About the Journal
The Distributed Learning and Broad Applications in Scientific Research (DLBASR) is a peer-reviewed, open-access journal that publishes original research papers, reviews, and brief communications in the field of distributed learning and its numerous applications in scientific research. DLBASR seeks articles from worldwide researchers to promote various perspectives and new methodologies.
Journal Snapshot
Journal Name: Distributed Learning and Broad Applications in Scientific Research (DLBASR)
ISSN: 2458-1232
Impact Factor: 7.2 (By ResearchBib)
Journal Initials: DLBASR
Research Scope: Distributed Learning, Machine Learning, Data Mining, Big Data Analytics, Computational Biology, Environmental Modeling, Scientific Simulations, AI Applications in Science
Publication Mode: Digital (On this Website)
Frequency: Annual (1 Volume a year)
Launch Year: 2015
Review Mode: Double Blind Peer Review
Plagiarism Allowed: 10% (as per Turnitin)
Coverage: Worldwide
Language: English
Current Issue
This issue of Distributed Learning and Broad Applications in Scientific Research presents a collection of innovative essays stressing recent developments in distributed learning technologies and their applications in numerous scientific fields. Advanced machine learning algorithms, deep learning frameworks, and artificial intelligence techniques applied in domains such computational biology, environmental modeling, and materials research are among the topics underlined.
Together with case studies showing real-world impact, this collection offers critical assessments of modern trends and future paths in distributed learning. The efforts of global experts and upcoming researchers highlight the journal's commitment to foster multidisciplinary interaction and forward scientific research by means of creativity.
Research these creative developments and keep leading edge distributed learning's ability to revolutionize science front and front.
