Forthcoming and Online First 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 Articles will not be published until the final proofs are validated by their authors.

Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.

Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.

Online First articles are published online here, before they appear in a journal issue. Online First articles are fully citeable, complete with a DOI. They can be cited, read, and downloaded. Online First articles are published as Open Access (OA) articles to make the latest research available as early as possible.

Open AccessArticles marked with this Open Access icon are Online First articles. They are freely available and openly accessible to all without any restriction except the ones stated in their respective CC licenses.

Register for our alerting service, which notifies you by email when new issues are published online.

International Journal of Embedded Systems (6 papers in press)

Regular Issues

  • A wrapper-based feature selection approach using osprey optimisation for software fault detection   Order a copy of this article
    by Pradeep Kumar Rath, Soumili Ghosh, Mahendra Kumar Gourisaria, Susmita Mahato, Himansu Das 
    Abstract: Advancements in machine learning, detecting faults in software development cycles alongside determining the level of correctness of said predictions, has improved significantly. This paper employed osprey optimisation in feature selection approach to improve the predictive capabilities of the models. In this paper, the proposed approach taking the feature generation process through two stages; exploration and exploitation phases. The propagation of weight updation through two stages makes sure that all relevant characteristics of the dataset are considered when generating the optimal subset of features. A total of four models were used, namely, K-nearest neighbours (KNN) classifier, na
    Keywords: software fault prediction; feature selection; optimisation techniques; classification; soft computing.
    DOI: 10.1504/IJES.2024.10068607
     
  • A hybrid ABC-SVM approach for multi-dimensional data classification with synthetic data balancing   Order a copy of this article
    by Weili Zhao, Yuan Xu, Chuzhen Wang 
    Abstract: User behaviour data plays a vital role in digital decision-making, especially in education, finance, and healthcare. However, traditional methods often fail to capture the complex characteristics of user behavior, perform poorly on multi-dimensional data, and struggle with class imbalance, which limits model performance. To overcome these challenges, this study constructs a dynamic user behavior dataset from the Chaoxing system and adopts the Synthetic Minority Oversampling Technique (SMOTE) to address data imbalance problem. The Artificial Bee Colony (ABC) algorithm is combined with the Support Vector Machine (SVM) to optimize model parameters and improve performance. Experimental results show that the proposed ABC-SVM model performs well in complex classification tasks with an accuracy of 97.9%, outperforming baseline and other optimization methods. This study highlights the potential of intelligent optimization algorithms in multidimensional data analysis and provides a reference for intelligent systems in other fields.
    Keywords: class imbalance; multi-dimensional data; support vector machine; algorithm optimisation; ideological and political education; educational assessment.
    DOI: 10.1504/IJES.2024.10068870
     
  • Ensemble-based classification algorithm to enhance stability of energy management in IoT-based smart grid networks   Order a copy of this article
    by Monire Norouzi, Zafer Utlu, Salaheddine Bendak, Alireza Souri 
    Abstract: The exponential increase in electricity consumption makes renewable energy management a necessity within the global warming context. Internet of Things (IoT) has a key role in effective data transmission for better managing of energy dissipation in smart grids. Since smart grid network deployment involves huge complexities due to the large data volume being generated, applying artificial intelligent methods is essential to better manage the process. Moreover, reducing energy consumption in a stable smart grid system and fault detection are important in managing electricity congestions, power failure and grid stability problems. This paper aims to present a novel prediction architecture involving Ensemble Bagging Trees and Analysis of Variance (ANOVA) as a feature selection strategy to improve stability of energy consumption and maximize prediction factors such as accuracy, precision, recall and F1-score in IoT-based smart grids. Experimental and simulation results show that the proposed architecture can decrease training time and improve accuracy of prediction with 99.999% on validation (train) data and 100% on test data than other state-of-the-are machine learning mechanisms.
    Keywords: internet of things; smart grid stability; energy management; ensemble learning; ANOVA; accuracy.
    DOI: 10.1504/IJES.2024.10069119
     
  • On the characteristic polynomials and the spectra of two classes of cyclic polyomino chains   Order a copy of this article
    by Yong-Hong Zhang, Ligong Wang 
    Abstract: Polyhedral graphs hold significant importance in graph theory as well as in other diverse fields. In graph theory, they serve as fundamental objects for understanding various structural properties and topological characteristics. Let A(G) and D(G) be the adjacency matrix and the diagonal matrix of vertex degrees of a graph G, respectively. The Laplacian matrix of G is denoted as L(G) = D(G) A(G), while the signless Laplacian matrix of G is denoted as Q(G) = D(G) + A(G). Additionally, the A-matrix of G can be defined as A(G) = D(G) + (1 )A(G), where [0, 1]. In this paper, our focus is on the linear cyclic polyomino chain Fn and the M
    Keywords: characteristic polynomial; polyomino chain; circulant matrix; symmetric circulant matrix; spectrum.
    DOI: 10.1504/IJES.2025.10069247
     
  • DCATNet: dilated convolution attention transformer network for medical image fusion   Order a copy of this article
    by Zenghui Wang, Mingliang Gao, Lina Liu, Xiangqin Zeng, Qilei Li, Monire Norouzi 
    Abstract: Medical image fusion is dedicated to extracting structural and functional information from medical images. However, existing medical image fusion methods usually rely on convolutional operations and ignore longdistance information transmission. To address this problem, we propose a Dilated Convolutional Attention Transformer Network (DCATNet) for medical image fusion. Specifically, to enhance the long-term dependence of the network on the input image, a transformer (TF) module is built. At the same time, a Dilated Convolutional Channel Attention (DCCA) module is built to realize the accurate extraction of feature and multi-scale information. This module combines the CPA module with the expansion convolution to enhance the robustness of the model. This enables the proposed method to handle the complexities of long-distance information transfer without losing important contextual and structural details. Experimental results demonstrate that the DCATNet outperforms competitors and proves its potential in medical image fusion for long-distance information transfer processes. Meanwhile, the results highlight the potential of DCATNet to advance medical image fusion, and it can lead to better clinical outcomes and more accurate diagnoses.
    Keywords: medical image fusion; transformer; dilated convolution attention; long distance information transmission; embedded system.
    DOI: 10.1504/IJES.2025.10069458
     
  • Efficient edge AI implementation for IoT device identification for hierarchical federated learning   Order a copy of this article
    by Sumitra Budania, Jeevan Kittur, Meetha V. Shenoy 
    Abstract: As more and more IoT devices are being introduced to the network, ubiquitous identification of the IoT devices connected to a network is essential for the efficient network resource management, planning, and identification of anomalous traffic. Federated Learning (FL) is an emerging paradigm that avoids the need for transmitting the data to a centralized location for training the model and is inherently privacy-preserving as well as saves the communication bandwidth. We present an efficient implementation of an AI model for IoT device identification at the embedded edge devices .We have curated and implemented various model compression techniques in a standalone as well as in a cascaded manner and analyzed their device identification performance Vs resource trade-off to identify the most suitable model compression strategy for edge-device implementation in the proposed federated learning model for IoT device identification.
    Keywords: edge AI; model compression; inference optimisation; deep learning; Internet of Things; device identification.
    DOI: 10.1504/IJES.2025.10069459