International Journal of Computational Science and Engineering (13 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;.
  
  
• Evaluating the effectiveness of large language models in detecting mental health disorders from social media  
  by Weili Zhao, Yuan Xu 
  Abstract: Mental health disorders affect over 25\% of the global population, making scalable detection essential for early intervention. Current social media datasets often use community-assigned or platform-inferred labels, which may lack semantic clarity and category consistency. This study explores whether large language models (LLMs), such as GPT-4, can generate more reliable annotations by leveraging prompt-based reasoning aligned with standardized symptom criteria. Using a Reddit dataset of 17,159 posts, we re-annotate entries using a Chain-of-Thought (CoT) framework guided by symptom profiles from screening instruments. We then evaluate these LLM-generated annotations against subreddit-derived labels via two downstream tasks: (1) classification performance under supervised learning, and (2) clustering under unsupervised methods. Results show that LLM-generated annotations yield higher consistency and improve downstream performance, particularly for depression and anxiety, demonstrating their potential to enhance mental health detection from online text.
  Keywords: large language models; LLMs; social media text analysis; mental health detection; chain-of-thought reasoning.
  DOI: 10.1504/IJCSE.2025.10073938
   
  
• An enhanced traffic-aware multipath routing scheme in software-defined networks  
  by U. Prabu, R. Venkata Sai, Sanjaya Kumar Panda, V. Geetha 
  Abstract: The growing complexity of modern networks due to diverse Internet services challenges efficient routing and load balancing. Traditional single-path routing fails to handle such dynamic requirements effectively. This paper proposes an enhanced traffic-aware multipath routing (TAMR) scheme in software-defined networks (SDNs), which leverages the centralized programmability of SDNs to dynamically measure real-time network bandwidth and select multiple optimal paths using Yen's algorithm. The proposed TAMR scheme achieves superior bandwidth utilization, reduced transmission delay, and improved load balancing under varying traffic conditions by integrating a packet monitoring module to manage out-of-order packets. The innovation lies in its adaptive multipath routing framework, which combines real-time network awareness and packet monitoring to ensure reliable data delivery. The results demonstrate the industrial applicability of TAMR in datacenter networks and high-demand environments requiring low latency and high throughput, highlighting its potential for enhancing network performance and resilience.
  Keywords: software defined networks; multipath routing; equal-cost multipath routing; load balancing; network monitoring; bandwidth.
  DOI: 10.1504/IJCSE.2025.10073986
   
  
• Feature selection using fossa optimisation algorithm for detection of epilepsy from EEG signal  
  by Pratiti Mishra, Hrishikesh Kumar, Himansu Das 
  Abstract: Epilepsy is a widespread neurological condition impacting people of all ages. Medical professionals use electroencephalography (EEG) as a monitoring tool to analyse neural activity and detect signs of epilepsy. This issue often stems from the inclusion of superfluous EEG features such as noise and irrelevant data that fail to support accurate diagnosis. Therefore, feature selection (FS) methods are necessary to filter out irrelevant features and retain the most diagnostically significant ones. This study proposes an innovative FS method using the fossa optimisation algorithm (FSFOA) to determine the most effective feature subset for improving classification accuracy. This method is compared with four widely recognised FS techniques: ACO, GA, PSO, and DE. The evaluation is conducted using five popular classifiers such as QDA, NB, DT, SVM and KNN. Experimental results reveal that the proposed FSFOA outperforms the aforementioned methods in selecting optimal features and enhancing classification performance.
  Keywords: epilepsy; electroencephalography; EEG; FSGA; FSPSO; FSDE; FSACO; FSFOA; ML classifiers.
  DOI: 10.1504/IJCSE.2025.10074012
   
