Machine Learning Applications
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Machine Learning Applications
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Author : Rik Das
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2020-04-20
Machine Learning Applications written by Rik Das 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 2020-04-20 with Computers categories.
The publication is attempted to address emerging trends in machine learning applications. Recent trends in information identification have identified huge scope in applying machine learning techniques for gaining meaningful insights. Random growth of unstructured data poses new research challenges to handle this huge source of information. Efficient designing of machine learning techniques is the need of the hour. Recent literature in machine learning has emphasized on single technique of information identification. Huge scope exists in developing hybrid machine learning models with reduced computational complexity for enhanced accuracy of information identification. This book will focus on techniques to reduce feature dimension for designing light weight techniques for real time identification and decision fusion. Key Findings of the book will be the use of machine learning in daily lives and the applications of it to improve livelihood. However, it will not be able to cover the entire domain in machine learning in its limited scope. This book is going to benefit the research scholars, entrepreneurs and interdisciplinary approaches to find new ways of applications in machine learning and thus will have novel research contributions. The lightweight techniques can be well used in real time which will add value to practice.
Machine Learning Applications
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Author : Indranath Chatterjee
language : en
Publisher: John Wiley & Sons
Release Date : 2023-12-08
Machine Learning Applications written by Indranath Chatterjee 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 2023-12-08 with Computers categories.
Machine Learning Applications Practical resource on the importance of Machine Learning and Deep Learning applications in various technologies and real-world situations Machine Learning Applications discusses methodological advancements of machine learning and deep learning, presents applications in image processing, including face and vehicle detection, image classification, object detection, image segmentation, and delivers real-world applications in healthcare to identify diseases and diagnosis, such as creating smart health records and medical imaging diagnosis, and provides real-world examples, case studies, use cases, and techniques to enable the reader’s active learning. Composed of 13 chapters, this book also introduces real-world applications of machine and deep learning in blockchain technology, cyber security, and climate change. An explanation of AI and robotic applications in mechanical design is also discussed, including robot-assisted surgeries, security, and space exploration. The book describes the importance of each subject area and detail why they are so important to us from a societal and human perspective. Edited by two highly qualified academics and contributed to by established thought leaders in their respective fields, Machine Learning Applications includes information on: Content based medical image retrieval (CBMIR), covering face and vehicle detection, multi-resolution and multisource analysis, manifold and image processing, and morphological processing Smart medicine, including machine learning and artificial intelligence in medicine, risk identification, tailored interventions, and association rules AI and robotics application for transportation and infrastructure (e.g., autonomous cars and smart cities), along with global warming and climate change Identifying diseases and diagnosis, drug discovery and manufacturing, medical imaging diagnosis, personalized medicine, and smart health records With its practical approach to the subject, Machine Learning Applications is an ideal resource for professionals working with smart technologies such as machine and deep learning, AI, IoT, and other wireless communications; it is also highly suitable for professionals working in robotics, computer vision, cyber security and more.
Machine Learning Applications In Software Engineering
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Author : Du Zhang
language : en
Publisher: World Scientific
Release Date : 2005-02-21
Machine Learning Applications In Software Engineering written by Du Zhang and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-02-21 with Computers categories.
Machine learning deals with the issue of how to build computer programs that improve their performance at some tasks through experience. Machine learning algorithms have proven to be of great practical value in a variety of application domains. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development and maintenance tasks could be formulated as learning problems and approached in terms of learning algorithms. This book deals with the subject of machine learning applications in software engineering. It provides an overview of machine learning, summarizes the state-of-the-practice in this niche area, gives a classification of the existing work, and offers some application guidelines. Also included in the book is a collection of previously published papers in this research area.
Handbook Of Machine Learning Applications For Genomics
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Author : Sanjiban Sekhar Roy
language : en
Publisher: Springer Nature
Release Date : 2022-06-23
Handbook Of Machine Learning Applications For Genomics written by Sanjiban Sekhar Roy and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-23 with Technology & Engineering categories.
