Special Issue "Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications."

Submission Deadline: 31 January 2021 (closed)
Guest Editors
Dr. Charlie (Seungmin) Rho, Sejong University, Republic of Korea.
Dr. Naveen Chilamkurti, La Trobe University, Australia.
Dr. Mohammad Hammoudeh, Manchester Metropolitan University, UK.


Significance & Novelty:

The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. The advent of the Internet itself, the IoT enables myriad applications ranging from the micro to the macro, and from the trivial to the critical. Till now there is no dedicated network stack defined for IOT, as it is a heterogeneous network of networks. High Performance Computing (HPC) applications have high impact in the cloud computing environment. Most of these applications require high capabilities of CPU and large data storage. IOT with HPC labs can harness supercomputing performance to do research on massive data sets including true artificial intelligence and deep learning.

High Performance Computing (HPC) in association with Artificial intelligence is often termed as Intelligent HPC, it drives a major shift in the paradigm with data analytics and subsequent data processing. The information in the data centre needs a highly securable and performance viable data processing in a highly secured environment. The today era in academia and industry perspectives need an intelligent HPC infrastructure to analyse, process and validate the data. AI is a good technique to support the various perspectives with a wide range of capability from analysis to storage with good retrieval. The traditional infrastructures in data centres concentrates on the fast retrieval mechanism, but AI based HPC enables a supercomputing mechanism and flexible access with the support of various machine learning and deep learning algorithms.

IOT Refers to uniquely identifiable objects and their virtual representations in an Internet like structure. IOT all together a new environment in which current Internet will be smartly utilized by all new range of embedded connected things. IOT provides real time monitoring and control possible for various applications. Till now there is no dedicated network stack defined for IOT, as it is a heterogeneous network of networks. The most popular protocols used for realization of IOT are ZigBee and 6LoWPAN (IEEE 802.15.4), Bluetooth and Wi-Fi. This special issues focuses the medical applications using the AI and IoT based high performance computing with the big medical data sets. This special issue will provide opportunities for researchers and practitioners to publish their latest innovative contributions in the areas of intelligent IoT systems, such as sensors, actuators, and data processing, in the context of rehabilitation and biomedical healthcare applications. The special issue will attract readers from different research areas, including novel algorithms and applications for healthcare infrastructures, big health data analysis, as well as devices and tools for health monitoring and rehabilitation.


This special addresses the below objective and scope

Objectives and scope:

• To identify Artificial Intelligence techniques in data Analytics and computing environment that are suitable for the IoT applications.

• To recognize a wide variety of learning algorithms and how to apply a variety of those algorithms to data.

• To have a good understanding of the fundamental issues and challenges of AI based deep learning: data, model selection, model complexity, etc.

• Integration of heterogeneous computing and big data analytics as a powerful new paradigm to implement the concept of high performance computing in science, medicine, and business

• To introduce the Big data analytics to the sources available and the possible challenges and techniques associated with Bioinformatics and healthcare domain using highperformance computing.

• To introduce the advancements in the computing field to effectively handle and make inferences from voluminous and heterogeneous healthcare data.

• State-of-the-art AI approaches need to be improved in terms of data integration, interpretability, security and temporal modeling to be effectively applied to the clinical and health care domain has been focused.


Topics of interest include, but are not limited to, the following scope:

• Deep learning methods for applications in object detection and identification, object tracking, human action recognition, cross-modal and multimodal data analysis

• High performance Computing systems for applications in Autonomous driving, Healthcare and recommendation

• Hyperspectral data analysis and intelligent systems

• AI augmented High Performance Computing

• Microarray data analysis, Sequence analysis, genomics based analytics, Disease network analysis, Techniques for big data Analytics and health information technology

• Mobile edge computing for Large-scale multimodal data acquisition techniques

• Neurocomputing/Neural Systems

• Mobile edge computing techniques for healthcare applications

• Swarm intelligence big data computing for healthcare applications

• Cognitive Based Intelligent Systems

• AI based assistive technologies

• AI based HPC for applications in Life Sciences, Molecular modelling, Quantum Chemistry, Bio-informatics

Big healthcare and rehabilitation data analytics, Hyperspectral data analysis and intelligent systems, High performance Computing, AI algorithms, Health analytics

