconsistency caused by modality information mismatch; 2) insufficient fine-grained details in synthesised images. In order to solve these problems, we propose global-local perception generative adversarial network (GLP-GAN) a single-stage framework featuring three innovative components: deep information matching module (DIMM) that aligns textual and visual semantics through cross-modal matching loss, dynamic refinement module (DRM) that progressively enhances image details via adaptive weight updating, and conditional instance-batch normalisation (CIBN) that stabilises training by learning semantic-visual correlation patterns. Experimental results demonstrate state-of-the-art performance on CUB and COCO datasets, achieving 10.8% FID reduction on CUB and 22.3% improvement on COCO compared to baseline model. The IS shows 3.3% gain on CUB and 6.1% enhancement on COCO, and the results demonstrate the effectiveness of the proposed method.
  Keywords: Information matching; text-to-image generation; generative adversarial networks; multimodality; attention mechanism.
  DOI: 10.1504/IJBIC.2025.10074735
   
  
• Psychological Health Detection Based on Transformer Stress EEG Signal Recognition Model  
  by Hongying Zhang 
  Abstract: In response to the problem of emotion recognition implied in electroencephalogram signals, this study conducts psychological health testing based on electroencephalogram signal recognition. Firstly, position embedding is added to the electroencephalogram signals, and a Transformer-based electroencephalogram signal recognition model is constructed. Then, using the attention mechanism to improve the long short-term memory network, an emotion recognition model based on the bidirectional network is constructed. The results demonstrated that the proposed model had high recognition accuracy on both datasets, with 94.67% and 95.06% respectively, and recall rates of over 90%. The recognition accuracy (91.46%) and F1 score (90.77%) of the complete model were the highest. The proposed emotion recognition model had the highest recognition accuracy (98.14%), the smallest loss value (0.02), and the fastest decline rate. The research results contribute to the development of psychological health monitoring technology.
  Keywords: Transformer; EEG signals; Psychological health; Multi-head attention; Long short-term memory.
  DOI: 10.1504/IJBIC.2025.10075039
   
  
• A Novel MSAO-F-SVM Classifier based on Feature Transformation and Parameter Optimisation  
  by Qian Wang, Qinghua Gu, Yan Wang 
  Abstract: The generalisation error performance of the support vector machine algorithm is affected by the radius-to-interval ratio. At the same time, the selection of model parameters largely determines the classification performance. However, the existing optimisation algorithms seldom take these two aspects into account. This article proposed a new radius-margin-based SVM model with improved Aquila optimizer called MSAO-F-SVM, which considers the maximisation of margin and the minimisation of radius information. Firstly, the AO algorithm is integrated with Tent chaos mapping, differential evolution algorithm, and the introduction of the adaptive weighting factor to search the optimal global parameters effectively. Secondly, the enhanced AO method is utilised to choose the ideal values for maximum performance. Finally, the model is solved in three steps: matrix initialisation, parameter optimisation and transformation matrix solution. Experimental results showed that the proposed MSAO-F-SVM algorithm has better classification accuracy compared to other models and is valuable for solving classification problems.
  Keywords: Support vector machine (SVM); Aquila optimizer (AO); Parameters optimization; Feature transformation; Algorithm improvement.
  DOI: 10.1504/IJBIC.2025.10075741
   
  
• Swarm Intelligence and its Application: an Overview  
  by Qianjin Guo, Hongye Wang, Shuqi Huangfu, Shuai Guo, WanRu Gao, Yazhou Hu 
  Abstract: Swarm intelligence is a fascinating concept inspired by decentralised and self-organised biological groups. It is valued for redundancy, robustness, diversity, and effective spatial coverage. This review examines its core concepts, methodologies, military applications, and challenges. Furthermore, we propose an innovative classification framework combining application scenarios and algorithm characteristics. Specifically, we overview the fundamental concepts, development, and working principles underlying swarm intelligence. The three essential elements and five key principles defining this approach are summarised. Then, we explore mainstream swarm intelligence algorithms, their applications, and improved iterations. A captivating aspect of swarm intelligence is its diverse military applications across aerospace, aviation, land, surface, and underwater domains. We elaborate on its use in these five fields, discussing current development and research progress. Finally, the major challenges swarm intelligence faces are analysed. In summary, this review provides a comprehensive exploration of swarm intelligence its principles, algorithms, military applications, challenges, and future prospects.
  Keywords: Intelligence swarm system; Swarm intelligence; Robot swarm; Multi-agent reinforcement learning; Intelligence algorithm.
  DOI: 10.1504/IJBIC.2025.10075743
   
