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.
Summary
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
Keywords
Big healthcare and rehabilitation data analytics, Hyperspectral data analysis and intelligent systems, High performance Computing, AI algorithms, Health analytics
Published Papers
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Open Access
ARTICLE
Design of an Information Security Service for Medical Artificial Intelligence
Yanghoon Kim, Jawon Kim, Hangbae Chang
CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 679-694, 2022, DOI:10.32604/cmc.2022.015610
（This article belongs to this Special Issue:
Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)
Abstract The medical convergence industry has gradually adopted ICT devices, which has led to legacy security problems related to ICT devices. However, it has been difficult to solve these problems due to data resource issues. Such problems can cause a lack of reliability in medical artificial intelligence services that utilize medical information. Therefore, to provide reliable services focused on security internalization, it is necessary to establish a medical convergence environment-oriented security management system. This study proposes the use of system identification and countermeasures to secure system reliability when using medical convergence environment information in medical artificial intelligence. We checked the life…
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Open Access
ARTICLE
Gastrointestinal Tract Infections Classification Using Deep Learning
Muhammad Ramzan, Mudassar Raza, Muhammad Sharif, Muhammad Attique Khan, Yunyoung Nam
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3239-3257, 2021, DOI:10.32604/cmc.2021.015920
（This article belongs to this Special Issue:
Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)
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…
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Open Access
ARTICLE
Machine Learning-Based Two-Stage Data Selection Scheme for Long-Term Influenza Forecasting
Jaeuk Moon, Seungwon Jung, Sungwoo Park, Eenjun Hwang
CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2945-2959, 2021, DOI:10.32604/cmc.2021.017435
（This article belongs to this Special Issue:
Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)
Abstract One popular strategy to reduce the enormous number of illnesses and deaths from a seasonal influenza pandemic is to obtain the influenza vaccine on time. Usually, vaccine production preparation must be done at least six months in advance, and accurate long-term influenza forecasting is essential for this. Although diverse machine learning models have been proposed for influenza forecasting, they focus on short-term forecasting, and their performance is too dependent on input variables. For a country’s long-term influenza forecasting, typical surveillance data are known to be more effective than diverse external data on the Internet. We propose a two-stage data selection…
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Open Access
ARTICLE
Enhanced Accuracy for Motor Imagery Detection Using Deep Learning for BCI
Ayesha Sarwar, Kashif Javed, Muhammad Jawad Khan, Saddaf Rubab, Oh-Young Song, Usman Tariq
CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3825-3840, 2021, DOI:10.32604/cmc.2021.016893
（This article belongs to this Special Issue:
Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)
Abstract Brain-Computer Interface (BCI) is a system that provides a link between the brain of humans and the hardware directly. The recorded brain data is converted directly to the machine that can be used to control external devices. There are four major components of the BCI system: acquiring signals, preprocessing of acquired signals, features extraction, and classification. In traditional machine learning algorithms, the accuracy is insignificant and not up to the mark for the classification of multi-class motor imagery data. The major reason for this is, features are selected manually, and we are not able to get those features that give…
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Open Access
ARTICLE
HealthyBlockchain for Global Patients
Shada A. Alsalamah, Hessah A. Alsalamah, Thamer Nouh, Sara A. Alsalamah
CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2431-2449, 2021, DOI:10.32604/cmc.2021.016618
（This article belongs to this Special Issue:
Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)
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…
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Open Access
ARTICLE
Gastric Tract Disease Recognition Using Optimized Deep Learning Features
Zainab Nayyar, Muhammad Attique Khan, Musaed Alhussein, Muhammad Nazir, Khursheed Aurangzeb, Yunyoung Nam, Seifedine Kadry, Syed Irtaza Haider
CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2041-2056, 2021, DOI:10.32604/cmc.2021.015916
（This article belongs to this Special Issue:
Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)
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…
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Open Access
ARTICLE
Classification of COVID-19 CT Scans via Extreme Learning Machine
Muhammad Attique Khan, Abdul Majid, Tallha Akram, Nazar Hussain, Yunyoung Nam, Seifedine Kadry, Shui-Hua Wang, Majed Alhaisoni
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1003-1019, 2021, DOI:10.32604/cmc.2021.015541