Published Papers

  • Gastrointestinal Tract Infections Classification Using Deep Learning
  • Abstract Automatic gastrointestinal (GI) tract disease recognition is an important application of biomedical image processing. Conventionally, microscopic analysis of pathological tissue is used to detect abnormal areas of the GI tract. The procedure is subjective and results in significant inter-/intra-observer variations in disease detection. Moreover, a huge frame rate in video endoscopy is an overhead for the pathological findings of gastroenterologists to observe every frame with a detailed examination. Consequently, there is a huge demand for a reliable computer-aided diagnostic system (CADx) for diagnosing GI tract diseases. In this work, a CADx was proposed for the diagnosis and classification of GI… More
  •   Views:688       Downloads:532        Download PDF

  • HealthyBlockchain for Global Patients
  • Abstract An emerging healthcare delivery model is enabling a new era of clinical care based on well-informed decision-making processes. Current healthcare information systems (HISs) fall short of adopting this model due to a conflict between information security needed to implement the new model and those already enforced locally to support traditional care models. Meanwhile, in recent times, the healthcare sector has shown a substantial interest in the potential of using blockchain technology for providing quality care to patients. No blockchain solution proposed so far has fully addressed emerging cross-organization information-sharing needs in healthcare. In this paper, we aim to study the… More
  •   Views:994       Downloads:959        Download PDF

  • Gastric Tract Disease Recognition Using Optimized Deep Learning Features
  • Abstract Artificial intelligence aids for healthcare have received a great deal of attention. Approximately one million patients with gastrointestinal diseases have been diagnosed via wireless capsule endoscopy (WCE). Early diagnosis facilitates appropriate treatment and saves lives. Deep learning-based techniques have been used to identify gastrointestinal ulcers, bleeding sites, and polyps. However, small lesions may be misclassified. We developed a deep learning-based best-feature method to classify various stomach diseases evident in WCE images. Initially, we use hybrid contrast enhancement to distinguish diseased from normal regions. Then, a pretrained model is fine-tuned, and further training is done via transfer learning. Deep features are… More
  •   Views:1076       Downloads:845       Cited by:4        Download PDF

  • Classification of COVID-19 CT Scans via Extreme Learning Machine
  • Abstract Here, we use multi-type feature fusion and selection to predict COVID-19 infections on chest computed tomography (CT) scans. The scheme operates in four steps. Initially, we prepared a database containing COVID-19 pneumonia and normal CT scans. These images were retrieved from the Radiopaedia COVID-19 website. The images were divided into training and test sets in a ratio of 70:30. Then, multiple features were extracted from the training data. We used canonical correlation analysis to fuse the features into single vectors; this enhanced the predictive capacity. We next implemented a genetic algorithm (GA) in which an Extreme Learning Machine (ELM) served… More
  •   Views:1452       Downloads:820       Cited by:4        Download PDF

  • A New Enhanced Arabic Light Stemmer for IR in Medical Documents
  • Abstract This paper introduces a new enhanced Arabic stemming algorithm for solving the information retrieval problem, especially in medical documents. Our proposed algorithm is a light stemming algorithm for extracting stems and roots from the input data. One of the main challenges facing the light stemming algorithm is cutting off the input word, to extract the initial segments. When initiating the light stemmer with strong initial segments, the final extracting stems and roots will be more accurate. Therefore, a new enhanced segmentation based on deploying the Direct Acyclic Graph (DAG) model is utilized. In addition to extracting the powerful initial segments,… More
  •   Views:1024       Downloads:935        Download PDF

  • Modeling Liver Cancer and Leukemia Data Using Arcsine-Gaussian Distribution
  • Abstract The main objective of this paper is to discuss a general family of distributions generated from the symmetrical arcsine distribution. The considered family includes various asymmetrical and symmetrical probability distributions as special cases. A particular case of a symmetrical probability distribution from this family is the Arcsine–Gaussian distribution. Key statistical properties of this distribution including quantile, mean residual life, order statistics and moments are derived. The Arcsine–Gaussian parameters are estimated using two classical estimation methods called moments and maximum likelihood methods. A simulation study which provides asymptotic distribution of all considered point estimators, 90% and 95% asymptotic confidence intervals are… More
  •   Views:1012       Downloads:753        Download PDF

  • Forecast the Influenza Pandemic Using Machine Learning
  • Abstract Forecasting future outbreaks can help in minimizing their spread. Influenza is a disease primarily found in animals but transferred to humans through pigs. In 1918, influenza became a pandemic and spread rapidly all over the world becoming the cause behind killing one-third of the human population and killing one-fourth of the pig population. Afterwards, that influenza became a pandemic several times on a local and global levels. In 2009, influenza ‘A’ subtype H1N1 again took many human lives. The disease spread like in a pandemic quickly. This paper proposes a forecasting modeling system for the influenza pandemic using a feed-forward… More
  •   Views:2403       Downloads:1413       Cited by:1        Download PDF