Nebojsa Bacanin – författare

Harnessing Automation and Machine Learning for Resource Recovery and Value Creation: From Waste to Value provides a comprehensive understanding of how automation and machine learning technologies can be used to convert waste into valuable resources. This book gives insight in the opportunities offered by automation and machine learning technologies in waste management and how they can help address the challenges associated with waste management and to discuss the benefits and potential of automation technologies. It examines the potential of machine learning algorithms in analyzing waste management data, identifying patterns, predicting future waste generation, and optimizing waste management processes. Moreover, this book showcases case studies from different industries and regions, highlighting the revolutionary applications of automation and machine learning in waste management. This book is an indispensable resource for researchers, waste management professionals, and policymakers interested in learning more about how automation and machine learning can contribute to waste management and the creation of a sustainable future.Provides insights into the potential of automation and machine learning in waste management inspiring readers to adopt sustainable waste management practicesOffers a comprehensive understanding of how waste management can be transformed into a profitable business by adopting innovative and sustainable solutions Offers an opportunity to explore case studies from different industries and regions to showcase the revolutionary applications of automation and machine learning in waste management Provides guidance for waste management professionals, policymakers, and business leaders to optimize waste management processes and improve their bottom line

Harnessing Automation and Machine Learning for Resource Recovery and Value Creation: From Waste to Value provides a comprehensive understanding of how automation and machine learning technologies can be used to convert waste into valuable resources. This book gives insight in the opportunities offered by automation and machine learning technologies in waste management and how they can help address the challenges associated with waste management and to discuss the benefits and potential of automation technologies. It examines the potential of machine learning algorithms in analyzing waste management data, identifying patterns, predicting future waste generation, and optimizing waste management processes. Moreover, this book showcases case studies from different industries and regions, highlighting the revolutionary applications of automation and machine learning in waste management. This book is an indispensable resource for researchers, waste management professionals, and policymakers interested in learning more about how automation and machine learning can contribute to waste management and the creation of a sustainable future.- Provides insights into the potential of automation and machine learning in waste management inspiring readers to adopt sustainable waste management practices- Offers a comprehensive understanding of how waste management can be transformed into a profitable business by adopting innovative and sustainable solutions- Offers an opportunity to explore case studies from different industries and regions to showcase the revolutionary applications of automation and machine learning in waste management- Provides guidance for waste management professionals, policymakers, and business leaders to optimize waste management processes and improve their bottom line

The book establishes how to design, develop, and test different hybrids of multi-objective optimization algorithms. It presents several application areas of multi-objective optimization algorithms.Presents a thorough analysis of equations, mathematical models, and mechanisms of multi-objective optimization algorithms.Explores different alternatives of multi-objective optimization algorithms to solve binary, multi-objective, noisy, dynamic, and combinatorial optimization problems.Illustrates how to design, develop, and test different hybrids of multi-objective optimization algorithms. Discusses multi-objective optimization techniques for cloud, fog, and edge computing.Highlights applications of multi-objective optimization in diverse sectors such as engineering, e-healthcare, and scheduling.The text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics, communications engineering, computer science and engineering, and mathematics.

The book establishes how to design, develop, and test different hybrids of multi-objective optimization algorithms. It presents several application areas of multi-objective optimization algorithms.Presents a thorough analysis of equations, mathematical models, and mechanisms of multi-objective optimization algorithms.Explores different alternatives of multi-objective optimization algorithms to solve binary, multi-objective, noisy, dynamic, and combinatorial optimization problems.Illustrates how to design, develop, and test different hybrids of multi-objective optimization algorithms. Discusses multi-objective optimization techniques for cloud, fog, and edge computing.Highlights applications of multi-objective optimization in diverse sectors such as engineering, e-healthcare, and scheduling.The text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics, communications engineering, computer science and engineering, and mathematics.

The book establishes how to design, develop, and test different hybrids of multi-objective optimization algorithms. It presents several application areas of multi-objective optimization algorithms. Presents a thorough analysis of equations, mathematical models, and mechanisms of multi-objective optimization algorithms. Explores different alternatives of multi-objective optimization algorithms to solve binary, multi-objective, noisy, dynamic, and combinatorial optimization problems. Illustrates how to design, develop, and test different hybrids of multi-objective optimization algorithms. Discusses multi-objective optimization techniques for cloud, fog, and edge computing. Highlights applications of multi-objective optimization in diverse sectors such as engineering, e-healthcare, and scheduling. The text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics, communications engineering, computer science and engineering, and mathematics.

The book establishes how to design, develop, and test different hybrids of multi-objective optimization algorithms. It presents several application areas of multi-objective optimization algorithms. Presents a thorough analysis of equations, mathematical models, and mechanisms of multi-objective optimization algorithms. Explores different alternatives of multi-objective optimization algorithms to solve binary, multi-objective, noisy, dynamic, and combinatorial optimization problems. Illustrates how to design, develop, and test different hybrids of multi-objective optimization algorithms. Discusses multi-objective optimization techniques for cloud, fog, and edge computing. Highlights applications of multi-objective optimization in diverse sectors such as engineering, e-healthcare, and scheduling. The text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics, communications engineering, computer science and engineering, and mathematics.

