Special Issues
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Recent Trends in Machine Intelligence respected to Medical Field Applications

Submission Deadline: 13 March 2021 (closed) View: 28

Guest Editors

Dr. Paulchamy Balaiyah, Hindusthan Institute of Technology, India.
Dr. Arun Kumar Sivaraman, Vellore Institute of Technology (VIT), India.
Prof. Vijayakumar Varadarajan, The University of New South Wales, Australia.
Dr. Muralidhar Appalaraju, Vellore Institute of Technology (VIT), India.


Artificial Intelligence plays the major role in the field of medical image processing which brings the enormous changes in the present technology. Advanced monitoring systems based on Machine learning approach and innovative technologies are rapidly evolving and their use in healthcare, activity monitoring, and performance assessment is promising. Virtual technology and Artificial Intelligence systems development have been fostered by a combination of advances in materials, Classification techniques, electronic and communication engineering, Internet of Things technologies, wireless Sensor networks. Such Machine learning approaches brings various changes in disparate scenarios and at different scopes.


• Innovative machine learning algorithm in medical imaging system
• Techniques in sensing systems, techniques and methods in Internet of Things
• Metrological characterization of Virtual devices and monitoring systems in Artificial Intelligence
• prototypes and applications in medicine and Real Time applications
• Signal processing and Machine learning with Data fusion techniques
• Study of Gene expression with innovative outcome in machine learning approaches
• Innovative applications and case studies

Published Papers

  • Open Access


    Mental Illness Disorder Diagnosis Using Emotion Variation Detection from Continuous English Speech

    S. Lalitha, Deepa Gupta, Mohammed Zakariah, Yousef Ajami Alotaibi
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3217-3238, 2021, DOI:10.32604/cmc.2021.018406
    (This article belongs to the Special Issue: Recent Trends in Machine Intelligence respected to Medical Field Applications)
    Abstract Automatic recognition of human emotions in a continuous dialog model remains challenging where a speaker’s utterance includes several sentences that may not always carry a single emotion. Limited work with standalone speech emotion recognition (SER) systems proposed for continuous speech only has been reported. In the recent decade, various effective SER systems have been proposed for discrete speech, i.e., short speech phrases. It would be more helpful if these systems could also recognize emotions from continuous speech. However, if these systems are applied directly to test emotions from continuous speech, emotion recognition performance would not be… More >

  • Open Access


    Early COVID-19 Symptoms Identification Using Hybrid Unsupervised Machine Learning Techniques

    Omer Ali, Mohamad Khairi Ishak, Muhammad Kamran Liaquat Bhatti
    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 747-766, 2021, DOI:10.32604/cmc.2021.018098
    (This article belongs to the Special Issue: Recent Trends in Machine Intelligence respected to Medical Field Applications)
    Abstract The COVID-19 virus exhibits pneumonia-like symptoms, including fever, cough, and shortness of breath, and may be fatal. Many COVID-19 contraction experiments require comprehensive clinical procedures at medical facilities. Clinical studies help to make a correct diagnosis of COVID-19, where the disease has already spread to the organs in most cases. Prompt and early diagnosis is indispensable for providing patients with the possibility of early clinical diagnosis and slowing down the disease spread. Therefore, clinical investigations in patients with COVID-19 have revealed distinct patterns of breathing relative to other diseases such as flu and cold, which… More >

  • Open Access


    Colouring of COVID-19 Affected Region Based on Fuzzy Directed Graphs

    Rupkumar Mahapatra, Sovan Samanta, Madhumangal Pal, Jeong-Gon Lee, Shah Khalid Khan, Usman Naseem, Robin Singh Bhadoria
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1219-1233, 2021, DOI:10.32604/cmc.2021.015590
    (This article belongs to the Special Issue: Recent Trends in Machine Intelligence respected to Medical Field Applications)
    Abstract Graph colouring is the system of assigning a colour to each vertex of a graph. It is done in such a way that adjacent vertices do not have equal colour. It is fundamental in graph theory. It is often used to solve real-world problems like traffic light signalling, map colouring, scheduling, etc. Nowadays, social networks are prevalent systems in our life. Here, the users are considered as vertices, and their connections/interactions are taken as edges. Some users follow other popular users’ profiles in these networks, and some don’t, but those non-followers are connected directly to… More >

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