Handbook Of Research On Automated Feature Engineering And Advanced Applications In Data Science

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Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science

Author : Panda, Mrutyunjaya
Publisher : IGI Global
ISBN 13 : 1799866610
Total Pages : 392 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science by : Panda, Mrutyunjaya

Download or read book Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science written by Panda, Mrutyunjaya and published by IGI Global. This book was released on 2021-01-08 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: In today’s digital world, the huge amount of data being generated is unstructured, messy, and chaotic in nature. Dealing with such data, and attempting to unfold the meaningful information, can be a challenging task. Feature engineering is a process to transform such data into a suitable form that better assists with interpretation and visualization. Through this method, the transformed data is more transparent to the machine learning models, which in turn causes better prediction and analysis of results. Data science is crucial for the data scientist to assess the trade-offs of their decisions regarding the effectiveness of the machine learning model implemented. Investigating the demand in this area today and in the future is a necessity. The Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science provides an in-depth analysis on both the theoretical and the latest empirical research findings on how features can be extracted and transformed from raw data. The chapters will introduce feature engineering and the recent concepts, methods, and applications with the use of various data types, as well as examine the latest machine learning applications on the data. While highlighting topics such as detection, tracking, selection techniques, and prediction models using data science, this book is ideally intended for research scholars, big data scientists, project developers, data analysts, and computer scientists along with practitioners, researchers, academicians, and students interested in feature engineering and its impact on data.

Automated Feature Engineering and Advanced Applications in Data Science

Author : Mrutyunjaya Panda
Publisher :
ISBN 13 : 9781799866602
Total Pages : pages
Book Rating : 4.8/5 (666 download)

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Book Synopsis Automated Feature Engineering and Advanced Applications in Data Science by : Mrutyunjaya Panda

Download or read book Automated Feature Engineering and Advanced Applications in Data Science written by Mrutyunjaya Panda and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "This edited book will start with an introduction to feature engineering and then move onto recent concepts, methods and applications with the use of various data types that includes : text, image, streaming data, social network data, financial data, biomedical data, bioinformatics etc. to help readers gain insight into how features can be extracted and transformed from raw data"--

Intelligent Analytics With Advanced Multi-Industry Applications

Author : Sun, Zhaohao
Publisher : IGI Global
ISBN 13 : 1799849643
Total Pages : 392 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Intelligent Analytics With Advanced Multi-Industry Applications by : Sun, Zhaohao

Download or read book Intelligent Analytics With Advanced Multi-Industry Applications written by Sun, Zhaohao and published by IGI Global. This book was released on 2021-01-08 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many fundamental technological and managerial issues surrounding the development and implementation of intelligent analytics within multi-industry applications remain unsolved. There are still questions surrounding the foundation of intelligent analytics, the elements, the big characteristics, and the effects on business, management, technology, and society. Research is devoted to answering these questions and understanding how intelligent analytics can improve healthcare, mobile commerce, web services, cloud services, blockchain, 5G development, digital transformation, and more. Intelligent Analytics With Advanced Multi-Industry Applications is a critical reference source that explores cutting-edge theories, technologies, and methodologies of intelligent analytics with multi-industry applications and emphasizes the integration of artificial intelligence, business intelligence, big data, and analytics from a perspective of computing, service, and management. This book also provides real-world applications of the proposed concept of intelligent analytics to e-SMACS (electronic, social, mobile, analytics, cloud, and service) commerce and services, healthcare, the internet of things, the sharing economy, cloud computing, blockchain, and Industry 4.0. This book is ideal for scientists, engineers, educators, university students, service and management professionals, policymakers, decision makers, practitioners, stakeholders, researchers, and others who have an interest in how intelligent analytics are being implemented and utilized in diverse industries.

