Preface to Special Issue on Scientific Computing and Learning Analytics for Smart Healthcare Systems (Part I)

  • Chinmay Chakraborty Birla Institute of Technology, Mesra, Jharkhand, India
  • Sayonara Barbosa Federal University of Santa Catarina, Brazil
  • Lalit Garg University of Malta, Msida, Malta

Abstract

This special issue introduces intelligent healthcare emerging technologies that incorporate big medical data, artificial intelligence, cloud/fog scientific computing, federated learning, bio-inspired computation, the internet of medical things, security and privacy, semantic databases, etc. The health monitoring and diagnosis for the target structure of interest are achieved by interpreting collected data. The advances in sensor technologies and data acquisition tools have led to a new era of big data, where different sensors collect massive amounts of medical data. This special issue offers valuable perceptions to researchers and engineers on designing intelligent healthcare technologies and improving patient information delivery care remotely. Sensors can enhance the decision-making process and early disease diagnosis by intelligently investigating and collecting large amounts of healthcare data (i.e., big data). Hence, there is a need for scalable machine learning, deep learning, and intelligent algorithms that lead to more interoperable  solutions and make effective decisions in emerging sensor technologies. More specifically, the proposed special issue intends to study the impact of integrating artificial intelligence, advanced learning and scientific computing systems, and the internet of medical things ideas on secured health data processing and analytics.

Keywords

References

1. S.K. Nanda, D. Ghai, P.V. Ingole, S. Pande, Soft computing techniques-based digital video forensics for fraud medical anomaly detection, Computer Assisted Methods in Engineering and Science, 30(2): 111–130, doi: 10.24423/cames.447.
2. Lakshmi T.K., J. Dheeba, Classification and segmentation of periodontal cyst for digital dental diagnosis using deep learning, Computer Assisted Methods in Engineering and Science, 30(2): 131–149, doi: 10.24423/cames.505.
3. S.P. Deshmukh, D. Choudhari, S. Amalraj, P.N. Matte, Hybrid deep learning method for detection of liver cancer, Computer Assisted Methods in Engineering and Science, 30(2): 151–165, doi: 10.24423/cames463.
4. G. Kaur, P. Gupta, Detection of distributed denial of service attacks for IoT-based healthcare systems, Computer Assisted Methods in Engineering and Science, 30(2): 167–186, doi: 10.24423/cames.450.
5. Sharma, A. Sharma, R. Malhotra, IoT-based monitoring system for diagnosing adverse drug reactions and enhancing drug compliance in TB patients, Computer Assisted Methods in Engineering and Science, 30(2): 187–201, doi: 10.24423/cames.451.
6. F. Sammy, S.M.C. Vigila, Decentralized device authentication for cloud systems with blockchain using skip graph algorithm, Computer Assisted Methods in Engineering and Science, 30(2): 203–221, doi: 10.24423/cames.443.
7. T. Sharma, A.K. Mohapatra, G. Tomar, Fuzzy-based firefly and ACO algorithm for densely deployed WSN, Computer Assisted Methods in Engineering and Science, 30(2): 223–246, doi: 10.24423/cames.438.
8. D. Bordoloi, V. Singh, K. Kaliyaperumal, M. Ritonga, M. Jawarneh, T. Kassanuk, J. Quiñonez-Choquecota, Classification and detection of skin disease based on machine learning and image processing evolutionary models, Computer Assisted Methods in Engineering and Science, 30(2): 247–256, doi: 10.24423/cames.479.
Published
Apr 21, 2023
How to Cite
CHAKRABORTY, Chinmay; BARBOSA, Sayonara; GARG, Lalit. Preface to Special Issue on Scientific Computing and Learning Analytics for Smart Healthcare Systems (Part I). Computer Assisted Methods in Engineering and Science, [S.l.], v. 30, n. 2, p. 107–109, apr. 2023. ISSN 2956-5839. Available at: <https://cames.ippt.gov.pl/index.php/cames/article/view/1286>. Date accessed: 23 nov. 2024.
Section
Articles