International Journal of Intelligent Systems Technologies and Applications (8 papers in press)

Regular Issues

• Path Selection for Intelligent Robot Mobile Obstacle Avoidance based on Variable Step Size Ant Colony Algorithm  
  by Qilong Li, Wei Zhang 
  Abstract: In order to overcome the problems of poor performance, low obstacle avoidance success rate, and long time consumption in traditional intelligent robot obstacle avoidance path selection methods, a path selection method for intelligent robot mobile obstacle avoidance based on variable step size ant colony algorithm is proposed. Using laser radar to scan the environment of intelligent robots and obtain observation data, building a kinematic model and probability grid map of intelligent robots based on the observation data. Combining variable step size ant colony algorithm with the kinematic model of intelligent robots in the probability grid map to achieve obstacle avoidance path selection during movement. The experimental results show that under the application of the proposed method, the intelligent robot did not collide during the movement process, the movement path was the shortest, the maximum obstacle avoidance success rate of the intelligent robot was 99.12%, and the minimum path selection time was 0.63 s.
  Keywords: variable step size ant colony algorithm; intelligent robot; obstacle avoidance; path selection; probability grid map.
  DOI: 10.1504/IJISTA.2025.10068141
   
  
• Analysis of Interference Cancellation under Limited Frequency Band Resources for B5G Communication System Application Scenarios  
  by Xin Wang, Na E, Ziyu Wang, Meng Wu 
  Abstract: In a bandwidth-constrained setting, precise self-interference cancellation in communication systems poses challenges. This research extends optical self-interference cancellation to propose an adaptive interference mitigation strategy tailored for advanced mobile communication systems beyond the fifth generation. By considering delay gains in reference and self-interference channels, along with multipath temporal aspects, an unconstrained optimisation algorithm is introduced. Additionally, an adaptive mitigation strategy for multipath channels is devised using orthogonal frequency-division multiplexing technology. Simulation experiments demonstrate the optimised algorithm's 29.8238.15% reduced sampling energy consumption compared to alternatives. The self-interference cancellation strategy saves energy, achieving 30.02 dB detection depth. Post-cancellation, the signal spectrums error vector magnitude is only 6.71%, a 71.72% decrease. The results highlight the effectiveness of this adaptive interference mitigation strategy in self-interference and multipath signal elimination in communication systems.
  Keywords: band-limited resources; interference mitigation; unconstrained optimisation algorithm; delay gains; multipath signals.
  DOI: 10.1504/IJISTA.2025.10068592
   
  
• Intelligent Recognition of Foul Action of Track and Field Athletes based on Bouguet Stereoscopic Correction method  
  by Hao Wu 
  Abstract: This article proposes a new intelligent recognition method of foul action of track and field athletes based on Bouguet stereoscopic correction method, aiming to shorten recognition time and improve recognition accuracy. This method first utilizes the principle of binocular vision to collect images of foul action by track and field athletes, and uses the Bouguet algorithm for image correction to eliminate distortion. Then, the background subtraction method is used to extract the characteristics of foul action by track and field athletes. Finally, the extracted features are input into a support vector machine for intelligent recognition. The experimental results show that the recognition time of this method in multiple data tests does not exceed 1.0s, and the average recognition accuracy reaches 94.388%. This method provides an effective solution for quickly and accurately recognizing foul action by track and field athletes.
  Keywords: Bouguet stereoscopic correction method; Track and field athletes; Foul actions; Intelligent recognition.
  DOI: 10.1504/IJISTA.2025.10068745
   
  
• Brain Tumour Segmentation for Overall Survival Prediction  
  by Novsheena Rasool, Javaid Iqbal Bhat 
  Abstract: Gliomas present significant challenges due to their heterogeneous and infiltrative nature, making accurate segmentation essential for effective treatment. Manual segmentation methods are highly labour-intensive and often inadequate. This study introduces a novel pipeline for improving glioma management, beginning with advanced MRI pre-processing. We propose two attention-gated UNet architectures, the dual convolution attention gated UNet and the channel attention gated UNet, for precise tumour segmentation. Radiomic features, including the grey-level co-occurrence matrix and grey-level dependence matrix, are extracted to capture detailed tumour characteristics. Clinical data, such as age and resection status, are integrated alongside radiomic features to enhance survival models. A stacking ensemble model, combining a random forest regressor and multilayer perceptron, predicts survival based on integrated data. Validation on the BraTS 2018 dataset shows that dual convolution attention gated UNet excels in both segmentation accuracy and survival prediction, highlighting the potential of these advanced technologies for glioma management.
  Keywords: Gliomas; Segmentation; Dual Channel Attention gated UNet; MRI; Channel Attention gated UNet; Survival prediction.
  DOI: 10.1504/IJISTA.2025.10069003
   