（This article belongs to this Special Issue:
Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)
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…
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Open Access
ARTICLE
A New Enhanced Arabic Light Stemmer for IR in Medical Documents
Ra’ed M. Al-Khatib, Taha Zerrouki, Mohammed M. Abu Shquier, Amar Balla, Asef Al-Khateeb
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1255-1269, 2021, DOI:10.32604/cmc.2021.016155
（This article belongs to this Special Issue:
Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)
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,…
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Open Access
ARTICLE
Modeling Liver Cancer and Leukemia Data Using Arcsine-Gaussian Distribution
Farouq Mohammad A. Alam, Sharifah Alrajhi, Mazen Nassar, Ahmed Z. Afify
CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2185-2202, 2021, DOI:10.32604/cmc.2021.015089
（This article belongs to this Special Issue:
Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)
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…
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Open Access
ARTICLE
Timing and Classification of Patellofemoral Osteoarthritis Patients Using Fast Large Margin Classifier
Mai Ramadan Ibraheem, Jilan Adel, Alaa Eldin Balbaa, Shaker El-Sappagh, Tamer Abuhmed, Mohammed Elmogy
CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 393-409, 2021, DOI:10.32604/cmc.2021.014446
（This article belongs to this Special Issue:
Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)
Abstract Surface electromyogram (sEMG) processing and classification can assist neurophysiological standardization and evaluation and provide habitational detection. The timing of muscle activation is critical in determining various medical conditions when looking at sEMG signals. Understanding muscle activation timing allows identification of muscle locations and feature validation for precise modeling. This work aims to develop a predictive model to investigate and interpret Patellofemoral (PF) osteoarthritis based on features extracted from the sEMG signal using pattern classification. To this end, sEMG signals were acquired from five core muscles over about 200 reads from healthy adult patients while they were going upstairs. Onset, offset,…
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Open Access
ARTICLE
On Computing the Suitability of Non-Human Resources for Business Process Analysis
Abid Sohail, Khurram Shahzad, P. D. D. Dominic, Muhammad Arif Butt, Muhammad Arif, Muhammad Imran Tariq
CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 303-319, 2021, DOI:10.32604/cmc.2021.014201
（This article belongs to this Special Issue:
Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)
Abstract Business process improvement is a systematic approach used by several organizations to continuously improve their quality of service. Integral to that is analyzing the current performance of each task of the process and assigning the most appropriate resources to each task. In continuation of our previous work, we categorize resources into human and non-human resources. For instance, in the healthcare domain, human resources include doctors, nurses, and other associated staff responsible for the execution of healthcare activities; whereas the non-human resources include surgical and other equipment needed for execution. In this study, we contend that the two types of resources…
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Open Access
ARTICLE
Classification of Fundus Images Based on Deep Learning for Detecting Eye Diseases
Nakhim Chea, Yunyoung Nam
CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 411-426, 2021, DOI:10.32604/cmc.2021.013390
（This article belongs to this Special Issue:
Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)
Abstract Various techniques to diagnose eye diseases such as diabetic retinopathy (DR), glaucoma (GLC), and age-related macular degeneration (AMD), are possible through deep learning algorithms. A few recent studies have examined a couple of major diseases and compared them with data from healthy subjects. However, multiple major eye diseases, such as DR, GLC, and AMD, could not be detected simultaneously by computer-aided systems to date. There were just high-performance-outcome researches on a pair of healthy and eye-diseased group, besides of four categories of fundus image classification. To have a better knowledge of multi-categorical classification of fundus photographs, we used optimal residual…
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Open Access
ARTICLE
An Efficient False-Positive Reduction System for Cerebral Microbleeds Detection
Sitara Afzal, Muazzam Maqsood, Irfan Mehmood, Muhammad Tabish Niaz, Sanghyun Seo
CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2301-2315, 2021, DOI:10.32604/cmc.2021.013966
（This article belongs to this Special Issue:
Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)
Abstract Cerebral Microbleeds (CMBs) are microhemorrhages caused by certain abnormalities of brain vessels. CMBs can be found in people with Traumatic Brain Injury (TBI), Alzheimer’s disease, and in old individuals having a brain injury. Current research reveals that CMBs can be highly dangerous for individuals having dementia and stroke. The CMBs seriously impact individuals’ life which makes it crucial to recognize the CMBs in its initial phase to stop deterioration and to assist individuals to have a normal life. The existing work report good results but often ignores false-positive’s perspective for this research area. In this paper, an efficient approach is…