  
• Reconfigurable FIR Filter Design Optimised with Hybrid Adolescent Identity Search Algorithm and Group Teaching Algorithm in FPGA for Biomedical application  
  by Senthil Kumar S, S. Karthick, S. Thillaikkarasi, Rajesh Kumar T 
  Abstract: Finite Impulse Response (FIR) filters are crucial in biomedical signal processing, providing precise filtering to extract relevant information from noisy signals. However, designing and implementing FIR filters for real-time applications on Field-Programmable Gate Arrays (FPGAs) presents challenges in performance optimisation and execution time reduction. To address these challenges, design and implementation of a reconfigurable FIR filter optimized with a hybrid Adolescent Identity Search Algorithm and Group Teaching Algorithm (Hyb-AISA-GTA) on FPGA for biomedical applications called RFIR-Hyb-AISA-GTA is proposed. The RFIR filter utilizes a Truncation and rounding-based scaling rounding approximation Multiplier (TOSAM) and the Error Reduction Carrying Prediction Approximate Adder (ERCPAA) for enhanced performance. Optimal filter coefficients are estimated using Hyb-AISA-GTA algorithm. The RFIR-AISA-GTA filter achieves lower stop band attenuation of 0.2340 dB and shorter execution time of 2.46seconds compared to existing methods. Tested on Virtex FPGA using Verilog in Xilinx ISE14.5, the filter effectively removes noise from ECG signals.
  Keywords: Biomedical application; Group Teaching Algorithm; hybrid Adolescent Identity Search Algorithm; Reconfigurable finite impulse response (RFIR) filter; maximum ripples.
  DOI: 10.1504/IJBIC.2025.10075752
   
  
• Talent Cultivation Method for Building Informatisation Based on Distributed Teaching Assistance Model  
  by Fang Wang 
  Abstract: This study proposes a teaching assistance model for the cultivation of building informatisation talents based on a distributed architecture. Through the integration of microservice architecture and cloud computing technology, the dynamic scheduling and efficient management of teaching resources are achieved. This model takes modular design as the core, combines key technologies such as service registration discovery and API gateway, and supports high concurrent access and elastic scalability. Experiments showed that the model significantly improved the prediction accuracy by introducing preference factors and attendance behaviour analysis in the performance prediction algorithm. Meanwhile, the system could still maintain a stable response speed in high-concurrency scenarios, and the delay was controlled within an acceptable range. The proposed distributed teaching assistance model is scalable, flexible, and highly available, providing theoretical and practical references for the cultivation of talents in building informatisation.
  Keywords: Distributed architecture; Microservice architecture; Teaching assistance model; Talents in architectural informatisation; Cultivation.
  DOI: 10.1504/IJBIC.2025.10075945
   
  
• Siamese Network Based on Self-Attention Dropout and Blur Pooling for Object tracking  
  by Ciyuan Wang, Jia Zhang, Zhiheng Wang, Xiaoyu Sun, Juan Wang 
  Abstract: Addressing the challenges faced by Siamese network-based object tracking algorithms, such as difficulties in effectively extracting salient features of the target due to the shallow architecture of AlexNet, and positional offsets caused by pooling downsampling. This paper first designs a saliency attention module inspired by the working principle of the biological visual system, and adds a saliency attention layer in the offline training process to improve the network's ability to learn significant features through adversarial learning. Then, a feature extraction network with Max Blur Pooling as the downsampling layer is constructed to reduce the aliasing effect during the downsampling process. Finally, the trained tracking network is tested and evaluated using the tracking benchmark dataset OTB100, and the saliency attention layer is not used in the online tracking test. The experimental results show that the comprehensive success rate and precision rate of the proposed ADB-SiamFC are 4% and 3% higher than those of the original SiamFC respectively, and the adaptability and robustness are significantly improved.
  Keywords: Object Tracking; Siamese Network; Visual Inspiration; Saliency Attention.
  DOI: 10.1504/IJBIC.2025.10076636
   