  
• Multimodal aspect-based sentiment analysis network with adaptive modality balancing  
  by Defang Liu, Taikui Zhang, Zhuoran Zhong, Yan Zhang, Jiajia Liu, Guangli Zhu 
  Abstract: Multimodal aspect-based sentiment analysis, as a fine-grained sentiment analysis task, aims to identify the sentiment polarity associated with a given aspect entity. Current methods mainly focus on fusing information from different sources (e.g., image and text). However, in real-world scenarios, the emotional contributions of different modalities are not balanced. Therefore, directly exploring modality fusion inevitably has its limitations. To address this issue, we propose a multimodal aspect-based sentiment analysis network with Adaptive Modality Balancing (AMB), which adaptively analyzes cross-modal emotional contributions. Specifically, we first separately aggregate the feature information of a single modality through self-attention. Furthermore, during modality fusion, each modality is assigned a different weight based on its feature entropy, which helps balance their emotional contributions. Experiments on two public datasets validate the effectiveness of the proposed network.
  Keywords: multimodal aspect-based sentiment analysis; modality fusion; cross-modal emotional contributions; self-attention; feature entropy.
  DOI: 10.1504/IJCSE.2025.10075607
   
  
• HAN-SL: hybrid attention networks integrated with sentiment lexicon  
  by Chunqing Wang, Yulei Zhang, Ziliang Li, Jiao Yixuan, Jiajia Liu, Shunxiang Zhang 
  Abstract: The existing aspect-based sentiment analysis methods have achieved significant success. However, they face the challenge of low accuracy in handling multiple-aspect words due to long-range dependencies and insufficient contextual information which results in interference from irrelevant words. A hybrid attention network and sentiment lexicon integrated model (HAN-SL) has been proposed to solve the problem. Introducing a phrase structure tree to segment long sentences, minimizing the interference of irrelevant words, and integrating a sentiment lexicon to generate a sentiment enhancement matrix that enriches contextual information. In addition, the HAN-SL constructs a matrix between aspect words and viewpoint words, capturing intrinsic grammatical features through aspect and context-aware attention. The fusion of a sentiment enhancement matrix with a sentiment lexicon effectively enriches contextual semantic information. A dual-channel GCN extracts representation information from two matrices to facilitate efficient information exchange. Extensive experimental results on four public datasets demonstrate the superiority and rationality of HAN-SL.
  Keywords: aspect-based sentiment analysis; sentiment lexicon; hybrid attention neural networks; phrase structure tree.
  DOI: 10.1504/IJCSE.2025.10075609
   
  
• Agri IoT: designed relay multiplexer for port optimisation for IoT-based agriculture platform  
  by Tusharkanta Padhy, Sunil Kumar Dhal, Biswaranjan Bhola, Chhabi Rani Panigrahi 
  Abstract: Increasing adoption of internet of things (IoT) technologies in agriculture necessitates reliable and scalable sensor networks for smart farming applications. This work proposed the design and evaluation of a relay-based multiplexer for IoT-enabled agricultural systems enhancing sensor interconnectivity while minimising input/output port requirements. To address limited connectivity and resource constraints commonly encountered in agricultural environments, the system employed cost-effective magnetic relay technology. Strategically placed relays enable seamless integration of a large number of spatially distributed sensors across extensive and challenging terrains. Adaptive sensor integration algorithms dynamically optimize data transmission channels, resulting in reduced energy consumption and improved network efficiency. Experimental results demonstrate that the proposed multiplexer can connect and manage a significantly higher number of sensors, leading to enhanced data accuracy, real-time monitoring, and informed decision-making for precision agriculture. The system supports the connection of S
  Keywords: agricultural IoT; port optimisation; multiplexer; sensor integration.
  DOI: 10.1504/IJCSE.2025.10076821
   
