International Journal of Intelligent Systems Technologies and Applications (11 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
   
  
• 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
   
  
• 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
   
  
• Forecasting of Short-Term Traffic Flow using LSTM Ensembled with Variational Mode Decomposition and Wavelet Denoising  
  by Lulu Wang, Meiqing An, Wenlong Zhu, Wanli Xiang, Ruichun He 
  Abstract: Accurate prediction of traffic flow is essential for effective traffic management. This paper proposes a Long Short-Term Memory network ensembled with Variational Mode Decomposition and Wavelet Denoising (VMD-WD-LSTM). First, the LSTM model is employed as a basic prediction model. Subsequently, Variational Mode Decomposition (VMD) is applied to decompose the time series, followed by a stationarity test on the resulting components. The Intrinsic Mode Functions (IMFs) with non-stationary characteristics are further denoised using Wavelet Denoising. Finally, the processed time series are input into the input layer of the LSTM model, respectively. The final short-term traffic flow prediction is obtained by reconstructing each time series prediction result. Six sets of traffic flow data are selected to test the model. The results show that the proposed model is superior to other four methods.
  Keywords: Short-term traffic flow; Variational Mode Decomposition; Wavelet Denoising; LSTM network; Forecasting.
  DOI: 10.1504/IJISTA.2026.10070999
   
  
• DARE-LSTMAE: LSTM Autoencoder model with attention mechanism and optimized reconstruction error for anomaly detection in biogas plants  
  by K. Meena 
  Abstract: This study develops a deep LSTM autoencoder with attention (DARE-LSTMAE) to enhance anomaly detection accuracy and enable real-time fault detection, investigating the use of neural networks to identify abnormalities in the functioning of biogas facilities. By leveraging advanced machine learning techniques, the system can continuously monitor and analyze data from various sensors, including temperature, pressure, methane and pH levels. The method for identifying irregularities in the operations of biogas plants presented in this study uses a deep long short-term memory autoencoder with an attention mechanism (DARE-LSTMAE). It is designed to learn the normal operational patterns of the biogas plant by encoding and reconstructing the input data. The attention mechanism helps the model focus on the most important properties, improving anomaly detection. Additionally, the reconstruction error is optimized to ensure that the model can precisely differentiate between normal and anomalous behaviors.
  Keywords: Anomaly detection; biogas plants; LSTM; reconstruction error; threshold; attention mechanism.
  DOI: 10.1504/IJISTA.2026.10071217
   
  
• Analysis of Deep Learning based Models for Automatic Target Recognition using Synthetic Aperture Radar Imagery  
  by Baldivya Mitra, Maroti Deshmukh, Abhimanyu Kumar 
  Abstract: Automatic Target Recognition (ATR) has become necessary for the military to enhance intelligence in autonomous weapons, ultimately minimize collateral damage, and autonomously tackle war conditions. Therefore, researchers from military weapons manufacturing organizations and academia built various techniques for automatic target recognition models. It is found that Synthetic Aperture Radar (SAR) imagery for automatic target recognition produces a better accuracy of target recognition than other sources of data [43], [52]. This paper reviews existing techniques for Automatic Target Recognition in autonomous weapons using SAR Imagery to analyse their performance in Standard Operating Conditions (SOC) and Extended Operating Conditions (EOC). A total of 182 research papers and articles were explored to get information of recent works done for ATR using SAR imagery, out of them 55 research papers were found relative and are analysed in this paper. Data sources as UCI ML repository, Kaggle, Sandia Digital Library, and Amazon are explored and it is found out that, Moving and Stationary Target Acquisition and Recognition (MSTAR) is the only SAR imagery based dataset available to conduct research for ATR.
  Keywords: Automatic Target Recognition (ATR); Synthetic Aperture radar (SAR); Moving and Stationary Target Acquisition and Recognition (MSTAR) I. INTRODUCTION.
  DOI: 10.1504/IJISTA.2026.10071682
   
  
• Research on Campus Safety Behavior Recognition Based on Data Enhancement and Multiscale Improved CNNs  
  by Wei Zhao, Hui Xu 
  Abstract: This study tackles the issue of low accuracy in campus safety behaviour recognition methods by proposing a model that integrates a data enhancement algorithm with an improved multi-scale convolutional neural network (MSCNN). The newly developed data-enhanced MSCNN model was tested against two commonly used recognition models across various regions and identified safety behaviours with significantly greater accuracy. Remarkably, it achieved a 96% accuracy rate for recognizing behaviours among students at a specific university and reached 97% for students across multiple colleges under similar conditions. The model demonstrated minimal errors in identifying diverse safety behaviours, highlighting its effectiveness in accurately recognising campus safety behaviours. Overall, the experimental results confirm that this model provides a robust tool for evaluating and analysing campus safety behaviours more effectively.
  Keywords: Data augmentation; CNN; Multi-scale; Campus safety behavior; Improved MSCNN model.
  DOI: 10.1504/IJISTA.2026.10073603
   
  
• A Track and Field Technical Evaluation Model based on DeepSORT Algorithm and YOLOv5 Algorithm  
  by Wu Wen 
  Abstract: This study presents a run-up speed measurement model for long jump athletes, combining a lightweight target detector (YOLOv5) with a real-time tracking algorithm (DeepSORT). Traditional tracking methods often suffer from high computational load and slow detection speeds; our model addresses these limitations by improving efficiency and accuracy. Experimental results on the Sports-1M and UCF101 datasets show reduced memory usage down to 64.88% and 68.32%, respectively after replacing the original detector. On UCF101, the model achieves a precision of 0.957, recall of 0.913, F1 score of 0.927, and accuracy of 0.940, significantly outperforming the baseline. The average speed measurement deviation remains within 0.1 m/s, confirming high accuracy. Overall, this model offers an effective tool for tracking and performance evaluation in track and field, providing valuable support for technical analysis.
  Keywords: Athletics; Technical evaluation; Target tracking; Speed measurement; DeepSORT; YOLOv5.
  
  
• Unmanned Aerial Vehicle Inspection Algorithm for Highway Maintenance Based on 5G Network Communication  
  by Dongdong Xie, Qingsong Wan, Meng Chi, Wenjiang Hao, Bo Mi 
  Abstract: This study delves into the potential of drones, supported by 5G network communication, to enhance the success rate of collision avoidance and the rationality of path planning in the context of highway maintenance and inspection. It considers both line-of-sight (LOS) and non-line-of-sight (NLOS) propagation losses, alongside large-scale spatial losses, to analyze the distribution of 5G network communication signals. By calculating the relative position and velocity between the drone and obstacles, dynamic collision avoidance control for the drone is achieved. Considering four key factors: inspection path length, flight altitude restrictions, risk cost, and smoothness, the study employs the cuckoo search algorithm to optimize and plan the inspection path for highway maintenance drones. The experimental results demonstrate that the proposed drone inspection algorithm boasts a high collision avoidance success rate, consistently exceeding 97%, and yields reasonable inspection path planning results, enabling comprehensive inspection of multiple objectives.
  Keywords: 5G network communication; Highway maintenance; Unmanned aerial vehicle inspection; Autonomous collision avoidance.
  DOI: 10.1504/IJISTA.2025.10074176