  
• RLaGA: Reinforcement Learning-Assisted Genetic Algorithm for Satellite Resource Scheduling  
  by Zhang Zhe, Chengyu Hu, Xuesong Yan, Wenyin Gong, Dongcheng Li 
  Abstract: Satellite communication is critical in modern information transmission, necessitating efficient resource scheduling to maximise bandwidth utilisation and transmission speed. To address the challenges posed by increasing scheduling difficulty under varying task scales, we develop a mathematical model for satellite communication resource scheduling that incorporates task density effects, resource constraints, and multi-objective optimisation. Specifically, the objective function aims to maximise the total weighted priority gain of scheduled tasks and the number of successfully completed tasks, using a weighted sum formulation. Furthermore, we propose a novel reinforcement learning-assisted genetic algorithm (RLaGA). By integrating Q-learning into the genetic algorithm, RLaGA dynamically adjusts crossover and mutation operations, significantly improving solution quality and accelerating convergence. Experimental results conducted across multiple task scales to simulate increasing scheduling complexity demonstrate that our approach outperforms traditional heuristic algorithms, delivering superior performance in large scale, high density scheduling scenarios.
  Keywords: Satellite communication; reinforcement learning; genetic algorithm; large-scale; scheduling difficulty.
  DOI: 10.1504/IJBIC.2025.10076712
   
  
• Mechanical Property Prediction of Non-oriented Electrical Steel using Machine Learning Methods  
  by Wangya Huang, Chaoyu Fan, Qi Deng, Qi Kang 
  Abstract: This paper combines fundamental theoretical knowledge in materials science with practical production experience to successfully develop an ensemble learning-based prediction model for the mechanical properties of non-oriented electrical steel, leveraging various machine learning methods such as Bootstrap Forest, K-Nearest Neighbors, Neural Networks, and Stepwise Regression. The model evaluation reveals an R-squared value of 0.978 or higher, indicating its robust performance. By successfully integrating this prediction model into the Manufacturing Execution System (MES), monitoring and prediction of mechanical properties have been achieved without the necessity of additional mechanical testing equipment.
  Keywords: Non-oriented Electrical Steel; Mechanical Property Prediction; Machine Learning.
  DOI: 10.1504/IJBIC.2025.10077197
   
  
• An explorative  
  by Qiong Wang, Jiahang Li 
  Abstract: Differential evolution (DE) is a powerful metaheuristic, yet it often struggles to balance exploration and exploitation, leading to premature convergence. To address this, this paper proposes CGODE, a novel DE variant combining a hybrid mutation operator with a modified opposition-based learning (OBL) strategy. The approach introduces three key contributions: a population diversity metric to assess search status, a mutation operator utilising Cauchy and Gaussian distributions to balance the search process, and an elite subpopulation OBL to accelerate convergence. CGODE was validated using 41 benchmark functions from the CEC 2017 and 2022 suites and two real-world problems. Results demonstrate that CGODE significantly outperforms existing DE variants, OBL methods, and state-of-the-art algorithms in over 65% of the tested functions, effectively solving the exploration-exploitation equilibrium problem.
  Keywords: Differential evolution; Population diversity; Exploration-exploitation; Opposition-based learning; Parameter control.
  DOI: 10.1504/IJBIC.2025.10077433
   
  
• Chronic Kidney Disease Diagnosis using Optimised Adaptive Auto Regressive Deep Recurrent Neural Network Model  
  by Karuppuchamy V, Palanivel Rajan S 
  Abstract: Chronic kidney disease (CKD) is a growing public health concern due to its silent progression and severe long-term complications. To address the limitations of existing diagnostic methods, this study proposes a novel optimised adaptive auto regressive deep recurrent neural network (Op_A2DRNN) model for early and accurate CKD detection. A hybrid Osprey hunger game optimisation (HOGO) method selects key features, while the enhanced gazelle optimisation algorithm (EGOA) tunes model parameters to reduce over-fitting and enhance convergence. The integration of adaptive auto regression with deep RNN improves training efficiency and learning capability. Experimental results demonstrate superior performance, achieving 98.2% accuracy and outperforming existing models like CNN-GRU and DBN.
  Keywords: Cholesky triangle; missing data; standard deviation; threshold parameters; hunger weight; local optima; standardization; autoregressive effects; elite matrix.
  DOI: 10.1504/IJBIC.2025.10077453
   
  
• ECG Reconstruction with Compressive Sensing and Enhanced Step Size Optimised Sparsity Adaptive Matching Pursuit Algorithm  
  by S. Karthikeyani, S. Sasipriya, M. Ramkumar 
  Abstract: Electrocardiogram (ECG) signal monitoring plays a critical role in the early detection and diagnosis of cardiovascular diseases. However, efficient transmission and storage of ECG data remain challenging due to the large volume of signals generated. To address these challenges, this work presents an effective compression-detection-based reconstruction method. The incoming ECG signals are first represented as sparse signals and then compressed. To improve compression efficiency, an optimisation strategy is proposed using the improved intelligent satin bowerbird optimiser (I2SBO). The original signal is subsequently reconstructed on the medical server using the observation matrix and an enhanced step size optimised sparsity adaptive matching pursuit (ESSO_SAMP) algorithm. The proposed method is validated using the MIT-BIH atrial fibrillation (AF) database. The model achieves a MSE of 495.52, and a reconstruction probability of 0.944. These results demonstrate the effectiveness of the proposed method in enhancing ECG signal reconstruction while maintaining a high compression ratio.
  Keywords: Observation matrix; restricted isometry property; time index; recovering signal; elitism; convergence; termination; judgement; stage index.
  DOI: 10.1504/IJBIC.2025.10077454
   
