Gholamreza Abdi – författare

Artificial Intelligence and Food Science examines a range of applications from pre- and post-harvest management to food packaging and the dairy industry, providing a thorough overview of how AI is revolutionizing these industries. The book's initial chapters introduce the fundamentals of AI and its recent advancements, covering key areas such as Constraint Satisfaction Problems, Adversarial Search, and Statistical Learning Theory. The book then moves on to explore AI applications in pre- and post-harvest management while also highlighting techniques such as machine vision and spectral imaging for non-destructive analysis of agricultural products.The role of AI in the fat and oil processing industry, specifically the use of bioinformatics predictive methods based on artificial neural network (ANN) is discussed in detail. Subsequent chapters probe the use of AI in the processing of fruits, vegetables, dairy, and meat, and detail how technologies like neural networks and Industry 4.0 innovations can drive improvements in product quality. Later chapters explore AI applications in the baking and confectionery industries, with a focus on automation and 3D printing technologies that are setting new standards for production and product design.Functions as an accessible book on artificial intelligence and smart techniques employed in the food industryDescribes how AI applications enhance efficiency and reliability in various food sector processesProvides in-depth information on the current challenges faced by the food industry in implementing AI, along with possible solutionsEnables food researchers and industrialists to make informed decisions about adopting AI and smart techniques over conventional methodsOffers PowerPoint slides and a test bank for instructors and students

Artificial Intelligence and Food Science examines a range of applications from pre- and post-harvest management to food packaging and the dairy industry, providing a thorough overview of how AI is revolutionizing these industries. The initial chapters introduce the fundamentals of AI and its recent advancements, covering key areas such as Constraint Satisfaction Problems, Adversarial Search, and Statistical Learning Theory. The book then moves on to explore AI applications in pre- and post-harvest management and highlights techniques such as machine vision and spectral imaging for non-destructive analysis of agricultural products.The role of AI in the fat and oil processing industry, specifically the use of bioinformatics predictive methods based on artificial neural network (ANN) is discussed in detail. Subsequent chapters probe the use of AI in the processing of fruits, vegetables, dairy, and meat, and detail how technologies like neural networks and Industry 4.0 innovations can drive improvements in product quality. Later chapters explore AI applications in the baking and confectionery industries, with a focus on automation and 3D printing technologies that are setting new standards for production and product design. - Functions as an accessible book on artificial intelligence and smart techniques employed in the food industry- Describes how AI applications enhance efficiency and reliability in various food sector processes- Provides in-depth information on the current challenges faced by the food industry in implementing AI and offers possible solutions- Enables food researchers and industrialists to make informed decisions about adopting AI and smart techniques over conventional methods- Offers PowerPoint slides and a test bank for instructors and students

This book focuses on exploring the potential of artificial intelligence and its respective components in drug designing from medicinal and aromatic plants. Different ancient civilizations have shown the significance of herbal formulation in healthcare, food and curing the ailments. However, with time the majority of this treasure has been lost due to overexploitation or generation gap (destruction of ancient civilization) and evolution of synthetic drugs. In recent decades, society again shifted toward herbal healthcare mainly due to side effects of synthetic drugs. Therefore, the proposal has been segregated into three sections: Section 1 (Chapter 1-4) will cover the basics of traditional practices, policies and screening methods. Section 2 (Chapter 5-10) will elaborate the disease specific applications of phytoformulations. Some of the common and prevalent diseases like blood borne infections, neurological diseases, viral infections, respiratory disorders and biofilm-associated nosocomial infections will be discussed. Section 3 (Chapter 11-15) will provide an overview of OMICS and advanced tools integration in drug development followed by emerging challenges with AI models.