Edited by:
Veljko Milutinovic, Indiana University Bloomington, USA
The aim of this Collection in the Journal of Big Data is to shed the newest light on the problems in science that could be effectively solved only with the application of Big Data techniques enhanced with the appropriate artificial intelligence algorithms.
The aim of this Collection in the Journal of Big Data is to shed the newest light on the problems in science that could be effectively solved only with the application of Big Data techniques enhanced with the appropriate artificial intelligence algorithms.
The scope of the articles is wide, and includes research results not only in the domain of algorithms and methods, but also in the domain of arhitectures and infrastructures. In addition to research articles, survey articles with deep messages are also welcome. The topics covered include all aspects of natural and life sciences, with informatics and heritage-preservation sciences, as well. More specifically, articles were welcome from a wide plethora of disciplines: geophysics, physics, chemistry, biology, genomics, medicine, data science.
Each research article includes the following issues explicitly:
A. What is the problem, why is it important, and why will its importance grow over time
B. What is the best existing solution of that problem, and what are its drawbacks, looking from the viewpoint of interest for this research
C. What is the proposed solution of your article, and why is it expected not to have the drawbacks of the best existing solution
D. What have you done to demonstrate the superiority of your proposed solution, for how much is it better, under what conditions, and what is the performance/complexity ratio of your proposed solution.
The survey articles include a taxonomy, a methodologically uniform presentation of all presented solutions to the given problem, as well as deep conclusion about possible future development trends.
Cardiovascular diseases are a global health challenge that necessitates improvements in diagnostic accuracy and efficiency. This study examines the potential of deep learning (DL) models for the classification...
When applying data mining or machine learning techniques to large and diverse datasets, it is often necessary to construct descriptive and predictive models. Descriptive models are used to discover relationshi...
In environmental monitoring, deep learning models are used where we can either use past observations or extrapolated values with high uncertainty as input. The lag scheme is commonly applied during the modelin...
Data based clinical decision support system is a boon for health care monitoring. Smart healthcare monitoring systems play a vital role in the early diagnosis and detection of the physical and mental health of...
The design of the novel metaheuristic method, called Topological Variable Neighborhood Search, is presented and its theoretical properties are elaborated. The proposed metaheuristic method is implemented, appl...
Customer churn is a common problem faced by many industries, including telecommunication industries. This has resulted in the development of advanced techniques for the prediction and prevention of customer ch...
One of the key challenges in Big Data for clinical research and healthcare is how to integrate new sources of data, whose relation to disease processes are often not well understood, with multiple classical cl...
The microarchitecture of general-purpose processors is continuously evolving to adapt to the new computation and memory demands of incoming workloads. In this regard, new circuitry is added to execute specific...
Hepatocellular carcinoma (HCC) presents a major health issue worldwide. This study introduces the HepScope gene set, developed through a hybrid approach that utilizes single-cell RNA sequencing (scRNA-seq), sp...
This review focuses on the usage of machine learning methods in sports. It closely follows the PRISMA framework for writing systematic reviews. We introduce the broader field of using sensor data for feedback ...
Medical imaging is an indispensable and very important step in the diagnosis and treatment of illnesses. However, due to large amounts of resources necessary for training the model, training from scratch may n...
Hyperdimensional Computing (HDC), also known as Vector Symbolic Architectures (VSA), is a neuro-inspired computing framework that exploits high-dimensional random vector spaces. HDC uses extremely parallelizab...
Psychiatric disorders are severe health challenges that exert a heavy public burden. Air pollution has been widely reported as related to psychiatric disorder risk, but their casual association and pathologica...
Subway button detection is paramount for passenger safety, yet the occurrence of inadvertent touches poses operational threats. Camera-based detection is indispensable for identifying touch occurrences, ascert...
The convergence of artificial intelligence (AI), big data (DB), and Internet of Things (IoT) in Society 5.0, has given rise to Marketing 5.0, revolutionizing personalized customer experiences. In this study, a...
Non-Insulin-Dependent Diabetes Mellitus (NIDDM) is a chronic health condition caused by high blood sugar levels, and if not treated early, it can lead to serious complications i.e. blindness. Human Activity Re...
The article introduces an innovative approach to global optimization and feature selection (FS) using the RIME algorithm, inspired by RIME-ice formation. The RIME algorithm employs a soft-RIME search strategy ...
This Collection welcomes submission of Research Articles. Should you wish to submit a different article type, please read our submission guidelines.
Articles for this Collection should be submitted via our submission system, Snapp. Please, select the appropriate Collection title “Big Data and Artificial Intelligence in Emerging Scientific Fields" under the “Details” tab during the submission stage.
Articles will undergo the journal’s standard peer-review process and are subject to all the journal’s standard policies. Articles will be added to the Collection as they are published.
The Editors have no competing interests with the submissions which they handle through the peer-review process. The peer-review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.