International Journal of Computing Science and Mathematics (6 papers in press)

Regular Issues

• A Synergic Deep Learning Approach for Efficient Grading of Glioma via MRI Images  
  by Nirmal Yadav 
  Abstract: Computer-aided diagnosis using deep learning approaches has made tremendous improvements in medical imaging for automatically detecting tumour area, tumour type, and grading of the tumour. These advancements are limited due to the fact that (1) medical images are often less in quantity, leading to over fitting, and (2) significant inter-class similarity and intra-class variation between the images. The main aim of the study is to develop a deep learning base model (Zhang et al., 2018; 2019; Krizhevsky et al., 2012) as a backbone for the automatic grading of glioma tumours. The synergic deep learning (SDL) architecture enables two pre-trained models to learn from each other mutually and allows them to perform better than vanilla pre-trained models. Our study uses T1-weighted sagittal tumour magnetic resonance imaging (MRI) slices from the REMBRANDT (Scarpace et al., 2019) dataset. The proposed architecture achieves an accuracy of 98.36%, showing that the model achieves excellent performance metrics on a small dataset.
  Keywords: Glioma Tumor Grading; Synergic Deep Learning; Transfer Learning; AlexNet; REMBRANDT.
  DOI: 10.1504/IJCSM.2024.10068238
   
  
• Offshore Wind Power Prediction Based on Chaotic Optimisation PSO-SCN-LSTM Model  
  by Xu Li, Lei Kou, Benfa Zhang, Zhen Wang, Jingya Wen, Fangfang Zhang, Jinyan Du, Yan Zhou, Wende Ke 
  Abstract: Offshore wind power, as a clean energy source, is receiving increasing attention worldwide. To enhance the economic and safety performance of offshore wind power, short-term forecasting of wind power is essential. This paper proposes a model based on chaos optimisation integrated with particle swarm optimisation (PSO), stochastic configuration network (SCN), and long short-term memory (LSTM) algorithm. Firstly, leveraging the randomness and ergodicity of the complex logistic chaos system, the collected power data from wind turbines is utilised as the input data source for the PSO, enhancing the randomness of the data. Subsequently, the SCN is employed to optimise the PSO, increasing the variation in the hidden layer during iterations and mitigating the PSOs tendency to fall into local optima, thereby obtaining initial prediction values. Finally, the mechanism model of the LSTM is utilised for secondary prediction, further improving prediction accuracy. Compared with traditional algorithms, the optimised algorithm significantly reduces errors and enhances prediction precision.
  Keywords: Chaos theory; particle swarm optimization algorithm; random configuration network; LSTM prediction model.
  DOI: 10.1504/IJCSM.2024.10068563
   
  
• A Visual Communication Method for Multi Feature Media Images Based on Interactive Modelling  
  by Ting Zhang, Shi-shun Wang, Xiang-yu Wei 
  Abstract: Multi feature media image visual communication can provide strong support for the development of computer vision and machine learning technology, therefore a visual communication method for multi feature media images based on interactive modelling is proposed. HSV colour histogram, DCT transform and GIST descriptor were used to extract colour, texture and scene content features of multi-feature media images, and the features were fused. The multi-feature media image is decomposed into semantically independent components, which are mapped to the surface of the 3D model, so as to realise the interactive modelling of the multi-feature media image. Combined with the modelling results, the image is enhanced and reconstructed to complete the visual transmission of the image. The experimental results show that the image detail features of the proposed method are significant, with high clarity. The average peak signal-to-noise ratio is 55.9dB, and the processing time remains below 0.4s.
  Keywords: Interactive modeling; Multi feature media; Visual communication; Color; Texture; Scene content; Semantically independent components; Enhanced.
  DOI: 10.1504/IJCSM.2024.10068569
   
  
• A Method for Capturing English Oral Pronunciation Errors based on Speech Recognition  
  by Wenna Dou 
  Abstract: To improve the accuracy and timeliness of capturing pronunciation errors, the paper proposes a new method for capturing English oral pronunciation errors based on the speech recognition process. Using a voice production system to collect raw English spoken pronunciation signals and extract the features of the speech signals. Then, after determining the confidence level of the intonation points, HMM classification algorithm is used to classify the intonation points and establish a spoken pronunciation comparison database containing standard state sequences. Finally, the degree component signal detection method is used to determine the spectral features of pronunciation errors. By comparing the spectral features with standard state sequences, incorrect English spoken pronunciation is captured. Experiment shows that the recognition accuracy of this method remains above 97%, and the maximum accuracy of capturing pronunciation errors can reach 98.74%. The capture time remains within 3s, indicating that this method has achieved the design expectations.
  Keywords: Speech recognition; English speaking test; Capture pronunciation errors; Classification of intonation points; Standard state sequence.
  DOI: 10.1504/IJCSM.2024.10068571
   
  
• Multi-Objective Optimisation of Cigarette Production Planning and Inventory Management  
  by Wanjiang Wang, Feng Zhao, Mingjun Wang, Qi Sun, Huihui Gao, Wei Jian, Renwang LI, Shulan Luo 
  Abstract: This paper proposed a dynamic model-based optimization method for cigarette production planning to meet market demand and optimize inventory management. The model contains three main objectives: Minimising the difference between annual demand and production output, balancing monthly production and demand, and minimising the average age of inventory. The model sets annual demand constraints, production capacity constraints, and considers seasonal adjustment and contingency inventory. The model is solved by a combination of linear programming and genetic algorithms, and the results show that the optimized production plan can effectively reduce total costs, avoid the risks of under-supply and over-stocking. Specific analysis shows that the monthly production quantity closely matches the actual allocation quantity, the inventory management is effective, and the ability to cope with peak demand is enhanced.
  Keywords: Dynamic stocking model; Cigarette production; Linear programming; Genetic algorithm; Inventory management.
  DOI: 10.1504/IJCSM.2025.10069527
   
  
• Construction of Course Arrangement System for College Sports Teaching Based on Adaptive Factor Optimised SGA  
  by Chunyu Yin, Li Shi 
  Abstract: The course arrangement method for college sports teaching has problems of low efficiency and low quality of course arrangement. This study proposes an intelligent course arrangement method based on improved dual population genetic algorithm. Firstly, course arrangement problems are described, and mathematical model is built. Then, the lowest running time, the highest teacher utilization rate and the best class distribution balance are taken as optimization objectives. Afterwards, an improved dual population genetic algorithm is used to solve course arrangement problems of physical education teaching. Finally, this algorithm is applied to the course arrangement system of college sports teaching for testing and analysis. Experimental results show that the proposed improved dual population genetic algorithm can solve course arrangement problems accurately and quickly. Furthermore, its average running time, average classroom utilisation rate and average class time distribution balance are 53.59s, 96.42% and 0.9, respectively, which are better than those of intelligent course arrangement methods based on ant colony algorithm, firefly algorithm, particle swarm optimisation algorithm, and grey wolf optimization algorithm.
  Keywords: adaptive factor; dual population genetic algorithm; competition mechanism; college sports teaching; intelligent course arrangement.
  DOI: 10.1504/IJCSM.2025.10070151