  
• The Application of Adaptive Residual Module Optimisation Transformer Model in Sports Training Human Pose Estimation  
  by Jinchi Yu, Xiaomin Gu 
  Abstract: To make more intelligent and objective comparisons of body posture in sports training, this study proposes a network model of adaptive residual module optimised Transformer. Existing methods often struggle with feature degradation in complex motion sequences and individual biomechanical variations during cross-subject analysis. The model optimises the information transfer process by using the residual transfer pathway between attention layers, effectively addressing gradient dissipation while preserving detailed kinematic features, while improving the comparison accuracy. In addition, the study employs a multi-view pose estimation method to collect and process data from key points of the human 3D skeleton. It also standardises and normalises their positional data through anthropometric scaling to improve the accuracy of motion posture analysis. The test results revealed that the improved model’s comparison accuracy is 7.3% higher than the standard Transformer, with notable advantages in self-occluded postures and fast transitional movements, boosting intelligent sports development.
  Keywords: Residuals; Transformer model; Posture; Comparison; Attention layer; Sports training.
  DOI: 10.1504/IJBIC.2025.10077455
   
  
• Recognition of Chinese sentence intention combining multi-channel attention convolution and graph neural network  
  by Xin Zhao, Qiansong Wang 
  Abstract: To improve the recognition accuracy for Chinese sentence intention, this paper proposes a Chinese sentence intention recognition model by combining multi-channel attention convolution and graph neural network. The semantic features of Chinese sentences are extracted by using the improved CNN network, and the syntactic relations of Chinese sentences are extracted by using the graph neural network. Finally, the semantic features and syntactic relations are fused and softmax classifier is used for classification. The results show that average precision, recall and F1 value of the proposed model for different Chinese sentence intention recognition tasks reach 93.52%, 93.87% and 93.06%. Thus, the proposed model can improve the Chinese sentence intention recognition precision.
  Keywords: intention recognition; CNN network; graph neural network; semantic feature; syntactic relation.
  DOI: 10.1504/IJBIC.2025.10077456
   
  
• Oversampling Classification of Multi-class Imbalanced Data based on Metaheuristic Algorithm  
  by Xin-ji Chen, Jing-Lun Zhu, Ze-yu Liu, Jian-wei Liu 
  Abstract: Classifying imbalanced data is one of the most critical tasks faced in modern data analysis. Particularly, when combined with other factors such as the presence of noise, class overlap issues, and ambiguities, data imbalance significantly impacts classification performance. Furthermore, with deeper investigations into the class imbalance problem, it’s found that multi-class imbalanced learning is more common in practice. Despite this, current research in the field of data imbalance largely focuses on binary classification problems, with relatively less study on the more challenging multiclass classification issues. In this paper, we introduce a novel oversampling technique the multi-class sine cosine evolutionary hybrid algorithm (MSCEHA). This method leverages the global search capability of meta-heuristic algorithms and the genetic characteristics of differential evolution algorithms to generate samples with diversity and robustness, and it is less affected by outliers compared to existing oversampling methods. Lastly, by introducing the triangular decomposition strategy for handling multi-class problems, MSCEHA is less affected by the loss of inter-class relationship information than traditional multi-class decomposition strategies. Experimental results on multi-class imbalanced benchmark datasets show that our method has higher robustness to noise and performs better compared to existing methods.
  Keywords: Metaheuristic Algorithms; Class Imbalance; Oversampling; Decomposition Strategy; Multi-class Data.
  DOI: 10.1504/IJBIC.2025.10077635
   
  
• Onsets and Frames Model based Piano Teaching Evaluation with Rhythm Tracking  
  by Weixin Zou, Shimin Wang, Ming Zhao 
  Abstract: To address the issue of uneven professional levels among real piano teachers and realise real-time error feedback on students electronic music score practice, this paper adopted an improved piano transcription algorithm to obtain note information and used rhythm tracking to complete the evaluation of piano teaching. In the piano transcription task, based on the onsets and frames model, the network structure was optimised for audio feature extraction, which effectively avoids information loss during transmission. In the rhythm tracking task, an adaptive harmonic superposition method was used to extract rhythms; significant energy enhancement can also be observed in the frequency spectrum at integer multiples of the signals fundamental frequency, indicating a remarkable effect on rhythm extraction. Through tests using piano practice audio data, the new model demonstrates significant improvements in noise resistance, enhanced performance in real-time piano music transcription tasks in real-world environments, and a favourable user experience.
  Keywords: Piano teaching; Teaching evaluation; Music transcription; Rhythm tracking.
  
