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Transformasi organisasi berbasis data analitik (data-driven organization) merupakan sebuah keniscayaan. Berbicara tentang data-driven organization, kita tidak hanya berbicara mengenai tools atau aplikasi, namun juga bagaimana data analitik menjadi bagian dari system, proses, dan strategi organisasi. Data analitik tidak hanya sekedar menjadi tren bagi insan yang berminat di bidang data, namun juga dapat menjadi sebuah budaya kerja bagi seluruh pegawai. Data analitycs memberi ruang yang luas bagi mereka untuk menyalurkan minat dan mendorong munculnya inovasi baru bagi organisasi yang sinergi, adaptif, berteknologi, dan unggul serta mampu memberikan dampak yang lebih kuat dan luas. Kompetisi Ke...
This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.
Ecological Informatics is defined as the design and application of computational techniques for ecological analysis, synthesis, forecasting and management. The book provides an introduction to the scope, concepts and techniques of this newly emerging discipline. It illustrates numerous applications of Ecological Informatics for stream systems, river systems, freshwater lakes and marine systems as well as image recognition at micro and macro scale. Case studies focus on applications of artificial neural networks, genetic algorithms, fuzzy logic and adaptive agents to current ecological management issues such as toxic algal blooms, eutrophication, habitat degradation, conservation of biodiversity and sustainable fishery.
This book constitutes the refereed proceedings of the sixth International Conference on Artificial Neural Networks - ICANN 96, held in Bochum, Germany in July 1996. The 145 papers included were carefully selected from numerous submissions on the basis of at least three reviews; also included are abstracts of the six invited plenary talks. All in all, the set of papers presented reflects the state of the art in the field of ANNs. Among the topics and areas covered are a broad spectrum of theoretical aspects, applications in various fields, sensory processing, cognitive science and AI, implementations, and neurobiology.
The premise of this book is that managers should act not only as decision makers, but also as designers. In a series of essays from a multitude of disciplines, the authors develop a theory of the design attitude in contrast to the more traditionally accepted and practiced decision attitude.
What is sensory marketing and why is it interesting and also important? Krishna defines it as marketing that engages the consumers’ senses and affects their behaviors. In this edited book, the authors discuss how sensory aspects of products, i.e., the touch , taste, smell, sound, and look of the products, affect our emotions, memories, perceptions, preferences, choices, and consumption of these products. We see how creating new sensations or merely emphasizing or bringing attention to existing sensations can increase a product’s or service’s appeal. The book provides an overview of sensory marketing research that has taken place thus far. It should facilitate sensory marketing by practitioners and also can be used for research or in academic classrooms.
Intended for the social scientist who must develop a rating on attitudes, values and opinions, this text provides information on the construction of more effective scales. It includes information on how to validate a scale and how to develop a summated rating scale based on classical test theory.
A large international conference on Advances in Machine Learning and Systems Engineering was held in UC Berkeley, California, USA, October 20-22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009). Machine Learning and Systems Engineering contains forty-six revised and extended research articles written by prominent researchers participating in the conference. Topics covered include Expert system, Intelligent decision making, Knowledge-based systems, Knowledge extraction, Data analysis tools, Computational biology, Optimization algorithms, Experiment designs, Complex system identification, Computational modeling, and industrial applications. Machine Learning and Systems Engineering offers the state of the art of tremendous advances in machine learning and systems engineering and also serves as an excellent reference text for researchers and graduate students, working on machine learning and systems engineering.
An interdisciplinary framework for learning methodologies—covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied—showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples making this an invaluable text.
We know that teachers make a profound difference in the lives of students and are the single most important school-related influence on student achievement. When it comes to teacher selection, district and building-level administrators are challenged to predict what kind of teacher a candidate will be, based on information collected through an application and one or two interviews. In this book, James H. Stronge and Jennifer L. Hindman explain how to take the guesswork out of hiring decisions. Their Teacher Quality Index (TQI) is a structured, research-based interview protocol built on the quality indicators explored in Stronge's best-selling Qualities of Effective Teachers. Here, educators ...