Special lssues

Explainable AI and Cybersecurity Techniques for IoT-Based Medical and Healthcare Applications

Submission Deadline: 31 March 2023 (closed)

Guest Editors

Prof. Mohamed Esmail Karar, Shaqra University, Saudi Arabia
Prof. Omar Reyad, Sohag University, Egypt
Prof. Abdel-Haleem Abdel-Aty, African Academy of Science, Kenya

Summary

Artificial Intelligence (AI), cybersecurity and the Internet of Things (IoT) enable technologies that can be integrated into medical and healthcare applications. Internet of Medical Things (IoMT) and Internet of Health Things (IoHT) have recently become an attractive topic for many researchers and physicians because these technologies connect all medical devices, sensors, and software applications through online computers and mobile communication networks. For instance, remote patient monitoring can be achieved through wearable sensors and WiFi-Internet connection at home, while medical doctors are tracking the patient health status at hospitals. This clinical situation was effective during the COVID-19 pandemic lockdown. Additionally, Explainable Artificial Intelligence (XAI), a recent competitive trend in AI, focuses on making traditional AI models more intelligible by using the models' decision-making and prediction outputs. The explainability factor gives real models new potential and gives physicians the confidence to interpret the decisions of machine learning (ML) and deep learning (DL) models used in the diagnosis and treatment procedures. Furthermore, privacy and security aspects of patient data should be highly considered in the framework of IoMT and IoT applications.


Keywords

This special Issue aims to compile original research and review articles presenting recent achievements in this field. The following proposed research topics, but are not limited to
• New theories, methods and evaluation metrics of XAI and cybersecurity in medicine
• Advanced XAI techniques for IoMT and IoHT
• Advanced cybersecurity techniques for IoMT and IoHT
• Explainable machine learning and deep learning methods
• Medical image encryption
• Lightweight encryption for IoMT and IoHT
• Smart decision-making in healthcare systems

Published Papers


  • Open Access

    ARTICLE

    SwinVid: Enhancing Video Object Detection Using Swin Transformer

    Abdelrahman Maharek, Amr Abozeid, Rasha Orban, Kamal ElDahshan
    Computer Systems Science and Engineering, Vol.48, No.2, pp. 305-320, 2024, DOI:10.32604/csse.2024.039436
    (This article belongs to this Special Issue: Explainable AI and Cybersecurity Techniques for IoT-Based Medical and Healthcare Applications)
    Abstract What causes object detection in video to be less accurate than it is in still images? Because some video frames have degraded in appearance from fast movement, out-of-focus camera shots, and changes in posture. These reasons have made video object detection (VID) a growing area of research in recent years. Video object detection can be used for various healthcare applications, such as detecting and tracking tumors in medical imaging, monitoring the movement of patients in hospitals and long-term care facilities, and analyzing videos of surgeries to improve technique and training. Additionally, it can be used in telemedicine to help diagnose… More >

  • Open Access

    ARTICLE

    DeepSVDNet: A Deep Learning-Based Approach for Detecting and Classifying Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images

    Anas Bilal, Azhar Imran, Talha Imtiaz Baig, Xiaowen Liu, Haixia Long, Abdulkareem Alzahrani, Muhammad Shafiq
    Computer Systems Science and Engineering, Vol.48, No.2, pp. 511-528, 2024, DOI:10.32604/csse.2023.039672
    (This article belongs to this Special Issue: Explainable AI and Cybersecurity Techniques for IoT-Based Medical and Healthcare Applications)
    Abstract Artificial Intelligence (AI) is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy (VTDR), which is a leading cause of visual impairment and blindness worldwide. However, previous automated VTDR detection methods have mainly relied on manual feature extraction and classification, leading to errors. This paper proposes a novel VTDR detection and classification model that combines different models through majority voting. Our proposed methodology involves preprocessing, data augmentation, feature extraction, and classification stages. We use a hybrid convolutional neural network-singular value decomposition (CNN-SVD) model for feature extraction and selection and an improved SVM-RBF with a Decision Tree (DT) and K-Nearest Neighbor (KNN)… More >

  • Open Access

    ARTICLE

    Cybersecurity Threats Detection Using Optimized Machine Learning Frameworks

    Nadir Omer, Ahmed H. Samak, Ahmed I. Taloba, Rasha M. Abd El-Aziz
    Computer Systems Science and Engineering, Vol.48, No.1, pp. 77-95, 2024, DOI:10.32604/csse.2023.039265
    (This article belongs to this Special Issue: Explainable AI and Cybersecurity Techniques for IoT-Based Medical and Healthcare Applications)
    Abstract Today’s world depends on the Internet to meet all its daily needs. The usage of the Internet is growing rapidly. The world is using the Internet more frequently than ever. The hazards of harmful attacks have also increased due to the growing reliance on the Internet. Hazards to cyber security are actions taken by someone with malicious intent to steal data, destroy computer systems, or disrupt them. Due to rising cyber security concerns, cyber security has emerged as the key component in the fight against all online threats, forgeries, and assaults. A device capable of identifying network irregularities and cyber-attacks… More >

  • Open Access

    ARTICLE

    Intrusion Detection and Prevention Model for Blockchain Based IoMT Applications

    Jameel Almalki
    Computer Systems Science and Engineering, Vol.48, No.1, pp. 131-152, 2024, DOI:10.32604/csse.2023.038085
    (This article belongs to this Special Issue: Explainable AI and Cybersecurity Techniques for IoT-Based Medical and Healthcare Applications)
    Abstract The recent global pandemic has resulted in growth in the medical and healthcare sectors. Applications used in these domains have become more advanced and digitally integrated. Sensor-based Internet of Things (IoT) devices are increasing in healthcare and medical units. The emerging trend with the use of IoT devices in medical healthcare is termed as Internet of Medical Things (IoMT). The instruments used in these healthcare units comprise various sensors that can record patient body observations. These recorded observations are streamed across Internet-based channels to be stored and analyzed in centralized servers. Patient diagnostics are performed based on the information retrieved… More >

  • Open Access

    ARTICLE

    Intelligent Networked Control of Vasoactive Drug Infusion for Patients with Uncertain Sensitivity

    Mohamed Esmail Karar, Amged Sayed A. Mahmoud
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 721-739, 2023, DOI:10.32604/csse.2023.039235
    (This article belongs to this Special Issue: Explainable AI and Cybersecurity Techniques for IoT-Based Medical and Healthcare Applications)
    Abstract Abnormal high blood pressure or hypertension is still the leading risk factor for death and disability worldwide. This paper presents a new intelligent networked control of medical drug infusion system to regulate the mean arterial blood pressure for hypertensive patients with different health status conditions. The infusion of vasoactive drugs to patients endures various issues, such as variation of sensitivity and noise, which require effective and powerful systems to ensure robustness and good performance. The developed intelligent networked system is composed of a hybrid control scheme of interval type-2 fuzzy (IT2F) logic and teaching-learning-based optimization (TLBO) algorithm. This networked IT2F… More >

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