  
• Layer-specific L2 regularisation for efficient forgetting in transformers  
  by Zhigao Huang, Shiyan Zheng, Musheng Chen, Miao Pan, Tianying Wu, Quanfa Li 
  Abstract: Language models face an inherent challenge in balancing memory capacity with the need to efficiently discard irrelevant patterns. We propose a novel adaptive layer-targeted regularisation framework that enables controlled forgetting by integrating temporal decay scheduling with exponential regularisation strength reduction, employing layer-specific targeting on final multi-layer perceptron (MLP) components, and applying L2 norm constraints selectively to memorisation-critical parameters. Our experiments on a 6-layer transformer reveal that targeted L2 regularisation on final-layer MLP weights maintains validation performance while improving inference speed by 19.2%. Validation on enwik8 dataset using generative pre-trained transformer 2 (GPT2)-small architecture demonstrates 6.58% speedup with only 0.52% validation loss increase, confirming broad applicability. We demonstrate that later transformer layers disproportionately influence memorisation patterns (containing 83% of memorisation capacity), enabling precise interventions without compromising linguistic capabilities. Our approach creates a predictable efficiency-performance trade-off with minimal training overhead. This work offers immediate practical benefits for reducing inference costs and energy consumption in production language models.
  Keywords: inference optimisation; intentional forgetting; layer-specific targeting; model efficiency; regularisation; transformer models.
  DOI: 10.1504/IJCSE.2025.10077380
   
  
• X-SSAS: human-machine interaction-driven framework for explainable and scalable similarity-based link prediction in social networks  
  by Mridula Dwivedi, Vipin Saxena, Babita Pandey 
  Abstract: This study proposes a novel unified similarity-based link prediction method, X-SSAS, that addresses the limitations posed by traditional methods and serves as a medium to explore human-machine interaction. X-SSAS combines both structural similarity and attribute similarity controlled by a tuning parameter to generate efficient similarity scores that considers both network topology and node attributes. The proposed study provides a novel approach to k-medoid clustering using X-SSAS scores and offers transparent predictions using explainable artificial intelligence. Extensive experiments are conducted on 14 real-world datasets and X-SSAS is tested using six well-known evaluation metrics. The results demonstrate that X-SSAS outperforms existing studies and four widely used similarity measures in their weighted version. The proposed X-SSAS framework attains 99.86% accuracy and 99.99% AUROC on the C. elegans datasets. X-SSAS provides enhanced accuracy, transparency, trustworthiness, and scalability across diverse real-world network datasets.
  Keywords: link prediction; explainable artificial intelligence; XAI; k-medoid clustering; HMI; social network; structural similarity; attribute similarity; Jaccard’s coefficient; Euclidean distance.
  DOI: 10.1504/IJCSE.2026.10077984
   
  
• Hybrid convolutional network with adaptive graph attention mechanism for HSI classification  
  by Sandeep Swamy, Shashidhar Sonnad 
  Abstract: This research introduces a novel hyperspectral image classification (HSIC) model, named 3D-2D hybrid convolution and enhanced graph attention mechanism (HCN-AMGAM), designed to overcome the challenges of high-dimensional spectral-spatial feature extraction and classification accuracy. HSI captures rich spectral information across multiple wavelengths, enabling precise identification of diverse ground objects. The proposed HCN-AMGAM integrates 3D-2D hybrid convolution with an enhanced graph attention mechanism to jointly exploit spatial continuity and spectral correlation. The hybrid convolution module effectively captures local-global dependencies while reducing computational complexity compared to conventional 3D CNNs. An adaptive graph construction strategy based on deep spectral-spatial embeddings strengthen feature connectivity, and the multi-feature fusion GAM enhances information integration while minimising redundancy. This synergy leads to more discriminative and robust hyperspectral representations. The proposed end-to-end framework not only achieves higher accuracy but also enhances model interpretability and scalability for real-world remote sensing tasks. Experiments conducted on Kennedy Space Center (KSC) and Pavia University (PU) datasets demonstrate that HCN-AMGAM achieves state-of-the-art performance, confirming its significant impact on advancing hyperspectral image classification and environmental monitoring applications.
  Keywords: hyperspectral image; HSIs; convolutional neural networks; CNNs; Pavia University; PU; Kennedy Space Center; KSC; graph attention mechanism; GAM.
  DOI: 10.1504/IJCSE.2026.10078381
   
