Hong Yan - Böcker

Food Powders is the first book that addresses key aspects of food powder technology. It is a comprehensive review on the characterization of ingredients, semi-processed and finished products when they are in a powdered form. It includes the description of different industrial unit operations such as drying, grinding, mixing, agglomeration, and encapsulation that supply food powders of different composition and microstructure. It also includes several analytical tools to characterize food powders by their particle size, size distribution, physical properties, and functionality. Written from an engineering perspective, the book is designed and developed as a useful reference for individuals in both the food industry and academia interested in an organized and updated review. It is a valuable addition to the food powder technology literature and promotes additional interest in advancing food powders research, development, education and implementation.

Food Powders is the first book that addresses key aspects of food powder technology. It is a comprehensive review on the characterization of ingredients, semi-processed and finished products when they are in a powdered form. It includes the description of different industrial unit operations such as drying, grinding, mixing, agglomeration, and encapsulation that supply food powders of different composition and microstructure. It also includes several analytical tools to characterize food powders by their particle size, size distribution, physical properties, and functionality. Written from an engineering perspective, the book is designed and developed as a useful reference for individuals in both the food industry and academia interested in an organized and updated review. It is a valuable addition to the food powder technology literature and promotes additional interest in advancing food powders research, development, education and implementation.

Artificial intelligence (AI) has become pervasive in most areas of research and applications.  While computation can significantly reduce mental efforts for complex problem solving, effective computer algorithms allow continuous improvement of AI tools to handle complexity—in both time and memory requirements—for machine learning in large datasets.  Meanwhile, data science is an evolving scientific discipline that strives to overcome the hindrance of traditional skills that are too limited to enable scientific discovery when leveraging research outcomes. Solutions to many problems in medicine and life science, which cannot be answered by these conventional approaches, are urgently needed for society.This edited book attempts to report recent advances in the complementary domains of AI, computation, and data science with applications in medicine and life science. The benefits to the reader are manifold as researchers from similar or different fields can be aware of advanced developments and novel applications that can be useful for either immediate implementations or future scientific pursuit.Features:Considers recent advances in AI, computation, and data science for solving complex problems in medicine, physiology, biology, chemistry, and biochemistryProvides recent developments in three evolving key areas and their complementary combinations: AI, computation, and data scienceReports on applications in medicine and physiology, including cancer, neuroscience, and digital pathologyExamines applications in life science, including systems biology, biochemistry, and even food technologyThis unique book, representing research from a team of international contributors, has not only real utility in academia for those in the medical and life sciences communities, but also a much wider readership from industry, science, and other areas of technology and education.

Artificial intelligence (AI) has become pervasive in most areas of research and applications.  While computation can significantly reduce mental efforts for complex problem solving, effective computer algorithms allow continuous improvement of AI tools to handle complexity—in both time and memory requirements—for machine learning in large datasets.  Meanwhile, data science is an evolving scientific discipline that strives to overcome the hindrance of traditional skills that are too limited to enable scientific discovery when leveraging research outcomes. Solutions to many problems in medicine and life science, which cannot be answered by these conventional approaches, are urgently needed for society.This edited book attempts to report recent advances in the complementary domains of AI, computation, and data science with applications in medicine and life science. The benefits to the reader are manifold as researchers from similar or different fields can be aware of advanced developments and novel applications that can be useful for either immediate implementations or future scientific pursuit.Features:Considers recent advances in AI, computation, and data science for solving complex problems in medicine, physiology, biology, chemistry, and biochemistryProvides recent developments in three evolving key areas and their complementary combinations: AI, computation, and data scienceReports on applications in medicine and physiology, including cancer, neuroscience, and digital pathologyExamines applications in life science, including systems biology, biochemistry, and even food technologyThis unique book, representing research from a team of international contributors, has not only real utility in academia for those in the medical and life sciences communities, but also a much wider readership from industry, science, and other areas of technology and education.

Machine learning and cybernetics play an important role in many modern electronic, computer and communications systems. Automated processing of information by these systems requires intelligent analysis of various types of data and optimal decision making. In recent years, we have witnessed a rapid expansion of research and development activities in machine learning and cybernetics. To provide opportunities for researchers in these areas to share their ideas and foster collaborations, the International Conference on Machines and Cybernetics (ICMLC) has been held annually since 2002. The conference series has achieved a great success in attracting a large number of paper submissions and participants and enabling fruitful exchanges among academic and industrial researchers and postgraduate students. In 2005, the conference (ICMLC 2005) received 2461 full paper submissions and the Program Committee selected 1050 of them for presentation. It is especially encouraging that the conference is attracting more and more international attention. This year, there are contributions from 21 countries and 211 universities worldwide. Out of the 1050 papers presented at the conference, we selected 114 papers to be published in this volume of Lecture Notes in Computer Science.

This text deals with the subject of fuzzy algorithms and their applications to image processing and pattern recognition. Subjects covered include membership functions; fuzzy clustering; fuzzy rulers and defuzzification; fuzzy classifiers; and combined classifiers.