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Enabling Cognitive Technologies for Data analysis Using Artificial Intelligence based Smart medical applications.
Closes 17 July, 2024Issue Pre-order form
Journal: Recent Advances in Computer Science and Communications
Guest editor(s): Dr. Vimal Shanmuganathan
Co-Guest Editor(s): Seungmin Rho
Big Data Analytics in Bioinformatics and Healthcare merges the fields of biology, technology, and medicine in order to present a comprehensive study on the emerging information processing applications necessary in the field of electronic medical record management. 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 care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. Artificial Intelligence is the enabling technology that accomplish the tasks completed by human and living beings. AI is a tool that combines the task performed in the computer with statistics using the complex database and dataset. AI is been applied in many domains such as social media networking, web analysis and many medical applications for diagnosing the human diseases. In addition, deep learning algorithms showed remarkable precision and accuracy in the diagnosis of those diseases. 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. 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. The Genomic sequences are the need of many biologist to survey the pathogens for modern smart medical applications. The genomic and sequence analysis has more implications in the disease prediction worldwide. The Next generation sequencing is highly relied on the genome research and the phenotype of the data. The genomic sequencing involves processing of the available biological data using the Big data and Cloud applications to deal the Terabytes of data in a high performance computing way. The processing time, management of data and analysis can be done using the AI algorithms in a smarter applications to handle those efficiently. 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. The main topics to be addressed are big data analytics problems in bioinformatics research such as Microarray data analysis, Sequence analysis, genomics based analytics, Disease network analysis, Techniques for big data Analytics in health information technology. This special issues also address the Big Data challenges in the genomic era
Artificial Intelligence ,Health care application,big data,cognitive computing