Advanced Deep Learning Applications In Big Data Analytics
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Advanced Deep Learning Applications In Big Data Analytics
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Author : Bouarara, Hadj Ahmed
language : en
Publisher: IGI Global
Release Date : 2020-10-16
Advanced Deep Learning Applications In Big Data Analytics written by Bouarara, Hadj Ahmed and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-16 with Computers categories.
Interest in big data has swelled within the scholarly community as has increased attention to the internet of things (IoT). Algorithms are constructed in order to parse and analyze all this data to facilitate the exchange of information. However, big data has suffered from problems in connectivity, scalability, and privacy since its birth. The application of deep learning algorithms has helped process those challenges and remains a major issue in today’s digital world. Advanced Deep Learning Applications in Big Data Analytics is a pivotal reference source that aims to develop new architecture and applications of deep learning algorithms in big data and the IoT. Highlighting a wide range of topics such as artificial intelligence, cloud computing, and neural networks, this book is ideally designed for engineers, data analysts, data scientists, IT specialists, programmers, marketers, entrepreneurs, researchers, academicians, and students.
Advanced Machine Learning Applications In Big Data Analytics
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Author : Taiyong Li
language : en
Publisher:
Release Date : 2023
Advanced Machine Learning Applications In Big Data Analytics written by Taiyong Li and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
With the development of computer technology and communication technology, various industries have collected a large amount of data in different forms, so-called big data. How to obtain valuable knowledge from these data is a very challenging task. Machine learning is such a direct and effective method for big data analytics. In recent years, a variety of advanced machine learning technologies have emerged, and they continue to play important roles in the era of big data. Considering advanced machine learning and big data together, we have selected a series of relevant works in this Special Issue to showcase the latest research advancements in this field. Specifically, a total of thirty-three articles are included in this Special Issue, which can be roughly categorized into six groups: time series analysis, evolutionary computation, pattern recognition, computer vision, image encryption, and others.
Data Analytics And Machine Learning
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Author : Pushpa Singh
language : en
Publisher: Springer Nature
Release Date :
Data Analytics And Machine Learning written by Pushpa Singh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Advanced Analytics And Deep Learning Models
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Author : Archana Mire
language : en
Publisher: John Wiley & Sons
Release Date : 2022-06-01
Advanced Analytics And Deep Learning Models written by Archana Mire 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 2022-06-01 with Computers categories.
Advanced Analytics and Deep Learning Models The book provides readers with an in-depth understanding of concepts and technologies related to the importance of analytics and deep learning in many useful real-world applications such as e-healthcare, transportation, agriculture, stock market, etc. Advanced analytics is a mixture of machine learning, artificial intelligence, graphs, text mining, data mining, semantic analysis. It is an approach to data analysis. Beyond the traditional business intelligence, it is a semi and autonomous analysis of data by using different techniques and tools. However, deep learning and data analysis both are high centers of data science. Almost all the private and public organizations collect heavy amounts of data, i.e., domain-specific data. Many small/large companies are exploring large amounts of data for existing and future technology. Deep learning is also exploring large amounts of unsupervised data making it beneficial and effective for big data. Deep learning can be used to deal with all kinds of problems and challenges that include collecting unlabeled and uncategorized raw data, extracting complex patterns from a large amount of data, retrieving fast information, tagging data, etc. This book contains 16 chapters on artificial intelligence, machine learning, deep learning, and their uses in many useful sectors like stock market prediction, a recommendation system for better service selection, e-healthcare, telemedicine, transportation. There are also chapters on innovations and future opportunities with fog computing/cloud computing and artificial intelligence. Audience Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in healthcare, telemedicine, transportation, and the financial sector. The book will also be a great source for software engineers and advanced students who are beginners in the field of advanced analytics in deep learning.
Big Data Analysis And Deep Learning Applications
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Author : Thi Thi Zin
language : en
Publisher: Springer
Release Date : 2018-06-06
Big Data Analysis And Deep Learning Applications written by Thi Thi Zin and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-06 with Technology & Engineering categories.
This book presents a compilation of selected papers from the first International Conference on Big Data Analysis and Deep Learning Applications (ICBDL 2018), and focuses on novel techniques in the fields of big data analysis, machine learning, system monitoring, image processing, conventional neural networks, communication, industrial information, and their applications. Readers will find insights to help them realize more efficient algorithms and systems used in real-life applications and contexts, making the book an essential reference guide for academic researchers, professionals, software engineers in the industry, and regulators of aviation authorities.
Advanced Deep Learning With R
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Author : Bharatendra Rai
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-12-17
Advanced Deep Learning With R written by Bharatendra Rai and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-17 with Computers categories.
Discover best practices for choosing, building, training, and improving deep learning models using Keras-R, and TensorFlow-R libraries Key FeaturesImplement deep learning algorithms to build AI models with the help of tips and tricksUnderstand how deep learning models operate using expert techniquesApply reinforcement learning, computer vision, GANs, and NLP using a range of datasetsBook Description Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. Advanced Deep Learning with R will help you understand popular deep learning architectures and their variants in R, along with providing real-life examples for them. This deep learning book starts by covering the essential deep learning techniques and concepts for prediction and classification. You will learn about neural networks, deep learning architectures, and the fundamentals for implementing deep learning with R. The book will also take you through using important deep learning libraries such as Keras-R and TensorFlow-R to implement deep learning algorithms within applications. You will get up to speed with artificial neural networks, recurrent neural networks, convolutional neural networks, long short-term memory networks, and more using advanced examples. Later, you'll discover how to apply generative adversarial networks (GANs) to generate new images; autoencoder neural networks for image dimension reduction, image de-noising and image correction and transfer learning to prepare, define, train, and model a deep neural network. By the end of this book, you will be ready to implement your knowledge and newly acquired skills for applying deep learning algorithms in R through real-world examples. What you will learnLearn how to create binary and multi-class deep neural network modelsImplement GANs for generating new imagesCreate autoencoder neural networks for image dimension reduction, image de-noising and image correctionImplement deep neural networks for performing efficient text classificationLearn to define a recurrent convolutional network model for classification in KerasExplore best practices and tips for performance optimization of various deep learning modelsWho this book is for This book is for data scientists, machine learning practitioners, deep learning researchers and AI enthusiasts who want to develop their skills and knowledge to implement deep learning techniques and algorithms using the power of R. A solid understanding of machine learning and working knowledge of the R programming language are required.
