International Journal of Computational Science and Engineering (7 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