  
• Using Custom Deep Networks optimised with Golden Jackal Algorithm Alzheimer's Disease Classification  
  by Jayanthi V. S, Baskar D, Sarankumar R, Jagadeesh P, Blessy C. Simon, Preethi Bhaskaran 
  Abstract: Alzheimer's disease (AD) is a common neurodegenerative disorder marked by memory loss and cognitive decline. Early detection is essential but challenging due to subtle initial symptoms and overlapping cognitive impairments. This research addresses these challenges by proposing eight deep learning models for identifying various stages of cognitive impairment and AD utilizing neuroimaging data from the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset. AlexNet, GoogleNet, ResNet, SqueezeNet, VGG-16, VGG-19, DenseNet, and LeNet are optimized using the Golden Jackal Algorithm (GJO) to enhance the classification performance. The proposed method significantly improves classification accuracy and shows up to 41.82% increase for AD detection compared to the existing models. This analysis exhibits the efficacy of deep learning for early AD diagnosis, and facilitating timely interventions.
  Keywords: RBF-Kernel-Based Adaptive Filtering; Cognitive Impairment; Classification; Golden Jackal Algorithm.
  DOI: 10.1504/IJBIC.2025.10077791
   
  
• A New Artificial Bee Colony Algorithm for Wireless Sensor Network Coverage optimization  
  by Feiyan Fan 
  Abstract: This paper introduces a novel artificial bee colony (ABC) algorithm to enhance the coverage of wireless sensor networks (WSNs). The new algorithm called NABC employs three improved strategies. Firstly, an external archive is built, and some best solutions are stored in the archive at each iteration. Secondly, three improved search equations are designed based on the archive. In the search process, these search equations are adaptively chosen to produce offspring. Thirdly, the scout bee search phase is modified by utilizing the information of the archive. To validate the search efficiency of NABC, eleven well-known benchmark problems are tested. Computational results show that NABC is superior to five other ABC variants. Finally, NABC is applied to optimize the coverage in WSNs. Simulation results demonstrate that NABC can improve the network coverage and quality of service of WSNs.
  Keywords: artificial bee colony; search strategy; coverage; wireless sensor network; optimization.
  DOI: 10.1504/IJBIC.2026.10077875
   
  
• Optimal Mobile Sink Placement with Voronoi based Node Deployment and Artificial Gazelle Optimisation  
  by Arikrishnaperumal Ramaswamy Aravind, Prianka RR, Seenuvasamurthi S, Sahaana G 
  Abstract: In wireless sensor networks (WSNs), mobile sinks have drawn interest as a solution to sinkhole or hotspot problems. Nonetheless, the path design of the mobile sink has a major impact on the networks lifetime and coverage, which is essential for many WSN applications that require delay-sensitive data collecting. In order to increase network coverage and lifetime, this study proposes an energy-efficient mobile sink deployment technique for WSNs. This approach makes use of the LEACH technique for cluster head (CH) selection and energy-efficient clustering, and it improves coverage through an improved Voronoi-based node deployment. The mobile sink collects CH data, and the proposed artificial gazelle optimisation (ArGo) algorithm, which combines the artificial hummingbird algorithm (AHA) with gazelle optimisation (GOA) for faster convergence and acquisition of the global best solution, is used to determine the optimal placement of the sink. The simulation scenarios proposed approach is implemented in Python. According to simulation results, proposed approach outperforms conventional optimisation strategies by greatly increasing data collection efficiency, obtaining values of 0.9986 for residual energy, 99.9900 for packet delivery ratio and 89.2079 for network lifespan.
  Keywords: optimal mobile sink placement; wireless sensor networks; meta-heuristic algorithm; energy efficiency; clustering.
  DOI: 10.1504/IJBIC.2026.10077953
   
