الصفحة 12
الصفحة 12
Advances in bioinformatics and computational biology ; 1st Brazilian symposium on bioinformatics, BSB 2005, Sao Leopoldo, Brazil, July 27-29, 2005, Proceedings
This book constitutes the refereed proceedings of the Brazilian Symposium on Bioinformatics, BSB 2005, The 15 revised full papers and 10 revised extended abstracts presented together with 3 invited papers were carefully reviewed and selected from 55 submissions. The papers address a broad range of current topics in computational biology and bioinformatics.
Advances in Artificial Intelligence ; Vol. 4013 : 19th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2006, Quebec City, Quebec, Canada, June 7-9, Proceedings
Constitutes the refereed proceedings of the 4th Hellenic Conference on Artificial Intelligence, SETN 2006, held at Heraklion, Crete, Greece in May 2006. The 43 revised full papers and extended abstracts of 34 revised short papers presented together with 2 invited contributions were carefully reviewed and selected from 125 submissions. The papers address any area of artificial intelligence; particular fields of interest include; logic programming, knowledge-based systems, intelligent information retrieval, machine learning, neural nets, genetic algorithms, data mining and knowledge discovery, hybrid intelligent systems and methods, intelligent agents, multi-agent systems, intelligent distributed systems, intelligent/natural interactivity, intelligent virtual environments, planning, scheduling, and robotics.
Advances in Artificial Intelligence ; 15th Conference of the Canadian Society for Computational Studies of Intelligence, AI 2002 Calgary, Canada, May 27-29, 2002 Proceedings
The AI conference series is the premier event sponsored by the Canadian - ciety for the Computational Studies of Intelligence / Soci´et´e canadienne pour l’´etude d’intelligence par ordinateur. Attendees enjoy our typically Canadian - mosphere –hospitable and stimulating. The Canadian AI conference showcases the excellent research work done by Canadians, their international colleagues, and others choosing to join us each spring. International participation is always high; this year almost 40% of the submitted papers were from non-Canadian - searchers. We accepted 24 papers and 8 poster papers from 52 full-length papers submitted. We also accepted eight of ten abstracts submitted to the Graduate Student Symposium. All of these accepted papers appear in this volume.
Advanced technique and future perspective for next generation optical fiber communications
Optical fiber communication industry has gained unprecedented opportunities and achieved rapid progress in recent years. However, with the increase of data transmission volume and the enhancement of transmission demand, the optical communication field still needs to be upgraded to better meet the challenges in the future development. Artificial intelligence technology in optical communication and optical network is still in its infancy, but the existing achievements show great application potential. In the future, with the further development of artificial intelligence technology, AI algorithms combining channel characteristics and physical properties will shine in optical communication. This reprint introduces some recent advances in optical fiber communication and optical network, and provides alternative directions for the development of the next generation optical fiber communication technology.
Advanced methods for knowledge discovery from complex data
An overview of the field, looking at the issues and challenges involved is followed by coverage of recent trends in data mining, including descriptions of some currently popular tools like genetic algorithms, neural networks and case-based reasoning. This provides the context for the subsequent chapters on methods and applications. Part I is devoted to the foundations of mining different types of complex data like trees, graphs, links and sequences. A knowledge discovery approach based on problem decomposition is also described. Part II presents important applications of advanced mining techniques to data in unconventional and complex domains, such as life sciences, world-wide web, image databases, cyber security and sensor networks. With a good balance of introductory material on the knowledge discovery process, advanced issues and state-of-the-art tools and techniques, as well as recent working applications this book provides a representative selection of the available methods and their evaluation in real domains. It will be useful to students at Masters and PhD level in Computer Science, as well as practitioners in the field.
Advanced Intelligent Computing Theories and Applications : With Aspects of Contemporary Intelligent Computing Techniques ; 3rd International Conference on Intelligent Computing, ICIC 2007 Qingdao, China, August 21-24, 2007 Proceedings
The International Conference on Intelligent Computing (ICIC) was formed to provide an annual forum dedicated to the emerging and challenging topics in artificial intelligence, machine learning, bioinformatics, and computational biology, etc. It aims to bring - gether researchers and practitioners from both academia and industry to share ideas, problems and solutions related to the multifaceted aspects of intelligent computing.This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications.
