International Journal of Intelligent Systems Technologies and Applications (11 papers in press)

Regular Issues

• Deep-Reinforcement Learning aided Dynamic Parameter Identification of Multi-Joints Manipulator  
  by Zhuoran Bi, Wenlong Zhao, Yichao Huang, Haoran Zhou, Li Qingdu 
  Abstract: To obtain more accurate dynamics equation parameters, this paper proposed a deep reinforcement learning (DRL) method for parameter identification. After using the least square (LS) method to identify the base parameters, we establish a training strategy where the friction coefficient serves as the DRL action. This strategy controls both the source and target manipulators, employing the concept of imitation learning. After using our strategy, the parameters of the target manipulator tend to converge to those of the source manipulator. In the experiment, we perform parameter identification of a 7-degree-of-freedom (DOF) manipulator in a real environment, and then identify friction coefficient for each joint based on the MuJoCo environment to theoretically validate the parameter identification using DRL. The identification results demonstrated that in a simulation environment, the use of DRL outperforms the traditional LS method, resulting in improved accuracy.
  Keywords: deep reinforcement learning; parameter identification; soft actor critic; joint friction.
  DOI: 10.1504/IJISTA.2024.10064051
   
  
• A combined Feature Selector using Jaya and Differential Evolution to Improve the Classification Accuracy for Dataset of Intrusion Detection System  
  by S.Appavu Alias Balamurugan, Karthik Kannan A. S, Millie Pant 
  Abstract: Cyberattacks are considered one of the largest threats to data security in this digital age. In the overall strategy for thwarting cyberattacks, intrusion detection systems (IDS) play a very important role. A high dimensional data flow poses a significant challenge for IDS when investigating all aspects. IDS's success rate is reduced as a result of the increases in computation cost. Feature selection in intrusion detection is proposed as a combined self-adapted Jaya optimisation algorithm. The goal of the proposed work is to maximize the classification accuracy (success rate) and to minimise the feature selection ratio by maximizing the fitness function. Three benchmark datasets (UNSW NB15, KDDCUP 99 and NLS KDD) were used to verify the proposed method performance. With reference to the analysis of comparison made, the proposed method outperforms than the existing methods.
  Keywords: Combined feature-selector; Classifier; System for Intrusion detection; Jaya optimization; Differential evolution.
  DOI: 10.1504/IJISTA.2024.10066581
   
  
• Enhancing VTOL Control using Twin Delayed Deep Deterministic Policy Gradient-Based Controller  
  by Haitham Alradhi, Khaled El-Metwally 
  Abstract: This study presents the application of a twin delayed deep deterministic policy gradient (TD3) based controller for vertical take-off and landing (VTOL) system control. The TD3 algorithm is a deep reinforcement learning (DRL) algorithm (actor-critic type) designed for continuous control problems. The DRL agent learns to optimise actions to maximise rewards by interacting with the environment. VTOL aircraft is a nonlinear system that shows high complexity with variable aerodynamic parameters during the flight. The proposed controller is implemented on a one-degree-of-freedom VTOL system. The performance of the proposed controller is compared to that of a PID controller and a deep deterministic policy gradient (DDPG) based controller, another DRL algorithm. The evaluation includes analysing step response characteristics and the sum square of errors. MATLAB and Simulink are utilised for the implementation and analysis. The results indicate that the TD3-based controller exhibits performance better than the PID controller with reduced settling time and free overshoot.
  Keywords: vertical take-off and landing; VTOL control; reinforcement learning; AI; deep learning; TD3-based controller; PID controller; actor-critic method.
  DOI: 10.1504/IJISTA.2024.10067251
   
  
• Edge Extraction Method for Multi-Frame 2D Animated Images Based on Spatiotemporal Filtering  
  by Ting Zhang, Xiang-yu Wei, Feng Qu 
  Abstract: In order to reduce the edge localization error of animated images and improve the accuracy of edge extraction, a multi-frame 2D animated image edge extraction method based on spatiotemporal filtering is proposed. Firstly, the spatiotemporal filtering algorithm is used to divide the regions to be enhanced in multiple 2D animated images into basic layers and detail layers, thereby enhancing the animated images. Secondly, the Canny operator and an extended Sobel gradient template are used for gradient calculation. Finally, by improving the adaptive threshold segmentation method and combining histogram analysis with the strategy of maximising inter-class variance, the edge extraction process of multi-frame 2D animated images is optimised to improve the accuracy and robustness of edge detection. The experimental results show that the signal-to-noise ratio of the method proposed in this paper remains above 35 dB, and the edge extraction accuracy reaches 99%.
  Keywords: Spatiotemporal domain filtering; Multi frame 2D animated images; Edge extraction; Sobel gradient template.
  DOI: 10.1504/IJISTA.2025.10067669
   
