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  • Open Access

    ARTICLE

    Deep Learning-Driven Anomaly Detection for IoMT-Based Smart Healthcare Systems

    Attiya Khan1, Muhammad Rizwan2, Ovidiu Bagdasar2,3, Abdulatif Alabdulatif4,*, Sulaiman Alamro4, Abdullah Alnajim5

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2121-2141, 2024, DOI:10.32604/cmes.2024.054380 - 31 October 2024

    Abstract The Internet of Medical Things (IoMT) is an emerging technology that combines the Internet of Things (IoT) into the healthcare sector, which brings remarkable benefits to facilitate remote patient monitoring and reduce treatment costs. As IoMT devices become more scalable, Smart Healthcare Systems (SHS) have become increasingly vulnerable to cyberattacks. Intrusion Detection Systems (IDS) play a crucial role in maintaining network security. An IDS monitors systems or networks for suspicious activities or potential threats, safeguarding internal networks. This paper presents the development of an IDS based on deep learning techniques utilizing benchmark datasets. We propose More >

  • Open Access

    ARTICLE

    Enhancing Healthcare Data Security and Disease Detection Using Crossover-Based Multilayer Perceptron in Smart Healthcare Systems

    Mustufa Haider Abidi*, Hisham Alkhalefah, Mohamed K. Aboudaif

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 977-997, 2024, DOI:10.32604/cmes.2023.044169 - 30 December 2023

    Abstract The healthcare data requires accurate disease detection analysis, real-time monitoring, and advancements to ensure proper treatment for patients. Consequently, Machine Learning methods are widely utilized in Smart Healthcare Systems (SHS) to extract valuable features from heterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities. These methods are employed across different domains that are susceptible to adversarial attacks, necessitating careful consideration. Hence, this paper proposes a crossover-based Multilayer Perceptron (CMLP) model. The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on the medical… More >

  • Open Access

    ARTICLE

    IoMT-Enabled Fusion-Based Model to Predict Posture for Smart Healthcare Systems

    Taher M. Ghazal1,2,*, Mohammad Kamrul Hasan1, Siti Norul Huda Abdullah1, Khairul Azmi Abubakkar1, Mohammed A. M. Afifi2

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2579-2597, 2022, DOI:10.32604/cmc.2022.019706 - 07 December 2021

    Abstract Smart healthcare applications depend on data from wearable sensors (WSs) mounted on a patient’s body for frequent monitoring information. Healthcare systems depend on multi-level data for detecting illnesses and consequently delivering correct diagnostic measures. The collection of WS data and integration of that data for diagnostic purposes is a difficult task. This paper proposes an Errorless Data Fusion (EDF) approach to increase posture recognition accuracy. The research is based on a case study in a health organization. With the rise in smart healthcare systems, WS data fusion necessitates careful attention to provide sensitive analysis of… More >

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