Special Issue "Computational Intelligence for Internet of Medical Things and Big Data Analytics"

Submission Deadline: 28 February 2021 (closed)
Guest Editors
Dr. Mazin Abed Mohammed, University of Anbar, Iraq
Prof. Oana Geman, Universitatea Stefan cel Mare din Suceava, Romania
Prof. Valentina Emilia Balas, Aurel Vlaicu University of Arad, Romania
Prof. Aniello Castiglione, University of Naples Parthenope, Italy


Traditionally, devices used in medical industry predominantly rely on medical images and sensor data; this medical data is processed to study the patient’s health condition and information. Presently, the medical industry requires more innovative technologies to process the large volume of data and improve the quality of service in patient care, and needs an intelligent system to detect early symptoms of diseases in the beginning stage and provide appropriate treatment. Internet of Medical Things (IoMT) and its recent advancements have included a new dimension towards enhancing the medical industry practices and realizing an intelligent system. In addition, the medical data of IoMT systems is constantly growing because of increasing peripherals introduced in patient care.


Conventionally, medical image processing and machine learning are used for any medical diagnosis, subsequent treatment and therapies. However, with increasing volume of data with increased dimensions and dynamics of medical data, machine learning takes a back seat over another powerful classification mechanism named deep learning. Deep learning can solve more complicated problems, unsolvable by machine learning, and produce highly accurate diagnoses. The medical industry is one of the biggest industries which implements deep learning algorithms. Deep learning can handle the large volume of medical data, including medical reports, patients’ records, and insurance records, helping medical experts to predict the necessary treatment. The scalability of deep learning which helps to process and manipulate this huge volume of data makes an indomitable paradigm for computer-aided diagnosis in medical informatics. The significance of deep learning is compounded by the ever-improving technological aspects towards acquiring precise and multidimensional IoMT data with an eye on improving the accuracy of diagnosis. Overall, incorporating deep learning into IoMT can provide radical innovations in medical image processing, disease diagnosing, medical big data analysis and pathbreaking medical applications.

The topics of interest include, but are not limited to:
• Computational Intelligence methodologies for medical data analysis;
• Computational Intelligence and block chain assisted medical efficient product designs;
• Computational Intelligence for medical big data analysis;
• Computational Intelligence for medical decision support systems in Parkinson's disease;
• Computational Intelligence for medical decision support systems in heart disease;
• Computational Intelligence for medical decision support systems in cancers diagnostic;
• Advancements in deep learning algorithms in health informatics;
• Computational Intelligence for wearable medical devices;
• Computational Intelligence management in IoMT devices;
• Deep learning for data analytics in body sensor networks;
• Machine learning applied to Healthcare Systems;
• Medical image recognition using AI technologies;
• Machine and deep learning approaches based observation in case of COVID-19;
• Computational methods for COVID-19 prediction and detection;
• Data mining and knowledge discovery in healthcare;
• COVID-19 analysis using Big Data;
• Medical Management system for COVID-19;
• Big Data Analytics for prediction and application for COVID-19;
• AI Methodologies;
• Soft Computing approaches;
• Optimizations methods in complex problems;
• Big Data Analytics for Wireless area network.

Published Papers
  • Investigating the Role of Trust Dimension as a Mediator on CC-SaaS Adoption
  • Abstract The public sector of Iraq has been struggling from poor management of resources and numerous difficulties that affect its governmental organization’s development, such as financial issues resulting from corruption, insecurity, and the lack of IT resources and infrastructure. Thus, cloud computing Software as a Service (CC-SaaS) can be a useful solution to help governmental organizations increase their service efficiency through the adoption of low-cost technology and provision of better services. The adoption of CC-SaaS remains limited in Iraqi public organizations due to numerous challenges, including privacy and protection, legal policy, and trust. Trust was found to be an effective facilitator… More
  •   Views:183       Downloads:184        Download PDF

