Special Issue "Advances in Artificial Intelligence and Machine learning in Biomedical and Healthcare Informatics"

Submission Deadline: 30 May 2022 (closed)
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Guest Editors
Dr. Manoj Gupta, JECRC University, Jaipur (Rajasthan), India.
Mr. Zulkurnain Sabir, Hazara University, Pakistan.
Dr. Achyuth Sarkar, National Institute of Technology, India.
Dr. Parma Nand, Sharda University, Greater Noida (UP), India.
Dr. Ramani Kannan, Universiti Teknologi PETRONAS (UTP), Malaysia.


The ever increasing population of the world has put tremendous pressure on the healthcare sector to provide quality treatment and healthcare services. Now, more than ever, people are demanding smart healthcare services, applications, and wearables that will help them to lead better lives and prolong their lifespan. The healthcare sector has always been one of the greatest proponents of innovative technology, and Artificial Intelligence and Machine Learning are no exceptions. Just as AI and ML permeated rapidly into the business and e-commerce sectors, they also found numerous use cases within the healthcare industry. In fact, Artificial Intelligence and Machine Learning has come to play a pivotal role in the realm of healthcare – from improving the delivery system of healthcare services, cutting down costs, and handling patient data to the development of new treatment procedures and drugs, remote monitoring and so much more.

This need for a ‘better’ healthcare service is increasingly creating the scope for artificial intelligence (AI) and machine learning (ML) applications to enter the biomedical and healthcare world. Today, AI, ML, and deep learning are affecting every imaginable domain, and healthcare, too, doesn’t remain untouched. Also, the fact that the healthcare sector’s data burden is increasing by the minute (owing to the ever-growing population and higher incidence of diseases) is making it all the more essential to incorporate Artificial Intelligence and Machine Learning into its canvas. With Artificial Intelligence and Machine Learning, there are endless possibilities. Through its cutting-edge applications, AI and ML are helping transform the healthcare industry for the better.

This Special issue covers the advances and application of artificial intelligence and machine learning in biomedical and healthcare. It will discuss integrating the principles of computer science, life science, healthcare, medical and statistics incorporated into statistical models using existing data, discovering patterns in data to extract the information, and predicting the changes and diseases based on this data and models. This SI will cover the practical applications of artificial intelligence and machine learning for disease prognosis & management. Further, the role of artificial intelligence and machine learning will discuss with reference to diseases like diabetes mellitus, cancer, mycobacterium tuberculosis, Covid-19 and others. This SI will be the working examples on how different types of biomedical ad healthcare data can be used to develop models and predict diseases using machine learning and artificial intelligence. This SI will also touch upon precision medicine, personalized medicine, and transfer learning, with the real examples. Further, it will also discuss the use of machine learning and artificial intelligence for visualization, prediction, detection, and diagnosis of Disease. This SI will be a valuable source of information for programmers, healthcare professionals, and researchers interested in understanding the applications of artificial intelligence and machine learning in biomedical and healthcare informatics.

The topics to be discussed in this special issue but are not limited to:

(1) AI and Machine Learning for Precision Medicine and Preventive Healthcare
(2) AI and Machine Learning for Identifying diseases and diagnosis
(3) AI and Machine Learning for Drug discovery and manufacturing
(4) AI and Machine Learning for Medical Imaging diagnosis
(5) AI and Machine Learning for Personalized medicine
(6) AI and Machine Learning based behavioural modification
(7) AI and Machine Learning for Smart health records
(8) AI and Machine Learning for Clinical Trial and research
(9) AI and Machine Learning based Crowd sourced data collection
(10) AI and Machine Learning in Better Radiotherapy and Radiology
(11) AI and Machine Learning in Outbreak Prediction
(12) AI and Machine Learning based Robotic Surgery
(13) AI, Machine learning and deep learning for Biomedical and Health Informatics
(14) AI and Machine Learning in Rule Based Expert Systems- Used in EHR (Electronic Health record)
(15) AI and ML Applications in Pharmacy
(16) AI and ML applications in Clinical Trial Research
(17) Physical Robots
(18) Natural Language Processing
(19) Robotic Process Automation
(20) AI and Machine Learning based Diagnosis and treatment Applications
(21) AI and Machine Learning for Precision Medicine and Preventive Healthcare
(22) Medicine 5.0: AI and Machine Learning algorithm in healthcare
(23) AI and Machine Learning for Predictive cardiovascular disease using electronic health record
(24) AI and Machine Learning based Electronic Medical Data
(25) AI and Machine Learning in Image Processing, Computer Vision and Pattern recognition
(26) AI and Machine Learning- Neural Network and Deep Learning
(27) AI and ML for Treatment and Prediction of disease: devices for disease (wearable bionic)
(28) Internet of Things (IoT) based applications
(29) Computational Intelligence and Soft Computing based applications
(30) Artificial Intelligence and Machine Learning based Biological Models
(31) AI and Machine Learning based Numerical Computing

Published Papers
  • Nonlinear Dynamics of Nervous Stomach Model Using Supervised Neural Networks
  • Abstract The purpose of the current investigations is to solve the nonlinear dynamics based on the nervous stomach model (NSM) using the supervised neural networks (SNNs) along with the novel features of Levenberg-Marquardt backpropagation technique (LMBT), i.e., SNNs-LMBT. The SNNs-LMBT is implemented with three different types of sample data, authentication, testing and training. The ratios for these statistics to solve three different variants of the nonlinear dynamics of the NSM are designated 75% for training, 15% for validation and 10% for testing, respectively. For the numerical measures of the nonlinear dynamics of the NSM, the Runge-Kutta scheme is implemented to form… More
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  • Sustainable-Security Assessment Through a Multi Perspective Benchmarking Framework
  • Abstract The current cyber-attack environment has put even the most protected systems at risk as the hackers are now modifying technologies to exploit even the tiniest of weaknesses and infiltrate networks. In this situation, it's critical to design and construct software that is both secure and long-lasting. While security is the most well-defined aspect of health information software systems, it is equally significant to prioritise sustainability because any health information software system will be more effective if it provides both security and sustainability to the customers at the same time. In this league, it is crucial to determine those characteristics in… More
  •   Views:723       Downloads:551        Download PDF

  • Hyperuricemia Prediction Using Photoplethysmogram and Arteriograph
  • Abstract Hyperuricemia is an alarming issue that contributes to cardiovascular disease. Uric acid (UA) level was proven to be related to pulse wave velocity, a marker of arterial stiffness. A hyperuricemia prediction method utilizing photoplethysmogram (PPG) and arteriograph by using machine learning (ML) is proposed. From the literature search, there is no available papers found that relates PPG with UA level even though PPG is highly associated with vessel condition. The five phases in this research are data collection, signal preprocessing including denoising and signal quality indexes, features extraction for PPG and SDPPG waveform, statistical analysis for feature selection and classification… More
  •   Views:757       Downloads:605        Download PDF

  • A Hybrid Deep Learning Scheme for Multi-Channel Sleep Stage Classification
  • Abstract Sleep stage classification plays a significant role in the accurate diagnosis and treatment of sleep-related diseases. This study aims to develop an efficient deep learning based scheme for correctly identifying sleep stages using multi-biological signals such as electroencephalography (EEG), electrocardiogram (ECG), electromyogram (EMG), and electrooculogram (EOG). Most of the prior studies in sleep stage classification focus on hand-crafted feature extraction methods. Traditional hand-crafted feature extraction methods choose features manually from raw data, which is tedious, and these features are limited in their ability to balance efficiency and accuracy. Moreover, most of the existing works on sleep staging are either single… More
  •   Views:817       Downloads:656       Cited by:1        Download PDF