الصفحة 33
الصفحة 33
Innovations in Machine Learning : Theory and Applications
Machine learning is currently one of the most rapidly growing areas of research in computer science. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. This book covers the three main learning systems; symbolic learning, neural networks and genetic algorithms as well as providing a tutorial on learning casual influences. Each of the nine chapters is self-contained. Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for Postgraduate since it shows the direction of current research.
Innovations in Intelligent Machines - 1
This book includes a collection of chapters on the state of art in the area of intelligent machines. This research would provide a sound basis to make autonomous systems human-like. The contributions include: An introduction to intelligent machines / Supervisory control of multiple UAVs / Intelligent autonomous UAV task allocation / UAV path planning / Dynamic path planning / Routing in UAVS State estimation of micro air vehicles / Architecture for soccer playing robots / Robot perception / Application engineers scientists and researchers will find this book useful.
Innovations in healthcare and outcome measurement : New approaches for a healthy lifestyle
Aims to bring up-to-date new ideas, opinions, development, and critical issues in healthcare and personalized medicine. We are interested in relevant articles covering a broad range of topics, such as: Advances in medical devices, Digitalization and data-driven technologies, AI and algorithm-based drug development (molecule building, enhancement, clinical trials), Diagnostic imaging, Personalized medicine, Nutrition, Oral health care, Healthcare management in certain diseases and population groups, Regulatory developments, Data management, Digital healthcare.
Innovations in Derivatives Markets : Fixed Income Modeling, Valuation Adjustments, Risk Management, and Regulation
This book presents 20 peer-reviewed chapters on current aspects of derivatives markets and derivative pricing. The contributions, written by leading researchers in the field as well as experienced authors from the financial industry, present the state of the art in: • Modeling counterparty credit risk: credit valuation adjustment, debit valuation adjustment, funding valuation adjustment, and wrong way risk. • Pricing and hedging in fixed-income markets and multi-curve interest-rate modeling. • Recent developments concerning contingent convertible bonds, the measuring of basis spreads, and the modeling of implied correlations.
Innovations in classification, data science, and information systems ; Proceedings of the 27th Annual Conference of the Gesellschaft für Klassifikation e.V., Brandenburg University of Technology, Cottbus, March 12-14, 2003
The volume presents innovations in data analysis and classification and gives an overview of the state of the art in these scientific fields and applications. Areas that receive considerable attention in the book are discrimination and clustering, data analysis and statistics, as well as applications in marketing, finance, and medicine. The reader will find material on recent technical and methodological developments and a large number of applications demonstrating the usefulness of the newly developed techniques.
Innovations in Bayesian Networks : Theory and Applications
Bayesian networks currently provide one of the most rapidly growing areas of research in computer science and statistics. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field.
Innovations and new developments in craniomaxillofacial reconstruction
Provides a comprehensive review of the new technologies that are having a tremendous impact on the complex field of craniomaxillofacial reconstructive surgery. The coverage encompasses the use of biomaterials and tissue engineering, virtual planning and CAD/CAM techniques, the various applications of computer-assisted surgery, and intraoperative navigation.
Injecting Salmonella Bacteria in to the Tumor Cures Cancer
The human body is inhabited by millions of tiny living organisms like good bacteria. We acquire these bacteria during birth and the first years of life, and they live with us throughout our lives. The human microbiomes are involved in healthy growth, in protecting the body from invaders, in helping digestion, and in regulating moods, but sometimes these bacteria can also be harmful. We need to take good care of our health to avoid the development of some diseases, like salmonella for example. Salmonella infections in humans can range from self-limiting gastroenteritis typically associated with non-typhoidal Salmonella (NTS) to typhoidal fever, which can be life-threatening. Salmonellosis causes considerable morbidity and mortality in both humans and animals, and has a significant socioeconomic impact worldwide.
