Artificial Neural Networks And Machine Learning Icann 2014
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Artificial Neural Networks And Machine Learning Icann 2014
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Author : Stefan Wermter
language : en
Publisher: Springer
Release Date : 2014-08-18
Artificial Neural Networks And Machine Learning Icann 2014 written by Stefan Wermter and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-08-18 with Computers categories.
The book constitutes the proceedings of the 24th International Conference on Artificial Neural Networks, ICANN 2014, held in Hamburg, Germany, in September 2014. The 107 papers included in the proceedings were carefully reviewed and selected from 173 submissions. The focus of the papers is on following topics: recurrent networks; competitive learning and self-organisation; clustering and classification; trees and graphs; human-machine interaction; deep networks; theory; reinforcement learning and action; vision; supervised learning; dynamical models and time series; neuroscience; and applications.
Proceedings Of International Conference On Computational Intelligence And Data Engineering
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Author : Nabendu Chaki
language : en
Publisher: Springer
Release Date : 2017-12-19
Proceedings Of International Conference On Computational Intelligence And Data Engineering written by Nabendu Chaki and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-19 with Technology & Engineering categories.
The book presents high quality research work in cutting edge technologies and most-happening areas of computational intelligence and data engineering. It contains selected papers presented at International Conference on Computational Intelligence and Data Engineering (ICCIDE 2017). The conference was conceived as a forum for presenting and exchanging ideas and results of the researchers from academia and industry onto a common platform and help them develop a comprehensive understanding of the challenges of technological advancements from different viewpoints. This book will help in fostering a healthy and vibrant relationship between academia and industry. The topics of the conference include, but are not limited to collective intelligence, intelligent transportation systems, fuzzy systems, Bayesian network, ant colony optimization, data privacy and security, data mining, data warehousing, big data analytics, cloud computing, natural language processing, swarm intelligence, and speech processing.
Proceedings Of Fourth International Conference On Computing And Communication Networks
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Author : Akshi Kumar
language : en
Publisher: Springer Nature
Release Date : 2025-05-24
Proceedings Of Fourth International Conference On Computing And Communication Networks written by Akshi Kumar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-24 with Technology & Engineering categories.
This book includes selected peer-reviewed papers presented at fourth International Conference on Computing and Communication Networks (ICCCN 2024), held at Manchester Metropolitan University, UK, during 17–18 October 2024. The book covers topics of network and computing technologies, artificial intelligence and machine learning, security and privacy, communication systems, cyber physical systems, data analytics, cyber security for industry 4.0, and smart and sustainable environmental systems.
Proceedings
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Author : Michel Verleysen
language : en
Publisher: Presses universitaires de Louvain
Release Date : 2015
Proceedings written by Michel Verleysen and has been published by Presses universitaires de Louvain this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Artificial Intelligence Xxxiv
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Author : Max Bramer
language : en
Publisher: Springer
Release Date : 2017-12-01
Artificial Intelligence Xxxiv written by Max Bramer and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-01 with Computers categories.
This book constitutes the proceedings of the 37th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2017, held in Cambridge, UK, in December 2017. The 25 full papers and 12 short papers presented in this volume were carefully reviewed and selected from 55 submissions. There are technical and application papers which were organized in topical sections named: machine learning and neural networks; machine learning, speech and vision and fuzzy logic; short technical papers; AI for healthcare; applications of machine learning; applications of neural networks and fuzzy logic; case-based reasoning; AI techniques; and short applications papers.
Genomics At The Nexus Of Ai Computer Vision And Machine Learning
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Author : Shilpa Choudhary
language : en
Publisher: John Wiley & Sons
Release Date : 2024-10-01
Genomics At The Nexus Of Ai Computer Vision And Machine Learning written by Shilpa Choudhary and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-01 with Computers categories.
The book provides a comprehensive understanding of cutting-edge research and applications at the intersection of genomics and advanced AI techniques and serves as an essential resource for researchers, bioinformaticians, and practitioners looking to leverage genomics data for AI-driven insights and innovations. The book encompasses a wide range of topics, starting with an introduction to genomics data and its unique characteristics. Each chapter unfolds a unique facet, delving into the collaborative potential and challenges that arise from advanced technologies. It explores image analysis techniques specifically tailored for genomic data. It also delves into deep learning showcasing the power of convolutional neural networks (CNN) and recurrent neural networks (RNN) in genomic image analysis and sequence analysis. Readers will gain practical knowledge on how to apply deep learning techniques to unlock patterns and relationships in genomics data. Transfer learning, a popular technique in AI, is explored in the context of genomics, demonstrating how knowledge from pre-trained models can be effectively transferred to genomic datasets, leading to improved performance and efficiency. Also covered is the domain adaptation techniques specifically tailored for genomics data. The book explores how genomics principles can inspire the design of AI algorithms, including genetic algorithms, evolutionary computing, and genetic programming. Additional chapters delve into the interpretation of genomic data using AI and ML models, including techniques for feature importance and visualization, as well as explainable AI methods that aid in understanding the inner workings of the models. The applications of genomics in AI span various domains, and the book explores AI-driven drug discovery and personalized medicine, genomic data analysis for disease diagnosis and prognosis, and the advancement of AI-enabled genomic research. Lastly, the book addresses the ethical considerations in integrating genomics with AI, computer vision, and machine learning. Audience The book will appeal to biomedical and computer/data scientists and researchers working in genomics and bioinformatics seeking to leverage AI, computer vision, and machine learning for enhanced analysis and discovery; healthcare professionals advancing personalized medicine and patient care; industry leaders and decision-makers in biotechnology, pharmaceuticals, and healthcare industries seeking strategic insights into the integration of genomics and advanced technologies.
