Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges, Hassanien Aboul Ella, Darwish Ashraf
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: 196810.00 T 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.
Автор: Tsihrintzis George A., Virvou Maria, Sakkopoulos Evangelos Название: Machine Learning Paradigms: Applications of Learning and Analytics in Intelligent Systems ISBN: 3030156273 ISBN-13(EAN): 9783030156275 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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.
Автор: Anandakumar Haldorai, Arulmurugan Ramu Название: Cognitive Social Mining Applications in Data Analytics and Forensics ISBN: 1522575227 ISBN-13(EAN): 9781522575221 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 189420.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Recently, there has been a rapid increase in interest regarding social network analysis in the data mining community. Cognitive radios are expected to play a major role in meeting this exploding traffic demand on social networks due to their ability to sense the environment, analyze outdoor parameters, and then make decisions for dynamic time, frequency, space, resource allocation, and management to improve the utilization of mining the social data.Cognitive Social Mining Applications in Data Analytics and Forensics is an essential reference source that reviews cognitive radio concepts and examines their applications to social mining using a machine learning approach so that an adaptive and intelligent mining is achieved. Featuring research on topics such as data mining, real-time ubiquitous social mining services, and cognitive computing, this book is ideally designed for social network analysts, researchers, academicians, and industry professionals.
Автор: Hassanien Aboul Ella, Darwish Ashraf Название: Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges ISBN: 3030593371 ISBN-13(EAN): 9783030593377 Издательство: Springer Цена: 186330.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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: Tsihrintzis George A., Virvou Maria, Sakkopoulos Evangelos Название: Machine Learning Paradigms: Applications of Learning and Analytics in Intelligent Systems ISBN: 3030156303 ISBN-13(EAN): 9783030156305 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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.
Автор: Maleh Yassine, Shojafar Mohammad, Alazab Mamoun Название: Machine Intelligence and Big Data Analytics for Cybersecurity Applications ISBN: 3030570231 ISBN-13(EAN): 9783030570231 Издательство: Springer Цена: 186330.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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.
Автор: Maleh Yassine, Shojafar Mohammad, Alazab Mamoun Название: Machine Intelligence and Big Data Analytics for Cybersecurity Applications ISBN: 3030570266 ISBN-13(EAN): 9783030570262 Издательство: Springer Рейтинг: Цена: 186330.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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.
Автор: Ramgopal Kashyap, A.V. Senthil Kumar Название: Challenges and Applications for Implementing Machine Learning in Computer Vision ISBN: 1799801837 ISBN-13(EAN): 9781799801832 Издательство: Mare Nostrum (Eurospan) Цена: 173190.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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.
As data holdings get bigger and questions get harder, data scientists and analysts must focus on the systems, the tools and techniques, and the disciplined process to get the correct answer, quickly Whether you work within industry or government, this book will provide you with a foundation to successfully and confidently process large amounts of quantitative data.
Here are just a dozen of the many questions answered within these pages:
What does quantitative analysis of a system really mean?
What is a system?
What are big data and analystics?
How do you know your numbers are good?
What will the future data science environment look like?
How do you determine data provenance?
How do you gather and process information, and then organize, store, and synthesize it?
How does an organization implement data analytics?
Do you really need to think like a Chief Information Officer?
What is the best way to protect data?
What makes a good dashboard?
What is the relationship between eating ice cream and getting attacked by a shark?
The nine chapters in this book are arranged in three parts that address systems concepts in general, tools and techniques, and future trend topics. Systems concepts include contrasting open and closed systems, performing data mining and big data analysis, and gauging data quality. Tools and techniques include analyzing both continuous and discrete data, applying probability basics, and practicing quantitative analysis such as descriptive and inferential statistics. Future trends include leveraging the Internet of Everything, modeling Artificial Intelligence, and establishing a Data Analytics Support Office (DASO).
Many examples are included that were generated using common software, such as Excel, Minitab, Tableau, SAS, and Crystal Ball. While words are good, examples can sometimes be a better teaching tool. For each example included, data files can be found on the companion website. Many of the data sets are tied to the global economy because they use data from shipping ports, air freight hubs, largest cities, and soccer teams. The appendices contain more detailed analysis including the 10 T's for Data Mining, Million Row Data Audit (MRDA) Processes, Analysis of Rainfall, and Simulation Models for Evaluating Traffic Flow.
Автор: Jena Om Prakash, Bhushan Bharat, Kose Utku Название: Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications ISBN: 1032126876 ISBN-13(EAN): 9781032126876 Издательство: Taylor&Francis Рейтинг: Цена: 132710.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The book incorporates the many facets of computational intelligence, such as machine learning and deep learning, to provide groundbreaking developments in healthcare applications. It discusses theory, analytical methods, numerical simulation, scientific techniques, analytical outcomes, and computational structuring.
Автор: Sujatha R., Aarthy S. L., Vettriselvan R. Название: Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics ISBN: 0367466635 ISBN-13(EAN): 9780367466633 Издательство: Taylor&Francis Рейтинг: Цена: 117390.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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.
Автор: Villalobos Alva Jalil Название: Beginning Mathematica and Wolfram for Data Science: Applications in Data Analysis, Machine Learning, and Neural Networks ISBN: 1484265939 ISBN-13(EAN): 9781484265932 Издательство: Springer Рейтинг: Цена: 30620.00 T Наличие на складе: Поставка под заказ. 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
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