Ingle's endodontics 7
Known as the “Bible of Endodontics” for half a century. It continues the tradition of including an international group of authors, contributing new cutting-edge knowledge and updates on topics that have formed the core of this book for years and also contributing new chapters that reflect the ways in which the field has evolved over the 50 years since its inception. include: Periradicular disease Dental innervations and pain of pulpal origin Cone beam computed tomography Magnetic resonance imaging Preparation for endodontic treatment Endodontic therapy in teeth with anatomical variations Achieving long-term success with endodontic therapy Management of teeth with immature apices Regenerative endodontics Intentional replantation of endodontically treated teeth Endodontic therapy in the elderly patient Endodontic therapy in the pediatric patient
Information theory and machine learning
The recent successes of machine learning, especially regarding systems based on deep neural networks, have encouraged further research activities and raised a new set of challenges in understanding and designing complex machine learning algorithms. New applications require learning algorithms to be distributed, have transferable learning results, use computation resources efficiently, convergence quickly on online settings, have performance guarantees, satisfy fairness or privacy constraints, incorporate domain knowledge on model structures, etc. A new wave of developments in statistical learning theory and information theory has set out to address these challenges.
Information Technologies in Environmental Engineering ; ITEE 2007 - 3rd International ICSC Symposium
Potentially dangerous environmental changes are happening in the atm- phere, oceans, animal habitats and places where hazardous materials are used, or have been discarded without adequate environmental protections. These increasing problems that also affect human health demand for int- disciplinary approaches where engineers, natural scientists, economists and computer scientists work together. This book publishes the results of the ITEE 2007 conference where information about the topics above has been presented and discussed among environmental engineers, computer scientists and economists.
Information Security ; Vol.3650 ; 8th International Conference, ISC 2005, Singapore, September 20-23, 2005, Proceedings
ISC 2005 brought together individuals from academia and - dustry involvedin manyresearchdisciplines of information security to foster the exchange of ideas. During recent years this conference has tried to place special emphasis on the practical aspects of information security, and since it passed from being an international workshop to being an international conference in 2001, it has become one of the most relevant forums at which researchers meet and discuss emerging security challenges and solutions. Advised by the ISC Steering Committee, and in order to provide students with more opportunities for publication, ISC 2005 accepted extra student papers - sides the regular papers. The initiative was very well accepted by the young sector of the scienti?c community, and we hope that the success of this idea will remainfornextISCevents. Another important factor for the success of ISC2005 was that selected papers in the proceedings will be invited for submission to a special issue of the International Journal of Information Security. The result was an incredible response to the call for papers; we received 271 submissions, the highest since ISC events started. It goes without saying that the paper selection process was more competitive and di?cult than ever before — only 33 regular papers were accepted, plus 5 student papers for a special student session.
Information Security ; Vol. 4176 ; 9th International Conference; ISC 2006, Samos Island, Greece, August 30 - September 2, 2006, Proceedings
th This volume contains the papers presented at the 9 Information Security Conference (ISC 2006) held on Samos Island, Greece, during August 30 – September 2, 2006. The Conference was organized by the University of the Aegean, Greece. ISC was first initiated as a workshop, ISW in Japan in 1997, ISW 1999 in Mal- sia, ISW 2000 in Australia and then changed to the current name ISC when it was held in Spain in 2001 (ISC 2001). The latest conferences were held in Brazil (ISC 2002), UK (ISC 2003), USA (ISC 2004), and Singapore (ISC 2005). ISC 2006 provided an international forum for sharing original research results and application experiences among specialists in fundamental and applied problems of - formation security. In response to the Call for Papers, 188 papers were submitted. Each paper was - viewed by three members of the PC, on the basis of their significance, novelty, and technical quality. Of the papers submitted, 38 were selected for presentation, with an acceptance rate of 20%.
Information Processing with Evolutionary Algorithms : From Industrial Applications to Academic Speculations
The last decade of the 20th century has witnessed a surge of interest in num- ical, computation-intensive approaches to information processing. The lines that draw the boundaries among statistics, optimization, arti cial intelligence and information processing are disappearing, and it is not uncommon to nd well-founded and sophisticated mathematical approaches in application - mains traditionally associated with ad-hoc programming. Heuristics has - come a branch of optimization and statistics. Clustering is applied to analyze soft data and to provide fast indexing in the World Wide Web. Non-trivial matrix algebra is at the heart of the last advances in computer vision. The breakthrough impulse was, apparently, due to the rise of the interest in arti cial neural networks, after its rediscovery in the late 1980s. Disguised as ANN, numerical and statistical methods made an appearance in the - formation processing scene, and others followed. A key component in many intelligent computational processing is the search for an optimal value of some function. Sometimes, this function is not evident and it must be made explicit in order to formulate the problem as an optimization problem. The search - ten takes place in high-dimensional spaces that can be either discrete, or c- tinuous or mixed. The shape of the high-dimensional surface that corresponds to the optimized function is usually very complex. Evolutionary algorithms are increasingly being applied to information processing applications that require any kind of optimization.
