Enhanced Bayesian Network Models for Spatial Time Series Prediction, Monidipa Das; Soumya K. Ghosh
Автор: Amit Konar; Diptendu Bhattacharya Название: Time-Series Prediction and Applications ISBN: 3319545965 ISBN-13(EAN): 9783319545967 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents machine learning and type-2 fuzzy sets for the prediction of time-series with a particular focus on business forecasting applications.
Описание: This book focuses on the fields of hybrid intelligent systems based on fuzzy systems, neural networks, bio-inspired algorithms and time series. Prediction errors are evaluated by the following metrics: Mean Absolute Error, Mean Square Error, Root Mean Square Error, Mean Percentage Error and Mean Absolute Percentage Error.
Описание: Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challenges in time series, using both traditional statistical and modern machine learning techniques.
Автор: A. Ravishankar Rao; Guillermo A. Cecchi Название: The Relevance of the Time Domain to Neural Network Models ISBN: 1461429927 ISBN-13(EAN): 9781461429920 Издательство: Springer Рейтинг: Цена: 26120.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Here is a unified view of how the time domain can be effectively employed in neural network models. Covers synchronization, phase-locking behavior, image processing, temporal pattern analysis, fMRI analyis, network topology and synchronizability and more.
Автор: Quan-Lin Li; Jinting Wang; Hai-Bo Yu Название: Stochastic Models in Reliability, Network Security and System Safety ISBN: 9811508631 ISBN-13(EAN): 9789811508639 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is dedicated to Jinhua Cao on the occasion of his 80th birthday. Jinhua Cao is one of the most famous reliability theorists. His main contributions include: published over 100 influential scientific papers; published an interesting reliability book in Chinese in 1986, which has greatly influenced the reliability of education, academic research and engineering applications in China; initiated and organized Reliability Professional Society of China (the first part of Operations Research Society of China) since 1981. The high admiration that Professor Cao enjoys in the reliability community all over the world was witnessed by the enthusiastic response of each contributor in this book. The contributors are leading researchers with diverse research perspectives. The research areas of the book iclude a broad range of topics related to reliability models, queueing theory, manufacturing systems, supply chain finance, risk management, Markov decision processes, blockchain and so forth.The book consists of a brief Preface describing the main achievements of Professor Cao; followed by congratulations from Professors Way Kuo and Wei Wayne Li, and by Operations Research Society of China, and Reliability Professional Society of China; and further followed by 25 articles roughly grouped together. Most of the articles are written in a style understandable to a wide audience. This book is useful to anyone interested in recent developments in reliability, network security, system safety, and their stochastic modeling and analysis.
Описание: This brief examines a deterministic, ODE-based model for gene regulatory networks (GRN) that incorporates nonlinearities and time-delayed feedback. One chapter is devoted to the analysis of GRNs under negative feedback with time delays and a special case of a homogenous GRN is considered.
Автор: Pole, Andy , West, Mike , Harrison, Jeff Название: Applied Bayesian Forecasting and Time Series Analysis ISBN: 0367449382 ISBN-13(EAN): 9780367449384 Издательство: Taylor&Francis Рейтинг: Цена: 9492.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is about practical forecasting and analysis of time series. It describes how to analyse time series data, how to identify structure, how to explain observed behaviour, how to model structures and behaviours, and how to use insight gained from the analysis to make informed forecasts.
Описание: Constructing Subject- and Disease-Specific Effect Maps: Application to Neurodegenerative Diseases.- BigBrain: Automated Cortical Parcellation and Comparison with Existing Brain Atlases.- LATEST: Local AdapTivE and Sequential Training for Tissue Segmentation of Isointense Infant Brain MR Images.- Landmark-based Alzheimer's Disease Diagnosis Using Longitudinal Structural MR Images.- Inferring Disease Status by non-Parametric Probabilistic Embedding.- A Lung Graph-Model for Pulmonary Hypertension and Pulmonary Embolism Detection on DECT Images.- Explaining Radiological Emphysema Subtypes with Unsupervised Texture Prototypes: MESA COPD Study.- Automatic Segmentation of Abdominal MRI Using Selective Sampling and Random Walker.- Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation.- Automatic Detection of Histological Artifacts in Mouse Brain Slice Images.- Lung Nodule Classification by Jointly Using Visual Descriptors and Deep Features.- Representation Learning for Cross-Modality Classification.- Guideline-based Machine Learning for Standard Plane Extraction in 3D Cardiac Ultrasound.- A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Brain Images.- Bayesian Multiview Manifold Learning Applied to Hippocampus Shape and Clinical Score Data.- Rigid Slice-To-Volume Medical Image Registration through Markov Random Fields.- Sparse Probabilistic Parallel Factor Analysis for the Modeling of PET and Task-fMRI data.- Non-local Graph-based Regularization for Deformable Image Registration.- Unsupervised Framework for Consistent Longitudinal MS Lesion Segmentation.
Автор: M. Jorge Cardoso; Ivor Simpson; Tal Arbel; Doina P Название: Bayesian and grAphical Models for Biomedical Imaging ISBN: 3319122886 ISBN-13(EAN): 9783319122885 Издательство: Springer Рейтинг: Цена: 5590.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
N3 Bias Field Correction Explained as a Bayesian Modeling Method.- A Bayesian Approach to Distinguishing Interdigitated Muscles in the Tongue from Limited Diffusion Weighted Imaging.- Optimal Joint Segmentation and Tracking of Escherichia Coli in the Mother Machine.- Physiologically Informed Bayesian Analysis of ASL fMRI Data.- Bone Reposition Planning for Corrective Surgery Using Statistical Shape Model: Assessment of Differential Geometrical Features.- An Inference Language for Imaging.- An MRF-Based Discrete Optimization Framework for Combined DCE-MRI Motion Correction and Pharmacokinetic Parameter Estimation.- Learning Imaging Biomarker Trajectories from Noisy Alzheimer's Disease Data Using a Bayesian Multilevel Model.- Four Neuroimaging Questions that P-Values Cannot Answer (and Bayesian Analysis Can).- Spherical Topic Models for Imaging Phenotype Discovery in Genetic Studies.- A Generative Model for Automatic Detection of Resolving Multiple Sclerosis Lesions.
Автор: BALL Название: NETWORK MODELS HORM 7 ISBN: 0444892923 ISBN-13(EAN): 9780444892928 Издательство: Elsevier Science Рейтинг: Цена: 8098.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This text presents papers reflecting the wide range of theories and applications of network models. Several chapters model issues in the domains of telecommunications and transportation.
Описание: Moreover, because adaptive networks are described in a network format as well, the approach can simply be applied iteratively, so that higher-order adaptive networks in which network adaptation itself is adaptive (second-order adaptation), too can be modeled just as easily.
Автор: Enrique Castillo; Jose M. Gutierrez; Ali S. Hadi Название: Expert Systems and Probabilistic Network Models ISBN: 1461274818 ISBN-13(EAN): 9781461274810 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Artificial intelligence and expert systems have seen a great deal of research in recent years, much of which has been devoted to methods for incorporating uncertainty into models. This book is devoted to providing a thorough and up-to-date survey of this field for researchers and students.
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