Artificial Intelligence and Animal Ecology - A Review

About the author

Lidia Ghosh (Gold-Medalist, M.Tech., JU) is an Assistant Professor in the Department of Computer Application at the RCC Institute of Information Technology, Kolkata, India. She was a Postdoctoral Fellow at Liverpool Hope University, UK, and has received multiple prestigious fellowships, including the Rashtriya Uchchatara Shiksha Abhiyan Doctoral Fellowship. She has published over 50 research papers and serves as a reviewer for top IEEE journals. Her research focuses on cognitive neuroscience, deep learning, type-2 fuzzy sets, and human memory formation.
Amiyangshu De is a PhD candidate at the Indian Institute of Technology Kharagpur, currently working on context dependent auditory encoding of simple and complex sounds in the mouse brain. He is a pharmacy graduate with diverse domains of expertise from various academic backgrounds like pharmaceutical chemistry, cognitive science, animal behavior, molecular and system neuroscience. He has worked in different domains like cognitive loads and working memory, philosophical aspects of the co-evolution of language and cognition, and transcriptional regulations of adult neurogenesis. He has valuable contributions in multiple journals and conference presentations.

Product details

• Authors: Lidia De Ghosh
• Assisted by: Amiyangshu De (Editor), Lidia Ghosh (Editor)
• Publisher: Taylor & Francis Ltd.
• Languages: English
• Product format: Hardback
• Release: 29.07.2025
• EAN: 9781032782348
• ISBN: 978-1-0-3278234-8
• No. of pages: 268
• Subjects: Humanities, art, music > Psychology > Theoretical psychology; Natural sciences, medicine, IT, technology > Biology > Botany; Artificial Intelligence, SCIENCE / Life Sciences / Zoology / General, Perception, SCIENCE / Life Sciences / Ecology, COMPUTERS / Computer Science, Biodiversity, Cognition and cognitive psychology, Botany & plant sciences, Agricultural science, Animal Ecology, Automatic control engineering, Zoology and animal sciences, Botany and plant sciences, COMPUTERS / Data Science / Machine Learning, Biodiversity / Ecosystems