  
• Student Employment Guidance and Teaching Management Based on LightGBM Algorithm  
  by Mei Huang 
  Abstract: With the rapid expansion of higher education, structural contradictions in graduate employment have intensified, exposing limitations in traditional guidance models regarding personalization and data utilization. To address these issues, this study proposes a collaborative model integrating Light Gradient Boosting Machine(LightGBM) with a feature-optimized Rotating Forest(RF) framework. A dynamic feature rotation mechanism was designed to enhance base classifier diversity, overcoming homogeneity in traditional ensembles. This was combined with an improved SimRank++ algorithm for cross-modal similarity propagation and a multi-objective K-Means for hierarchical demand clustering. Results demonstrate that the integrated model significantly improves guidance accuracy, achieving a stable graduate employment success rate of 0.94-0.96. It consistently outperformed control groups, with core metrics surpassing mainstream algorithms. Cross-modal accuracy reached 0.89, and six distinct employment demand types were identified. This framework offers valuable support for advancing data-driven university employment and teaching management.
  Keywords: LightGBM; Rotating forest; Principal component analysis; IK-Means; Employment; Teaching.
  DOI: 10.1504/IJBIC.2026.10077963
   
  
• DynSpike: a spiking neural network for effective long-term temporal dependency capture in dynamic graphs  
  by Zijing Yuan, Tianfang Lu, Masaaki Omura, Shangce Gao 
  Abstract: Dynamic graph learning is crucial for modelling systems with evolving node features and graph structures. Spiking neural networks offer low-power solutions but struggle with long-term dependencies and multi-scale dynamics. To address these, we propose DynSpike, a novel spiking neural network framework for dynamic graph processing. DynSpike introduces a spatio-temporal multi-scale fusion strategy, modelling local/global structures and short/long-term dependencies through parallel spatio-temporal branches. These branches are processed by the aggregation-spiking-attention module, combining neighbourhood aggregation, spike-driven encoding, and multi-head attention for fine-grained node representations. Experiments on benchmark datasets show DynSpike outperforms existing methods in node classification, effectively capturing multi-scale spatio-temporal patterns with high efficiency.
  Keywords: deep learning; graph neural network; spiking neural networks; dynamic graph data.
  DOI: 10.1504/IJBIC.2026.10078104
   
  
• A liquid level approximation algorithm based on echo sound pressure model  
  by Bin Zhang, Shuqi Zang, Yuejuan Wei, Zong Yao, Qing Li, Yan Qiang 
  Abstract: This study achieves dual innovations in the field of non-contact liquid level detection, encompassing the generalisation of a physical model and the development of a targeted automated algorithm. First, systematic validation confirms the sound pressure calculation model established in prior research exhibits excellent universality. Experimental studies using containers made of four materials, namely aluminium alloy, stainless steel, glass, and plastic, demonstrate that the model delivers robust performance across different materials, while also revealing the influence of material properties on detection performance. Specifically, metallic containers exhibit superior measurement resolution due to lower acoustic energy loss, whereas plastic and glass materials show significantly reduced detection sensitivity owing to higher attenuation. Second, to address the limitation of existing threshold-based methods in accurately identifying key features under noisy or signal-attenuated conditions, an innovative automated approximation algorithm was developed. This intelligent algorithm, grounded in physical mechanisms, automatically determines liquid level height, and its output is designed for direct integration into industrial automation systems. Ultimately, this research provides a validated automated solution that offers effective technical support for liquid level detection in specialised application scenarios.
  Keywords: detection model; elastic transfer function; paraxial approximation; liquid level.
  DOI: 10.1504/IJBIC.2026.10078176
   
  
• Background music generation method integrating time series model and GAN  
  by Ji Lu 
  Abstract: This study proposes an intelligent background music generation method based on an improved multi-track sequence generative adversarial network (MTSGAN). The core objectives are threefold: resolving note fragmentation and rhythm imbalance through cyclic structural optimisation of generators; achieving multi-track harmony via dual time-series models (FG and TG) with chord compatibility constraints; enhancing feature smoothness through self-recurrent CNN and hybrid optimisation algorithms. During the process, a music sample set was constructed based on the Lakh Pianorol dataset and pre-processed to remove note fragmentation. Simultaneously optimising neural network parameters to improve generation efficiency and music quality. The results show that the research method exhibits superior performance on various instrument data, with a qualified note rate of 92.89% on piano sample data; The qualified note rate on the string sample data reached 96.05%. This work provides a technical solution for cross-modal background music generation, addressing critical gaps in artistic fluency and real-time adaptability.
  Keywords: multi-track sequence generation adversarial network; music generation; feature extraction; convolutional neural networks; CNN.
  DOI: 10.1504/IJBIC.2026.10078177
   
