Sanjay Chakraborty

This book provides a practical, end-to-end introduction to computational biodiversity science—showing how artificial intelligence, machine learning, graph theory, and natural language processing transform ecological monitoring, modeling, and conservation. Centered on mangrove ecosystems and climate resilience, it connects ecological foundations to reproducible analytics and policy-facing tools. This book explains the data revolution in ecology—from species-occurrence and environmental layers to image, text, and networked observations. It demonstrates machine learning for species distribution modeling, invasive-species prediction, and habitat classification from satellite imagery; introduces ecological network analysis to reveal keystone species and fragile interactions using degree, PageRank, and modularity; and shows how graph neural networks predict missing links in food webs and mutualistic networks. A dedicated chapter details building ecological Knowledge Graphs from literature with NLP—entity and relation extraction, ontology design, and semantic enrichment—and deploying them using tools such as Neo4j and PyKEEN. The closing chapter connects research to real decisions via AI-driven Decision Support Systems. It outlines scenario modeling for restoration planning, collapse risk, and resilience assessment; compares AI pipelines with conventional workflows for efficiency, accuracy, and reproducibility; and highlights community engagement, translating technical outputs into accessible formats that empower local stakeholders.

This book explores intricate world of data classification with 'Multi-Objective, Multi-Class, and Multi-Label Data Classification.' This book studies sophisticated methods and strategies for working with complicated data sets, tackling the difficulties of various classes, many objectives, and complicated labelling tasks. This resource fosters a deeper grasp of multi-dimensional data analysis in today's data-driven world by providing readers with the skills and insights needed to navigate the subtleties of modern classification jobs, from algorithmic techniques to practical applications.

O livro, tanto quanto do conhecimento do autor, trata de alguns aspectos tradicionais e modernos da "intera o c rebro-computador humana baseada na aprendizagem autom tica". Fornece uma an lise aprofundada dos modelos e algoritmos de aprendizagem autom tica e demonstra as suas aplica es em problemas do mundo real no que diz respeito intera o c rebro-computador. O livro abrange seis cap tulos no total. Come a com a perspetiva comportamental do c rebro humano, com a an lise detalhada do sistema nervoso e das v rias actividades neurais. A discuss o final sobre quest es de investiga o futura motiva os outros investigadores a fazer investiga o futura nesta rea. O livro nico pela diversidade dos seus conte dos, pela clareza e precis o da sua apresenta o e pela exaustividade global dos seus cap tulos.

Das Buch befasst sich nach bestem Wissen des Autors mit einigen traditionellen und modernen Aspekten der "auf maschinellem Lernen basierenden Mensch-Gehirn-Computer-Interaktion‖. Es bietet eine eingehende Analyse der Modelle und Algorithmen des maschinellen Lernens und demonstriert ihre Anwendungen in realen Problemen der Gehirn-Computer-Interaktion. Das Buch besteht aus insgesamt sechs Kapiteln. Es beginnt mit der Verhaltensperspektive des menschlichen Gehirns mit einer detaillierten Analyse des Nervensystems und verschiedener neuronaler Aktivit ten. Die abschlie ende Diskussion zuk nftiger Forschungsfragen motiviert andere Forscher, in diesem Bereich weiter zu forschen. Das Buch zeichnet sich durch seine inhaltliche Vielfalt, die Klarheit und Pr zision der Darstellung und die Vollst ndigkeit der einzelnen Kapitel aus.

Ce livre, la connaissance de l'auteur, traite de certains aspects traditionnels et modernes de l'interaction cerveau-ordinateur bas e sur l'apprentissage automatique‖. Il fournit une analyse approfondie des mod les et algorithmes d'apprentissage automatique et d montre leurs applications dans les probl mes du monde r el en ce qui concerne l'interaction cerveau-ordinateur. Le livre comprend six chapitres au total. Il commence par la perspective comportementale du cerveau humain avec l'analyse d taill e du syst me nerveux et des diff rentes activit s neuronales. La discussion finale sur les questions de recherche futures motive les autres chercheurs effectuer des recherches dans ce domaine. Ce livre est unique par la diversit de son contenu, la clart et la pr cision de sa pr sentation et l'exhaustivit de ses chapitres.

Il libro, al meglio delle conoscenze dell'autore, tratta alcuni aspetti tradizionali e moderni della -Interazione cervello-computer umano basata sull'apprendimento automatico‖. Fornisce un'analisi approfondita dei modelli e degli algoritmi di apprendimento automatico e dimostra le loro applicazioni in problemi del mondo reale rispetto all'interazione cervello-computer. Il libro comprende complessivamente sei capitoli. Inizia con la prospettiva comportamentale del cervello umano, con l'analisi dettagliata del sistema nervoso e delle varie attivit neurali. La discussione finale sui problemi di ricerca futuri motiva gli altri ricercatori a svolgere ricerche future in questo settore. Il libro unico per la diversit dei contenuti, la chiarezza e la precisione della presentazione e la completezza complessiva dei capitoli.

This book covers various cutting-edge computing technologies and their applications over data. It discusses in-depth knowledge on big data and cloud computing, quantum computing, cognitive computing, and computational biology with respect to different kinds of data analysis and applications. In this book, authors describe some interesting models in the cloud, quantum, cognitive, and computational biology domains that provide some useful impact on intelligent data (emotional, image, etc.) analysis. They also explain how these computing technologies based data analysis approaches used for various real-life applications. The book will be beneficial for readers working in this area.

This book is a comprehensive, hands-on guide to the basics of data mining and machine learning with a special emphasis on supervised and unsupervised learning methods. The book lays stress on the new ways of thinking needed to master in machine learning based on the Python, R, and Java programming platforms. This book first provides an understanding of data mining, machine learning and their applications, giving special attention to classification and clustering techniques. The authors offer a discussion on data mining and machine learning techniques with case studies and examples. The book also describes the hands-on coding examples of some well-known supervised and unsupervised learning techniques using three different and popular coding platforms: R, Python, and Java. This book explains some of the most popular classification techniques (K-NN, Naïve Bayes, Decision tree, Random forest, Support vector machine etc,) along with the basic description of artificial neural network and deep neural network. The book is useful for professionals, students studying data mining and machine learning, and researchers in supervised and unsupervised learning techniques.

This book covers various cutting-edge computing technologies and their applications over data. It discusses in-depth knowledge on big data and cloud computing, quantum computing, cognitive computing, and computational biology with respect to different kinds of data analysis and applications. In this book, authors describe some interesting models in the cloud, quantum, cognitive, and computational biology domains that provide some useful impact on intelligent data (emotional, image, etc.) analysis. They also explain how these computing technologies based data analysis approaches used for various real-life applications. The book will be beneficial for readers working in this area.

This book is a comprehensive, hands-on guide to the basics of data mining and machine learning with a special emphasis on supervised and unsupervised learning methods. The book lays stress on the new ways of thinking needed to master in machine learning based on the Python, R, and Java programming platforms. This book first provides an understanding of data mining, machine learning and their applications, giving special attention to classification and clustering techniques. The authors offer a discussion on data mining and machine learning techniques with case studies and examples. The book also describes the hands-on coding examples of some well-known supervised and unsupervised learning techniques using three different and popular coding platforms: R, Python, and Java. This book explains some of the most popular classification techniques (K-NN, Naïve Bayes, Decision tree, Random forest, Support vector machine etc,) along with the basic description of artificial neural network and deep neural network. The book is useful for professionals, students studying data mining and machine learning, and researchers in supervised and unsupervised learning techniques.