Forthcoming Articles

International Journal of Critical Computer-Based Systems

International Journal of Critical Computer-Based Systems (IJCCBS)

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 Articles will not be published until the final proofs are validated by their authors.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

Regular Issues

  • Evaluating the Performance of Predictive Models for Water Flow Rate in Storage Tanks: An Analysis of a Cyber-Physical System   Order a copy of this article
    by Abhishek M. B, Neelawar Shekar Vittal Shet 
    Abstract: Excessive or insufficient water flow in the water storage system impacts water management, thereby unsatisfying the water demands of the people. Hence, this paper presents an efficient hybrid forecasting model to develop an effective water management system, regulate water usage, and fulfill water demands. Assisted water monitoring and CPS’s distribution bolstered the task of providing the respective utility with incessant water (24/7) from the storage tanks. Here, the CPS computation unit that works on WFR and TS data is considered. WFR data is acquired and further categorized into daily and monthly data at a sampling interval of 15 minutes. ARIMA, SMA, NN, MLP, and HW are the utilized forecasting models. The output metrics like the MAE, MAPE, and RMSE are wielded. This recommends that the proposed hybrid forecasting method permits taking the necessary steps to finish the CPS decision-making activities. This is utilized to preserve and distribute water whenever required.
    Keywords: Forecasting techniques; Computation Cyber-physical system; Auto Regressive Integrated Moving Average; Simple Moving Average; Neural Networks; Holt-Winters; Multilayer Perceptron.
    DOI: 10.1504/IJCCBS.2026.10078676
     
  • A Secure federated learning mechanism for privacy preservation of IoMT data   Order a copy of this article
    by Hajer Khriji, Salim El Khediri, Salah Zidi, Najoua Bennaji 
    Abstract: The internet of medical things (IoMT) has revolutionised healthcare by enabling continuous real-time patient monitoring. However, the increasing interconnection of medical devices raises major security and privacy concerns regarding sensitive healthcare data. To address these challenges, this paper investigates the integration of federated learning (FL) with secure aggregation (SA) to support decentralised and privacy-preserving medical data analysis. Secure aggregation protects individual model updates during the FL process while maintaining collaborative learning efficiency. Experimental evaluations on IoMT-based datasets demonstrate that the proposed secure federated learning (SFL) framework significantly reduces the impact of adversarial data perturbation. The framework improves model accuracy on attacked datasets from approximately 97% to 99% while reducing adversarial attack effects by nearly 8090% without affecting convergence. Although SA introduces a slight increase in performance variability, the results confirm that combining IoMT, FL, and SA provides an effective balance between privacy preservation, security, and reliable healthcare intelligence.
    Keywords: IoMT ; ML; Federatting Learning ; Secure aggregation ; Data Security ; Data Privacy ; Adversarial Attacks.
    DOI: 10.1504/IJCCBS.2026.10079423
     

 

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