  
• The SSJ method: identifying hostile individuals in multi-agent systems  
  by Fengying Yang, Huichao Liu, Zia Ur Rehman, Ahmad Din 
  Abstract: This study investigates the identification of hostile agents and their attack strategies in multi-agent systems (MASs), critical for enhancing security and reliability of distributed control. We propose a single-step jump algorithm (SSJ) that evaluates stepwise deviations between an agents current and historical opinions using predefined thresholds. This mechanism differentiates honest agents from hostile ones and recognises two adversarial strategies: the overt Berserk strategy and the deceptive Pretend strategy. Extensive experiments were conducted on a 50-agent MASs in more than ten thousand simulated scenarios, including pure Berserk, pure Pretend, and Mixed attacks. Performance was assessed using five metrics: accuracy, hostile detection rate, undetected rate, false-hostile rate, and strategy detection rate. Comparative analysis with the classical W-MSR algorithm shows that SSJ achieves superior accuracy, robustness, and stability across varying hostile ratios, which demonstrate that SSJ provides an effective and reliable approach to improving resilience and fault tolerance in adversarial MASs.
  Keywords: multi-agent systems; MASs; single-step jump algorithm; adversarial strategy; distributed control; robustness.
  DOI: 10.1504/IJBIC.2026.10078178
   
  
• Adversarial Network with Block Chain adopted Intrusion Detection Scheme for Progressive Wasserstein Generative Enhancing Privacy and Security in IoT Environment  
  by A. Saleem Raja, V. Balamurugan, B. Sundaravadivazhagan, Robin Cyriac, R. Karthikeyan 
  Abstract: The growth of smart cities depends on Internet of Things (IoT) for efficient urban management and public services. However, IoT's security, privacy and scalability challenges hinder its adoption, requiring innovative approaches to secure and optimise interconnected urban systems. Traditional approaches face issues like limited mobility, high power usage and also challenges such as security, privacy, trust, scalability, verifiability and centralization hinder its faster adoption. This study proposes, Blockchain-based Delegated Proof of Stake (DPoS) with Progressive Wasserstein GAN (PWGAN) for Intrusion Detection (BC-DPoS-PWGAN-IDS). The BC-DPoS-PWGAN-IDS has two level privacy preservation; DPoS-based blockchain for secure consensus and Adjusted Quick Shift model for data conversion. PWGAN is optimised with Nomadic People Optimiser (NPO) for improved intrusion detection. The method shows 98% F-score, 99% accuracy and 30s of execution time when analysed with two datasets. The proposed system effectively improves IoT security by addressing key challenges and shows solid performance across key metrics.
  Keywords: Block Chain; Intrusion Detection in Internet of Things; Nomadic People Optimizer; Progressive Wasserstein generative adversarial network.
  DOI: 10.1504/IJBIC.2025.10078447
   
  
• A hybrid ACO Algorithm with SOS and 3-Opt for Solving Citrus Picking Sequence planning  
  by Wenxuan Huang, Yu Tang, Zhiping Tan, Huasheng Huang 
  Abstract: Traditional ant colony optimization (ACO) algorithms often exhibit slow convergence and are prone to being trapped in local optima when applied to the picking sequence planning problem.To address this problem, we propose a hybrid strategy ant colony algorithm (3SACO), based on the 3-Opt heuristic method and the symbiotic organisms search (SOS). First, the SOS is used to adjust three key parameters of the ACO algorithm adaptively. Second, the 3-Opt local search strategy is integrated into the algorithm to improve the convergence rate and obtain the optimal path. Third, different traveling salesman problem (TSP) instances in TSPLIB are used to evaluate the performance of the proposed algorithm. The experimental results prove that the proposed algorithm significantly outperforms other algorithms in terms of robustness and the quality of the optimal solution. In addition, the proposed 3SACO is employed to solve a real-world citrus picking sequence planning problem.
  Keywords: Ant colony algorithm; Symbiotic organisms search; 3-Opt algorithm; Citrus picking sequence planning.
  DOI: 10.1504/IJBIC.2026.10078602
   