Applications of Big Data in Large- and Small-Scale Systems

Author : Goundar, Sam
Publisher : IGI Global
ISBN 13 : 1799866750
Total Pages : 377 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Applications of Big Data in Large- and Small-Scale Systems by : Goundar, Sam

Download or read book Applications of Big Data in Large- and Small-Scale Systems written by Goundar, Sam and published by IGI Global. This book was released on 2021-01-15 with total page 377 pages. Available in PDF, EPUB and Kindle. Book excerpt: With new technologies, such as computer vision, internet of things, mobile computing, e-governance and e-commerce, and wide applications of social media, organizations generate a huge volume of data and at a much faster rate than several years ago. Big data in large-/small-scale systems, characterized by high volume, diversity, and velocity, increasingly drives decision making and is changing the landscape of business intelligence. From governments to private organizations, from communities to individuals, all areas are being affected by this shift. There is a high demand for big data analytics that offer insights for computing efficiency, knowledge discovery, problem solving, and event prediction. To handle this demand and this increase in big data, there needs to be research on innovative and optimized machine learning algorithms in both large- and small-scale systems. Applications of Big Data in Large- and Small-Scale Systems includes state-of-the-art research findings on the latest development, up-to-date issues, and challenges in the field of big data and presents the latest innovative and intelligent applications related to big data. This book encompasses big data in various multidisciplinary fields from the medical field to agriculture, business research, and smart cities. While highlighting topics including machine learning, cloud computing, data visualization, and more, this book is a valuable reference tool for computer scientists, data scientists and analysts, engineers, practitioners, stakeholders, researchers, academicians, and students interested in the versatile and innovative use of big data in both large-scale and small-scale systems.

Data Science Advancements in Pandemic and Outbreak Management

Author : Asimakopoulou, Eleana
Publisher : IGI Global
ISBN 13 : 1799867382
Total Pages : 255 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Data Science Advancements in Pandemic and Outbreak Management by : Asimakopoulou, Eleana

Download or read book Data Science Advancements in Pandemic and Outbreak Management written by Asimakopoulou, Eleana and published by IGI Global. This book was released on 2021-04-09 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pandemics are disruptive. Thus, there is a need to prepare and plan actions in advance for identifying, assessing, and responding to such events to manage uncertainty and support sustainable livelihood and wellbeing. A detailed assessment of a continuously evolving situation needs to take place, and several aspects must be brought together and examined before the declaration of a pandemic even happens. Various health organizations; crisis management bodies; and authorities at local, national, and international levels are involved in the management of pandemics. There is no better time to revisit current approaches to cope with these new and unforeseen threats. As countries must strike a fine balance between protecting health, minimizing economic and social disruption, and respecting human rights, there has been an emerging interest in lessons learned and specifically in revisiting past and current pandemic approaches. Such approaches involve strategies and practices from several disciplines and fields including healthcare, management, IT, mathematical modeling, and data science. Using data science to advance in-situ practices and prompt future directions could help alleviate or even prevent human, financial, and environmental compromise, and loss and social interruption via state-of-the-art technologies and frameworks. Data Science Advancements in Pandemic and Outbreak Management demonstrates how strategies and state-of-the-art IT have and/or could be applied to serve as the vehicle to advance pandemic and outbreak management. The chapters will introduce both technical and non-technical details of management strategies and advanced IT, data science, and mathematical modelling and demonstrate their applications and their potential utilization within the identification and management of pandemics and outbreaks. It also prompts revisiting and critically reviewing past and current approaches, identifying good and bad practices, and further developing the area for future adaptation. This book is ideal for data scientists, data analysts, infectious disease experts, researchers studying pandemics and outbreaks, IT, crisis and disaster management, academics, practitioners, government officials, and students interested in applicable theories and practices in data science to mitigate, prepare for, respond to, and recover from future pandemics and outbreaks.

