Ontology, originally a fundamental part of philosophical enquiry, is concerned with the analysis and categorization of what exists. The advent of complex information systems which rely on robust and coherent formal representations of their subject matter has led to a renewed focus on ontological enquiry, and the systematic study of such representations are at the center of the modern discipline of formal ontology. This is now a research focus in domains as diverse as conceptual modeling, database design, software engineering, artificial intelligence, computational linguistics, the life sciences, bioinformatics, geographic information science, knowledge engineering, information retrieval and the semantic web. This book presents the proceedings of the 9th edition of the Formal Ontology in Information Systems conference (FOIS 2016) held in Annecy, France, in July 2016. It contains the 25 full papers delivered at the conference (an acceptance rate of 30.9% for the main track), as well as the abstracts of the 3 keynotes by Gilberto Câmara, Stephen Mumford and Friederike Moltmann. The remainder of the book is divided into the sections: Foundations; Space, Time and Change; Cognition, Language and Semantics; Empiricism and Measurement; Ontology for Engineering; Biomedical Ontologies; and Ontology of Social Reality. The domains addressed by the papers include geography, biomedicine, economics, social reality and engineering, and the book will be of interest to all those working in these fields, as well as to anybody with an interest in formal ontology.

This book constitutes the refereed proceedings of the XXIInd International Conference on AIxIA 2023 – Advances in Artificial Intelligence, AIxIA 2023, held in Rome, Italy, during November 6–10, 2023. The 33 full papers included in this book were carefully reviewed and selected from 53 submissions. They were organized in topical sections as follows: Argumentation and Logic Programming, Natural Language Processing, Machine Learning, Hybrid AI and Applications of AI.

Food is a necessary aspect of human life, and agriculture is crucial to any country’s global economy. Because the food business is essential to both a country’s economy and global economy, artificial intelligence (AI)-based smart solutions are needed to assure product quality and food safety. The agricultural sector is constantly under pressure to boost crop output as a result of population growth. This necessitates the use of AI applications. Artificial Intelligence Applications in Agriculture and Food Quality Improvement discusses the application of AI, machine learning, and data analytics for the acceleration of the agricultural and food sectors. It presents a comprehensive view of how these technologies and tools are used for agricultural process improvement, food safety, and food quality improvement. Covering topics such as diet assessment research, crop yield prediction, and precision farming, this premier reference source is an essential resource for food safety professionals, quality assurance professionals, agriculture specialists, crop managers, agricultural engineers, food scientists, computer scientists, AI specialists, students, libraries, government officials, researchers, and academicians.

This book constitutes the thoroughly refereed post-conference proceedings of the 4th International Conference on Pattern Recognition, ICPRAM 2015, held in Lisbon, Portugal, in January 2015. The 20 revised full papers were carefully reviewed and selected from 145 submissions and describe up-to-date applications of pattern recognition techniques to real-world problems, interdisciplinary research, experimental and/or theoretical studies yielding new insights that advance pattern recognition methods.

Cognitive Design for Artificial Minds explains the crucial role that human cognition research plays in the design and realization of artificial intelligence systems, illustrating the steps necessary for the design of artificial models of cognition. It bridges the gap between the theoretical, experimental, and technological issues addressed in the context of AI of cognitive inspiration and computational cognitive science. Beginning with an overview of the historical, methodological, and technical issues in the field of cognitively inspired artificial intelligence, Lieto illustrates how the cognitive design approach has an important role to play in the development of intelligent AI technologies and plausible computational models of cognition. Introducing a unique perspective that draws upon Cybernetics and early AI principles, Lieto emphasizes the need for an equivalence between cognitive processes and implemented AI procedures, in order to realize biologically and cognitively inspired artificial minds. He also introduces the Minimal Cognitive Grid, a pragmatic method to rank the different degrees of biological and cognitive accuracy of artificial systems in order to project and predict their explanatory power with respect to the natural systems taken as a source of inspiration. Providing a comprehensive overview of cognitive design principles in constructing artificial minds, this text will be essential reading for students and researchers of artificial intelligence and cognitive science.

Globalization has transformed agri-food markets, creating a single global market with reduced trade barriers. In theory, this should bring increased food security, yet challenges persist. Small farmers often need help integrating into global sourcing networks and meeting stringent food safety regulations. Additionally, there is increasing pressure on businesses and governments to address the environmental and resource consequences of agri-food production. Advanced Computational Methods for Agri-Business Sustainability offers a comprehensive analysis of agricultural sector challenges and provides practical solutions. It identifies potential issues in agri-food management and supply chains, offers mitigation strategies, and highlights opportunities for sustainable development. The book aims to bridge the gap between theory and practice, providing insights for academics, policymakers, and industry professionals.