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Open Access
ARTICLE
Evaluation of Pencil Lead Based Electrodes for Electrocardiogram Monitoring in Hot Spring
Ratha Yeu, Namhui Ra, Seong-A Lee, Yunyoung Nam
CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1411-1425, 2021, DOI:10.32604/cmc.2020.013761
（This article belongs to this Special Issue:
Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)
Abstract Electrocardiogram (ECG) electrodes are conductive pads applied to the skin to measure cardiac activity. Ag/AgCl electrodes are the commercial product which widely used to obtain ECGs. When monitoring the ECG in a hot spring, Ag/AgCl electrodes must be waterproofed; however, this is time-consuming, and the adhesive may tear the skin on removal. For solving the problem, we developed the carbon pencil lead (CPL) electrodes for use in hot springs. Both CPL and Ag/AgCl electrodes were connected to ECG100C’s cables. The Performance was evaluated in three conditions as following: hot spring water with and without bubble, and in cold water. In…
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Open Access
ARTICLE
Early Detection of Diabetic Retinopathy Using Machine Intelligence through Deep Transfer and Representational Learning
Fouzia Nawaz, Muhammad Ramzan, Khalid Mehmood, Hikmat Ullah Khan, Saleem Hayat Khan, Muhammad Raheel Bhutta
CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1631-1645, 2021, DOI:10.32604/cmc.2020.012887
（This article belongs to this Special Issue:
Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)
Abstract Diabetic retinopathy (DR) is a retinal disease that causes irreversible blindness. DR occurs due to the high blood sugar level of the patient, and it is clumsy to be detected at an early stage as no early symptoms appear at the initial level. To prevent blindness, early detection and regular treatment are needed. Automated detection based on machine intelligence may assist the ophthalmologist in examining the patients’ condition more accurately and efficiently. The purpose of this study is to produce an automated screening system for recognition and grading of diabetic retinopathy using machine learning through deep transfer and representational learning.…
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Open Access
ARTICLE
An IoT-Cloud Based Intelligent Computer-Aided Diagnosis of Diabetic Retinopathy Stage Classification Using Deep Learning Approach
K. Shankar, Eswaran Perumal, Mohamed Elhoseny, Phong Thanh Nguyen
CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1665-1680, 2021, DOI:10.32604/cmc.2020.013251
（This article belongs to this Special Issue:
Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)
Abstract Diabetic retinopathy (DR) is a disease with an increasing prevalence and the major reason for blindness among working-age population. The possibility of severe vision loss can be extensively reduced by timely diagnosis and treatment. An automated screening for DR has been identified as an effective method for early DR detection, which can decrease the workload associated to manual grading as well as save diagnosis costs and time. Several studies have been carried out to develop automated detection and classification models for DR. This paper presents a new IoT and cloud-based deep learning for healthcare diagnosis of Diabetic Retinopathy (DR). The…
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Open Access
ARTICLE
Forecast the Influenza Pandemic Using Machine Learning
Muhammad Adnan Khan, Wajhe Ul Husnain Abidi, Mohammed A. Al Ghamdi, Sultan H. Almotiri, Shazia Saqib, Tahir Alyas, Khalid Masood Khan, Nasir Mahmood
CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 331-340, 2021, DOI:10.32604/cmc.2020.012148
（This article belongs to this Special Issue:
Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)
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…
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Open Access
ARTICLE
Smart Healthcare Using Data-Driven Prediction of Immunization Defaulters in Expanded Program on Immunization (EPI)
Sadaf Qazi, Muhammad Usman, Azhar Mahmood, Aaqif Afzaal Abbasi, Muhammad Attique, Yunyoung Nam
CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 589-602, 2021, DOI:10.32604/cmc.2020.012507
（This article belongs to this Special Issue:
Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)
Abstract Immunization is a noteworthy and proven tool for eliminating lifethreating infectious diseases, child mortality and morbidity. Expanded Program
on Immunization (EPI) is a nation-wide program in Pakistan to implement immunization activities, however the coverage is quite low despite the accessibility of
free vaccination. This study proposes a defaulter prediction model for accurate
identification of defaulters. Our proposed framework classifies defaulters at five
different stages: defaulter, partially high, partially medium, partially low, and
unvaccinated to reinforce targeted interventions by accurately predicting children
at high risk of defaulting from the immunization schedule. Different machine
learning algorithms are applied on Pakistan Demographic and…
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