  
• Detection of Waterlogging in Urban Road Traffic Based on Improved YOLOv5-seg and Ellipse Fitting Algorithm  
  by Jianqiang Liu, Rui Chen, Xiaoyan Zhao, Xingyao Li, Yujie Shang, Peng Geng 
  Abstract: This article proposes an innovative method for acquiring precise waterlogging depth data utilising images from traffic surveillance systems. Initially, the YOLOv5 algorithm identifies the vehicle type and determines its tire specifications accordingly. Subsequently, an enhanced version of the YOLOv5-seg model segments and masks the tire instances, while an ellipse fitting algorithm extracts the geometric parameters of the submerged tires to shape a complete ellipse. With the vehicle tires as benchmarks, a mathematical model for waterlogging depth is formulated, which computes the depth using crucial parameters from the ellipse. The experimental outcomes demonstrate that this algorithm achieves an average localisation accuracy of 96.4%, a mask segmentation accuracy of 95.6%, and maintains a detection error within 5 cm for 90% of the waterlogged depths measured. These findings confirm that the image-based tire detection method for waterlogging measurement is both effective and practical.
  Keywords: waterlogging depth detection; deep learning; ellipse fitting; YOLOv5-seg.
  DOI: 10.1504/IJISTA.2025.10069443
   
  
• Control of Camless Electrohydraulic Valvetrain in Internal Combustion Engine under Extended Kalman Filter  
  by Lei Zhang, Xiaoqin Yang 
  Abstract: In order to solve the problems of low success rate, long response time of control signals, and high failure rate in traditional valvetrain control methods, a control method of camless electrohydraulic valvetrain in internal combustion engine under extended Kalman filter is proposed. Build a mathematical model for the camless electrohydraulic valvetrain of internal combustion engine, combine the constructed mathematical model with extended Kalman filtering to estimate the state of the camless electrohydraulic valvetrain, and determine the relevant control signals. Input the control signals into the neural network PID controller to camless electrohydraulic valvetrain control of internal combustion engine. The experimental results show that the control success rate curve of the proposed method varies from 95.3% to 98.1%, the average response time of the control signal is 39.09ms, and the failure rate varies from 0.8% to 3.1%, demonstrating high precision and efficiency.
  Keywords: Extended Kalman Filter; Internal combustion engine; Camless electrohydraulic valvetrain; Mathematical model; Neural network PID controller.
  DOI: 10.1504/IJISTA.2025.10069474
   
  
• Anti Tampering Method of Private Data in Industrial Internet for Intelligent Manufacturing  
  by Yafan Men, Jie Lu 
  Abstract: To improve the integrity of industrial Internet privacy data and reduce the tamper response time, an intelligent manufacturing oriented industrial Internet privacy data tamper prevention method was proposed. Firstly, in the context of intelligent manufacturing, through association rule mining methods, analyse the characteristics and relationships of industrial Internet privacy data sets, and mine industrial Internet privacy data. Secondly, through sparse fraction method and L1 norm minimisation strategy, key features of industrial Internet privacy data are extracted, and feature selection process is optimised to improve the accuracy and efficiency of data processing. Finally, by deploying monitoring scripts, encryption processing and key generation algorithms, an industrial Internet privacy data tamper prevention system is built to ensure data integrity, improve security, and prevent unauthorised tampering. The experimental results show that compared to existing tamper proof methods, the data integrity of our method is higher and the response time is the shortest.
  Keywords: Intelligent manufacturing; Industrial Internet; Privacy data; Anti tampering.
  DOI: 10.1504/IJISTA.2025.10069477
   
  
• A Dynamic Resource Allocation Method for Edge Computing of Industrial Internet of Things based on Bee Colony Algorithm  
  by Yanrong Wang, Xiaokun Huang, Ying Li 
  Abstract: In order to solve the problems of poor stability and low efficiency of industrial IoT resource allocation system, a dynamic resource allocation method based on bee colony algorithm for industrial IoT edge computing was proposed. First of all, an architecture integrating the advantages of cloud computing and edge computing was built. Secondly, an energy consumption calculation model was constructed and optimization objective functions and constraints were set. Then, the bee colony algorithm is used to optimise the dynamic resource allocation of edge computing. This algorithm simulates the honey harvesting behaviour of bees and efficiently searches for the optimal resource allocation plan in the solution space through the collaborative effect of hiring bees, observing bees, and reconnaissance bees. Experimental results have shown that the proposed method consistently maintains a throughput of over 45Mbit/s, with high efficiency in processing task requests and resource allocation, and better system stability.
  Keywords: Internet of Things; Dynamic resource allocation; Edge computing; Bee Colony Algorithm.
  DOI: 10.1504/IJISTA.2025.10069479