International Journal of Computing Science and Mathematics (8 papers in press)

Regular Issues

• A Cross-modal Evolution Reasoning Framework for Mountainous Transmission Line Safety via Edge-deployed Vision-Language Models and Dynamic Knowledge Graphs  
  by Qi Cao, Song Yang, Hao Lin Li, Xin Qiang Wu, Ming Liu, Zhijun Qin, Feng Wang 
  Abstract: Mountainous transmission lines face compound hazards, while traditional monitoring suffers from semantic ambiguity and isolated multi-modal data. This paper proposes a cross-modal reasoning framework integrating edge vision-language models (Edge-VLM) and cross-modal dynamic knowledge graphs (CDKG). We design a lightweight Edge-VLM with decoupled contrastive loss and NF4 quantisation, reaching mAP@50=0.94 on Jetson Orin Nano. A semantic-guided binocular reconstruction achieves millimeter-level depth precision complying with power industry standards. CDKG fuses InSAR, meteorology and visual data via R-GCN for causal risk propagation. Tested on Jilin-Mount-24 dataset, the framework delivers a 10-hour advance early warning for geological disasters with full reasoning interpretability.
  Keywords: Transmission Line Inspection; Vision-Language Models; Knowledge Graphs; Edge Computing; Multi-modal Fusion; InSAR.
  DOI: 10.1504/IJCSM.2026.10080881
   
  
• Optimization and Simulation of Pickup and Transportation Scheduling for Digital Freight Platform  
  by Wenwen Zhang, Zixia Chen, Qianqian Wang, Haosheng Zhang, Xiaoping Chen 
  Abstract: This study addresses vehicle dispatching in pickup transportation for digital freight platforms, tackling multi-depot dynamic scheduling complexity and real-time response limitations. A novel KNN-GA two-stage hybrid algorithm is proposed, integrating K-nearest neighbours (KNNs) clustering for spatial decomposition and an improved Genetic Algorithm for route optimisation. The multi-objective model considers dispatching costs, temporal costs, and load factors. KNN preprocessing reduces search space complexity by 83.3%, while elite preservation and dynamic parameter adjustment enhance convergence. Experiments on automotive supply chain data demonstrate convergence to a fitness value of 0.015 within 30 iterations, with 26.5% computation time reduction (180 s vs. 245 s), 85.3% average load factor, and an 8.7% empty-running rate (p < 0.01, Cohens d = 1.82). The algorithm enables minute-level dynamic rescheduling for 50-vehicle fleets at $2.10 per AWS EC2 run, facilitating the transportation management system (TMS) integration via RESTful APIs. This hybrid integration of spatial clustering and evolutionary optimisation fills the research gap in closed-loop simulation for order-dispatch-pickup processes.
  Keywords: Fuzzy Cognitive Map (FCM); Ant Colony Algorithm (ACA); Niche Technique; Logistics Demand Forecasting.
  DOI: 10.1504/IJCSM.2026.10080885
   
  
• Collaborative Optimisation of Inspection Strategies and Predictive Maintenance Decisions for Hydropower Generators under Complex Operating Conditions  
  by Yingbing Ran, Xihui Zhang, Jiajin Yuan, Kongyun Chen, Yujie Zhao, Jian Wang 
  Abstract: Hydropower generators play a vital role in ensuring the stability and reliability of large-scale power systems. However, existing studies on condition monitoring and maintenance often treat inspection and maintenance decisions separately, overlooking the coupling between fault evolution, maintenance imperfection, and inspection timing. To address these challenges, this paper proposes a collaborative optimization framework for inspection strategies and predictive maintenance decisions of hydropower generators. An imperfect maintenance-aware reliability model based on the Weibull degradation law and residual defect accumulation is constructed to describe the nonlinear evolution of defect probability after maintenance. Moreover, a predictive maintenance optimization model integrating inspection intervals, repair thresholds, and costrisk trade-offs is developed, solved using a multi-objective evolutionary algorithm. Experimental validation using real hydropower station data demonstrates that the proposed framework achieves higher diagnostic accuracy. The results indicate that jointly optimizing inspection and maintenance decisions can significantly reduce operational risk and life-cycle costs.
  Keywords: Hydropower generator; collaborative optimization; inspection strategy; predictive maintenance decision.
  DOI: 10.1504/IJCSM.2026.10081030
   
  
• Dynamic early warning of coal mine floor water inrush using cost-sensitive random forest  
  by Lou Jie, Chen Baohui, Yang Xiaoyong, Ye Wenchao, An Xiang, Guo Xin, Chen Weiming 
  Abstract: Addressing extreme level imbalance and insufficient dynamic response in coal mine floor water inrush evaluation, this paper proposes an early warning method integrating game theory weighting and cost-sensitive random forest. The mining process is discretised into operational time slices, incorporating factors like 'distance to working face' to construct a dynamicstatic multi-source indicator system. Logarithmic transformation normalises long-tail data, while game theory's Nash equilibrium optimises subjective and objective weights, validating the water inrush coefficient's mechanical contribution. For prediction, traditional random forest is enhanced with an asymmetric cost matrix and safety threshold optimisation, replacing accuracy with expected risk minimisation. This overcomes low recognition rates for Level IV critical risks under small-sample conditions. Test results show the model effectively suppresses majority level sample bias via asymmetric cost constraints, achieving complete recall of critical water inrush risks. It provides a quantitative foundation for active defence and precise decision-making in deep mine water hazard management.
  Keywords: coal mine water hazard; combination weighting; random forest; safety threshold optimisation; imbalanced learning.
  DOI: 10.1504/IJCSM.2026.10080254
   
