Special lssues
Table of Content

Intelligent Computational Models based on Machine Learning and Deep Learning for Diagnosis System

Submission Deadline: 15 January 2023 (closed)

Guest Editors

Dr. Ahmed Mostafa Khalil, Al-Azhar University, Egypt.
Dr. Hager Saleh, South Valley University, Egypt.
Dr. Rana Muhammad Zulqarnain, University of the Management and Technology, Pakistan.
Dr. Hu Zhao, Xi'an Polytechnic University, China.


Recently, Intelligent Computational Models have demonstrated remarkable results in many applications such as engineering and smart healthcare. In addition, the volume of healthcare data is steadily increasing at an unprecedented pace across various disparate and incompatible data sources. For example, wearable devices generate a massive amount of healthcare data, and extracting useful information from data and analyzing data to provide a fast and accurate diagnosis is challenging. Also, social media platforms are rich in medical knowledge that is utilized increasingly for health and medicinal objectives, including sharing data about diabetes, determining the potential adverse drug, diagnosing breast cancer, and others. Also, Artificial Intelligence (AI), including deep learning and machine learning models, is adopted in the healthcare industry to provide health systems with higher accuracy with time-sensitive data processing. Additionally, they improve healthcare professionals' ability to understand better the day-to-day habits and desires of the people they care about, allowing them to provide better input, advice, and encouragement to keep healthy. Therefore, there is a need for an intelligent model based on machine learning and deep learning that can automatically handle the enormous data, analyze and extract the hidden knowledge from data, predict a patient's health condition and develop a diagnosis system.


This Special Issue aims to address the areas of deep learning models and machine learning models for intelligent healthcare
• Deep learning models for healthcare applications
• Machine learning models for healthcare applications
• Ensemble Deep learning models in healthcare
• Applications of natural language processing in healthcare
• Intelligent Computational Models in healthcare
• Wearable sensors in healthcare monitoring systems
• AI and ML decision-making in medical expert models
• Swarm Intelligence
• Evolutionary Algorithms
• Artificial Intelligence Applications
• Deep Learning
• Time Series and Forecasting

Published Papers

  • Open Access


    Developing a Breast Cancer Resistance Protein Substrate Prediction System Using Deep Features and LDA

    Mehdi Hassan, Safdar Ali, Jin Young Kim, Muhammad Sanaullah, Hani Alquhayz, Khushbakht Safdar
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1643-1663, 2023, DOI:10.32604/cmc.2023.038578
    (This article belongs to this Special Issue: Intelligent Computational Models based on Machine Learning and Deep Learning for Diagnosis System)
    Abstract Breast cancer resistance protein (BCRP) is an important resistance protein that significantly impacts anticancer drug discovery, treatment, and rehabilitation. Early identification of BCRP substrates is quite a challenging task. This study aims to predict early substrate structure, which can help to optimize anticancer drug development and clinical diagnosis. For this study, a novel intelligent approach-based methodology is developed by modifying the ResNet101 model using transfer learning (TL) for automatic deep feature (DF) extraction followed by classification with linear discriminant analysis algorithm (TLRNDF-LDA). This study utilized structural fingerprints, which are exploited by DF contrary to conventional molecular descriptors. The proposed in… More >

  • Open Access


    Developed Fall Detection of Elderly Patients in Internet of Healthcare Things

    Omar Reyad, Hazem Ibrahim Shehata, Mohamed Esmail Karar
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1689-1700, 2023, DOI:10.32604/cmc.2023.039084
    (This article belongs to this Special Issue: Intelligent Computational Models based on Machine Learning and Deep Learning for Diagnosis System)
    Abstract Falling is among the most harmful events older adults may encounter. With the continuous growth of the aging population in many societies, developing effective fall detection mechanisms empowered by machine learning technologies and easily integrable with existing healthcare systems becomes essential. This paper presents a new healthcare Internet of Health Things (IoHT) architecture built around an ensemble machine learning-based fall detection system (FDS) for older people. Compared to deep neural networks, the ensemble multi-stage random forest model allows the extraction of an optimal subset of fall detection features with minimal hyperparameters. The number of cascaded random forest stages is automatically… More >

  • Open Access


    A Novel Multi-Stage Bispectral Deep Learning Method for Protein Family Classification

    Amjed Al Fahoum, Ala’a Zyout, Hiam Alquran, Isam Abu-Qasmieh
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1173-1193, 2023, DOI:10.32604/cmc.2023.038304
    (This article belongs to this Special Issue: Intelligent Computational Models based on Machine Learning and Deep Learning for Diagnosis System)
    Abstract Complex proteins are needed for many biological activities. Folding amino acid chains reveals their properties and functions. They support healthy tissue structure, physiology, and homeostasis. Precision medicine and treatments require quantitative protein identification and function. Despite technical advances and protein sequence data exploration, bioinformatics’ “basic structure” problem—the automatic deduction of a protein’s properties from its amino acid sequence—remains unsolved. Protein function inference from amino acid sequences is the main biological data challenge. This study analyzes whether raw sequencing can characterize biological facts. A massive corpus of protein sequences and the Globin-like superfamily’s related protein families generate a solid vector representation.… More >

  • Open Access


    Effectiveness of Deep Learning Models for Brain Tumor Classification and Segmentation

    Muhammad Irfan, Ahmad Shaf, Tariq Ali, Umar Farooq, Saifur Rahman, Salim Nasar Faraj Mursal, Mohammed Jalalah, Samar M. Alqhtani, Omar AlShorman
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 711-729, 2023, DOI:10.32604/cmc.2023.038176
    (This article belongs to this Special Issue: Intelligent Computational Models based on Machine Learning and Deep Learning for Diagnosis System)
    Abstract A brain tumor is a mass or growth of abnormal cells in the brain. In children and adults, brain tumor is considered one of the leading causes of death. There are several types of brain tumors, including benign (non-cancerous) and malignant (cancerous) tumors. Diagnosing brain tumors as early as possible is essential, as this can improve the chances of successful treatment and survival. Considering this problem, we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models (Resnet50, Vgg16, Vgg19, U-Net) and their integration for computer-aided detection and localization systems in brain tumors. These pre-trained and integrated… More >

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