  
• Modular Design of Underground Rescue Drilling Rigs Based on the Fusion of Knowledge and Genetic Clustering  
  by Dong Fan 
  Abstract: To address transportation difficulties and low assembly efficiency of large-diameter casing drilling rigs in underground coal mine rescue, this study proposes a knowledge-driven modular design method, integrating domain design knowledge (DSM), algorithm optimisation knowledge (MDL), and engineering evaluation knowledge (adaptability evaluation) to establish a systematic solution. A quantifiable design structure matrix was established based on a three-dimensional knowledge weighting model (structural: 0.68, geometric: 0.08, functional: 0.24). Combining MDL theory with K-means and genetic algorithms achieved optimal knowledge encapsulation, yielding seven module schemes (MDL: 522.17). Developed core modules including a dual-rotation high-torque power head (250 kNm) and independently-driven feed unit (1,604 kN) through multi-source knowledge integration. Knowledge-enhanced entropy-weighted TOPSIS evaluation showed outstanding performance (technical: 85 +- 2.3; economic: 92 +- 1.7; overall: 89.21). Field results demonstrated 60% faster assembly, 40% lower maintenance costs, and compliance with 4,000 x 1,750 x 1,800 mm roadway limits, establishing a smart equipment design standard for mine rescue.
  Keywords: knowledge-driven; GA-Optimized Clustering Method; underground rescue drill rig; modular design; design structure matrix.
  DOI: 10.1504/IJBIC.2025.10078878
   
  
• Portfolio optimisation model based on structural modelling from the perspective of ESG and its intelligent solution  
  by Junlong Shen, Kaiyan Tang, Qijie Zhuang, Binhua Yu, Renbin Xiao 
  Abstract: To address the structural deficiencies of traditional portfolio models in integrating numerous environmental, social, and governance (ESG) factors, this paper, based on interpretive structural modelling (ISM), first extracts and systematically analyses 30 ESG influencing factors from six dimensions, including environmental, social, and governance aspects. Then, through ISM partitioning, it reveals the multi-level structural relationships of portfolio problems from an ESG perspective. Based on this, a multi-objective optimisation model integrating seven objectives with financial and ESG indicators is constructed, with nine types of practical constraints. A hybrid NSGA-III/AROA algorithm embedding niche strategies is proposed to intelligently solve the established multi-objective optimisation model, demonstrating excellent performance during the solution process. This research, through its structural model optimisation model intelligent solution processing architecture, embodies the holistic thinking of financial systems engineering and can provide effective support for financial theory innovation and practical research.
  Keywords: portfolio; ESG; structural modelling; optimisation model; many-objective optimisation; intelligent solving; hybrid algorithm; financial systems engineering.
  DOI: 10.1504/IJBIC.2026.10078907
   
  
• Optimised Map Fusion for Collaborative SLAM in Multi-Robot Systems  
  by Luigi Maciel Ribeiro, Nadia Nedjah, Paulo Carvalho 
  Abstract: Map fusion remains one of the central challenges in Collaborative Simultaneous Localization and Mapping (C-SLAM), largely due to structural redundancies, viewpoint changes across robots, and the difficulty of reliably identifying correspondences between independently generated maps. This research proposes an innovative approach to optimize map fusion, combining Fourier Transform, Pearson correlation coefficient, and Particle Swarm Optimization. The method identifies distinctive environmental features, guides the selection of robot pairs, and determines rotation and translation parameters, ensuring accurate and integrated mapping. Validated through simulations, the method demonstrated significant advancements after fusing all maps, in completeness (96.91%), accuracy (86.41%), precision (68.70%), and efficiency (66.67%). The research highlights the importance of map fusion for a comprehensive representation of shared environments, offering innovative solutions to practical challenges.
  Keywords: Collaborative SLAM; Map fusion; Multi-robot systems; Fourier transform; Pearson correlation coefficient; Particle swarm optimization;.
  DOI: 10.1504/IJBIC.2026.10078908
   
  
• Maximum Power Point Extraction with Hybrid Optimisation Tuned FOPID Controller in Solar PV Systems with Partial Shading Conditions  
  by Nilesh Mendhe, Abhay Vidyarthi 
  Abstract: The study introduces a novel hybrid optimisation algorithm called the dwarf mongoose sparrow (DM-Spa) algorithm to enhance the maximum power point tracking (MPPT) under PSCs in solar PV systems. The DM-Spa algorithm is used to optimally tune the fractional order proportional-integral-derivative (FOPID) controller parameters, which in turn optimises the duty cycle of a superlift Luo converter to maximise power output. The DM-Spa algorithm combines features from the dwarf mongoose optimisation algorithm (DMA) and the sparrow search optimisation algorithm (SSA). The results show that the proposed dwarf mongoose sparrow (DM-Spa) algorithm outperforms other methods in precision, achieving a significantly lower error value of 1.5089
  Keywords: Photovoltaic System System; Partial shading condition; Maximum Power Point Trackingand Optimization.
  DOI: 10.1504/IJBIC.2026.10079071