International Journal of Computing Science and Mathematics (5 papers in press)

Regular Issues

• Design of personalised knowledge teaching platform based on C4.5 algorithm and association rules  
  by Shasha Chen 
  Abstract: To address the limitations of traditional teaching models in accommodating individual student differences, this study developed a personalised knowledge instruction platform integrating C4.5 algorithm and association rules. The model first constructs personalised knowledge profiles using C4.5 algorithm to extract learning behaviours and preferences. By combining an enhanced Apriori algorithm with data snapshot and hash index mechanisms, it significantly improves rule generation efficiency and recommendation accuracy. Experimental results demonstrate that the model achieves 98.4% accuracy in FIC dataset and 98.2% in ASSISTments dataset, with F1 scores reaching 91.14%. Key metrics including support, confidence, and gain reach 90.73%, 84.22%, and 93.86% respectively, outperforming competing models like LightGBM and XGBoost. This study validates the platforms effectiveness and practical value in personalised teaching recommendations, providing a scalable technical framework for intelligent education systems.
  Keywords: C4.5 algorithm; association rules; teaching; personalisation; recommendation.
  DOI: 10.1504/IJCSM.2026.10078105
   
  
• Mental health diagnosis model based on multi-task and multi-view learning  
  by Xiaoyu Yang 
  Abstract: Mental health diagnosis remains a global challenge due to limitations in existing models, which often rely on single-task learning or unimodal data sources, leading to low accuracy and poor generalisation across diverse user groups. To address these issues, this paper proposes a novel mental health diagnostic model based on multi-task and multi-view (MTMV) learning. A self-constructed dataset combining smartphone behavioural data and psychological scale responses was preprocessed using a Word2Vec-based Skin gram model, with signal-to-noise ratio (SNR) and information loss rate used to evaluate preprocessing performance. The proposed MTMV framework integrates attention-enhanced feature fusion, expert gating, and cross-task representation sharing to jointly diagnose anxiety, stress, and depression. Experimental results show that the model achieves 97.6% accuracy and 89.2% recall for students, and demonstrates superior performance across multiple occupational groups. These findings indicate that the MTMV-MH model offers a robust and scalable solution for intelligent mental health assessment.
  Keywords: multi-task; multi-view; mental health; Word2Vec; MTMV; SNR.
  DOI: 10.1504/IJCSM.2026.10078231
   
  
• A Study of Film Music Emotion Recognition via Multimodal Fusion Strategy  
  by Li Zhan, Na Wan, Ming Zhao 
  Abstract: Film music constitutes one of the most essential components in cinematic creation. It is widely adopted to shape atmospheric tones, portray characters’ inner psychological states, and reinforce the dramatic emotions conveyed in films. To scientifically verify whether film music can induce varied emotional responses and specific affective categories in audiences, this study extracts valid features from both film music and scripts. Each single-modal input is then imported into its corresponding pre-trained large model to capture modality-specific emotional information. After that, the multi-source emotional outputs are integrated and analyzed to categorize emotional types. Experimental results indicate that the improved multimodal fusion approach presented in this paper significantly boosts the performance and reliability of emotion recognition, benefiting from the complementary characteristics of distinct modalities. Accordingly, this work offers meaningful references for the field of film music emotion recognition.
  Keywords: Emotion recognition; Multimodal; Film music; Film script; Multimodal fusion.
  DOI: 10.1504/IJCSM.2026.10079339
   
  
• Approximate bisimulation of semi-algebraic transition systems based on dynamically weighted metrics  
  by Guxuan Li, Weidong Tang, Meiling Liu 
  Abstract: With the widespread application of embedded systems, program verification faces challenges of state space explosion and non-deterministic branch redundancy. To address the static error control limitations of traditional singular value decomposition (SVD) methods in approximate bisimulation, this paper proposes a dynamically weighted approximate bisimulation (DyWAB). The method integrates Wus characteristic set method with SVD techniques, introducing a reconciliation-based error compensation mechanism derived from the harmonic mean principle. A dynamic weighting allocation strategy is designed based on singular value characteristics to suppress error propagation. Combined with row-minimal matrix determination and multi-candidate selection optimisation, DyWAB achieves system simplification under strict error control. Experimental results demonstrate that, compared to static threshold baselines, DyWAB reduces validation errors by 5177% and improves state approximation accuracy by 22%, validating its effectiveness in synergistic optimisation of error control and system simplification.
  Keywords: program verification; dynamically weighted; SVD; approximate bisimulation; Wu's characteristic set method; multi-candidate selection.
  DOI: 10.1504/IJCSM.2026.10079567
   
  
• Experimental Simulation of Logistics Demand Forecasting Based on Fuzzy Cognitive Map Model and Ant Colony Algorithm  
  by Wenqian Han, Zixia Chen, Yue Diao 
  Abstract: This study has established a logistics demand forecasting model based on Fuzzy Cognitive Map (FCM), optimizes the FCM weight matrix using the Ant Colony Algorithm (ACA), and introduces the Niche Technique for the design of the Ant Colony Algorithm-Fuzzy Cognitive Map (ACA-FCM) based logistics demand forecasting method. To effectively evaluate the application effect of ACA-FCM model on logistics demand forecasting, this paper innovatively proposes a logistics demand forecasting method based on ACA-FCM model, and uses Pandas and Python tools for experimental simulation and analysis of logistics demand forecasting. The results demonstrate that the ACA-FCM model outperforms the GWO-FCM and GA-FCM models in three aspects: comparative testing of convergence performance across different models using both standard and noisy logistics datasets, testing predictive performance by introducing accuracy, precision, and recall metrics, and in terms of stability and predictive accuracy for logistics demand forecasting, achieving the best overall performance.
  Keywords: Fuzzy Cognitive Map (FCM); Ant Colony Algorithm (ACA); Niche Technique; Logistics Demand Forecasting.
  DOI: 10.1504/IJCSM.2026.10079622