Information processing in medical imaging ; 19th International conference, IPMI 2005, Glenwood Springs, CO, USA, July 10-15, 2005, Proceedings
The nineteenth biennial International Conference on Information Processing in Medical Imaging (IPMI) was held July 11–15, 2005 in Glenwood Springs, CO, USA on the Spring Valley campus of the Colorado Mountain College. Following the successful meeting in beautiful Ambleside in England, this year’s conference addressed important recent developments in a broad range of topics related to the acquisition, analysis and application of biomedical images. Interest in IPMI has been steadily growing over the last decade. This is p- tially due to the increased number of researchers entering the ?eld of medical imagingasaresultoftheWhitakerFoundationandtherecentlyformedNational Institute of Biomedical Imaging and Bioengineering. This year, there were 245 full manuscripts submitted to the conference which was twice the number s- mitted in 2003 and almost four times the number of submissions in 2001. Of these papers, 27 were accepted as oral presentations, and 36 excellent subm- sions that could not be accommodated as oral presentations were presented as posters. Selection of the papers for presentation was a di?cult task as we were unable to accommodate many of the excellent papers submitted this year. All accepted manuscripts were allocated 12 pages in these proceedings.
Information Hiding ; 10th International Workshop, IH 2008, Santa Barbara, CA, USA, May 19-21, 2008, Revised Selected Papers
This book constitutes the thoroughly refereed post-workshop proceedings of the 10th International Workshop on Information Hiding, IH 2008, held in Santa Barbara, CA, USA, in May 2008.The 25 revised full papers presented were carefully reviewed and selected from 64 submissions. The papers are organized in topical sections on anonymity and privacy, steganography, forensics, novel technologies and applications, watermarking, steganalysis, other hiding domains, network security, and fingerprinting.
Information Geometry : Near Randomness and Near Independence
This volume will be useful to practising scientists and students working in the application of statistical models to real materials or to processes with perturbations of a Poisson process, a uniform process, or a state of independence for a bivariate process. We use information geometry to provide a common differential geometric framework for a wide range of illustrative applications including amino acid sequence spacings in protein chains, cryptology studies, clustering of communications and galaxies, cosmological voids, coupled spatial statistics in stochastic fibre networks and stochastic porous media, quantum chaology. Introduction sections are provided to mathematical statistics, differential geometry and the information geometry of spaces of probability density functions.
Information extraction : Algorithms and prospects in a retrieval context
The book focuses on content recognition in text. It elaborates on the past and current most successful algorithms and their application in a variety of domains (e.g., news filtering, mining of biomedical text, intelligence gathering, competitive intelligence, legal information searching, and processing of informal text). An important part discusses current statistical and machine learning algorithms for information detection and classification and integrates their results in probabilistic retrieval models. The book also reveals a number of ideas towards an advanced understanding and synthesis of textual content.
Information criteria and statistical modeling
One of the main objectives of this book is to provide comprehensive explanations of the concepts and derivations of the AIC and related criteria, including Schwarz’s Bayesian information criterion (BIC), together with a wide range of practical examples of model selection and evaluation criteria. A secondary objective is to provide a theoretical basis for the analysis and extension of information criteria via a statistical functional approach. A generalized information criterion (GIC) and a bootstrap information criterion are presented, which provide unified tools for modeling and model evaluation for a diverse range of models, including various types of nonlinear models and model estimation procedures such as robust estimation, the maximum penalized likelihood method and a Bayesian approach.
Information and Complexity in Statistical Modeling
The main theme in this book is to teach modeling based on the principle that the objective is to extract the information from data that can be learned with suggested classes of probability models. The intuitive and fundamental concepts of complexity, learnable information, and noise are formalized, which provides a firm information theoretic foundation for statistical modeling.
