Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models focuses on the relationship between three different multidisciplinary branches of engineering: Biomedical Engineering, Cognitive Science and Computer Science through Artificial Intelligence models. These models will be used to study how the nervous system and musculoskeletal system obey movement orders from the brain, as well as the mental processes of the information during cognition when injuries and neurologic diseases are present in the human body.
The interaction between these three areas are studied in this book with the objective of obtaining AI models on injuries and neurologic diseases of the human body, studying diseases of the brain, spine and the nerves that connect them with the musculoskeletal system. There are more than 600 diseases of the nervous system, including brain tumors, epilepsy, Parkinson's disease, stroke, and many others. These diseases affect the human cognitive system that sends orders from the central nervous system (CNS) through the peripheral nervous systems (PNS) to do tasks using the musculoskeletal system. These actions can be detected by many Bioinstruments (Biomedical Instruments) and cognitive device data, allowing us to apply AI using Machine Learning-Deep Learning-Cognitive Computing models through algorithms to analyze, detect, classify, and forecast the process of various illnesses, diseases, and injuries of the human body.
Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models provides readers with the study of injuries, illness, and neurological diseases of the human body through Artificial Intelligence using Machine Learning (ML), Deep Learning (DL) and Cognitive Computing (CC) models based on algorithms developed with MATLAB(R) and IBM Watson(R).
Автор: Nathalie Japkowicz; Stan Matwin Название: Discovery Science ISBN: 3319242814 ISBN-13(EAN): 9783319242811 Издательство: Springer Рейтинг: Цена: 7826.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Bilinear Prediction using Low Rank Models.- Finding Hidden Structure in Data with Tensor Decompositions.- Turning Prediction Tools Into Decision Tools.- Overcoming obstacles to the adoption of machine learning by domain Experts.- Resolution transfer in cancer classification based on amplification patterns.- Very Short-Term Wind Speed Forecasting using Spatio-Temporal Lazy Learning.- Discovery of Parameters for Animation of Midge Swarms.- No Sentiment is an Island: Author's activity and sentiments transactions in sentiment classification.- Active Learning for Classifying Template Matches in Historical Maps.- An evaluation of score descriptors combined with non-linear models of expressive dynamics in music.- Geo-Coordinated Parallel Coordinates (GCPC): A Case Study of Environmental Data Analysis.- Generalized Shortest Path Kernel on Graphs.- Ensembles of extremely randomized trees for multi-target regression.- Clustering-Based Optimised Probabilistic Active Learning (COPAL).- Predictive Analysis on Tracking Emails for Targeted Marketing.- Semi-supervised Learning for Stream Recommender Systems.- Detecting Transmembrane Proteins Using Decision Trees.- Change point detection for information diffusion tree.- Multi-label Classification via Multi-target Regression on Data Streams.- Periodical Skeletonization for Partially Periodic Pattern Mining.- Predicting Drugs Adverse Side-Effects using a recommender-system.- Dr. Inventor Framework: extracting structured information from scientific publications.- Predicting Protein Function and Protein-Ligand Interaction with the 3D Neighborhood Kernel.- Hierarchical Multidimensional Classification of web documents with MultiWebClass.- Evaluating the Effectiveness of Hashtags as Predictors of the Sentiment of Tweets.- On the Feasibility of Discovering Meta-Patterns from a Data Ensemble.- An Algorithm for Influence Maximization in a Two-Terminal Series.- Parallel Graph and Its Application to a Real Network.- Benchmarking Stream Clustering for Churn Detection in Dynamic Networks .- Canonical Correlation Methods for Exploring Microbe-Environment Interactions in Deep Subsurface.- KeCo: Kernel-based Online Co-agreement Algorithm.- Tree PCA for Extracting Dominant Substructures from Labeled Rooted Trees.- Enumerating Maximal Clique Sets with Pseudo-Clique Constraint.
Автор: Akihiro Yamamoto; Takuya Kida; Takeaki Uno; Tetsuj Название: Discovery Science ISBN: 3319677853 ISBN-13(EAN): 9783319677859 Издательство: Springer Рейтинг: Цена: 9083.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the proceedings of the 20th International Conference on Discovery Science, DS 2017, held in Kyoto, Japan, in October 2017, co-located with the International Conference on Algorithmic Learning Theory, ALT 2017.
