International Journal of Computational Science and Engineering (8 papers in press)

Regular Issues

• Self-supervised learning with split batch repetition strategy for long-tail recognition  
  by Zhangze Liao, Liyan Ma, Xiangfeng Luo, Shaorong Xie 
  Abstract: Deep neural networks cannot be well applied to balance testing when the training data present a long tail distribution. Existing works improve the performance of the model in long tail recognition by changing the model training strategy, data expansion, and model structure optimisation. However, they tend to use supervised approaches when training the model representations, which makes the model difficult to learn the features of the tail classes. In this paper, we use self-supervised representation learning (SSRL) to enhance the model's representations and design a three-branch network to merge SSRL with decoupled learning. Each branch adopts different learning goals to enable the model to learn balanced image features in the long-tail data. In addition, we propose a Split Batch Repetition strategy for long-tailed datasets to improve the model. Our experiments on the Imbalance CIFAR-10, Imbalance CIFAR-100, and ImageNet-LT datasets outperform existing similar methods. The ablation experiments prove that our method performs better on more imbalanced datasets. All experiments demonstrate the effectiveness of incorporating the self-supervised representation learning model and split batch repetition strategy.
  Keywords: long-tail recognition; self-supervised learning; decoupled learning; image classification; deep learning; neural network; computer vision;.
  
  
• ESPS: an effective software pipelining scheme for high performance computing in vector VLIW DSP  
  by Yonghua Hu, Tang Zhuoyou, Cheng Aobo, Xiong JingCheng, Wei Liang 
  Abstract: Processors based on very long instruction word (VLIW) architecture have rich registers and computing resources. Making full use of these resources can improve code performance effectively. Software pipelining is an important optimisation technology known to improve the utilisation of computing units. However, unsuccessful instruction pipelining often appears in traditional software pipelining, so there are still many chances to further improve the arrangement of instruction sequences during the optimisation process. In this paper, to generate more efficient programs and make software pipelining optimisation more general, we present an effective software pipelining scheme (ESPS) in Vector VLIW DSP for high performance computing. The loop optimised by ESPS gets higher instruction level parallelism and more instruction scheduling space. We show that ESPS will not fail and improves the execution performance of some representative digital signal processing algorithms relative to loop unrolling optimisation by up to 5%
  Keywords: vector VLIW DSP; loop optimisation; software pipelining; high performance computing.
  DOI: 10.1504/IJCSE.2026.10078985
   
  
• Deep transfer learning method for aero-engine blade encoded character detection and recognition based on YOLO-GE  
  by Yifan Xiao, Shuxia Wang, Guanfeng Wang, Shixin Wu, Mingrui Zhang, Jianghong Li 
  Abstract: The automatic detection and recognition of aero-engine blade encoded characters plays a crucial role in the intelligent manufacturing of the aviation industry, but challenges arise due to the structural characteristics of dot matrix characters, complex storage and assembly environment, limited sample size, and non-uniform data category distribution. This study presented a deep transfer learning method to detect and recognize the aero-engine blade encoded character. The target dataset is constructed through data augmentation to enhance the diversity and scale, integrating public datasets with self-generated simulated datasets to pre-train the model addressing the issue of sample imbalance. Then, an improved YOLO-GE model is proposed to enhance feature extraction capabilities, along with the design of a corresponding transfer fine-tuning strategy. Experimental results show that, compared to other specialized OCR technologies and models, the proposed method can achieve a good balance between accuracy and efficiency, with strong potential for deployment in practical industrial systems.
  Keywords: aero-engine blades; industrial character recognition; deep transfer learning; small sample dataset; network model.
  DOI: 10.1504/IJCSE.2026.10079196
   
  
• Uncertainty-aware web spam detection: a fuzzy integral approach  
  by Abdolhamid Nazerpanahi, Mohammadali Hossein Beigi, Amir Hosein Keyhanipour 
  Abstract: Web spamming is not merely a technical nuisance but a significant economic burden on search engines, leading to wasted crawling resources, storage inefficiencies, and revenue loss due to degraded user trust. Traditional detection methods, which rely on high-dimensional feature engineering, incur substantial computational costs and operational latency, limiting their scalability in real-time industrial environments. To address these economic and technical bottlenecks, this paper proposes a cost-effective, uncertainty-aware framework. By shifting the paradigm from expensive content extraction to an evidence-centric aggregation strategy using Bayesian probability and fuzzy integrals (Sugeno and Choquet), we minimise the dependency on hardware-intensive processes. Validated on WEBSPAM-UK benchmarks, our Sugeno-based model achieves exceptional precision (up to 98.9%). Critically, from an industrial standpoint, this approach yields a 40-60% reduction in processing overhead compared to feature-heavy baselines. From a deployment perspective, the proposed framework can be integrated as an early-stage filtering or decision layer in large-scale search engine pipelines, enabling cost-efficient spam screening before expensive crawling and ranking processes. This efficiency directly translates to lower operational expenses (OPEX) and faster index updates, offering a high-ROI solution for maintaining search engine integrity and profitability.
  Keywords: web spam detection; uncertainty handling; fuzzy integral operators; Sugeno integral; choquet integral.
  DOI: 10.1504/IJCSE.2026.10079328
   