Currently, machine learning is playing a pivotal role in the progress of genomics. The applications of machine learning are helping all to understand the emerging trends and the future scope of genomics. This book provides comprehensive coverage of machine learning applications such as DNN, CNN, and RNN, for predicting the sequence of DNA and RNA binding proteins, expression of the gene, and splicing control. In addition, the book addresses the effect of multiomics data analysis of cancers using tensor decomposition, machine learning techniques for protein engineering, CNN applications on genomics, challenges of long noncoding RNAs in human disease diagnosis, and how machine learning can be used as a tool to shape the future of medicine. More importantly, it gives a comparative analysis and validates the outcomes of machine learning methods on genomic data to the functional laboratory tests or by formal clinical assessment. The topics of this book will cater interest to academicians, practitioners working in the field of functional genomics, and machine learning. Also, this book shall guide comprehensively the graduate, postgraduates, and Ph.D. scholars working in these fields.
Data Science And Machine Learning Applications In Subsurface Engineering
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Author : Daniel Asante Otchere
language : en
Publisher: CRC Press
Release Date : 2024-02-06
Data Science And Machine Learning Applications In Subsurface Engineering written by Daniel Asante Otchere and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-06 with Science categories.
This book covers unsupervised learning, supervised learning, clustering approaches, feature engineering, explainable AI and multioutput regression models for subsurface engineering problems. Processing voluminous and complex data sets are the primary focus of the field of machine learning (ML). ML aims to develop data-driven methods and computational algorithms that can learn to identify complex and non-linear patterns to understand and predict the relationships between variables by analysing extensive data. Although ML models provide the final output for predictions, several steps need to be performed to achieve accurate predictions. These steps, data pre-processing, feature selection, feature engineering and outlier removal, are all contained in this book. New models are also developed using existing ML architecture and learning theories to improve the performance of traditional ML models and handle small and big data without manual adjustments. This research-oriented book will help subsurface engineers, geophysicists, and geoscientists become familiar with data science and ML advances relevant to subsurface engineering. Additionally, it demonstrates the use of data-driven approaches for salt identification, seismic interpretation, estimating enhanced oil recovery factor, predicting pore fluid types, petrophysical property prediction, estimating pressure drop in pipelines, bubble point pressure prediction, enhancing drilling mud loss, smart well completion and synthetic well log predictions.
Machine Learning Applications In Finance
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Author : Dr. Hemant N. Patel
language : en
Publisher: Xoffencerpublication
Release Date : 2023-07-17
Machine Learning Applications In Finance written by Dr. Hemant N. Patel and has been published by Xoffencerpublication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-17 with Computers categories.
In order to tackle the computer challenge, we will need an algorithm. A collection of instructions that must be carried out in order to transform an input into an outcome is referred to as an algorithm. One illustration of this would be the development of an algorithm to produce a classification. Your ordered list is the result, and the input is a series of numerical values to be arranged. You might be interested in discovering the most effective algorithm, which either needs fewer instructions or less memory or both, and you might discover that there are numerous algorithms for the same work. On the other hand, we do not have an algorithm for certain tasks, such as determining what constitutes spam and what constitutes legitimate e-mail. We are aware of the nature of the entry, which is a simple typeface file contained within an email document. We are aware of the expected outcome, which is a yes/no answer signifying whether or not the communication should be considered spam. We are not familiar with the process of converting information to output. The definition of what constitutes spam shifts over time and differs from one individual to the next. Using statistics, we are able to compensate for our dearth of understanding. We are able to quickly collect thousands of example messages, some of which we are aware are spam and would like to "learn" more about how they are constructed. Therefore, we would like the computer (machine) to automatically determine the procedure that should be used for this work. There is no need for you to learn how to arrange numbers because we already have algorithms for that; however, there are many applications with example data that do not require an algorithm. Because of developments in computer technology, we are now able to store and analyze large quantities of data, as well as retrieve this data from geographically dispersed locations through the use of a computer network. Most data acquisition instruments today are computerized and capture accurate data.