The rapid development in AI and quantum computing has resulted in a new domain termed Quantum-Inspired Neural Networks (QINNs). These models utilize ideas from quantum mechanics, including superposition, entanglement, and quantum probability, to improve the efficiency and performance of classical neural networks. This book examines the theoretical underpinnings, frameworks, and practical implementations of QINNs, rendering it an essential resource for scholars, academics, and industry experts. It examines mathematical frameworks behind quantum-inspired models, their implementation methodologies, and their relevance in diverse fields, including healthcare, finance, cybersecurity, and natural language processing. It serves as a comprehensive guide for individuals seeking to comprehend and apply QINNs in practical situations, utilizing theoretical insights, algorithmic frameworks, and case examples. The book is distinct due to its emphasis on the present and future of quantum-inspired deep learning. It integrates discussions on hybrid quantum-classical architectures, optimization strategies, and scalability difficulties, addressing the gap between quantum computing and classical AI, which are often treated separately in previous literature. Furthermore, it examines the constraints and future potential of QINNs, providing a framework for the shift from traditional deep learning to quantumaugmented models. Readers will acquire a profound comprehension of how quantum-inspired methodologies might transform the AI domain and propel innovation in nascent technologies.Key Features: Investigates the integration of quantum computing concepts with neural networks, a dynamically advancing domain with transformational capabilities Connects quantum computing, artificial intelligence, and machine learning, making it applicable across several fields Appeals to both academic researchers and industry professionals by addressing theoretical advancements and practical applications Explores the security implications of quantum AI and ethical concerns, making it relevant for policymakers and tech leaders Caters to researchers, academics, AI practitioners, and students looking to explore next-gen AI technologies

The rapid development in AI and quantum computing has resulted in a new domain termed Quantum-Inspired Neural Networks (QINNs). These models utilize ideas from quantum mechanics, including superposition, entanglement, and quantum probability, to improve the efficiency and performance of classical neural networks. This book examines the theoretical underpinnings, frameworks, and practical implementations of QINNs, rendering it an essential resource for scholars, academics, and industry experts. It examines mathematical frameworks behind quantum-inspired models, their implementation methodologies, and their relevance in diverse fields, including healthcare, finance, cybersecurity, and natural language processing. It serves as a comprehensive guide for individuals seeking to comprehend and apply QINNs in practical situations, utilizing theoretical insights, algorithmic frameworks, and case examples. The book is distinct due to its emphasis on the present and future of quantum-inspired deep learning. It integrates discussions on hybrid quantum-classical architectures, optimization strategies, and scalability difficulties, addressing the gap between quantum computing and classical AI, which are often treated separately in previous literature. Furthermore, it examines the constraints and future potential of QINNs, providing a framework for the shift from traditional deep learning to quantumaugmented models. Readers will acquire a profound comprehension of how quantum-inspired methodologies might transform the AI domain and propel innovation in nascent technologies.Key Features: Investigates the integration of quantum computing concepts with neural networks, a dynamically advancing domain with transformational capabilities Connects quantum computing, artificial intelligence, and machine learning, making it applicable across several fields Appeals to both academic researchers and industry professionals by addressing theoretical advancements and practical applications Explores the security implications of quantum AI and ethical concerns, making it relevant for policymakers and tech leaders Caters to researchers, academics, AI practitioners, and students looking to explore next-gen AI technologies

The rapid development in AI and quantum computing has resulted in a new domain termed Quantum-Inspired Neural Networks (QINNs). These models utilize ideas from quantum mechanics, including superposition, entanglement, and quantum probability, to improve the efficiency and performance of classical neural networks. This book examines the theoretical underpinnings, frameworks, and practical implementations of QINNs, rendering it an essential resource for scholars, academics, and industry experts. It examines mathematical frameworks behind quantum-inspired models, their implementation methodologies, and their relevance in diverse fields, including healthcare, finance, cybersecurity, and natural language processing. It serves as a comprehensive guide for individuals seeking to comprehend and apply QINNs in practical situations, utilizing theoretical insights, algorithmic frameworks, and case examples. The book is distinct due to its emphasis on the present and future of quantum-inspired deep learning. It integrates discussions on hybrid quantum-classical architectures, optimization strategies, and scalability difficulties, addressing the gap between quantum computing and classical AI, which are often treated separately in previous literature. Furthermore, it examines the constraints and future potential of QINNs, providing a framework for the shift from traditional deep learning to quantumaugmented models. Readers will acquire a profound comprehension of how quantum-inspired methodologies might transform the AI domain and propel innovation in nascent technologies.Key Features:Investigates the integration of quantum computing concepts with neural networks, a dynamically advancing domain with transformational capabilitiesConnects quantum computing, artificial intelligence, and machine learning, making it applicable across several fieldsAppeals to both academic researchers and industry professionals by addressing theoretical advancements and practical applicationsExplores the security implications of quantum AI and ethical concerns, making it relevant for policymakers and tech leadersCaters to researchers, academics, AI practitioners, and students looking to explore next-gen AI technologies

The book explores how machine learning (ML), deep learning (DL), and metaheuristic optimization techniques can revolutionize plant disease detection and agricultural intelligence. Unlike traditional agronomic approaches, this book bridges advanced computational methods with real-world agricultural needs. It emphasizes both the scientific and practical dimensions—focusing on image-based disease detection, sensor data interpretation, optimization of predictive models, and real-world deployment strategies. The book looks at the subject from a technology, biological & agricultural, computational optimization, practical and social impact perspective.This edited book is designed for researchers, academicians, and professionals working in Artificial Intelligence, Machine Learning, Deep Learning, Metaheuristics, and agricultural sciences. It will also benefit agricultural engineers, data scientists, agritech industries, policymakers, and undergraduate, postgraduate, and doctoral students interested in AI-driven plant disease detection and smart agriculture applications.