  
• Multi-Dimensional Evaluation Method of Chinese Online Teaching Effect based on Learning Behaviour Data  
  by Bingxin Zhao 
  Abstract: In order to overcome the problems of low recall and precision of learning behaviour data, as well as low evaluation accuracy in traditional multi-dimensional evaluation methods for Chinese online teaching effectiveness, a new multi-dimensional evaluation method of Chinese online teaching effect based on learning behaviour data is proposed. Using K-means algorithm and Ada Boos algorithm to mine learning behaviour data, Chinese online learning state recognition is performed based on the mined learning behaviour data and Kalman filtering. Combining the results of Chinese online learning state recognition with Markov chain, multi-dimensional evaluation of Chinese online teaching effectiveness is achieved. The experimental results show that the average recall rate and precision rate of the learning behaviour data of the proposed method are 96.78% and 97.34%, respectively. The accuracy of multi-dimensional evaluation of the effectiveness of Chinese online teaching varies within the range of 93.8% to 97.3%, indicating high accuracy.
  Keywords: Learning behaviour data; Chinese Online teaching effectiveness; Multi-dimensional evaluation; Kalman filtering; Markov chain.
  DOI: 10.1504/IJISTA.2025.10067670
   
  
• A Specific Action Pose recognition of Hierarchical Dance based on Pose Feature Matching  
  by Yu Zhang, Jun Wang 
  Abstract: To enhance the traditional method's limitations of low accuracy and prolonged feature matching times for specific dance action posture matching, we introduce a hierarchical approach for dance-specific action posture recognition. Initially, we utilise Kinect devices to capture real-time data and extract pertinent physical features. Subsequently, the K-means clustering algorithm is employed to extract keyframe features from the sequence, followed by image reconstruction using the active contour lasso method. Next, hierarchical dance movements are identified through two-dimensional manifold analysis, which enables us to derive the distribution function of edge contour features. Finally, the posture feature matching method is applied to align the functional outcomes, leading to recognition of specific action postures. Experimental results demonstrate that this method achieves a pose feature matching accuracy of 99.8% while reducing the matching time to 1.5 seconds. This method improves the performance of recognising specific movements and postures in graded dance.
  Keywords: Active contour lasso method;Two-dimensional manifold analysis;Postural features;K-means clustering algorithm;Feature Matching.
  DOI: 10.1504/IJISTA.2025.10067674
   
  
• Study on Internet of Things Anomaly Data Mining Method based on Improved Differential Evolution Automatic Clustering  
  by Aihua He 
  Abstract: In this paper, an IoT anomaly data mining method based on improved differential evolution automatic clustering is proposed. Through dimensionality reduction of the collected data, the overfitting problem of the mining results is avoided and the mining efficiency is improved. Expand the multi-stage feature selection to obtain the best feature. Based on this, a differential evolution algorithm is introduced to determine and adjust cluster centers by improving variation factors and cross factors through adaptive strategies, and the K-means automatic clustering algorithm is used to complete abnormal data mining in the network. The results show that the NMI value and ARI value of the proposed method can reach more than 0.95, the AUC value is close to 1, and the mining time is 1.8s, which has a good clustering effect and can accurately realize the mining of abnormal data.
  Keywords: Internet of things; Abnormal data mining; Multi stage feature selection; Improved differential evolution; Automatic clustering.
  DOI: 10.1504/IJISTA.2025.10067675
   
