Special Issue on Artificial Intelligence Empowered Big Data Analytical Patterns for Medical Applications

Medical Applications are the emerging areas where most of the clinical treatments and diseases have been treated with Artifical Intelligence. Big Data presents significant challenges to deep learning, including large scale, heterogeneity, noisy labels, and non-stationary distribution, among many others. In order to realize the full potential of Big Data, we need to address these technical challenges with new ways of thinking and transformative solutions. The research challenges posed by Big Data are not only timely, but will also bring ample opportunities for deep learning. Together, they will provide major advances in science, medicine, and business. The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of patient analytics and disease monitoring. The Clinical data process and diagnosis procedures for various Medical problems  should be automated that may help in improving medical treatment diagnosis. Medical data deals with several challenges including non-availability of sophisticated large size databases, high dimensional samples, and class imbalance to name a few. AI based analytical patterns can handle large scale data more efficiently as compared to the traditional machine learning methods and based on the inference , the diagnosis process can be carried in the medical fields.

This special issues focuses high-quality papers from academics and industry-related researchers of healthcare big data to address the tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems with a analytical pattern of diseases using the AI algorithms.

Topics (but not limited to):

  • AI with Big data Analytics for medical applications
  • AI and Cross-Media Methods for Big Data Representation in medical applications
  • Data-driven feature learning via AI methods for medical applications
  • Deep learning methods for applications in multimodal data analysis
  • AI based applications in Healthcare, biomedical and bioscience
  • Hyperspectral data analysis and intelligent systems  for medical applications
  • Heterogeneous big data in medical applications
  • Deep learning and its applications in medicine
  • Analytics based health care
  • Social networking and AI based suggestions for Medical applications
  • Big data based Medical diagnosis using sensor analytical patterns
  • Optimization of AI architectures and designing new loss functions

Guest Editors:

Dr. S. Vimal (Lead Guest Editor)
Department of Information Technology,National Engineering College, Tamil Nadu, India, svimalnec@nec.edu.in, svimalphd@gmail.com
Dr. Seungmin Rho
Department Software, Sejong University,#621 Innovation Center, Sejong University, 209 Neungdong-ro, Gwangjin-gu, 05006 Seoul, Republic of Korea, smrho@sejong.ac.kr
Dr. Danilo Pelusi
Faculty of Communication Sciences, University of Teramo, Via Balzarini, 1, 64100 Teramo, Italy, dpelusi@unite.it

Important Dates: 

Deadline for submissions:    15th March 2021
Deadline for review:             19th April 2021
Decisions, 1st revision:         25th May 2021
Deadline for revised version by authors: 10th June 2021
Deadline for 2nd review:      10th July 2021
Final decisions:                     15th September 2021

Submission Guideline

Authors from academia and industry working on the above research topics are invited to submit original manuscripts that have not been published and are not currently under review by other journals or conferences. Previously published conference papers should be clearly identified by the authors at the submission stage and an explanation should be provided about how such papers have been extended.  At least 30% of new content is expected.

Papers should be prepared by following the instructions for authors of Neural Processing Letters at https://springer.com/11063,  and the authors should submit their manuscript based on the following steps:

1. Submit manuscript on the submission website of Neural Processing Letters https://www.editorialmanager.com/nepl/default.aspx.  

2. In the ‘Additional Information’ section, answer ‘Yes’ to the question ‘Does this manuscript belong to a special issue?’  

3. Select ‘SI: AI Empowered Big Data Analytical Patterns for Medical Applications’.

The review process will be done by following the standard review process of this journal with, in general, two reviewing rounds. After this, guest editors will make their initial decision and the EIC will send the final decision.