Current Trends and Advances in Computer-Aided Intelligent Environmental Data Engineering merges computer engineering and environmental engineering. The book presents the latest finding on how data science and AI-based tools are being applied in environmental engineering research. This application involves multiple domains such as data science and artificial intelligence to transform the data collected by intelligent sensors into relevant and reliable information to support decision-making. These tools include fuzzy logic, knowledge-based systems, particle swarm optimization, genetic algorithms, Monte Carlo simulation, artificial neural networks, support vector machine, boosted regression tree, simulated annealing, ant colony algorithm, decision tree, immune algorithm, and imperialist competitive algorithm. This book is a fundamental information source because it is the first book to present the foundational reference material in this new research field. Furthermore, it gives a critical overview of the latest cross-domain research findings and technological developments on the recent advances in computer-aided intelligent environmental data engineering. Captures the application of data science and artificial intelligence for a broader spectrum of environmental engineering problems Presents methods and procedures as well as case studies where state-of-the-art technologies are applied in actual environmental scenarios Offers a compilation of essential and critical reviews on the application of data science and artificial intelligence to the entire spectrum of environmental engineering

Machine Learning and Deep Learning for Smart Agriculture and Applications delves into the captivating realm of artificial intelligence and its pivotal role in transforming the landscape of modern agriculture. With a focus on precision agriculture, digital farming, and emerging concepts, this book illuminates the significance of sustainable food production and resource management in the face of evolving digital hardware and software technologies. Geospatial technology, robotics, the Internet of Things (IoT), and data analytics converge with machine learning and big data to unlock new possibilities in agricultural management. This book explores the synergy between these disciplines, offering cutting-edge insights into data-intensive processes within operational agricultural environments. From automated irrigation systems and agricultural drones for field analysis to crop monitoring and precision agriculture, the applications of machine learning are far-reaching. Animal identification and health monitoring also benefit from these advanced techniques. With practical case studies on vegetable and fruit leaf disease detection, drone-based agriculture, and the impact of pesticides on plants, this book provides a comprehensive understanding of the applications of machine learning and deep learning in smart agriculture. It also examines various modeling techniques employed in this field and showcases how artificial intelligence can revolutionize plant disease detection. This book serves as a comprehensive guide for researchers, practitioners, and students seeking to harness the power of AI in transforming the agricultural landscape.

This book discusses major issues in the field of agriculture: crop diseases, lack of storage management, pesticide control, weed management, lack of irrigation and water management and their effective resolution via automation, including IoT, wireless communications, machine learning, artificial intelligence, and deep learning. It further discusses the sterile insect technique, which is a replacement of conventional pesticide and fertilizer techniques. Hydroponics and vertical farming, two of the top-ranked agricultural engineering accomplishments of the past century, are also discussed. Features: Offers in-depth insights regarding the fundamentals of technologies associated with the agriculture sector Synthesizes earlier works of researchers and inventors in this field Sheds light on the challenges and problems of supply and demand worldwide Encourages the reader to innovate and ideate upon those issues Analyses the wide array of services provided by companies worldwide and discusses recent breakthroughs in agriculture automation This book is aimed at the work of many researchers to obtain a concise overview of the current implementation of automation in agriculture and derive important insight into its upcoming challenges.

With the development of innovative technologies, the study of industrial automation is obtaining extraordinary attention from academia, governments, researchers, and various industrial communities. The technical innovations in advanced technologies such as deep learning, blockchain, artificial intelligence of things (AIOT), and more have unlocked the potential for bringing intelligent automation and efficiency to the industries to control their various operations. These new technologies provide an efficient and secure way for industries to manage and develop products intelligently. Revolutionizing Industrial Automation Through the Convergence of Artificial Intelligence and the Internet of Things includes the recent advancements in exploring and developing AIOT-powered strategies and mechanisms for future industrial automation and transforming industrial functions and architectures to help improve various industrial operations. Covering topics such as convolutional neural networks, smart urban logistics, and industrial automation, this premier reference source is an excellent resource for computer scientists, IT managers, engineers, business leaders and executives, logistics managers, students and faculty of higher education, libraries, researchers, and academicians.