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  • Open Access


    Integration of Digital Twins and Artificial Intelligence for Classifying Cardiac Ischemia

    Mohamed Ammar1,*, Hamed Al-Raweshidy2,*

    Journal on Artificial Intelligence, Vol.5, pp. 195-218, 2023, DOI:10.32604/jai.2023.045199

    Abstract Despite advances in intelligent medical care, difficulties remain. Due to its complicated governance, designing, planning, improving, and managing the cardiac system remains difficult. Oversight, including intelligent monitoring, feedback systems, and management practises, is unsuccessful. Current platforms cannot deliver lifelong personal health management services. Insufficient accuracy in patient crisis warning programmes. No frequent, direct interaction between healthcare workers and patients is visible. Physical medical systems and intelligent information systems are not integrated. This study introduces the Advanced Cardiac Twin (ACT) model integrated with Artificial Neural Network (ANN) to handle real-time monitoring, decision-making, and crisis prediction. THINGSPEAK… More >

  • Open Access


    Privacy Preserving Image Encryption with Deep Learning Based IoT Healthcare Applications

    Mohammad Alamgeer1, Saud S. Alotaibi2, Shaha Al-Otaibi3, Nazik Alturki3, Anwer Mustafa Hilal4,*, Abdelwahed Motwakel4, Ishfaq Yaseen4, Mohamed I. Eldesouki5

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1159-1175, 2022, DOI:10.32604/cmc.2022.028275

    Abstract Latest developments in computing and communication technologies are enabled the design of connected healthcare system which are mainly based on IoT and Edge technologies. Blockchain, data encryption, and deep learning (DL) models can be utilized to design efficient security solutions for IoT healthcare applications. In this aspect, this article introduces a Blockchain with privacy preserving image encryption and optimal deep learning (BPPIE-ODL) technique for IoT healthcare applications. The proposed BPPIE-ODL technique intends to securely transmit the encrypted medical images captured by IoT devices and performs classification process at the cloud server. The proposed BPPIE-ODL technique… More >

  • Open Access


    Lightweight Algorithm for MQTT Protocol to Enhance Power Consumption in Healthcare Environment

    Anwar D. Alhejaili*, Omar H. Alhazmi

    Journal on Internet of Things, Vol.4, No.1, pp. 21-33, 2022, DOI:10.32604/jiot.2022.019893

    Abstract Internet of things (IoT) is used in various fields such as smart cities, smart home, manufacturing industries, and healthcare. Its application in healthcare has many advantages and disadvantages. One of its most common protocols is Message Queue Telemetry Transport (MQTT). MQTT protocol works as a publisher/subscriber which is suitable for IoT devices with limited power. One of the drawbacks of MQTT is that it is easy to manipulate. The default security provided by MQTT during user authentication, through username and password, does not provide any type of data encryption, to ensure confidentiality or integrity. This… More >

  • Open Access


    Class Imbalance Handling with Deep Learning Enabled IoT Healthcare Diagnosis Model

    T. Ragupathi1,*, M. Govindarajan1, T. Priyaradhikadevi2

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1351-1366, 2022, DOI:10.32604/iasc.2022.025756

    Abstract The rapid advancements in the field of big data, wearables, Internet of Things (IoT), connected devices, and cloud environment find useful to improve the quality of healthcare services. Medical data classification using the data collected by the wearables and IoT devices can be used to determine the presence or absence of disease. The recently developed deep learning (DL) models can be used for several processes such as classification, natural language processing, etc. This study presents a bacterial foraging optimization (BFO) based convolutional neural network-gated recurrent unit (CNN-GRU) with class imbalance handling (CIH) model, named BFO-CNN-GRU-CIH… More >

  • Open Access


    Intelligent Disease Diagnosis Model for Energy Aware Cluster Based IoT Healthcare Systems

    Wafaa Alsaggaf1,*, Felwa Abukhodair1, Amani Tariq Jamal2, Sayed Abdel-Khalek3, Romany F. Mansour4

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1189-1203, 2022, DOI:10.32604/cmc.2022.022469

    Abstract In recent days, advancements in the Internet of Things (IoT) and cloud computing (CC) technologies have emerged in different application areas, particularly healthcare. The use of IoT devices in healthcare sector often generates large amount of data and also spent maximum energy for data transmission to the cloud server. Therefore, energy efficient clustering mechanism is needed to effectively reduce the energy consumption of IoT devices. At the same time, the advent of deep learning (DL) models helps to analyze the healthcare data in the cloud server for decision making. With this motivation, this paper presents… More >

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