Advanced Intelligent Computing Theories and Applications : With Aspects of Artificial Intelligence ; 3rd International Conference on Intelligent Computing, ICIC 2007, Qingdao, China, August 21-24, 2007, Proceedings
The International Conference on Intelligent Computing (ICIC) was formed to provide an annual forum dedicated to the emerging and challenging topics in artificial intelligence, machine learning, bioinformatics, and computational biology, etc. It aims to bring gether researchers and practitioners from both academia and industry to share ideas, problems and solutions related to the multifaceted aspects of intelligent computing.This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications.
Advanced driver assistance system (ADAS)
The purpose of Advanced Driver Assistance Systems (ADAS) is to reduce or eliminate driver errors, and to enhance efficiency in traffic and transportation. Our project is a means and a great contribution to safe driving, and the user does not need to install sensors or hard tools to the vehicle, and through it, the cost can be reduced and maintenance cost can be eliminated. The images are processed and segmented to find different features in the image. Segmented images are used for identification and classification based on various machine learning algorithms and neural networks. The main focus of ADAS technologies is to contribute to factors such as safety management and automated, stress-free driving for the driver
Advanced Decision Sciences Based on Deep Learning and Ensemble Learning Algorithms : A Practical Approach Using Python
Describes the deep learning models and ensemble approaches applied to decision-making problems. The authors have addressed the concepts of deep learning, convolutional neural networks, recurrent neural networks, and ensemble learning in a practical sense providing complete code and implementation for several real-world examples. The authors of this book teach the concepts of machine learning for undergraduate and graduate-level classes and have worked with Fortune 500 clients to formulate data analytics strategies and operationalise these strategies.
Adaptive Learning of Polynomial Networks : Genetic Programming, Backpropagation and Bayesian Methods
This book provides theoretical and practical knowledge for develop ment of algorithms that infer linear and nonlinear models. It offers a methodology for inductive learning of polynomial neural network mod els from data. The design of such tools contributes to better statistical data modelling when addressing tasks from various areas like system identification, chaotic time-series prediction, financial forecasting and data mining. The main claim is that the model identification process involves several equally important steps: finding the model structure, estimating the model weight parameters, and tuning these weights with respect to the adopted assumptions about the underlying data distrib ution. When the learning process is organized according to these steps, performed together one after the other or separately, one may expect to discover models that generalize well.
Adaptive Business Intelligence
In the modern information era, managers must recognize the competitive opportunities represented by decision-support tools. Adaptive business intelligence systems combine prediction and optimization techniques to assist decision makers in complex, rapidly changing environments. These systems address the fundamental questions: What is likely to happen in the future? And what is the best decision right now? Adaptive Business Intelligence includes elements of data mining, predictive modeling, forecasting, optimization, and adaptability.
Adaptive and Natural Computing Algorithms ; 8th International Conference, ICANNGA 2007, Warsaw, Poland, April 11-14, 2007, Proceedings, Part II
The ICANNGA series of conferences has been organized since 1993 and has a long history of promoting the principles and understanding of computational intelligence paradigms within the scientifc community. the ICANNGA series has established itself as a reference for scientists and practitioners in this area. The series has also been of value to young researchers wishing both to extend their knowledge and experience and to meet experienced professionals in their ?elds. In a rapidly advancing world, where technology and engineering change d- matically, new challenges in computer science compel us to broaden the c- ference scope in order to take into account new developments.
Adaptive and Natural Computing Algorithms ; 8th International Conference, ICANNGA 2007, Warsaw, Poland, April 11-14, 2007, Proceedings, Part I
Constitutes the refereed proceedings of the 8th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2007, held in Warsaw, Poland, in April 2007. The 178 revised full papers presented were carefully reviewed and selected from a total of 474 submissions. The 94 papers of the first volume are organized in topical sections on evolutionary computation, genetic algorithms, particle swarm optimization, learning, optimization and games, fuzzy and rough systems, just as classification and clustering. The second volume contains 84 contributions related to neural networks, support vector machines, biomedical signal and image processing, biometrics, computer vision, as well as to control and robotics.
A Matrix Algebra Approach to Artificial Intelligence
The book consists of two parts: the first discusses the fundamentals of matrix algebra in detail, while the second focuses on the applications of matrix algebra approaches in AI. Highlighting matrix algebra in graph-based learning and embedding, network embedding, convolutional neural networks and Pareto optimization theory, and discussing recent topics and advances, the book offers a valuable resource for scientists, engineers, and graduate students in various disciplines
