International Journal of Computational Science and Engineering (19 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;.
  
  
• Prediction model for recruitment of railway bureaus and enrolment of railway schools based on deep learning  
  by Haijun Wang, Wei He, Junlun Sun 
  Abstract: With urbanization accelerating, the demand for railway transportation is increasing, making it essential to plan recruitment for railway bureaus and adjust enrollment at railway schools. This study aims to accurately predict recruitment needs using historical data. We applied deep learning models, including Back Propagation Neural Network (BP Neural Network), Long Short-Term Memory (LSTM), and LSTM-Attention, to forecast recruitment numbers for eight positions across 18 railway bureaus in 2025, yielding MAE values of 100000, 0.16, and 0.13, respectively. We also used Linear Regression, Ridge Regression, LASSO Regression, and Random Forests to predict the number of remaining graduates in eight major railway programs for 2025, with most models showing MSE values between 0 and 4. Finally, we established upper and lower limits for vocational student enrollment quotas in 2025 by applying factors of 80% and 75% to the predicted recruitment numbers. These findings provide valuable insights for recruitment and enrollment planning, enhancing the application of deep learning in railway recruitment forecasting.
  Keywords: long short-term memory; LSTM; attention mechanism; ridge regression; LASSO regression; random forest; prediction model.
  DOI: 10.1504/IJCSE.2025.10070809
   
  
• FPrune: a parameter pruning algorithm based on federated deep classification model  
  by Xinjing Li, Zheng Huo, Teng Wang 
  Abstract: Federated learning is a distributed machine learning framework that enables multiple participants to train models collaboratively without sharing raw data. However, significant data transmission is required for parameter communication. As deep neural network models grow in size, deploying federated learning in complex network environments results in substantially increased communication costs. To address this challenge, we propose a pruning algorithm for deep federated text classification models, called FPrune. This algorithm evaluates the importance of locally trained models during the federated learning training stage by calculating the importance of each filter. Filters with lower importance are pruned. Additionally, we introduce a bidirectional pruning strategy that prunes filters on both the client and server sides. Experimental results demonstrate that the FPrune/25% and FPrune/50% algorithms reduce the communication cost by 70.22% and 42.03%, respectively, compared to FedAvg. Furthermore, the model’s performance loss is limited to approximately 1.34%, demonstrating that the FPrune algorithm can effectively reduce communication costs while maintaining minimal performance degradation.
  Keywords: federated learning; deep classification; model pruning; TextCNN.
  DOI: 10.1504/IJCSE.2025.10070810
   
  
• Enhancing fairness in deep learning: key tasks, measurement methods, and experimental validation  
  by Xiaoqian Liu, Weiyu Shi 
  Abstract: Deep learning is an important field in machine learning research. It has powerful feature extraction capabilities and superior performance in numerous applications, including computer vision, natural language processing, and speech recognition etc. However, unfairness in deep learning models has increasingly harmed people's interests. Therefore, designing methods to effectively enhance fairness has become a major trend in the development of deep learning. This work reviews key tasks and fairness measurement methods in deep learning. In addition, we conduct experiments on typical fair deep learning datasets to implement individual fairness. The experimental results show that a balance is achieved between accuracy and fairness of classification tasks.
  Keywords: deep learning; algorithmic bias; individual fairness.
  DOI: 10.1504/IJCSE.2025.10071366
   
  
• Temporal similarity-constraint graph networks for stock prediction with stock relations  
  by Jincheng Hu, Yu Zhang 
  Abstract: Stock prediction aims to enhance investment decisions by forecasting future stock trends, traditionally using time-series data. While deep learning has advanced time-series modeling, most existing methods treat stocks as independent entities, overlooking the rich relationships between them. Additionally, conventional approaches frame stock prediction as a regression problem focused on price prediction, which does not align directly with investment goals. To address these issues, we propose Temporal Similarity-constraint Graph Networks (TSCGN), a novel framework that incorporates stock relations and selects stocks with the highest return ratio. TSCGN embeds sequential stock data into features and constructs a stock knowledge graph to capture interactions between stocks. By integrating temporal similarity constraints, TSCGN enhances prediction accuracy and robustness. Experiments on real-world datasets (NASDAQ and NYSE) demonstrate that TSCGN outperforms state-of-the-art models in prediction accuracy and investment returns, making it a valuable tool for financial decision-making.
  Keywords: graph networks; similarity constraint; stock prediction; stock relation.
  DOI: 10.1504/IJCSE.2025.10072064
   