  
• An optimised inception capsule-based LSTM with fuzzy SVM model for effective cardiovascular detection and classification  
  by Uma Pyla, Venkata Lakshmi Panga 
  Abstract: Cardiovascular disease (CVD) is a serious concern in the healthcare industry due to its global prevalence and fatality rates. Traditional machine learning (ML) and deep learning (DL) models are widely used for CVD diagnosis; existing models often suffer from data imbalance, limited feature representation, lack of model parameter tuning, and lack of model interpretability analysis, which reduces diagnostic reliability in clinical environments. To overcome these issues, this study proposes an optimised inception capsule based long short-term memory with fuzzy support vector machine (ICPA-LSTM-FSVM) model for accurate cardiovascular disease detection and classification. The proposed method attained 98.74% accuracy, 98.12% precision, 97.95% recall, and 98.03% F1-score, outperforming state-of-the-art methods. Experiment results demonstrated that the proposed model greatly enhances the identification of CVD over the currently available methods, and also provides a reliable and interpretable solution for early CVD diagnosis in intelligent healthcare systems.
  Keywords: cardiovascular disease; machine learning; ML; deep learning; explainable AI; XAI; inception capsule; long short-term memory; orangutan optimisation algorithm.
  DOI: 10.1504/IJCSE.2026.10079443
   
  
• GS-LVPose: Fast and Accurate Camera Pose Estimation under Large Viewpoint Variations  
  by Fenghua Wang, Keji Song, Wenze Wu 
  Abstract: Camera pose estimation is fundamental to autonomous driving, augmented reality, and robotic navigation. However, existing methods often struggle with large viewpoint variations, which limits their reliability in complex industrial scenarios. This paper presents GS-LVPose, a novel framework designed for fast and accurate pose estimation under significant viewpoint discrepancies. By integrating 3D Gaussian Splatting (3DGS) with feature matching, our approach achieves efficient scene representation without relying on CAD models or extensive training. We propose a coarse-to-fine optimization scheme using a custom uniform sampling algorithm to enhance robustness against large angular differences. Experimental results demonstrate that GS-LVPose effectively handles large-viewpoint challenges, achieving a 97% success rate with an average inference time of 1.34s. With strong robustness to viewpoint changes and competitive efficiency, GS-LVPose offers a practical and effective solution for real-world camera pose estimation tasks.
  Keywords: pose estimation; geometric feature extraction; 3D Gaussian Splatting.
  
  
• Adaptive Sparse Feature Pyramid Networks for Enhanced Lightweight Small Object Detection  
  by Junyi Zhang, Dezhi Han, Xiang Shen, Hung-Wei Li, Meng-Yen Hsieh 
  Abstract: UAV-based small object detection is critical for automated aerial surveillance, yet it faces severe challenges, including extreme scale variations, dense clutter, sub-pixel information loss, and stringent computational constraints on edge devices. To address these critical bottlenecks, this paper proposes EL-YOLO, a highly optimized, lightweight object detection architecture tailored for resource-constrained UAV and Synthetic Aperture Radar (SAR) imagery. The framework introduces three core innovations: (1) a Spatial-to-Depth Convolution (SPDConv) backbone to preserve fine-grained spatial details that are typically obliterated during conventional downsampling; (2) an Adaptive Sparse-Connected Asymptotic Feature Pyramid Network (ASCAFPN) to bridge semantic gaps across non-adjacent layers and dynamically suppress background noise; and (3) a systematic integration of Depthwise Separable Convolutions (DSConv) to strictly bound computational overhead. Extensive experiments demonstrate the significant impact of the proposed method: EL-YOLO achieves a highly competitive mAP@50 of 31.5\% on the challenging VisDrone2019 dataset and 93.4\% on the HRSID dataset, outperforming state-of-the-art lightweight baselines (e.g., YOLOv11-n, YOLOv12-n) by up to 7.4\% in absolute mAP@50.
  Keywords: YOLO11; Feature fusion; Small Target Detection; Deep Learning; Unmanned aerial vehicle (UAV) aerial images; Lightweight.
  
  
• A dynamic trust-based federated learning framework for securing crowdsensing in edge networks  
  by Jiantao Xu, Yuxing Li, Jianbo Xu 
  Abstract: Mobile crowdsensing underpins smart cities and environmental monitoring, yet open participant pools expose federated learning (FL) systems to malicious contributions. Existing defenses rely on static reputation rules or one-shot filters that fail against adaptive adversaries. We propose federated trust management (FTM), a dynamic trust-aware FL framework comprising: a multi-signal trust score fusing contribution quality, global deviation, and historical consistency; an anomaly-aware penalisation rule with temporal-smoothness regularisation to prevent trust whitewashing and oscillation; and a Stackelberg-game formulation modelling server-client strategic interaction with provable trust-dynamics convergence. Trust updates require only O(d) computation and one scalar per upload, enabling lightweight edge deployment. Experiments on MNIST and CIFAR-10 under classic and non-stationary attacks show FTM improves accuracy by up to 14.3% over FedAvg, sustains 92% accuracy with 50% adversarial clients, and incurs less than 4% computational and 0.1 KB communication overhead per round.
  Keywords: federated learning; crowdsensing; trust management; adversarial robustness; game theory; anomaly detection.
  DOI: 10.1504/IJCSE.2026.10080290