Machine Learning Applications In Civil Engineering
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Author : Kundan Meshram
language : en
Publisher: Elsevier
Release Date : 2023-09-29
Machine Learning Applications In Civil Engineering written by Kundan Meshram and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-29 with Technology & Engineering categories.
Machine Learning Applications in Civil Engineering discusses machine learning and deep learning models for different civil engineering applications. These models work for stochastic methods wherein internal processing is done using randomized prototypes. The book explains various machine learning model designs that will assist researchers to design multi domain systems with maximum efficiency. It introduces Machine Learning and its applications to different Civil Engineering tasks, including Basic Machine Learning Models for data pre-processing, models for data representation, classification models for Civil Engineering Applications, Bioinspired Computing models for Civil Engineering, and their case studies. Using this book, civil engineering students and researchers can deep dive into Machine Learning, and identify various solutions to practical Civil Engineering tasks. - Introduces various ML models for Civil Engineering Applications that will assist readers in their analysis of design and development interfaces for building these applications - Reviews different lacunas and challenges in current models used for Civil Engineering scenarios - Explores designs for customized components for optimum system deployment - Explains various machine learning model designs that will assist researchers to design multi domain systems with maximum efficiency
Machine Learning Applications Using Python
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Author : Puneet Mathur
language : en
Publisher: Apress
Release Date : 2018-12-12
Machine Learning Applications Using Python written by Puneet Mathur and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-12 with Computers categories.
Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you’ll see examples that demonstrate how to use machine learning as a tool for business enhancement. As a domain expert, you will not only discover how machine learning is used in finance, healthcare, and retail, but also work through practical case studies where machine learning has been implemented. Machine Learning Applications Using Python is divided into three sections, one for each of the domains (healthcare, finance, and retail). Each section starts with an overview of machine learning and key technological advancements in that domain. You’ll then learn more by using case studies on how organizations are changing the game in their chosen markets. This book has practical case studies with Python code and domain-specific innovative ideas for monetizing machine learning. What You Will Learn Discover applied machine learning processes and principles Implement machine learning in areas of healthcare, finance, and retail Avoid the pitfalls of implementing applied machine learning Build Python machine learning examples in the three subject areas Who This Book Is For Data scientists and machine learning professionals.
Handbook Of Research On Machine Learning Applications And Trends Algorithms Methods And Techniques
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Author : Olivas, Emilio Soria
language : en
Publisher: IGI Global
Release Date : 2009-08-31
Handbook Of Research On Machine Learning Applications And Trends Algorithms Methods And Techniques written by Olivas, Emilio Soria and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-08-31 with Computers categories.
"This book investiges machine learning (ML), one of the most fruitful fields of current research, both in the proposal of new techniques and theoretic algorithms and in their application to real-life problems"--Provided by publisher.
Innovative Machine Learning Applications In The Aerospace Industry
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Author : Ponnada, Venkata Tulasiramu
language : en
Publisher: IGI Global
Release Date : 2025-06-17
Innovative Machine Learning Applications In The Aerospace Industry written by Ponnada, Venkata Tulasiramu and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-17 with Computers categories.
The aerospace industry evolves with the integration of machine learning (ML) applications. From optimizing flight operations and predictive maintenance to advancing autonomous navigation and air traffic management, ML enables efficiency, safety, and performance. As aerospace systems grow more complex, ML offers the ability to analyze data in real-time, uncover hidden patterns, and support intelligent decision-making. This emerging collaboration between aerospace engineering and AI reshapes traditional practices while opening new frontiers in exploration and innovation. Innovative Machine Learning Applications in the Aerospace Industry explores the potential of machine learning applications, examining its impact on various sectors. It investigates the diverse realms of machine learning applications and their profound implications for the future. This book covers topics such as drone navigation, aerial images, and computer vision, and is a useful resource for business owners, engineers, academicians, researchers, and computer scientists.