  
• QGA-optimised BL xLSTM MLP model for portfolio  
  by Meng Li, Zhihui Song, Jiaxu Feng 
  Abstract: The Black-Litterman (BL) model integrates market conditions and investor judgments. However, existing research on generating expert insights focuses on either regressing returns against external variables or modeling return series as time series, without integrating both approaches. Furthermore, traditional BL portfolio optimization neglects transaction costs and fails to optimize hyperparameters, limiting its adaptability to varying market conditions. To address these, we propose a QGA-optimized BL_xLSTM_MLP model, that combines external variable regression (via MLP) and time-series modeling(via xLSTM) ,which integrates temporal dependencies and macroeconomic features into expert views, while optimizing hyperparameters and transaction costs using a Quantum Genetic Algorithm (QGA). The QGA adopts the sum of the Sharpe ratio and Information ratio (accounting for transaction costs) as the fitness function, effectively addressing the traditional BL model’s neglect of transaction costs. Finally, we conducted experiments on USA 30 industry portfolio demonstrating that our method achieved state-of-the-art performance.
  Keywords: Black-Litterman model; quantum genetic algorithm; xLSTM model; asset allocation; portfolio.
  DOI: 10.1504/IJCSE.2025.10072092
   
  
• Achieve Sim2Real based on semantic constrained cycle generative adversarial network  
  by Xiangfeng Luo, Hongbin Huo, Xinzhi Wang 
  Abstract: In the field of vision-based control systems, the discrepancy between simulator and real-world environments renders models trained in simulators ineffective in real-world scenarios. Previous approaches have attempted to mitigate this issue by mapping the simulator and realworld into a shared latent space, but this can result in the loss of semantic information relevant to decision-making in the images. In this paper, we propose a method called Semantically Constrained CycleGAN (SCCGan) to address these limitations. SCCGan extracts semantic information from generated images and compares it with the original images to ensure consistency. Experimental results demonstrate that our method preserves the semantic information of the original images during the generation process, enabling the transfer of decision models from simulators to the real world. By leveraging semantic constraints, SCCGan facilitates the effective migration of decision models, bridging the gap between simulated and real-world environments in vision-based control systems.
  Keywords: simulator to reality; CycleGAN; reinforcement learning; autonomous decision making.
  DOI: 10.1504/IJCSE.2025.10072111
   
  
• Uplink transmission interference suppression technique for ultra-dense networks based on locally weighted regression  
  by Sujuan Li 
  Abstract: Abnormal transmission signal is the main reason for serious uplink transmission interference in ultra-dense networks, which leads to insufficient transmission interference suppression effect. Therefore, a local weighted regression based uplink transmission interference suppression for ultra-dense networks is proposed. Firstly, the robust local weighted regression is used to analyze the abnormal transmission signals of the uplink transmission link in the ultra-dense network, and improved the suppression effect of the uplink transmission interference. Secondly, the adaptive time-frequency analysis is used to extract the characteristics of abnormal signals and determine the existence of uplink transmission interference. Finally, the residual neural network is used to identify the interference signal, and the interference reconstruction and suppression are combined to achieve the interference suppression of the uplink transmission in the ultra-dense network. Experimental results show that the reference signal received power gain, signal-to-noise ratio, and information average rate gain of the uplink station are optimized with the proposed interference suppression, and the maximum received power gain is up to 53.79 dB.
  Keywords: locally weighted regression; ultra-dense network; uplink transmission; interference suppression; residual neural network; signal-to-noise ratio.
  DOI: 10.1504/IJCSE.2025.10073102
   