Industry Use Cases on Blockchain Technology Applications in IoT and the Financial Sector

Author : Mahmood, Zaigham
Publisher : IGI Global
ISBN 13 : 1799866521
Total Pages : 400 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Industry Use Cases on Blockchain Technology Applications in IoT and the Financial Sector by : Mahmood, Zaigham

Download or read book Industry Use Cases on Blockchain Technology Applications in IoT and the Financial Sector written by Mahmood, Zaigham and published by IGI Global. This book was released on 2021-03-18 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Blockchain technology presents numerous advantages that include increased transparency, reduced transaction costs, faster transaction settlement, automation of information, increased traceability, improved customer experience, improved digital identity, better cyber security, and user-controlled networks. These potential applications are widespread and diverse including funds transfer, smart contracts, e-voting, efficient supply chain, and more in nearly every sector of society including finance, healthcare, law, trade, real estate, and other important areas. However, there are challenges and limitations that exist such as high energy consumption, limited scalability, complexity, security, network size, lack of regulations, and other critical issues. Nevertheless, blockchain is an attractive technology and has much to offer to the modern-day industry. Industry Use Cases on Blockchain Technology Applications in IoT and the Financial Sector investigates blockchain technology’s adoption and effectiveness in multiple industries and for the internet of things (IoT)-based applications, presents use cases from industrial and financial sectors as well as from other transaction-based services, and fills a gap in this respect by extending the existing body of knowledge in the suggested field. While highlighting topics such as cybersecurity, use cases, and models for blockchain implementation, this book is ideal for business managers, financial accountants, practitioners, researchers, academicians, and students interested in blockchain technology’s role and implementation in IoT and the financial sector.

Advanced Machine Intelligence and Signal Processing

Author : Deepak Gupta
Publisher : Springer Nature
ISBN 13 : 9811908400
Total Pages : 859 pages
Book Rating : 4.8/5 (119 download)

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Book Synopsis Advanced Machine Intelligence and Signal Processing by : Deepak Gupta

Download or read book Advanced Machine Intelligence and Signal Processing written by Deepak Gupta and published by Springer Nature. This book was released on 2022-06-25 with total page 859 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the latest advancements in the areas of machine learning, computer vision, pattern recognition, computational learning theory, big data analytics, network intelligence, signal processing, and their applications in real world. The topics covered in machine learning involve feature extraction, variants of support vector machine (SVM), extreme learning machine (ELM), artificial neural network (ANN), and other areas in machine learning. The mathematical analysis of computer vision and pattern recognition involves the use of geometric techniques, scene understanding and modeling from video, 3D object recognition, localization and tracking, medical image analysis, and so on. Computational learning theory involves different kinds of learning like incremental, online, reinforcement, manifold, multitask, semi-supervised, etc. Further, it covers the real-time challenges involved while processing big data analytics and stream processing with the integration of smart data computing services and interconnectivity. Additionally, it covers the recent developments to network intelligence for analyzing the network information and thereby adapting the algorithms dynamically to improve the efficiency. In the last, it includes the progress in signal processing to process the normal and abnormal categories of real-world signals, for instance signals generated from IoT devices, smart systems, speech, videos, etc., and involves biomedical signal processing: electrocardiogram (ECG), electroencephalogram (EEG), magnetoencephalography (MEG), and electromyogram (EMG).

Developing Linear Algebra Codes on Modern Processors: Emerging Research and Opportunities

Author : Catalán Pallarés, Sandra
Publisher : IGI Global
ISBN 13 : 1799870847
Total Pages : 279 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Developing Linear Algebra Codes on Modern Processors: Emerging Research and Opportunities by : Catalán Pallarés, Sandra

Download or read book Developing Linear Algebra Codes on Modern Processors: Emerging Research and Opportunities written by Catalán Pallarés, Sandra and published by IGI Global. This book was released on 2022-10-14 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimized linear algebra (LA) libraries that are able to exploit the underlying hardware are always of interest in the high-performance computing community. The implementation of LA software has evolved along with computer architecture, while the specification remains unaltered almost from the beginning. It is important to differentiate between the specification of LA libraries and their implementation. Because LA libraries pursue high performance, the implementation for a given architecture needs to be optimized for it specifically. However, the type of operations included in the libraries, the input/output parameters, and the data types to be handled are common to all of them. This is why, while the specification remains constant, the implementation evolves with the creation of new architectures. Developing Linear Algebra Codes on Modern Processors: Emerging Research and Opportunities presents the main characteristics of LA libraries, showing the differences between the standards for sparse and dense versions. It further explores relevant linear algebra problems and shows, in a clear and understandable way, how to solve them using different computer architectures. Covering topics such as programming models, batched computing, and distributed memory platforms, this premier reference source is an excellent resource for programmers, computer scientists, engineers, students and faculty of higher education, librarians, researchers, and academicians.

