Special Issues

Intelligent Systems for Smart and Sustainable Healthcare

Submission Deadline: 30 June 2022 (closed) View: 1

Guest Editors

Dr. Muhammad Irfan, Najran University, Saudi Arabia.
Dr. Adam Glowacz, AGH University of Science and Technology, Poland.
Prof. Dr. Thompson Sarkodie-Gyan, University of Texas at El Paso, USA.


The aim of this special issue is to publish the latest research on modern and sustainable health infrastructure, modern technologies for smart diagnostics, e-health and reliable decisions. The increasing world population is causing an increase in natural resources usage, the forests are being replaced with the buildings, food consumption has been increased resulting in an increase of waste, more traffic on the roads causing a more polluted environment. Consequently, human health is at great risk and faces global challenges of epidemics and pandemics. Thus, the greatest challenge for researchers in the near future will be to design and innovate smart systems that can meet the increasing demand for healthcare and build a sustainable healthcare system. The special issue is intended for researchers, local governments, graduate students and practicing engineers with an interest in the technologies related to sustainable healthcare for smart cities. It will cover the applications of Artificial Intelligence, the Internet of Things (IoT), Biomaterials and Nanotechnologies to solve several issues of the community such as modern healthcare systems.

The modern healthcare systems need to be simpler and easier to access.

Therefore, this special issue will focus on but not limited to the following topics:

Potential topics include but are not limited to the following:

AI for future health management systems

Image processing for biomedical engineering

Signal processing for biomedical engineering

Condition monitoring of medical instruments

Medical data security

IoT and AI for early diagnosis of cancer

IoT and AI for early diagnosis of brain tumors

Impact of environmental changes on human health


Cancer diagnosis; Intelligent healthcare systems; Artificial intelligence; Machine learning; Deep Learning; Internet of things; Big data; Smart cities; Smart healthcare; Smart resource management; Cybersecurity attacks on medical data

Published Papers

  • Open Access


    Computing and Implementation of a Controlled Telepresence Robot

    Ali A. Altalbe, Aamir Shahzad, Muhammad Nasir Khan
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1569-1585, 2023, DOI:10.32604/iasc.2023.039124
    (This article belongs to the Special Issue: Intelligent Systems for Smart and Sustainable Healthcare)
    Abstract The development of human-robot interaction has been continuously increasing for the last decades. Through this development, it has become simpler and safe interactions using a remotely controlled telepresence robot in an insecure and hazardous environment. The audio-video communication connection or data transmission stability has already been well handled by fast-growing technologies such as 5G and 6G. However, the design of the physical parameters, e.g., maneuverability, controllability, and stability, still needs attention. Therefore, the paper aims to present a systematic, controlled design and implementation of a telepresence mobile robot. The primary focus of this paper is… More >

  • Open Access


    A Novel Convolutional Neural Networks-Fused Shallow Classifier for Breast Cancer Detection

    Sharifa Khalid Alduraibi
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1321-1334, 2022, DOI:10.32604/iasc.2022.025021
    (This article belongs to the Special Issue: Intelligent Systems for Smart and Sustainable Healthcare)
    Abstract This paper proposes a fused methodology based upon convolutional neural networks and a shallow classifier to diagnose and differentiate breast cancer between malignant lesions and benign lesions. First, various pre-trained convolutional neural networks are used to calculate the features of breast ultrasonography (BU) images. Then, the computed features are used to train the different shallow classifiers like the tree, naïve Bayes, support vector machine (SVM), k-nearest neighbors, ensemble, and neural network. After extensive training and testing, the DenseNet-201, MobileNet-v2, and ResNet-101 trained SVM show high accuracy. Furthermore, the best BU features are merged to increase… More >

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