Описание: The accepted chapters covered many themes including artificial intelligence and data mining applications, machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and security with big data.
Автор: V. Sathiyamoorthi, Atilla Elci Название: Challenges and Applications of Data Analytics in Social Perspectives ISBN: 1799825671 ISBN-13(EAN): 9781799825678 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 29522.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Provides innovative insights into the prevailing challenges in data analytics and its application on social media and focuses on various machine learning and deep learning techniques in improving practice and research. The publication examines topics that include collaborative filtering, data visualization, and edge computing.
Описание: Machine learning allows for non-conventional and productive answers for issues within various fields, including problems related to visually perceptive computers. Applying these strategies and algorithms to the area of computer vision allows for higher achievement in tasks such as spatial recognition, big data collection, and image processing. There is a need for research that seeks to understand the development and efficiency of current methods that enable machines to see. Challenges and Applications for Implementing Machine Learning in Computer Vision is a collection of innovative research that combines theory and practice on adopting the latest deep learning advancements for machines capable of visual processing. Highlighting a wide range of topics such as video segmentation, object recognition, and 3D modelling, this publication is ideally designed for computer scientists, medical professionals, computer engineers, information technology practitioners, industry experts, scholars, researchers, and students seeking current research on the utilization of evolving computer vision techniques.
Описание: Data science revolves around two giants, which are big data analytics and deep learning. It is becoming challenging to handle and retrieve useful information due to how fast data is expanding. This book presents the technologies and tools to simplify and streamline the formation of big data along with deep learning systems.
Описание: iMIMIC 2021 Workshop.- Interpretable Deep Learning for Surgical Tool Management.- Soft Attention Improves Skin Cancer Classification Performance.- Deep Gradient based on Collective Arti cial Intelligence for AD Diagnosis and Prognosis.- This explains That: Congruent Image-Report Generation for Explainable Medical Image Analysis with Cyclic Generative Adversarial Networks.- Visual Explanation by Unifying Adversarial Generation and Feature Importance Attributions.- The Effect of the Loss on Generalization: Empirical Study on Synthetic Lung Nodule Data.- Voxel-level Importance Maps for Interpretable Brain Age Estimation.- TDA4MedicalData Workshop.- Lattice Paths for Persistent Diagrams.- Neighborhood complex based machine learning (NCML) models for drug design.- Predictive modelling of highly multiplexed tumour tissue images by graph neural networks.- Statistical modeling of pulmonary vasculatures with topological priors in CT volumes.- Topological Detection of Alzheimer's Disease using Betti Curves.
Автор: Christine Preisach; Hans Burkhardt; Lars Schmidt-T Название: Data Analysis, Machine Learning and Applications ISBN: 3540782397 ISBN-13(EAN): 9783540782391 Издательство: Springer Рейтинг: Цена: 27944.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Contains the selected papers in the field of data analysis, machine learning and applications presented during the 31st Annual Conference of the German Classification Society (Gesellschaft fur Klassifikation - GfKl), which was held at the Albert-Ludwigs-University in Freiburg, Germany, in March 2007.
Описание: Presents research that combines theory and practice on adopting the latest deep learning advancements for machines capable of visual processing. The book highlights a wide range of topics such as video segmentation, object recognition, and 3D modelling.
Описание: This book presents the latest advances in machine intelligence and big data analytics to improve early warning of cyber-attacks, for cybersecurity intrusion detection and monitoring, and malware analysis.
Описание: 1. Introductiona. What is Data science?b. Data science and Statisticsc. Data scientist 2. Introduction to Mathematicaa. Why Mathematica?b. Wolfram Languagec. Structure of Mathematicad. Notebooks e. How Mathematica worksf. Input Form 3. Data Manipulation a. Listsb. Lists of objectsc. Manipulating listsd. Operations with listse. Indexed Tablesf. Working with data framesg. Datasets 4. Data Analysisa. Data Import and exportb. Wolfram data repositoryc. Statistical Analysisd. Visualizing datae. Making reports 5. Machine learning with Wolfram Languagea. Linear Regressionb. Multiple Regressionc. Logistic Regressiond. Decision Tresse. Data Clustering 6. Neural networks with Wolfram Languagea. Network Data and structureb. Network Layersc. Perceptron Modeld. Multi-layer Neural Networke. Using preconstructed nets from Wolfram Neural net repositoryf. LeNet Neural net for text recognition
Описание: This book embodies principles and applications of advanced soft computing approaches in engineering, healthcare and allied domains directed toward the researchers aspiring to learn and apply intelligent data analytics techniques.
Описание: This book is the inaugural volume in the new Springer series on Learning and Analytics in Intelligent Systems. The series aims at providing, in hard-copy and soft-copy form, books on all aspects of learning, analytics, advanced intelligent systems and related technologies. These disciplines are strongly related and mutually complementary; accordingly, the new series encourages an integrated approach to themes and topics in these disciplines, which will result in significant cross-fertilization, research advances and new knowledge creation. To maximize the dissemination of research findings, the series will publish edited books, monographs, handbooks, textbooks and conference proceedings. This book is intended for professors, researchers, scientists, engineers and students. An extensive list of references at the end of each chapter allows readers to probe further into those application areas that interest them most.
Описание: This book is the inaugural volume in the new Springer series on Learning and Analytics in Intelligent Systems. accordingly, the new series encourages an integrated approach to themes and topics in these disciplines, which will result in significant cross-fertilization, research advances and new knowledge creation.
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