Dimensionality Reduction In Machine Learning
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Author : Jamal Amani Rad
language : en
Publisher: Morgan Kaufmann
Release Date : 2025-02-04
Dimensionality Reduction In Machine Learning written by Jamal Amani Rad and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-04 with Computers categories.
Dimensionality Reduction in Machine Learning covers both the mathematical and programming sides of dimension reduction algorithms, comparing them in various aspects. Part One provides an introduction to Machine Learning and the Data Life Cycle, with chapters covering the basic concepts of Machine Learning, essential mathematics for Machine Learning, and the methods and concepts of Feature Selection. Part Two covers Linear Methods for Dimension Reduction, with chapters on Principal Component Analysis and Linear Discriminant Analysis. Part Three covers Non-Linear Methods for Dimension Reduction, with chapters on Linear Local Embedding, Multi-dimensional Scaling, and t-distributed Stochastic Neighbor Embedding.Finally, Part Four covers Deep Learning Methods for Dimension Reduction, with chapters on Feature Extraction and Deep Learning, Autoencoders, and Dimensionality reduction in deep learning through group actions. With this stepwise structure and the applied code examples, readers become able to apply dimension reduction algorithms to different types of data, including tabular, text, and image data. - Provides readers with a comprehensive overview of various dimension reduction algorithms, including linear methods, non-linear methods, and deep learning methods - Covers the implementation aspects of algorithms supported by numerous code examples - Compares different algorithms so the reader can understand which algorithm is suitable for their purpose - Includes algorithm examples that are supported by a Github repository which consists of full notebooks for the programming code
Feature Engineering For Machine Learning And Data Analytics
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Author : Guozhu Dong
language : en
Publisher: CRC Press
Release Date : 2018-03-14
Feature Engineering For Machine Learning And Data Analytics written by Guozhu Dong and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-14 with Business & Economics categories.
Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features. The first chapter defines the concepts of features and feature engineering, offers an overview of the book, and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering, including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering, namely feature selection, feature transformation based feature engineering, deep learning based feature engineering, and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection, software management, and Twitter-based applications respectively. This book can be used as a reference for data analysts, big data scientists, data preprocessing workers, project managers, project developers, prediction modelers, professors, researchers, graduate students, and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering, or as a supplement for courses on machine learning, data mining, and big data analytics.
Intelligent Systems And Applications
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Author : Kohei Arai
language : en
Publisher: Springer
Release Date : 2018-11-08
Intelligent Systems And Applications written by Kohei Arai and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-08 with Technology & Engineering categories.
Gathering the Proceedings of the 2018 Intelligent Systems Conference (IntelliSys 2018), this book offers a remarkable collection of chapters covering a wide range of topics in intelligent systems and computing, and their real-world applications. The Conference attracted a total of 568 submissions from pioneering researchers, scientists, industrial engineers, and students from all around the world. These submissions underwent a double-blind peer review process, after which 194 (including 13 poster papers) were selected to be included in these proceedings. As intelligent systems continue to replace and sometimes outperform human intelligence in decision-making processes, they have made it possible to tackle many problems more effectively. This branching out of computational intelligence in several directions, and the use of intelligent systems in everyday applications, have created the need for such an international conference, which serves as a venue for reporting on cutting-edge innovations and developments. This book collects both theory and application-based chapters on all aspects of artificial intelligence, from classical to intelligent scope. Readers are sure to find the book both interesting and valuable, as it presents state-of-the-art intelligent methods and techniques for solving real-world problems, along with a vision of future research directions.
Five Pillars Of The Mind Redesigning Education To Suit The Brain
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Author : Tracey Tokuhama-Espinosa
language : en
Publisher: W. W. Norton & Company
Release Date : 2019-03-05
Five Pillars Of The Mind Redesigning Education To Suit The Brain written by Tracey Tokuhama-Espinosa and has been published by W. W. Norton & Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-05 with Education categories.
From the author of Neuromyths, a revolutionary look at teaching and learning via the logical pathways of the brain. A review of the research on brain networks reveals, surprisingly, that there are just five basic pillars through which all learning takes place: Symbols, Patterns, Order, Categories, and Relationships. Dr. Tokuhama-Espinosa proposes that redesigning school curriculum around these five pillars—whether to augment or replace traditional subject categories—could enable students to develop the transdisciplinary problem-solving skills that are often touted as the ultimate goal of education. Heralding a potential paradigm shift in education, Five Pillars of the Mind explores how aligning instruction with the brain's natural design might just be the key to improving students' learning outcomes.