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Advances, Challenges, and Opportunities of IoT-Based Big Data in Healthcare Industry 4.0

Submission Deadline: 25 June 2023 (closed)

Guest Editors

Dr. Anwar Ghani, International Islamic University, Pakistan.
Dr. Shehzad Ashraf Chaudhry, Abu Dhabi University, UAE; Nisantasi University, Turkey.
Dr. Rashid Ahmad, COMSATS University Islamabad, Pakistan; Jeju National University, Republic of Korea.


Industries are migrating to version 4.0 and progressing towards digitalisation with the help of Internet of Things (IoT) technologies. Wireless communication technologies play a significant role in industry digitisation. The medical sector is the one that is facing that change. Modernised medical equipment, upgraded and precise scanning procedures, image processing, and intelligence-based medical instruments, among other things, have all made significant advances in technological development. Analysing MRI and CT scan data to diagnose Alzheimer's disease necessitate substantial human labour. This is where big data received increasing attention to meet these initiatives.


In the healthcare industry, data is exceedingly complicated. Medical data, such as pictures, prescriptions, sensor data, and electronic patient records, must be stored and handled by a big data system in healthcare, along with other operational and payment data. Most medical tools, the internet of devices, and healthcare software have become smart in healthcare. The majority of patient records are stored in a big data storage system that the patient can access and from any location. The doctor might consult the patient's medical records to give other therapies. This improves the treatment's quality while also saving time. Using Big data and IoT devices, every difficulty in a patient's health may be easily tracked. Big Data assists in extracting information from patient data, identifying trends, and recommending therapy and medicine. The big data system develops its analysing algorithm based on the result and a continual feedback mechanism. Big data analysis is used to detect medical insurance fraud claims and to anticipate future fraud claims. It also aids in the early detection of Alzheimer's disease. It has a lot of advantages, but it also has some disadvantages. The healthcare business should not rely solely on the results of big data analysis; before treatment, experts or medical professionals should be consulted. The IoT increases the amount of complex data, necessitating ultra-efficient data models and a more significant number of devices. This raises the cost of the system and the ultimate cost to the consumers. Big data analysis has a high rate of mistake susceptibility early, and establishing a well-trained big data model takes time. Because the sensor's accuracy cannot be guaranteed at all times, a backup system is necessary for sensor failure. Big data analysis will not perform well when exposed to new data sets.


This article examines how the IoT and its applications have aided tremendous growth in the healthcare business. We support all research and creative ideas to address the threats posed by Industry 4.0 to deliver smart health care.


Related Topics:
1. The Coming Rise of Cognitive Cyber-Physical Systems for Industry 4.0
2. Setting the Future of Medical informatics with a Cyber-Physical Human System
3. Innovative Applications of Cyber-Physical and Medical System technology foundations
4. The Progress with Cognitive Cyber-Physical Systems: IoT and Connected Devices
5. Industrial informatics based on Intelligent and Multi-Modal Cognitive System
6. Trends and Objectives of Humanized Computing and Cognitive Ambient Intelligence
7. Intelligent Cyber-Physical Systems Solutions in Medical Informatics
8. Designing Smart Cyber-Medical Systems: Constraints and Opportunities
9. Medical informatics in the context of a Cyber-Physical Human System
10. Cloud and Data Science for IoT Cognitive Statistics, and Knowledge Management
11. IoMT for Abnormal heart rate and breathing problems
12. Big Data Analytics for body sensor network using deep learning methods
13. A Systematic analysis of big data analytics and healthcare convergence

Published Papers

  • Open Access


    Smart MobiNet: A Deep Learning Approach for Accurate Skin Cancer Diagnosis

    Muhammad Suleman, Faizan Ullah, Ghadah Aldehim, Dilawar Shah, Mohammad Abrar, Asma Irshad, Sarra Ayouni
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3533-3549, 2023, DOI:10.32604/cmc.2023.042365
    (This article belongs to this Special Issue: Advances, Challenges, and Opportunities of IoT-Based Big Data in Healthcare Industry 4.0)
    Abstract The early detection of skin cancer, particularly melanoma, presents a substantial risk to human health. This study aims to examine the necessity of implementing efficient early detection systems through the utilization of deep learning techniques. Nevertheless, the existing methods exhibit certain constraints in terms of accessibility, diagnostic precision, data availability, and scalability. To address these obstacles, we put out a lightweight model known as Smart MobiNet, which is derived from MobileNet and incorporates additional distinctive attributes. The model utilizes a multi-scale feature extraction methodology by using various convolutional layers. The ISIC 2019 dataset, sourced from the International Skin Imaging Collaboration,… More >

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