Big Data Machine And Deep Learning

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Big Data Machine And Deep Learning

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Author : Rajesh Kumar Mishra
language : en
Publisher: GRIN Verlag
Release Date : 2025-04-11

Big Data Machine And Deep Learning written by Rajesh Kumar Mishra and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-11 with Computers categories.

Scientific Study from the year 2025 in the subject Computer Sciences - Artificial Intelligence, , language: English, abstract: In recent times, developments in artificial intelligence (AI) and machine learning (ML) have propelled improvements in systems and control engineering. We exist in a time of extensive data, where AI and ML can evaluate large volumes of information instantly to enhance efficiency and precision in decisions based on data. In control engineering, for instance, AI algorithms can anticipate system behaviors and autonomously modify controls to enhance performance for better efficiency and dependability. ML models, with their ability to learn, consistently enhance their predictions and choices as they handle additional data, enabling systems to dynamically adjust to evolving environments and operational circumstances. This swift adjustment enhances the functions of current systems and enables the creation of groundbreaking solutions, like self-driving cars and intelligent power grids, which were previously deemed unfeasible. The rapid expansion of digital data has propelled significant advancements in Big Data analytics, Machine Learning, and Deep Learning. These technologies are increasingly integrated across industries, facilitating automated decision-making, predictive modeling, and advanced pattern recognition. This chapter provides an in-depth review of recent progress in these domains, emphasizing breakthroughs in scalable data processing frameworks, cloud and edge computing, AutoML, explainable AI, transformer architectures, self-supervised learning, and generative models. Furthermore, it explores key applications in healthcare, finance, and autonomous systems, along with challenges such as data privacy, ethical concerns, and computational constraints. The discussion concludes with future directions, highlighting the potential of federated learning, neuromorphic computing, and novel algorithmic improvements to further expand AI's impact across disciplines.

Recent Advances In Big Data Machine And Deep Learning For Precision Agriculture

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Author : Muhammad Fazal Ijaz
language : en
Publisher: Frontiers Media SA
Release Date : 2024-02-19

Recent Advances In Big Data Machine And Deep Learning For Precision Agriculture written by Muhammad Fazal Ijaz and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-19 with Science categories.

The Fundamentals Of Data Science Big Data Deep Learning And Machine Learning What You Need To Know About Data Science And Why It Matters

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Author : Vlad Sozonov
language : en
Publisher: Vinco Publishing
Release Date : 2019-11-21

The Fundamentals Of Data Science Big Data Deep Learning And Machine Learning What You Need To Know About Data Science And Why It Matters written by Vlad Sozonov and has been published by Vinco Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-21 with Computers categories.

Data science is no easy term to define. While there are many definitions available that point out its statistical or logical aspects, others focus on its machine learning impacts. Today only, get this Amazon book for just $19.99 for a limited time. Regularly priced at $35.99. The truth is, data science is a process that requires an understanding of multiple fields, methods, techniques, and more. Data science cannot be easily labeled because, when applied, it looks different to each person, business, or organization utilizing it. While the term may not be easy to define, what it is used for, can be used for, and approaches to it can be more easily understood. And that is precisely what this book aims to do. Scroll Up & Click to Buy Now! Here Is A Preview Of What You'll Discover...In this step-by-step book: This book will not only thoroughly go over all the skills, people, and steps involved in data science, it will also look closely at: ● What big data is and how data science came from it. ● How data has evolved, resulting in new methods for understanding it. ● How data science influenced artificial intelligence. ● How data science is used in machine learning and deep learning. ● How data science revolutionizes the way we train machines and set up neural networks. Data science, big data, machine learning, and deep learning tend to intimidate people. Many believe it is too complicated or technology-centered for them to break into these fields. This book is designed to simplify these complex areas in a way that anyone can understand the fundamentals. Whether you are just hearing about data science, are a student studying it in college, or looking to expand your career, this book has something to offer every type of data enthusiast. Order your copy today! Take action right away by purchase this book "The Fundamentals of Data Science: Big Data, Deep Learning, and Machine Learning: What you need to know about data science and why it matters.", for a limited time discount of only $19.99! Hurry Up!! Tags: ● data science quick ● data science strategy ● data science trading ● data science journal ● insight data science ● data science salary ● data science jobs ● data science espanol ● data science case study ● data science beginner guide

Recent Advances In Big Data Machine And Deep Learning For Precision Agriculture Volume Ii

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Author : Muhammad Fazal Ijaz
language : en
Publisher: Frontiers Media SA
Release Date : 2025-12-17

Recent Advances In Big Data Machine And Deep Learning For Precision Agriculture Volume Ii written by Muhammad Fazal Ijaz and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-17 with Science categories.