Автор: Jan M. Zytkow; Mohamed Quafafou Название: Principles of Data Mining and Knowledge Discovery ISBN: 3540650687 ISBN-13(EAN): 9783540650683 Издательство: Springer Рейтинг: Цена: 12577.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The refereed proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery. There are 26 revised papers and 30 poster presentations organized in topical sections, including rule evaluation, visualization, association rules and text mining, and tree construction.
Автор: Qiang Yang; Zhi-Hua Zhou; Zhiguo Gong; Min-Ling Zh Название: Advances in Knowledge Discovery and Data Mining ISBN: 3030161447 ISBN-13(EAN): 9783030161446 Издательство: Springer Рейтинг: Цена: 11459.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The three-volume set LNAI 11439, 11440, and 11441 constitutes the thoroughly refereed proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019, held in Macau, China, in April 2019. The 137 full papers presented were carefully reviewed and selected from 542 submissions.
Автор: Michele Berlingerio; Francesco Bonchi; Thomas G?rt Название: Machine Learning and Knowledge Discovery in Databases ISBN: 3030109240 ISBN-13(EAN): 9783030109240 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The three volume proceedings LNAI 11051 - 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, held in Dublin, Ireland, in September 2018. Part III: ADS data science applications;
Автор: Leong Hou U.; Hady W. Lauw Название: Trends and Applications in Knowledge Discovery and Data Mining ISBN: 3030261417 ISBN-13(EAN): 9783030261412 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 14th Pacific Asia Workshop on Intelligence and Security Informatics (PAISI 2019).- A Supporting Tool for IT System Security Specification Evaluation Based on ISO/IEC 15408 and ISO/IEC 18045.- An Investigation on Multi View based User Behavior towards Spam Detection in Social Networks.- A Cluster Ensemble Strategy for Asian Handicap Betting.- Designing an Integrated Intelligence Center: New Taipei City Police Department as an Example.- Early Churn User Classification in Social Networking Service Using Attention-based Long Short-Term Memory.- PAKDD 2019 Workshop on Weakly Supervised Learning: Progress and Future (WeL 2019).- Weakly Supervised Learning by a Confusion Matrix of Contexts.- Learning a Semantic Space for Modeling Images, Tags and Feelings in Cross-media Search.- Adversarial Active Learning in the Presence of Weak and Malicious Oracles.- The Most Related Knowledge First: A Progressive Domain Adaptation Method.- Learning Data Representation for Clustering (LDRC 2019).- Deep Architectures for Joint Clustering and Visualization with Self-Organizing Maps.- Deep cascade of extra trees.- Algorithms for an Efficient Tensor Biclustering.- Change point detetion in periodic panel data using a mixture-model-based approach.- The 8th Workshop on Biologically-inspired Techniques for Knowledge Discovery and Data Mining (BDM 2019).- Neural Network-Based Deep Encoding for Mixed-Attribute Data Classification.- Protein Complexes Detection Based on Deep Neural Network.- Predicting Auction Price of Vehicle License Plate with Deep Residual Learning.- Mining Multispectral Aerial Images for Automatic Detection of Strategic Bridge Locations for Disaster Relief Missions.- Chinese Word Segmentation with Feature Alignment.- Spike Sorting with Locally Weighted Co-association Matrix-based Spectral Clustering.- Label Distribution Learning Based Age-Invariant Face Recognition.- Overall Loss For Deep Neural Networks.- Sentiment Analysis Based on LSTM Architecture with Emoticon Attention.- Aspect Level Sentiment Analysis with Aspect Attention.- The 1st Pacific Asia Workshop on Deep Learning for Knowledge Transfer (DLKT 2019).- Transfer Channel Pruning for Compressing Deep Domain Adaptation Models.- A Heterogeneous Domain Adversarial Neural Network for Trans-Domain Behavioral Targeting.- Natural Language Business Intelligence Question Answering through SeqtoSeq Transfer Learning.- Robust Faster R-CNN: Increasing Robustness to Occlusions and multi-scale objects.- Effectively Representing Short Text via the Improved Semantic Feature Space Mapping.- Probabilistic Graphical Model Based Highly Scalable Directed Community Detection Algorithm.- Hilltop based recommendation in co-author networks.- Neural Variational Collaborative Filtering for Top-K Recommendation.