  
• Generating empathetic responses via knowledge augmentation and chain-of-thought in conversational information retrieval systems  
  by Zhinan Gou, Yan Li, Mengyao Jia, Siyu Liu 
  Abstract: Conversational information retrieval systems stand as a crucial research area at the intersection of information retrieval and artificial intelligence. However, existing research largely neglects the generation of emotional responses. This paper proposes a novel framework for generating empathetic responses by integrating knowledge augmentation and chain-of-thought reasoning. Firstly, we leverage the knowledge-augmented capabilities of the large language model to generate semantically similar questions, acting as supplementary data to expand the coverage of dataset and enrich the contextual diversity. Then, relevant top answers are retrieved using the original and semantically similar questions, with semantic similarity from datasets as the key metric. Moreover, we establish a chain of thought that guides the large language models to generate empathetic responses, enabling them to offer targeted suggestions for alleviating emotions associated with questions of users. Experimental results show that our method significantly outperforms the baselines in terms of emotional expression and user-perceived empathy.
  Keywords: empathetic response; knowledge augmentation; chain-of-thought; large language model.
  DOI: 10.1504/IJCSE.2026.10078433
   
  
• A software fault prediction model using driving training-inspired metaheuristic feature selection framework  
  by Himansu Das, Pratiti Mishra, Sanat Kumar Patro, Sushruta Mishra, Mahendra Gourisaria, Saurabh Bilgaiyan 
  Abstract: In machine learning-based SFP, software metrics and defect data are utilised as independent features, and the datasets typically have high dimensionality. To reduce dimensionality and improve model performance, an effective feature selection (FS) approach is necessary. This study proposes a new FS method called driving training-based optimisation (FSDTBO). Unlike existing FS methods, the proposed algorithm maintains a healthy balance between exploration and exploitation, thereby identifying the most relevant features for SFP. The performance of FSDTBO was compared with four widely used FS methods: FSACO, FSGA, FSDE, and FSPSO. Four classifiers NB, KNN, QDA, and DT were employed for evaluation. Experimental results demonstrate that FSDTBO achieves higher classification accuracy and selects a more optimal subset of features compared to the other methods. Moreover, statistical analysis further confirms the superiority of the proposed approach over existing FS algorithms.
  Keywords: feature selection; wrapper-based approach; software fault prediction; SFP; classifiers; driving training-based optimisation.
  DOI: 10.1504/IJCSE.2026.10078586
   
  
• SMA-CLMPNet: spatial multiscale attention enabled convolutional distributed memory network for intra-frame video forgery detection  
  by Neha Dhiman, Hakam Singh, Abhishek Abhishek 
  Abstract: Video forgery detection presents a vital threat to digital media authenticity because it allows harmful modifications through deepfakes, splicing, and frame duplication methods. Earlier techniques lack the sufficient capability to capture long-range temporal dependencies and maintaining cross-channel correlations which degrade their detection performance. Hence, this research proposes a spatial multiscale attention coupled convolutional distributed long short-term memory based modified pooling network (SMA-CLMPNet) for detecting intra-frame video forgeries. The framework process spatial, channel, and multi-scale features to capture complex forged patterns. Additionally, the distributed LSTM and modified pooling techniques in CLMPNet addresses temporal inconsistencies by selectively aggregating the most informative features while reducing the loss of critical information. The experimental results are analysed using the Face Forensics++ dataset. At the training percentage of 90%, the framework attained an accuracy of 97.92%, sensitivity of 97.34%, and specificity of 98.20%, respectively showcasing it as the prominent solution for detecting manipulations and forgeries in videos.
  Keywords: video forgery detection; digital forensics; deep learning; digital video security; feature extraction; attention mechanism.
  DOI: 10.1504/IJCSE.2026.10078605