Forthcoming Articles

International Journal of Embedded Systems

International Journal of Embedded Systems (IJES)

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 Articles will not be published until the final proofs are validated by their authors.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 Embedded Systems (2 papers in press)

Regular Issues

  • Energy-efficient fixed priority scheduling for imprecise mixed-criticality tasks on multi-processor platforms   Order a copy of this article
    by Yi-Wen Zhang, Chen Ouyang, Rong-Kun Chen 
    Abstract: The multiprocessor platform has become the mainstream in embedded systems, and energy consumption has become a challenge for these systems. In this paper, we consider the energy-efficient fixed priority partitioned scheduling problem of the imprecise mixed-criticality (IMC) tasks on a multiprocessor platform. We propose a novel IMC task partitioning algorithm (IMCSGA) to minimize energy consumption under the Criticality Rate Monotonic Scheduling. The experimental results show that IMCSGA can save about 8.48% energy consumption compared with the existing methods.
    Keywords: energy-aware; imprecise mixed-criticality; fixed priority; partitioned scheduling; genetic algorithm.
    DOI: 10.1504/IJES.2025.10077359
     
  • Adaptive question difficulty and news classification with scaled soft voting: beyond machine and deep learning models   Order a copy of this article
    by Aradhana Saxena, SanthanaVijayan Arumugam 
    Abstract: Accurate classification of question difficulty is vital for adaptive learning but often suffers from instability and high computational cost in existing models. This study evaluates 15 machine learning classifiers and three deep learning architectures using a dataset of 9,692 questions categorised as simple, average, and tough. To enhance performance, LinearSVC, SGD classifier, and RBF SVM are integrated into a scaled soft voting ensemble, where classifier contributions are weighted based on class-level accuracy. The proposed ensemble achieves a micro-average AUC of 0.95. Cross-domain validation on the AG News benchmark further demonstrates robustness, achieving an overall accuracy of 89%. The ensemble consistently outperforms individual models and alternative strategies while maintaining competitive performance with deep learning approaches at significantly lower training and inference cost. These results highlight that a lightweight weighted ensemble provides an efficient, interpretable, and scalable solution for adaptive testing and text classification tasks
    Keywords: question difficulty classification; ensemble learning; soft voting; support vector machines; natural language processing; NLP; adaptive learning.
    DOI: 10.1504/IJES.2026.10078383
     

 

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