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 Articles will not be published until the final proofs are validated by their authors.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 (3 papers in press)

Regular Issues

  • Service Capability Aware Big Data Workflow Scheduling Approach in Cloud Datacenter   Order a copy of this article
    by Jie Cao, Jinchao Xu, Bo Wang 
    Abstract: With the increasing application of cloud computing, big data workflow scheduling in cloud datacentre also become an important focus of research. How to guarantee minimal scheduling length is the main challenge in scheduling workflow in cloud-based environments. The main limitation of proposed approaches stems is that they overlook the service capability support levels of the virtual machines and service capability requirement levels of the different tasks in a workflow, thus risking resulting in extremely poor processing efficiency. We propose a service dynamic level scheduling algorithm in cloud datacentre (Cloud-SDLS) that consists of three stages: Virtual machines' service capability support computation, tasks' service capability requirement computation, and service dynamic level scheduling. Experimental results show that the proposed algorithms effectively satisfy the QoS in service capability requirement. It is significant to shorten workflow completion time in practice.
    Keywords: cloud computing; service capability requirement; service capability support; workflow scheduling.
    DOI: 10.1504/IJISTA.2024.10059902
  • Developing A method to Detect Driver Drowsiness Based on A single EEG Channel and Discriminated Features   Order a copy of this article
    by Raed Hussein, Loay George, Firas Sabar Miften 
    Abstract: Driver drowsiness is one of the leading causes of road deaths and transportation industry dangers. Due to its direct evaluation of neurophysiological brain activity, electroencephalography (EEG) has been regarded as one of the most reliable physiological indicators for identifying driver drowsiness. This study proposes a straightforward, cost-effective method for detecting driver drowsiness using a single channel. The contribution of this research is the discovery of drowsiness using discriminated features [moments features (M1, M2, M3, M4), roughness features (R1, R2, R3, R4), zero crossing rate (ZCR), sample entropy (SE) and median absolute deviation (MAD)] from publicly available datasets. A novel model was introduced in this study, which involved the fusion of wavelet transform Daubechies order 4 (WTDB4) and residue decomposition (RD) techniques for feature extraction. Various classification algorithms, including the least-square support vector machine (LSSVM) and ensemble models were compared in terms of their performance metrics. The algorithm that exhibited superior accuracy with reduced computational time was chosen to classify the driver's status into two groups: awake and drowsy. Notably, the proposed model achieved an impressive accuracy of 97.95%.
    Keywords: drowsy driving detection; electroencephalography; least square support vector machine; residue decomposition; Wavelet transform.
    DOI: 10.1504/IJISTA.2024.10060208
  • Boosting Speech Recognition Performance: A Robust and Accurate Ensemble Method based on HMMs   Order a copy of this article
    by Samira Hazmoune, Fateh Bougamouza, Smaine Mazouzi, Mohamed Benmohammed 
    Abstract: In this paper, we propose an ensemble method based on hidden Markov models (HMMs) for speech recognition. Our objective is to reduce the impact of the initial setting of training parameters on the final model while improving accuracy and robustness, particularly in speaker independent systems. The main idea is to exploit the sensitivity of HMMs to the initial setting of training parameters, thus creating diversity among the ensemble members. Additionally, we perform an experimental study to investigate the potential relationship between initial training parameters and ten diversity measures from literature. The proposed method is assessed on a standard dataset from the UCI machine-learning repository. Results demonstrate its effectiveness in terms of accuracy and robustness to intra-class variability, surpassing basic classifiers (HMM, KNN, NN, SVM) and some previous works in the literature including those using deep learning algorithms such as convolutional neural networks (CNNs) and long short-term memory (LSTM).
    Keywords: speech recognition; inter-speaker variability; robustness; accuracy; HMM; multiple modelling; ensemble methods; diversity.
    DOI: 10.1504/IJISTA.2024.10060581