Artificial Intelligence Trends For Data Analytics Using Machine Learning And Deep Learning Approaches
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Author : K. Gayathri Devi
language : en
Publisher: CRC Press
Release Date : 2020-10-07
Artificial Intelligence Trends For Data Analytics Using Machine Learning And Deep Learning Approaches written by K. Gayathri Devi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-07 with Computers categories.
Artificial Intelligence (AI), when incorporated with machine learning and deep learning algorithms, has a wide variety of applications today. This book focuses on the implementation of various elementary and advanced approaches in AI that can be used in various domains to solve real-time decision-making problems. The book focuses on concepts and techniques used to run tasks in an automated manner. It discusses computational intelligence in the detection and diagnosis of clinical and biomedical images, covers the automation of a system through machine learning and deep learning approaches, presents data analytics and mining for decision-support applications, and includes case-based reasoning, natural language processing, computer vision, and AI approaches in real-time applications. Academic scientists, researchers, and students in the various domains of computer science engineering, electronics and communication engineering, and information technology, as well as industrial engineers, biomedical engineers, and management, will find this book useful. By the end of this book, you will understand the fundamentals of AI. Various case studies will develop your adaptive thinking to solve real-time AI problems. Features Includes AI-based decision-making approaches Discusses computational intelligence in the detection and diagnosis of clinical and biomedical images Covers automation of systems through machine learning and deep learning approaches and its implications to the real world Presents data analytics and mining for decision-support applications Offers case-based reasoning
Deep Learning In Data Analytics
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Author : Debi Prasanna Acharjya
language : en
Publisher: Springer
Release Date : 2021-09-21
Deep Learning In Data Analytics written by Debi Prasanna Acharjya and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-21 with Technology & Engineering categories.
This book comprises theoretical foundations to deep learning, machine learning and computing system, deep learning algorithms, and various deep learning applications. The book discusses significant issues relating to deep learning in data analytics. Further in-depth reading can be done from the detailed bibliography presented at the end of each chapter. Besides, this book's material includes concepts, algorithms, figures, graphs, and tables in guiding researchers through deep learning in data science and its applications for society. Deep learning approaches prevent loss of information and hence enhance the performance of data analysis and learning techniques. It brings up many research issues in the industry and research community to capture and access data effectively. The book provides the conceptual basis of deep learning required to achieve in-depth knowledge in computer and data science. It has been done to make the book more flexible and to stimulate further interest in topics. All these help researchers motivate towards learning and implementing the concepts in real-life applications.
Applications Of Machine Learning In Big Data Analytics And Cloud Computing
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Author : Subhendu Kumar Pani
language : en
Publisher: CRC Press
Release Date : 2022-09-01
Applications Of Machine Learning In Big Data Analytics And Cloud Computing written by Subhendu Kumar Pani and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-01 with Technology & Engineering categories.
Cloud Computing and Big Data technologies have become the new descriptors of the digital age. The global amount of digital data has increased more than nine times in volume in just five years and by 2030 its volume may reach a staggering 65 trillion gigabytes. This explosion of data has led to opportunities and transformation in various areas such as healthcare, enterprises, industrial manufacturing and transportation. New Cloud Computing and Big Data tools endow researchers and analysts with novel techniques and opportunities to collect, manage and analyze the vast quantities of data. In Cloud and Big Data Analytics, the two areas of Swarm Intelligence and Deep Learning are a developing type of Machine Learning techniques that show enormous potential for solving complex business problems. Deep Learning enables computers to analyze large quantities of unstructured and binary data and to deduce relationships without requiring specific models or programming instructions. This book introduces the state-of-the-art trends and advances in the use of Machine Learning in Cloud and Big Data Analytics. The book will serve as a reference for Data Scientists, systems architects, developers, new researchers and graduate level students in Computer and Data science. The book will describe the concepts necessary to understand current Machine Learning issues, challenges and possible solutions as well as upcoming trends in Big Data Analytics.
Big Data Analytics Methods
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Author : Peter Ghavami
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2019-12-16
Big Data Analytics Methods written by Peter Ghavami and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-16 with Business & Economics categories.
Big Data Analytics Methods unveils secrets to advanced analytics techniques ranging from machine learning, random forest classifiers, predictive modeling, cluster analysis, natural language processing (NLP), Kalman filtering and ensembles of models for optimal accuracy of analysis and prediction. More than 100 analytics techniques and methods provide big data professionals, business intelligence professionals and citizen data scientists insight on how to overcome challenges and avoid common pitfalls and traps in data analytics. The book offers solutions and tips on handling missing data, noisy and dirty data, error reduction and boosting signal to reduce noise. It discusses data visualization, prediction, optimization, artificial intelligence, regression analysis, the Cox hazard model and many analytics using case examples with applications in the healthcare, transportation, retail, telecommunication, consulting, manufacturing, energy and financial services industries. This book's state of the art treatment of advanced data analytics methods and important best practices will help readers succeed in data analytics.