  
• Trajectory Correction Control Method for Autonomous Mobile Robots based on Embedded Laser Ranging  
  by Lei Zhang, Baochen Yang, Wenlian Guo 
  Abstract: In order to reduce the movement deviation of robots, a trajectory correction control method for autonomous mobile robots based on embedded laser ranging is proposed. Firstly, a two-dimensional embedded laser rangefinder is used to obtain point cloud data of the robot's movement, and the robot's movement distance is analysed. Next, proceed with developing the robot's kinematic model and formulating the generalised dynamic equations. Ultimately, the adaptive Monte Carlo algorithm is applied to precisely determine the robot's location and compute the variance between the robot's coordinates and the primary point. Leveraging the classic PID algorithm, a control system for correcting distance and angle is established to enable precise trajectory adjustments for the robot. Notably, empirical findings demonstrate a remarkable 98.4% success rate in robot positioning using the proposed method, while discrepancies in the X-axis, Y-axis, and yaw angle closely correspond to the true values.
  Keywords: Embedded laser ranging; Autonomous mobile robots; Trajectory correction control; Kinematic model.
  DOI: 10.1504/IJISTA.2025.10067676
   
  
• Tackling Cyberbullying: A Multilingual Approach to Cyberbullying Detection in India  
  by Shahwar Nawshad, Umar Farooq, Parvinder Singh, Surinder Singh Khurana, Anam Bansal 
  Abstract: In India’s evolving digital world, women are particularly vulnerable to cyberbullying due to differences in education, limited digital literacy, and pervasive cybersecurity risks. This research focuses on creating a system to detect cyberbullying targeting Indian women. Acknowledging the country's linguistic diversity, we adopt a multilingual approach, constructing a dataset incorporating English, Hindi-English swear words, common Indian slang, and offensive lexicons. We apply various machine learning algorithms to classify cyberbullying incidents. Upon evaluating the results, the relevance vector machine (RVM) algorithm emerged as the most effective, achieving 82.61% and 84.82% accuracy scores in detecting cyberbullying over English and Hinglish datasets, respectively. These findings provide crucial insights for crafting strategies to safeguard Indian women in the digital space. However, more sophisticated and hybrid models are planned for the future to address image, video, and audio-based cyberbullying against women.
  Keywords: cyberbullying; multilingual classification; machine learning; relevance vector machine; RVM; SVM.
  DOI: 10.1504/IJISTA.2025.10067745
   
  
• Optimization Design of Wireless Communication Antennas Based on Proxy Model and Sparrow Search  
  by Zhenhua Han, Liang Li, Lina Shen 
  Abstract: Antennas are vital for wireless communication systems, but existing optimisation methods face challenges like lengthy training and numerous variables. This study introduces a new approach: using backpropagation neural networks to create a proxy model and enhancing it with a sparrow search algorithm for parameter adjustment. Results show the model’s minimum mean square error is 7.51, minimum average percentage absolute error is 10.97, with a maximum coefficient of determination of 0.88, and prediction errors within the (1, 1) range. Optimal antenna efficiency is achieved at 7.2 metres width and 10.8 metres length. The model achieves a minimum mapping parameter of 32 decibels and maximum gain of 28 with 250 iterations. This approach offers clear advantages and feasibility in antenna design optimisation, promising significant potential in wireless communication.
  Keywords: proxy model; sparrow search; wireless communication; antenna; parameter.
  DOI: 10.1504/IJISTA.2024.10067939
   
  
• An Improved EEMD-PE-BiLSTM Model for Forecasting Traffic Flow  
  by Liang Zhu, Wenlong Zhu, Wanli Xiang, Xuelei Meng, Chunmin Zhang 
  Abstract: Accurately predicting traffic flow is of paramount importance for enhancing road network traffic efficiency and alleviating urban traffic congestion. However, in the presence of external influences, the raw traffic flow data exhibited noticeable noise, thereby increasing prediction complexity. To address this challenge, we propose the ATMS-EEMD method. Specifically, we address the issue of mode mixing in EEMD by combining signal-energy calculations and introducing an adaptive stopping criterion. Subsequently, we employed the permutation entropy (PE) algorithm to assess the complexity of the decomposed components and reconstruct new subsequences with similar complexity. Finally, we constructed a bidirectional long short-term memory (BiLSTM) neural network for traffic flow prediction. We validated the performance of the proposed model using traffic flow data from three detectors. The experimental results demonstrate that ATMS-EEMD-PE-BiLSTM achieves an average absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of 1.422, 2.183, and 3.603, respectively. This method exhibits outstanding performance in traffic flow prediction, overcomes challenges posed by external factors, and achieves high-precision forecasting.
  Keywords: traffic flow forecasting; ITS; ATMS-EEMD; permutation entropy; BiLSTM.
  DOI: 10.1504/IJISTA.2024.10068006