  
• An imbalanced small-sample hydropower unit shaft trajectory recognition method based on improved adaptive empirical wavelet transform and convolutional neural network  
  by Yingbing Ran, Xihui Zhang, Yujie Zhao, Jiajin Yuan, Kongyun Chen, Jian Wang 
  Abstract: To address limited sample size and class imbalance in shaft centre trajectory signals of hydropower units, which degrade the accuracy and generalisation of conventional classifiers, this paper proposes an imbalanced small-sample identification method integrating empirical wavelet transform (EWT) and convolutional neural network (CNN). EWT first performs multimodal decomposition to suppress noise and extract frequency-band-specific features. A particle swarm optimisation (PSO) algorithm then adaptively refines EWT modal boundaries, generating optimised multi-channel trajectory images. To mitigate imbalance and underfitting, a binary ensemble strategy decomposes the multi-class task into several independent binary classifications. The optimised multi-channel images are subsequently input into a CNN for feature learning and recognition. Experimental results show that the proposed approach outperforms traditional support vector machine (SVM) and single-channel CNN models across multiple trajectory identification scenarios. PSO optimisation improves overall accuracy by about 5% points over the baseline, enhancing robustness and reliability.
  Keywords: hydropower unit; shaft centre trajectory; fault diagnosis; imbalanced small-sample; empirical wavelet transform; convolutional neural network; particle swarm optimisation; binary ensemble classification.
  DOI: 10.1504/IJCSM.2026.10080888
   
  
• Failure modes and effects analysis using data envelopment analysis under neutrosophic environments  
  by Reihaneh Hafizi Atabak, Hossein Sayyadi Tooranloo, Mohammad Zarei Mahmoudabadi 
  Abstract: Failure mode and effects analysis (FMEA) is a widely used method to identify defects in the design of a product or process, but it has limitations in handling uncertain or incomplete information. Therefore, this study proposes an input-oriented data envelopment analysis (DEA) model that is combined and improved with netrosophic logic to transform FMEA analysis in environments full of uncertainty. In contrast to the traditional FMEA's reliance on deterministic numerical scores, this model uses netrosophic set theory to simultaneously capture correctness, uncertainty, and incorrectness, allowing for a three-dimensional representation of the uncertainty inherent in expert judgements. Designed to assess the relative efficiency of organisations with comparable input-output structures, this framework extends the scope of FMEA to complex scenarios. In order to demonstrate the effectiveness of the model, a numerical example is introduced to show how the model can handle uncertainty and provides a transformative advance in risk assessment methodology.
  Keywords: FMEA; failure modes and effects analysis; DEA; data envelopment analysis; neutrosophic; risk management.
  DOI: 10.1504/IJCSM.2026.10080886
   
  
• Ada-ISO-PINN: solving parabolic partial differential equation using optimised hybrid loss-based physics-informed neural network  
  by Ganesh B. Dapke, Govardhan G. Bhuttampalle 
  Abstract: Partial differential equations (PDEs) are utilised to learn all types of common phenomena and to describe various physical phenomena. Several methods have been developed to offer accurate estimations of parabolic PDEs, but they have certain limitations, such as lower performance, higher computational cost, and time complexity. Hence, an adaptive intelligent searching optimisation enabled physics informed neural network (Ada-ISOPINN) model is proposed to solve the parabolic PDEs. The efficiency of the Ada-ISO-PINN model is obtained by incorporating the Ada-ISO algorithm, which aids in resolving the parabolic PDEs accurately with minimal errors in the solutions. The utilisation of hybrid loss functions helps to measure the error value accurately; moreover, it supports the Ada-ISO-PINN model to learn in a more informative and meaningful manner, which helps to minimise the error value. The validation results demonstrate that the Ada-ISO-PINN model attains an RMSE of 2.07, a correlation of 0.93, MAPE of 9.09, MAE of 1.13, R² of 0.88, and MSE of 4.29, which is far better than the traditional methods.
  Keywords: deep learning; adaptive intelligent searching optimisation; parabolic partial differential equations; hybrid loss function; physics informed neural network.
  DOI: 10.1504/IJCSM.2026.10080543
   
  
• Double rough set models for acquiring more accurate knowledge at lower cost  
  by Wanting Wang, Qingzhao Kong 
  Abstract: At present, almost all rough set models (RSMs) are proposed by using one information table. It is difficult to effectively reduce the cost of collecting data labels and accurately acquire knowledge when applying these traditional RSMs to analyse data. To address this dilemma, for any given concept, the samples in the positive and boundary regions obtained from the original information table are considered as the universe of the second information table, and three types of dual rough set models (DRSMs) are established based on different learning tasks. Research shows that DRSMs not only greatly reduce the cost of collecting data labels but also effectively improve the accuracy of acquired knowledge.
  Keywords: double rough set; more accurate knowledge; lower cost; information table; positive and boundary regions.
  DOI: 10.1504/IJCSM.2026.10080891