Harnessing AI for Invasive Species Management and Biodiversity Conservation

Name: Harnessing AI for Invasive Species Management and Biodiversity Conservation
Price: 393.60 AUD
Availability: OutOfStock
ISBN: 9798337346168

Adnène Arbi, Walid Ben Ameur

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Hardback

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English

Igi Global Scientific Publishing
06 January 2026

Biodiversity; Pest control; Artificial intelligence

Invasive species represent a major threat to biodiversity, ecosystems, and global economies, contributing to habitat destruction, species loss, and the disruption of ecological functions. Conventional methods for managing these species often prove inadequate due to limitations in resources, inefficiencies in monitoring large areas, delayed responses, and challenges in forecasting their spread. With increasing pressure from factors like climate change and human activities, there is a pressing need for more effective, scalable, and adaptable approaches. Artificial intelligence applications in conservation efforts may transform the management of invasive species and strengthen biodiversity conservation efforts, enhancing early detection, tracking, and control. Harnessing AI for Invasive Species Management and Biodiversity Conservation examines the challenges of integrating AI into ecological management, including concerns about data privacy, algorithm transparency, and the need for rigorous validation processes. It highlights how AI can serve as a powerful complement to traditional conservation strategies, providing innovative and sustainable solutions to combat the growing issue of invasive species. This book covers topics such as climate change, ecology, and sustainable development, and is a useful resource for business owners, engineers, policymakers, biologists, academicians, researchers, and environmental scientists.

Edited by: Adnène Arbi, Walid Ben Ameur
Imprint: Igi Global Scientific Publishing
Dimensions: Height: 254mm, Width: 178mm, Spine: 19mm
Weight: 771g
ISBN: 9798337346168
Pages: 530
Publication Date: 06 January 2026
Audience: General/trade , ELT Advanced
Format: Hardback
Publisher's Status: Active

Adnène Arbi is an Associate Professor at the National Institute of Applied Sciences and Technology (INSAT). His current research activities are in the field of Applied Mathematics and Artificial Intelligence (AI). Major research projects include modelling of mathematical neural networks and model predictive control. He published more than 34 papers in peer-reviewed international journals since 2014. He edited two books on Neural Networks, Iot, AI, and Data-driven control published in 2025-2026. He is a member of the editorial board of the journals: Journal of Algorithms & Computational Technology, Innovative Applied Mathematics and Computational Sciences, Frontiers in Applied Mathematics and Statistics and Karshi Multidisciplinary International Scientific Journal. He served as a reviewer for more than 100 papers for international journals such as the IMA Journal of Mathematical Control and Information, Mathematics and Computers in Simulation, Chaos, Solitons & Fractals, Journal of Intelligent & Fuzzy Systems, Computational and Applied Mathematics. American Association for Sciences and Technology, Mathematical Methods in the Applied Sciences, Neural Networks, Asian Journal of Control, Neural processing letters, International Journal of Statistical Distributions and Applications, IEEE Transactions on Neural Networks and Learning Systems, IMA Journal of Mathematical Control and Information, JCAS: International Journal of Control, Automation and Systems. He is an Expert/Consultant for the Science for Africa Foundation (2023). He has co-organized two international workshops and conferences since 2022. He is a member of the Laboratory of Engineering Mathematics at Tunisia Polytechnic School, University of Carthage, Tunisia. Besides, he is a member of international member of laboratoire Artificial Intelligence Research and Applications (AIRA), Hassan I University, Settat, Morocco. H-index: 19; Total citations: 721; Expertise: Time scales, Dynamical Systems, Time delay systems, Neural Networks, Model Predictive Control.