  
• 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
   
  
• Feature selection using war strategy optimisation algorithm for software fault prediction  
  by Pradeep Kumar Rath, Roshan Samantaray, Susmita Mahato, Sushruta Mishra, Sanat Kumar Patro, Himansu Das 
  Abstract: Identifying problematic software modules early on in development process can help programmers create software that is highly efficient and dependable. In this paper, a novel feature selection (FS) approach using war strategy optimisation (FSWSO) is proposed that applies ancient war strategy planning principles to the selection of features or variables in software fault prediction (SFP). This approach seeks to identify the most relevant features for SFP by simulating army operations and evaluating the performance of different feature subsets in a simulated war space. In this experiment, we have compared the proposed FSWSO algorithm's performance to that of other FS techniques including FSACO, FSDE, FSGA, and FSPSO in order to assess the algorithm's accuracy. In the majority of cases, FSWSO has provided better performance with fewer chosen features. The suggested approach has been validated and proven to be superior to prior approaches in choosing an optimal selection of features using the Friedman and Holm tests.
  Keywords: software fault prediction; SFP; war strategy optimisation; metaheuristic; machine learning; feature selection; FS; classification.
  DOI: 10.1504/IJCSE.2025.10069977
   
  
• DHARM: recurrent convolutional neural network for real-time activity recognition with smartphone sensor data  
  by Sourav Bera, Anukampa Behera, Chhabi Rani Panigrahi 
  Abstract: Recently, the field of activity recognition using sensor data has been quickly growing worldwide, leveraging the concepts of deep learning (DL) and artificial intelligence (AI) with numerous use cases like fall detection, patient activity tracking, fitness monitoring, gesture detection, etc. The proposed dynamic human activity recognition model (DHARM) is built on a recurrent convolution neural network with long-short-term memory (RCNN-LSTM) trained on a motion sensors based custom collected time series dataset along with two publicly available datasets, MobiAct and SysFall. DHARM is a lightweight model with approximately 43 ms of inference latency, which makes it extensible to edge computing devices like mobile phones, smartwatches, etc. for real-time human activity detection. DHARM has been tested to show a training accuracy of 96.82% and a test accuracy of 98.32%.
  Keywords: dynamic human activity recognition model; DHARM; recurrent convolutional neural network using long-short-term memory; RCNN-LSTM; deep learning; accelerometer; gyroscope.
  DOI: 10.1504/IJCSE.2025.10070638
   
  
• An irony detection model for social media comments based on topic context  
  by Yanhui Wang, Yuhao Zhou, Zixuan Zhang, Shunxiang Zhang, Lei Chen 
  Abstract: Due to the ever-expanding amount of information available on social media comments, the need for reliable and efficient irony detection mechanisms becomes evident. However, most current methods focus on analysing the comment text, ignoring the role of the topic context in promoting the understanding of ironic semantics. In order to provide a more efficient framework for extracting ironic text features, we propose an irony detection model for social media comments based on topic context (SMC-TC). The model is mainly composed of the information union module and the sentiment-guided module. In the information union module, the topic-comment text features are obtained through Bi-GRU and attention-over-attention (AOA) to realise the combination of topic context and comments. In the sentiment-guided module, the comment word embeddings are input into LSTM and combined with the self-attention mechanism to get the sentimental semantic features. Based on the above two modules, the sentimental semantic features and topic-comment text features are concatenated and then fed into a sigmoid function to acquire the final irony detection result. Through extensive experiments on the publicly available 'ToSarcasm dataset', our model can enhance the F1-score to 74.04%.
  Keywords: irony detection; social media comments; topic context; Bi-GRU; attention-over-attention; AOA.
  DOI: 10.1504/IJCSE.2025.10072093
   
  
• Charge prediction model enhanced by supplying legal knowledge  
  by Guangli Zhu, Yixuan Jiao, Jiajia Liu, Yuanyuan Ding, Yulei Zhang, Ziliang Li, Shunxiang Zhang 
  Abstract: Legal charge prediction aims to determine charges based on the fact description of a case. Existing works take case descriptions as the sole input to determine charges, and ignore the significant role of external legal knowledge related to charges. In this paper, we propose a charge prediction model that simultaneously leverages historical precedents and external knowledge from a legal knowledge base. Specifically, we design a similar case matching module, which matches historical precedents with a given case via cosine similarity. According to the charge labels of similar cases, legal knowledge aligned with each charge is obtained. Furthermore, in the charge prediction module, we propose a gated attention to selectively retain and integrate the obtained knowledge into the given case. By this way, model can identify the criminal constitutive information in the case for charge prediction. Experimental results on CAIL2018 dataset demonstrate that the introduction of legal knowledge can improve the charge prediction performance.
  Keywords: legal charge prediction; LCP; legal knowledge; gated attention mechanism; similar case matching; cosine similarity.
  DOI: 10.1504/IJCSE.2025.10072349
   
