Special lssues

Continual Learning Techniques for Mobile-based Smart Healthcare Information Systems

Submission Deadline: 18 January 2023 (closed)

Guest Editors

Dr. Muhammad Zubair Asghar, Gomal University; University of kuala lumpur
Dr. Ibrahim Hameed, Norwegian University of Science and Technology
Dr. Shakeel Ahmad, King Abdulaziz University


Artificial intelligence (AI), specifically machine learning and deep learning, is gaining practical applications in mobile healthcare information systems. Advanced deep learning systems for mobile healthcare information systems like a continual learner is a model that continuously learns and evolves based on a larger number of input values while retaining previously acquired information. The network can learn and change its behavior while remembering the original task thanks to supervised training.

The systems used by Netflix and Amazon are famous examples of this type of learning in action. These systems automatically collect new tagged data when mobile users interact with the model's output. Continuous learning models work well when they are tasked with helping the doctor with activities like diagnosis and decision making. The model would need to apply what it has learned in the past to fresh data, optimize the task that has been assigned to it, or perhaps gradually uncover new tasks.

Advanced deep learning-based mobile computing concepts and technology may be advantageous to the healthcare industry and its clients (patients, physicians, and so on). Health care professionals may be better able to understand the requirements and preferences of the public with the help of the data obtained through these ways. This special issue brings together experts from academia and business to discuss problems and offer solutions for the creation of appropriate frameworks for mobile-based continual deep learning and traditional deep learning powered healthcare sector informatics. By examining cutting-edge new challenges, this special issue will investigate this new dimension.


1. Mobile computing and continual deep learning in healthcare information systems
2. Methodologies of continual deep learning in mobile information systems
3 Medical AI applications that use mobile devices and a Continual learning framework
4. The Recognition of Emotions in mobile-enabled Surveillance Systems for the Healthcare Industry
5. Evaluating the success of the cure by analysing patient responses
6. Investigations of pharmacogenomics using mobile devices, machine learning, and deep learning
7. Post-marketing tracking in the field of health informatics through the creation of machine and deep learning-based mobile information systems
8. Creating mobile-enabled machine learning and deep learning algorithms for monitoring user tone in healthcare paradigm
9. Comparing the creation of a mobile-driven, machine-readable database of adverse reactions and health issues
10. A system that operates with continual deep learning and takes into account everyday health data together with inputs from trained professionals

Published Papers

  • Open Access


    Noise-Filtering Enhanced Deep Cognitive Diagnosis Model for Latent Skill Discovering

    Jing Geng, Huali Yang, Shengze Hu
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1311-1324, 2023, DOI:10.32604/iasc.2023.038481
    (This article belongs to this Special Issue: Continual Learning Techniques for Mobile-based Smart Healthcare Information Systems)
    Abstract Educational data mining based on student cognitive diagnosis analysis can provide an important decision basis for personalized learning tutoring of students, which has attracted extensive attention from scholars at home and abroad and has made a series of important research progress. To this end, we propose a noise-filtering enhanced deep cognitive diagnosis method to improve the fitting ability of traditional models and obtain students’ skill mastery status by mining the interaction between students and problems nonlinearly through neural networks. First, modeling complex interactions between students and problems with multidimensional features based on cognitive processing theory can enhance the interpretability of… More >

Share Link