Blockchain and AI Technology in the Industrial Internet of Things

Author : Pani, Subhendu Kumar
Publisher : IGI Global
ISBN 13 : 1799866955
Total Pages : 317 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Blockchain and AI Technology in the Industrial Internet of Things by : Pani, Subhendu Kumar

Download or read book Blockchain and AI Technology in the Industrial Internet of Things written by Pani, Subhendu Kumar and published by IGI Global. This book was released on 2021-01-08 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: Blockchain and artificial intelligence (AI) in industrial internet of things is an emerging field of research at the intersection of information science, computer science, and electronics engineering. The radical digitization of industry coupled with the explosion of the internet of things (IoT) has set up a paradigm shift for industrial and manufacturing companies. There exists a need for a comprehensive collection of original research of the best performing methods and state-of-the-art approaches in this area of blockchain, AI, and the industrial internet of things in this new era for industrial and manufacturing companies. Blockchain and AI Technology in the Industrial Internet of Things compares different approaches to the industrial internet of things and explores the direct impact blockchain and AI technology have on the betterment of the human life. The chapters provide the latest advances in the field and provide insights and concerns on the concept and growth of the industrial internet of things. While including research on security and privacy, supply chain management systems, performance analysis, and a variety of industries, this book is ideal for professionals, researchers, managers, technologists, security analysts, executives, practitioners, researchers, academicians, and students looking for advanced research and information on the newest technologies, advances, and approaches for blockchain and AI in the industrial internet of things.

New Approaches to Data Analytics and Internet of Things Through Digital Twin

Author : Karthikeyan, P.
Publisher : IGI Global
ISBN 13 : 1668457245
Total Pages : 326 pages
Book Rating : 4.6/5 (684 download)

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Book Synopsis New Approaches to Data Analytics and Internet of Things Through Digital Twin by : Karthikeyan, P.

Download or read book New Approaches to Data Analytics and Internet of Things Through Digital Twin written by Karthikeyan, P. and published by IGI Global. This book was released on 2022-09-30 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: Even though many data analytics tools have been developed in the past years, their usage in the field of cyber twin warrants new approaches that consider various aspects including unified data representation, zero-day attack detection, data sharing across threat detection systems, real-time analysis, sampling, dimensionality reduction, resource-constrained data processing, and time series analysis for anomaly detection. Further study is required to fully understand the opportunities, benefits, and difficulties of data analytics and the internet of things in today’s modern world. New Approaches to Data Analytics and Internet of Things Through Digital Twin considers how data analytics and the internet of things can be used successfully within the field of digital twin as well as the potential future directions of these technologies. Covering key topics such as edge networks, deep learning, intelligent data analytics, and knowledge discovery, this reference work is ideal for computer scientists, industry professionals, researchers, scholars, practitioners, academicians, instructors, and students.

Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance

Author : Rana, Dipti P.
Publisher : IGI Global
ISBN 13 : 1799873730
Total Pages : 309 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance by : Rana, Dipti P.

Download or read book Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance written by Rana, Dipti P. and published by IGI Global. This book was released on 2021-06-04 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last two decades, researchers are looking at imbalanced data learning as a prominent research area. Many critical real-world application areas like finance, health, network, news, online advertisement, social network media, and weather have imbalanced data, which emphasizes the research necessity for real-time implications of precise fraud/defaulter detection, rare disease/reaction prediction, network intrusion detection, fake news detection, fraud advertisement detection, cyber bullying identification, disaster events prediction, and more. Machine learning algorithms are based on the heuristic of equally-distributed balanced data and provide the biased result towards the majority data class, which is not acceptable considering imbalanced data is omnipresent in real-life scenarios and is forcing us to learn from imbalanced data for foolproof application design. Imbalanced data is multifaceted and demands a new perception using the novelty at sampling approach of data preprocessing, an active learning approach, and a cost perceptive approach to resolve data imbalance. Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance offers new aspects for imbalanced data learning by providing the advancements of the traditional methods, with respect to big data, through case studies and research from experts in academia, engineering, and industry. The chapters provide theoretical frameworks and the latest empirical research findings that help to improve the understanding of the impact of imbalanced data and its resolving techniques based on data preprocessing, active learning, and cost perceptive approaches. This book is ideal for data scientists, data analysts, engineers, practitioners, researchers, academicians, and students looking for more information on imbalanced data characteristics and solutions using varied approaches.