Автор: Amini Название: Machine Learning and Knowledge Discovery in Databases ISBN: 3031264088 ISBN-13(EAN): 9783031264085 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The multi-volume set LNAI 13713 until 13718 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, which took place in Grenoble, France, in September 2022. The 236 full papers presented in these proceedings were carefully reviewed and selected from a total of 1060 submissions. In addition, the proceedings include 17 Demo Track contributions. The volumes are organized in topical sections as follows: Part I: Clustering and dimensionality reduction; anomaly detection; interpretability and explainability; ranking and recommender systems; transfer and multitask learning; Part II: Networks and graphs; knowledge graphs; social network analysis; graph neural networks; natural language processing and text mining; conversational systems; Part III: Deep learning; robust and adversarial machine learning; generative models; computer vision; meta-learning, neural architecture search; Part IV: Reinforcement learning; multi-agent reinforcement learning; bandits and online learning; active and semi-supervised learning; private and federated learning; . Part V: Supervised learning; probabilistic inference; optimal transport; optimization; quantum, hardware; sustainability; Part VI: Time series; financial machine learning; applications; applications: transportation; demo track.
Описание: This book is devoted to the emerging field of integrated visual knowledge discovery that combines advances in artificial intelligence/machine learning and visualization/visual analytic. A long-standing challenge of artificial intelligence (AI) and machine learning (ML) is explaining models to humans, especially for live-critical applications like health care. A model explanation is fundamentally human activity, not only an algorithmic one. As current deep learning studies demonstrate, it makes the paradigm based on the visual methods critically important to address this challenge. In general, visual approaches are critical for discovering explainable high-dimensional patterns in all types in high-dimensional data offering "n-D glasses," where preserving high-dimensional data properties and relations in visualizations is a major challenge. The current progress opens a fantastic opportunity in this domain. This book is a collection of 25 extended works of over 70 scholars presented at AI and visual analytics related symposia at the recent International Information Visualization Conferences with the goal of moving this integration to the next level. The sections of this book cover integrated systems, supervised learning, unsupervised learning, optimization, and evaluation of visualizations. The intended audience for this collection includes those developing and using emerging AI/machine learning and visualization methods. Scientists, practitioners, and students can find multiple examples of the current integration of AI/machine learning and visualization for visual knowledge discovery. The book provides a vision of future directions in this domain. New researchers will find here an inspiration to join the profession and to be involved for further development. Instructors in AI/ML and visualization classes can use it as a supplementary source in their undergraduate and graduate classes.
Автор: Qiang Yang; Zhi-Hua Zhou; Zhiguo Gong; Min-Ling Zh Название: Advances in Knowledge Discovery and Data Mining ISBN: 3030161412 ISBN-13(EAN): 9783030161415 Издательство: Springer Рейтинг: Цена: 11459.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The three-volume set LNAI 11439, 11440, and 11441 constitutes the thoroughly refereed proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019, held in Macau, China, in April 2019. The 137 full papers presented were carefully reviewed and selected from 542 submissions.
Автор: Tapio Elomaa; Heikki Mannila; Hannu Toivonen Название: Principles of Data Mining and Knowledge Discovery ISBN: 3540440372 ISBN-13(EAN): 9783540440376 Издательство: Springer Рейтинг: Цена: 11179.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Constitutes the proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery held in Finland in 2002. Papers cover kernel methods, probabilistic methods, association rule mining, rough sets, sampling algorithms, pattern discovery, web text mining and more.
Автор: Phung Название: Advances in Knowledge Discovery and Data Mining ISBN: 3319930338 ISBN-13(EAN): 9783319930336 Издательство: Springer Рейтинг: Цена: 12577.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This three-volume set, LNAI 10937, 10938, and 10939, constitutes the thoroughly refereed proceedings of the 22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018, held in Melbourne, VIC, Australia, in June 2018.
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