This Research Topic is part of the series: Recent Advances in Big Data, Machine, and Deep Learning for Precision Agriculture. Precision agriculture can be defined as applying real-time, reliable information to optimize the use of resources and the management of farming practices, minimizing environmental impacts. With the evolution of remote sensing technologies, big data that must be converted to information is being generated in the agricultural sector. When analysed with machine and deep learning approaches applied to remote sensing products, this data has been used successfully. The computational power using cloud-based systems and recent advances in farm machinery equipment providing data collection, processing, and analysis opens several opportunities to develop and adopt new technologies. Large-scale farm precision experimentation conducted in partnership with commercial farms and the appearance of new sensors on board UAVs, crop duster aeroplanes, and satellites such as radar technologies that allow daily remote data collection under cloudy skies are exciting and require more investigation. In addition, new equipment and sensors are enabling improved crop monitoring and land use mapping on a regional scale. Recent advances in imaging and information technology have led to the massive production of digital images of plant specimens and living plants worldwide. Computer vision and machine learning approaches are up-and-coming technologies to investigate and interpret digitized images of wild and domesticated taxa. Deep learning technologies have been recently shown to achieve impressive performance on a variety of predictive tasks such as automated species identification, trait detection, organ counting, measurement, and recognition. This Research Topic aims to explore how big data, machine, and deep learning algorithms are being applied to precision agriculture and plant health. This topic will investigate how these tools could be used and improved in the future to aid food security, mainly involving the integration of state-of-the-art technologies. We hope to increase the recognition and accessibility of AI/ML tools in agricultural and plant research. This Research Topic will bring together researchers from diverse fields and specializations, such as plant bioinformatics, computer engineering, computer science, agricultural engineering, environmental engineering, food engineering, information technology, and mathematics. This Research Topic welcomes diverse articles including original research, reviews, and perspective papers. Potential topics include, but are not limited to: • Big data, machine, and deep learning for plant and fruit disease classification • Features optimization for plant disease classification • Classification of plant types using big data, machine, and deep learning • Recognition of plant and fruit diseases using big data, machine, and deep learning • On-farm precision experimentation • Monitoring and surveillance using hyperspectral images • Monitoring crop areas • Convolutional Neural Network-based fruit and crop disease detection • Fusion of fully connected layers for classification of plant disease • Selection of optimal features for plant disease

Machine Learning And Big Data Analytics Paradigms Analysis Applications And Challenges

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Author : Aboul Ella Hassanien
language : en
Publisher: Springer Nature
Release Date : 2020-12-14

Machine Learning And Big Data Analytics Paradigms Analysis Applications And Challenges written by Aboul Ella Hassanien and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-14 with Computers categories.

This book is intended to present the state of the art in research on machine learning and big data analytics. 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. It is a valuable resource for researchers in the area of big data analytics and its 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 Computers 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.

Deep Learning Convergence To Big Data Analytics

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Author : Murad Khan
language : en
Publisher: Springer
Release Date : 2018-12-30

Deep Learning Convergence To Big Data Analytics written by Murad Khan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-30 with Computers categories.

This book presents deep learning techniques, concepts, and algorithms to classify and analyze big data. Further, it offers an introductory level understanding of the new programming languages and tools used to analyze big data in real-time, such as Hadoop, SPARK, and GRAPHX. Big data analytics using traditional techniques face various challenges, such as fast, accurate and efficient processing of big data in real-time. In addition, the Internet of Things is progressively increasing in various fields, like smart cities, smart homes, and e-health. As the enormous number of connected devices generate huge amounts of data every day, we need sophisticated algorithms to deal, organize, and classify this data in less processing time and space. Similarly, existing techniques and algorithms for deep learning in big data field have several advantages thanks to the two main branches of the deep learning, i.e. convolution and deep belief networks. This book offers insights into these techniques and applications based on these two types of deep learning. Further, it helps students, researchers, and newcomers understand big data analytics based on deep learning approaches. It also discusses various machine learning techniques in concatenation with the deep learning paradigm to support high-end data processing, data classifications, and real-time data processing issues. The classification and presentation are kept quite simple to help the readers and students grasp the basics concepts of various deep learning paradigms and frameworks. It mainly focuses on theory rather than the mathematical background of the deep learning concepts. The book consists of 5 chapters, beginning with an introductory explanation of big data and deep learning techniques, followed by integration of big data and deep learning techniques and lastly the future directions.

Deep Learning Innovations And Their Convergence With Big Data

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Author : Karthik, S.
language : en
Publisher: IGI Global
Release Date : 2017-07-13

Deep Learning Innovations And Their Convergence With Big Data written by Karthik, S. and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-13 with Computers categories.

The expansion of digital data has transformed various sectors of business such as healthcare, industrial manufacturing, and transportation. A new way of solving business problems has emerged through the use of machine learning techniques in conjunction with big data analytics. Deep Learning Innovations and Their Convergence With Big Data is a pivotal reference for the latest scholarly research on upcoming trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. Featuring extensive coverage on a broad range of topics and perspectives such as deep neural network, domain adaptation modeling, and threat detection, this book is ideally designed for researchers, professionals, and students seeking current research on the latest trends in the field of deep learning techniques 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.

Deep Learning Techniques And Optimization Strategies In Big Data Analytics

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Author : Thomas, J. Joshua
language : en
Publisher: IGI Global
Release Date : 2019-11-29

Deep Learning Techniques And Optimization Strategies In Big Data Analytics written by Thomas, J. Joshua and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-29 with Computers categories.

Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there’s a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.