Special Issues
Table of Content

Big Data Analysis for Healthcare Applications

Submission Deadline: 30 May 2023 (closed) View: 34

Guest Editors

Dr. Rahman Ali, University of Peshawar, Pakistan.
Dr. Taqdir Ali, University of British Columbia, Canada.
Dr. Syed Hafiz Bilal Ali, National University of Sciences and Technology (NUST), Pakistan.

Summary

Data science deals with vast volumes of data from all fields of life, such as healthcare, security, and surveillance, business, banking, education, engineering, management, gaming, image recognition, and many others (for example), using modern deep learning, machine learning, modeling, statistics, programming, database, data analytics and visualization tools and techniques to find unseen patterns, derive meaningful information, and make informed intelligent decisions. To accomplish this task, five distinct stages of the data science lifecycle, comprising Data Acquisition, Maintenance, Processing, Analyzing, and Communication, are used. To drive advancement in the field of data science, challenging research problems shall be pursued by the research community. As data science is a multidisciplinary area, it borrows methods from computer science, statistics, and other disciplines to solve challenging research issues. 
The aim of this special issue is to invite high-quality original research and review articles that cover novel, cutting-edge technologies and methods concerned with the scientific design, development and implementation of decision support systems using latest advancement in the area of data science. This call is also aimed to invite researchers working in the area of integrating AI with data science, with the objective to improve the quality and accuracy of the decisions, generated by these systems across a range of diverse applications. 


Keywords

Data science, Data analytics, Business intelligence, Data mining, Big data analytics, Deep Learning, Machine learning, Predictive modeling

Published Papers


  • Open Access

    ARTICLE

    Spatiotemporal Prediction of Urban Traffics Based on Deep GNN

    Ming Luo, Huili Dou, Ning Zheng
    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 265-282, 2024, DOI:10.32604/cmc.2023.040067
    (This article belongs to the Special Issue: Big Data Analysis for Healthcare Applications)
    Abstract Traffic prediction already plays a significant role in applications like traffic planning and urban management, but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of traffic data. As well as to fulfil both long-term and short-term prediction objectives, a better representation of the temporal dependency and global spatial correlation of traffic data is needed. In order to do this, the Spatiotemporal Graph Neural Network (S-GNN) is proposed in this research as a method for traffic prediction. The S-GNN simultaneously accepts various traffic data as inputs and investigates the non-linear correlations… More >

  • Open Access

    ARTICLE

    Towards Cache-Assisted Hierarchical Detection for Real-Time Health Data Monitoring in IoHT

    Muhammad Tahir, Mingchu Li, Irfan Khan, Salman A. Al Qahtani, Rubia Fatima, Javed Ali Khan, Muhammad Shahid Anwar
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2529-2544, 2023, DOI:10.32604/cmc.2023.042403
    (This article belongs to the Special Issue: Big Data Analysis for Healthcare Applications)
    Abstract Real-time health data monitoring is pivotal for bolstering road services’ safety, intelligence, and efficiency within the Internet of Health Things (IoHT) framework. Yet, delays in data retrieval can markedly hinder the efficacy of big data awareness detection systems. We advocate for a collaborative caching approach involving edge devices and cloud networks to combat this. This strategy is devised to streamline the data retrieval path, subsequently diminishing network strain. Crafting an adept cache processing scheme poses its own set of challenges, especially given the transient nature of monitoring data and the imperative for swift data transmission,… More >

  • Open Access

    ARTICLE

    Classification of Brain Tumors Using Hybrid Feature Extraction Based on Modified Deep Learning Techniques

    Tawfeeq Shawly, Ahmed Alsheikhy
    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 425-443, 2023, DOI:10.32604/cmc.2023.040561
    (This article belongs to the Special Issue: Big Data Analysis for Healthcare Applications)
    Abstract According to the World Health Organization (WHO), Brain Tumors (BrT) have a high rate of mortality across the world. The mortality rate, however, decreases with early diagnosis. Brain images, Computed Tomography (CT) scans, Magnetic Resonance Imaging scans (MRIs), segmentation, analysis, and evaluation make up the critical tools and steps used to diagnose brain cancer in its early stages. For physicians, diagnosis can be challenging and time-consuming, especially for those with little expertise. As technology advances, Artificial Intelligence (AI) has been used in various domains as a diagnostic tool and offers promising outcomes. Deep-learning techniques are… More >

  • Open Access

    ARTICLE

    Recognizing Breast Cancer Using Edge-Weighted Texture Features of Histopathology Images

    Arslan Akram, Javed Rashid, Fahima Hajjej, Sobia Yaqoob, Muhammad Hamid, Asma Irshad, Nadeem Sarwar
    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1081-1101, 2023, DOI:10.32604/cmc.2023.041558
    (This article belongs to the Special Issue: Big Data Analysis for Healthcare Applications)
    Abstract Around one in eight women will be diagnosed with breast cancer at some time. Improved patient outcomes necessitate both early detection and an accurate diagnosis. Histological images are routinely utilized in the process of diagnosing breast cancer. Methods proposed in recent research only focus on classifying breast cancer on specific magnification levels. No study has focused on using a combined dataset with multiple magnification levels to classify breast cancer. A strategy for detecting breast cancer is provided in the context of this investigation. Histopathology image texture data is used with the wavelet transform in this… More >

  • Open Access

    ARTICLE

    Blockchain and IIoT Enabled Solution for Social Distancing and Isolation Management to Prevent Pandemics

    Muhammad Saad, Maaz Bin Ahmad, Muhammad Asif, Muhammad Khalid Khan, Toqeer Mahmood, Elsayed Tag Eldin, Hala Abdel Hameed
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 687-709, 2023, DOI:10.32604/cmc.2023.038335
    (This article belongs to the Special Issue: Big Data Analysis for Healthcare Applications)
    Abstract Pandemics have always been a nightmare for humanity, especially in developing countries. Forced lockdowns are considered one of the effective ways to deal with spreading such pandemics. Still, developing countries cannot afford such solutions because these may severely damage the country’s economy. Therefore, this study presents the proactive technological mechanisms for business organizations to run their standard business processes during pandemic-like situations smoothly. The novelty of this study is to provide a state-of-the-art solution to prevent pandemics using industrial internet of things (IIoT) and blockchain-enabled technologies. Compared to existing studies, the immutable and tamper-proof contact… More >

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