Statistical Modeling in Machine Learning

Author : Tilottama Goswami
Publisher : Academic Press
ISBN 13 : 0323972527
Total Pages : 398 pages
Book Rating : 4.3/5 (239 download)

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Book Synopsis Statistical Modeling in Machine Learning by : Tilottama Goswami

Download or read book Statistical Modeling in Machine Learning written by Tilottama Goswami and published by Academic Press. This book was released on 2022-10-29 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Modeling in Machine Learning: Concepts and Applications presents the basic concepts and roles of statistics, exploratory data analysis and machine learning. The various aspects of Machine Learning are discussed along with basics of statistics. Concepts are presented with simple examples and graphical representation for better understanding of techniques. This book takes a holistic approach – putting key concepts together with an in-depth treatise on multi-disciplinary applications of machine learning. New case studies and research problem statements are discussed, which will help researchers in their application areas based on the concepts of statistics and machine learning. Statistical Modeling in Machine Learning: Concepts and Applications will help statisticians, machine learning practitioners and programmers solving various tasks such as classification, regression, clustering, forecasting, recommending and more. Provides a comprehensive overview of the state-of-the-art in statistical concepts applied to Machine Learning with the help of real-life problems, applications and tutorials Presents a step-by-step approach from fundamentals to advanced techniques Includes Case Studies with both successful and unsuccessful applications of Machine Learning to understand challenges in its implementation, along with worked examples

Feature Engineering for Machine Learning and Data Analytics

Author : Guozhu Dong
Publisher : CRC Press
ISBN 13 : 1351721275
Total Pages : 400 pages
Book Rating : 4.3/5 (517 download)

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Book Synopsis Feature Engineering for Machine Learning and Data Analytics by : Guozhu Dong

Download or read book Feature Engineering for Machine Learning and Data Analytics written by Guozhu Dong and published by CRC Press. This book was released on 2018-03-14 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features. The first chapter defines the concepts of features and feature engineering, offers an overview of the book, and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering, including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering, namely feature selection, feature transformation based feature engineering, deep learning based feature engineering, and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection, software management, and Twitter-based applications respectively. This book can be used as a reference for data analysts, big data scientists, data preprocessing workers, project managers, project developers, prediction modelers, professors, researchers, graduate students, and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering, or as a supplement for courses on machine learning, data mining, and big data analytics.

Research Anthology on Ecosystem Conservation and Preserving Biodiversity

Author : Management Association, Information Resources
Publisher : IGI Global
ISBN 13 : 1668456796
Total Pages : 1915 pages
Book Rating : 4.6/5 (684 download)

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Book Synopsis Research Anthology on Ecosystem Conservation and Preserving Biodiversity by : Management Association, Information Resources

Download or read book Research Anthology on Ecosystem Conservation and Preserving Biodiversity written by Management Association, Information Resources and published by IGI Global. This book was released on 2022-04-08 with total page 1915 pages. Available in PDF, EPUB and Kindle. Book excerpt: In today’s rapidly evolving world, it has never been more critical to consider key environmental issues such as climate change, pollution, and endangered species. Society faces an unknown future where the fate of the environment is continuously in flux based on current preservation initiatives that governments develop. In order to ensure the world is protected moving forward, further study on the importance of securing environments, ecosystems, and species is necessary to successfully implement change. The Research Anthology on Ecosystem Conservation and Preserving Biodiversity considers the best practices and strategies for protecting our current ecosystems as well as the potential ramifications of failing to implement policies. Society is at a crossroads where if we continue to ignore the danger and warning signs brought about by environmental issues, we will be unable to maintain a healthy environment. Covering essential topics such as extinction, climate change, and pollution, this major reference work is ideal for scientists, industry professionals, researchers, academicians, policymakers, scholars, practitioners, instructors, and students.