  
• Inpainting method of generative adversarial network based on improved reconstructed loss function  
  by Zhang Yin'e, Ye Xiao Wen, Zhou Qi 
  Abstract: In processing image restoration tasks, more and more researchers are using generative adversarial networks, and have made many good achievements. However, the loss function used in current repair methods does not give different weights to the losses of areas with different repair effects. Therefore, we propose a generative adversarial network repair method based on improved reconstructed loss, which assigns different weights to the losses of areas with different repair effects by improving the reconstructed loss, enhancing the repair effect on areas with poor repair effects. We will also introduce improved reconstruction loss into perception loss and style loss to enhance the restoration effect of image details. This method was tested using the Paris StreetView and Celebra datasets. From the test results, it can be seen that our PSNR and SSIM have been improved compared to the comparative method, demonstrating the effectiveness of our approach.
  Keywords: image inpainting; dilated convolution; generative adversarial network; GAN; improved reconstruction loss function; residual spatial attention module; RSAM.
  DOI: 10.1504/IJCSE.2025.10071304
   
  
• Decision network for interpretable UAV decision making under human supervision  
  by Vinicius Abrão Da Silva Marques, Ana Lídia De Almeida Castro, Braulio M. Horta, Ney R. Moscati, Carlos Henrique Quartucci Forster 
  Abstract: With the increasing number of drones flying simultaneously under the supervision of a limited number of humans, it is essential to delegate more decision-making authority to the autopilot system to minimise intervention. The decision making capabilities of autonomous UAVs have a direct impact on mission safety and reliability. In this paper we propose a decision-making model for UAVs based on decision networks. The non-dominated sorting genetic algorithm (NSGA III) is used to train the model from a provided set of cases. Separate selected cases with the respective expected outcomes were used to test the model, showcasing its capacity to accurately represent cases while maintaining interpretability. We demonstrate that this model can facilitate mission accomplishment with risk minimisation under uncertainty in stochastic environments. In particular, the model provides a meaningful interpretation of the parameters and how they are taken into account when making decisions.
  Keywords: decision network; decision making; unmanned aerial vehicle; UAV; optimisation.
  DOI: 10.1504/IJCSE.2025.10070933
   
  
• Design and implementation of personalised recommendation system for university library based on GNN and data fusion  
  by Jie Yang 
  Abstract: Traditional libraries face challenges such as sparse data, cold start issues, and insufficient personalisation in resource recommendations due to their resource-centric service model. To address these issues, this study developed a personalised recommendation model using natural language processing and graph neural networks. The model integrates multi-dimensional data from university students and faculty at the feature layer and analyses the influence of neighbours in different graphs to predict user preferences more accurately. Experiments on Yelp and library datasets demonstrated that the proposed model outperformed six other recommendation systems, achieving the lowest mean absolute error (MAE) of 0.149 and a stable root mean square error (RMSE) of 0.2451. By leveraging social data to enhance user behaviour analysis, this approach alleviates cold start problems and improves recommendation accuracy. The method also indirectly boosts reader satisfaction, offering practical value for personalised book recommendations in libraries.
  Keywords: data fusion; user portrait; graph neural network; GNN; library; personalisation; recommendation; Word2Vec.
  DOI: 10.1504/IJCSE.2025.10071858
   
  
• Chinese text-oriented sentiment analysis models, corpus, and recent advancesz  
  by Zhongliang Wei, Chang Ge, Chang Su, Jun Zhu, Guangli Zhu 
  Abstract: Sentiment analysis of Chinese text presents unique challenges due to the distinct characteristics of the Chinese language, including cultural nuances, word formation styles, and the dynamic nature of certain terms on social media. This paper reviews recent methods for Chinese sentiment analysis, which can be broadly classified into three categories: dictionary-based, traditional machine learning-based, and advanced deep learning-based. A comparative analysis highlights the strengths and limitations of each method across various applications. Additionally, commonly used Chinese corpora, sentiment analysis systems and tools are introduced. Furthermore, this paper discusses the potential directions for future research, such as recognising complex sentiment states, multimodal sentiment analysis, and cross-cultural sentiment analysis.
  Keywords: sentiment analysis; machine learning; Chinese social networking.
  DOI: 10.1504/IJCSE.2025.10071176