  • Predicting COVID-19 Based on Environmental Factors With Machine Learning
  • Abstract The coronavirus disease 2019 (COVID-19) has infected more than 50 million people in more than 100 countries, resulting in a major global impact. Many studies on the potential roles of environmental factors in the transmission of the novel COVID-19 have been published. However, the impact of environmental factors on COVID-19 remains controversial. Machine learning techniques have been used effectively in combating the COVID-19 epidemic. However, researches related to machine learning on weather conditions in spreading COVID-19 is generally lacking. Therefore, in this study, three machine learning models (Convolution Neural Network (CNN), ADtree Classifier and BayesNet) based on the confirmed cases… More
  •   Views:347       Downloads:278        Download PDF

  • ECG Encryption Enhancement Technique with Multiple Layers of AES and DNA Computing
  • Abstract Over the decades, protecting the privacy of a health cloud using the design of a fog computing network is a very important field and will be more important in the near future. Current Internet of Things (IoT) research includes security and privacy due to their extreme importance in any growing technology that involves the implementation of cryptographic Internet communications (ICs) for protected IC applications such as fog computing and cloud computing devices. In addition, the implementation of public-key cryptography for IoT-based DNA sequence testing devices requires considerable expertise. Any key can be broken by using a brute-force attack with ample… More
  •   Views:216       Downloads:202        Download PDF

  • An Enhanced Convolutional Neural Network for COVID-19 Detection
  • Abstract The recent novel coronavirus (COVID-19, as the World Health Organization has called it) has proven to be a source of risk for global public health. The virus, which causes an acute respiratory disease in persons, spreads rapidly and is now threatening more than 150 countries around the world. One of the essential procedures that patients with COVID-19 need is an accurate and rapid screening process. In this research, utilizing the features of deep learning methods, we present a method for detecting COVID-19 and a screening model that uses pulmonary computed tomography images to differentiate COVID-19 pneumonia from healthy cases. In… More
  •   Views:449       Downloads:351        Download PDF

  • Computational Intelligence Approach for Municipal Council Elections Using Blockchain
  • Abstract Blockchain is an innovative technology that disrupts different industries and offers decentralized, secure, and immutable platforms. Its first appearance is connected with monetary cryptocurrency transactions, followed by adaptation in several domains. We believe that blockchain can provide a reliable environment by utilizing its unique characteristics to offer a more secure, costless, and robust mechanism suitable for a voting application. Although the technology has captured the interest of governments worldwide, blockchain as a service is still limited due to lack of application development experience, technology complexity, and absence of standardized design, architecture, and best practices. Therefore, this study aims to build… More
  •   Views:405       Downloads:441        Download PDF

  • Threshold Parameters Selection for Empirical Mode Decomposition-Based EMG Signal Denoising
  • Abstract Empirical Mode Decomposition (EMD) is a data-driven and fully adaptive signal decomposition technique to decompose a signal into its Intrinsic Mode Functions (IMF). EMD has attained great attention due to its capabilities to process a signal in the frequency-time domain without altering the signal into the frequency domain. EMD-based signal denoising techniques have shown great potential to denoise nonlinear and nonstationary signals without compromising the signal’s characteristics. The denoising procedure comprises three steps, i.e., signal decomposition, IMF thresholding, and signal reconstruction. Thresholding is performed to assess which IMFs contain noise. In this study, Interval Thresholding (IT), Iterative Interval Thresholding (IIT),… More
  •   Views:250       Downloads:240        Download PDF

  • Decision Support System Tool for Arabic Text Recognition
  • Abstract The National Center for Education Statistics study reported that 80% of students change their major or institution at least once before getting a degree, which requires a course equivalency process. This error-prone process varies among disciplines, institutions, regions, and countries and requires effort and time. Therefore, this study aims to overcome these issues by developing a decision support tool called TiMELY for automatic Arabic text recognition using artificial intelligence techniques. The developed tool can process a complete document analysis for several course descriptions in multiple file formats, such as Word, Text, Pages, JPEG, GIF, and JPG. We applied a comparative… More
  •   Views:716       Downloads:265        Download PDF