Mind genomics is the next level of apprehension of human behavior. It seeks to comprehend what drives consumers and people in general in their day-to-day decision making. It reaches out where surveys and focus groups are inadequate tools to help us better understand how people feel and how they will behave in a particular situation. While allowing for a more encapsulating method that works well with big data, mind genomics offers a more nuanced view of the complex societal and political reality. Applying Mind Genomics to Social Sciences presents an overview of mind genomics as applied to the food industry, commerce, business services, tourism, healthcare, and even legal service. Its focus, however, explores the new avenues of mind genomics in social and political sciences. The book offers a combination of rich data combined with a new methodological approach and fresh analytical insights, which helps us better grasp and understand the complex reality of society. Covering topics such as human thought, decision making, and cognitive science, this premier reference source is a dynamic resource for political scientists, sociologists, psychologists, business leaders, marketers, government officials, journalists, students and faculty of higher education, libraries, doctoral and postdoctoral candidates, researchers, and academicians.
Software Engineering Techniques Applied to Agricultural Systems presents cutting-edge software engineering techniques for designing and implementing better agricultural software systems based on the object-oriented paradigm and the Unified Modeling Language (UML). The book is divided in two parts: the first part presents concepts of the object-oriented paradigm and the UML notation of these concepts, and the second part provides a number of examples of applications that use the material presented in the first part. The examples presented illustrate the techniques discussed, focusing on how to construct better models using objects and UML diagrams. More advanced concepts such as distributed systems and examples of how to build these systems are presented in the last chapter of the book. The book presents a step-by-step approach for modeling agricultural systems, starting with a conceptual diagram representing elements of the system and their relationships. Furthermore, diagrams such as sequential and collaboration diagrams are used to explain the dynamic and static aspects of the software system.
Data Mining in Agriculture represents a comprehensive effort to provide graduate students and researchers with an analytical text on data mining techniques applied to agriculture and environmental related fields. This book presents both theoretical and practical insights with a focus on presenting the context of each data mining technique rather intuitively with ample concrete examples represented graphically and with algorithms written in MATLAB®.
This authored monograph features ongoing research in the inter-disciplinary field of mind genomics while also serves as a stepping stone toward a rapidly growing field that has much to contribute to the empirical science by transforming the everyday individual experiences and mindsets"--
This book presents the latest research on the use of ICTs in agriculture that aims at modernizing the agricultural production systems. It provides the most updated work in areas such as wireless sensor networks for local and national range, bio-sensors for ease and environmentally friendly use, remote sensing and GIS use, image processing for agricultural system analysis, automation technologies, decision support systems, farm management information systems, and big data management"--
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