Social Media and Crowdsourcing

Author : Sujoy Chatterjee
Publisher : CRC Press
ISBN 13 : 1003804861
Total Pages : 259 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Social Media and Crowdsourcing by : Sujoy Chatterjee

Download or read book Social Media and Crowdsourcing written by Sujoy Chatterjee and published by CRC Press. This book was released on 2023-12-06 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: Social media and crowdsourcing are important tools for solving complex problems. The benefit of crowdsourcing is that it leverages the power of human intelligence cost effectively and with less time. Social Media and Crowdsourcing: Application and Analytics examines the concepts of social media and crowdsourcing as well as their analytical aspects. It explores how these technologies contribute to the real world and examines such applications as promoting social good, agriculture, healthcare, tourism, disaster management, education, crime control, and cultural heritage. The book also looks at ethical issues in crowdsourcing and future scenarios and challenges for policy. Highlights of the book include the following: A crowdsourcing application in agriculture Crowdsourcing outline for a contemporary aided medicinal backup system Crowdsourcing-based recommendation in the tourism industry Crowdsourcing mechanisms for reviving cultural heritage Expanding the overarching concept of utilizing social media and crowdsourcing to solve various real-life problems, this book discusses how to bring together the wisdom of crowds for various decision-making problems in agriculture, disaster management, and healthcare. It addresses the various ethical issues arising out of various crowdsourcing-based applications. It puts forward diverse methodologies to involve crowdsourcing in education to implement new strategies to enhance learning outcomes. This book also addresses various problem-solving techniques for recommender applications in the travel and tourism industry. Providing a systematic discussion of the many sectors using crowdsourcing as an essential part of social innovation, this book is a theoretical and methodological look at the application of social media.

Machine Intelligence Techniques for Data Analysis and Signal Processing

Author : Dilip Singh Sisodia
Publisher : Springer Nature
ISBN 13 : 9819900859
Total Pages : 879 pages
Book Rating : 4.8/5 (199 download)

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Book Synopsis Machine Intelligence Techniques for Data Analysis and Signal Processing by : Dilip Singh Sisodia

Download or read book Machine Intelligence Techniques for Data Analysis and Signal Processing written by Dilip Singh Sisodia and published by Springer Nature. This book was released on 2023-05-30 with total page 879 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book comprises the proceedings of the 4th International Conference on Machine Intelligence and Signal Processing (MISP2022). The contents of this book focus on research advancements in machine intelligence, signal processing, and applications. The book covers the real-time challenges involved while processing big data analytics and stream processing with the integration of smart data computing services and interconnectivity. It also includes the progress in signal processing to process the normal and abnormal categories of real-world signals such as signals generated from IoT devices, smart systems, speech, and videos and involves biomedical signal processing: electrocardiogram (ECG), electroencephalogram (EEG), magnetoencephalography (MEG), electromyogram (EMG), etc. This book proves a valuable resource for those in academia and industry.

Feature Engineering for Machine Learning

Author : Alice Zheng
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491953195
Total Pages : 218 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Feature Engineering for Machine Learning by : Alice Zheng

Download or read book Feature Engineering for Machine Learning written by Alice Zheng and published by "O'Reilly Media, Inc.". This book was released on 2018-03-23 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering. Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples. You’ll examine: Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transforms Natural text techniques: bag-of-words, n-grams, and phrase detection Frequency-based filtering and feature scaling for eliminating uninformative features Encoding techniques of categorical variables, including feature hashing and bin-counting Model-based feature engineering with principal component analysis The concept of model stacking, using k-means as a featurization technique Image feature extraction with manual and deep-learning techniques