Forthcoming and Online First Articles

International Journal of Intelligent Systems Technologies and Applications

International Journal of Intelligent Systems Technologies and Applications (IJISTA)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Intelligent Systems Technologies and Applications (2 papers in press)

Regular Issues

  • Deep-Reinforcement Learning aided Dynamic Parameter Identification of Multi-Joints Manipulator   Order a copy of this article
    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   Order a copy of this article
    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
     

 

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