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Research and Reviews: A Journal of Neuroscience Cover

Research and Reviews: A Journal of Neuroscience

E-ISSN: 2277-6427 | P-ISSN: 2348-7925 | Peer-Reviewed Journal (Refereed Journal) | Hybrid Open Access

About the Journal

Research and Reviews: A Journal of Neuroscience [2277-6427(e)] is a peer-reviewed hybrid open access journal launched in 2011 and focused on the rapid publication of fundamental research papers on all areas of neurosciences. The scope of neuroscience has broadened to include different approaches used to study the molecular, cellular, developmental, structural, functional, evolutionary, computational, and medical aspects of the nervous system.

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Journal Information

Title: Research and Reviews: A Journal of Neuroscience
Abbreviation: rrjons
Issues Per Year: 3 Issues
P-ISSN: 2348-7925
E-ISSN: 2277-6427
Publisher: STM Journals, An imprint of Consortium e-Learning Network Pvt. Ltd.
DOI: 10.37591/RRJoNS
Starting Year: 2011
Subject: Neuroscience
Publication Format: Hybrid Open Access
Language: English
Copyright Policy: CC BY-NC-ND
Type: Peer-reviewed Journal (Refereed Journal)

Address:

STM Journals, An imprint of Consortium e-Learning Network Pvt. Ltd. A-118, 1st Floor, Sector-63, Noida, U.P. India, Pin - 201301

Editorial Board

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rrjons maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.

Editor in Chief

Editor

Dr. Praveen Kumar Yadav, Consultant Neurologist

The Mission Hospital, West Bengal-, India,

Email :

Institutional Profile Link:

Journal: Research and Reviews: A Journal of Neuroscience

Latest Articles

Ahead of Print

Artificial Intelligence for Tracking Cognitive Deviation in Aging Populations: A Comprehensive Review of Techniques, Challenges, and Ethical Concerns

Population aging is accelerating worldwide, and with it the burden of cognitive health conditions such as mild cognitive impairment (MCI), Alzheimer’s disease (AD), and dementia.

Cognitive deviation, artificial intelligence, aging populations, early detection, explainable AI, deep learning, dementia

Machine Learning Approach to Detect and Analyze Attention-Deficit/Hyperactivity Disorder

Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder characterized by difficulties with attention, impulse control, behavioral regulation, and daily functioning that persist across childhood and adulthood.

Attention-deficit/hyperactivity disorder, rs-fMRI, deep learning, CNN, functional connectivity, default mode network

Comparative Study of Handwriting in Sitting as Well as Standing Position

Handwriting is a complex motor activity influenced by posture, muscle coordination, balance, and environmental conditions. Handwriting analysis is very important in forensic science to determine authenticity of documents in various criminal cases.

Motor skills, class and individual characteristics, strokes, embellishments, pen pressure

Brain Stroke Detection Using Deep Learning and Grad-CAM Explainability Framework

Seconds matter when a brain stroke occurs; it is a race against time where rapid, precise intervention is the only way to preserve a patient’s quality of life.

Brain stroke, deep learning, efficientNetB0, Grad-CAM, explainable AI, MRI

The Early Brain Hemorrhage Prediction System Using Machine Learning

Brain hemorrhage is a critical medical emergency that requires immediate attention, as delays in diagnosis can result in severe neurological damage or death.

Brain Hemorrhage, Machine Learning, Random Forest, XGBoost, Deep Learning, Medical Imaging

Nutrient–Xenobiotic Crosstalk in Dairy Cattle: Implications for Metabolism, Immunity, Health, and Productivity in Resilient Dairy Development

Dairy cattle are routinely exposed to a wide range of xenobiotics, including mycotoxins, heavy metals, pesticides, residues from veterinary drugs, and plant secondary metabolites, through feed, water, and environmental sources.

Dairy cattle, immunity, metabolism, productivity, xenobiotic