Modeling and Optimization of Signals Using Machine Learning Techniques
Modeling and Optimization of Signals Using Machine Learning Techniques

Author: Chandra Singh

Publisher: John Wiley & Sons

Published: 2024-08-23

Total Pages: 421

ISBN-13: 1119847699

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Explore the power of machine learning to revolutionize signal processing and optimization with cutting-edge techniques and practical insights in this outstanding new volume from Scrivener Publishing. Modeling and Optimization of Signals using Machine Learning Techniques is designed for researchers from academia, industries, and R&D organizations worldwide who are passionate about advancing machine learning methods, signal processing theory, data mining, artificial intelligence, and optimization. This book addresses the role of machine learning in transforming vast signal databases from sensor networks, internet services, and communication systems into actionable decision systems. It explores the development of computational solutions and novel models to handle complex real-world signals such as speech, music, biomedical data, and multimedia. Through comprehensive coverage of cutting-edge techniques, this book equips readers with the tools to automate signal processing and analysis, ultimately enhancing the retrieval of valuable information from extensive data storage systems. By providing both theoretical insights and practical guidance, the book serves as a comprehensive resource for researchers, engineers, and practitioners aiming to harness the power of machine learning in signal processing. Whether for the veteran engineer, scientist in the lab, student, or faculty, this groundbreaking new volume is a valuable resource for researchers and other industry professionals interested in the intersection of technology and agriculture.

Machine Learning for Algorithmic Trading
Machine Learning for Algorithmic Trading

Author: Stefan Jansen

Publisher: Packt Publishing Ltd

Published: 2020-07-31

Total Pages: 822

ISBN-13: 1839216786

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Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key FeaturesDesign, train, and evaluate machine learning algorithms that underpin automated trading strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage NLP and deep learning to extract tradeable signals from market and alternative dataBook Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes dataWho this book is for If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.

Machine Learning in Bio-Signal Analysis and Diagnostic Imaging
Machine Learning in Bio-Signal Analysis and Diagnostic Imaging

Author: Nilanjan Dey

Publisher: Academic Press

Published: 2018-11-30

Total Pages: 348

ISBN-13: 012816087X

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Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical signals and images that cover both supervised and unsupervised machine learning models, standards, algorithms, and their applications, along with the difficulties and challenges faced by healthcare professionals in analyzing biomedical signals and diagnostic images. These intelligent recommender systems are designed based on machine learning, soft computing, computer vision, artificial intelligence and data mining techniques. Classification and clustering techniques, such as PCA, SVM, techniques, Naive Bayes, Neural Network, Decision trees, and Association Rule Mining are among the approaches presented. The design of high accuracy decision support systems assists and eases the job of healthcare practitioners and suits a variety of applications. Integrating Machine Learning (ML) technology with human visual psychometrics helps to meet the demands of radiologists in improving the efficiency and quality of diagnosis in dealing with unique and complex diseases in real time by reducing human errors and allowing fast and rigorous analysis. The book's target audience includes professors and students in biomedical engineering and medical schools, researchers and engineers. - Examines a variety of machine learning techniques applied to bio-signal analysis and diagnostic imaging - Discusses various methods of using intelligent systems based on machine learning, soft computing, computer vision, artificial intelligence and data mining - Covers the most recent research on machine learning in imaging analysis and includes applications to a number of domains

Fluorescent Nanodiamonds
Fluorescent Nanodiamonds

Author: Huan-Cheng Chang

Publisher: John Wiley & Sons

Published: 2018-11-12

Total Pages: 294

ISBN-13: 1119477085

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The most comprehensive reference on fluorescent nanodiamond physical and chemical properties and contemporary applications Fluorescent nanodiamonds (FNDs) have drawn a great deal of attention over the past several years, and their applications and development potential are proving to be manifold and vast. The first and only book of its kind, Fluorescent Nanodiamonds is a comprehensive guide to the basic science and technical information needed to fully understand the fundamentals of FNDs and their potential applications across an array of domains. In demonstrating the importance of FNDs in biological applications, the authors bring together all relevant chemistry, physics, materials science and biology. Nanodiamonds are produced by powerful cataclysmic events such as explosions, volcanic eruptions and meteorite impacts. They also can be created in the lab by high-pressure high-temperature treatment of graphite or detonating an explosive in a reactor vessel. A single imperfection can give a nanodiamond a specific, isolated color center which allows it to function as a single, trapped atom. Much smaller than the thickness of a human hair, a nanodiamond can have a huge surface area that allows it to bond with a variety of other materials. Because of their non-toxicity, nanodiamonds may be useful in biomedical applications, such as drug delivery and gene therapy. The most comprehensive reference on a topic of rapidly increasing interest among academic and industrial researchers across an array of fields Includes numerous case studies and practical examples from many areas of research and industrial applications, as well as fascinating and instructive historical perspectives Each chapter addresses, in-depth, a single integral topic including the fundamental properties, synthesis, mechanisms and functionalisation of FNDs The first book published by the key patent holder with his research group in the field of FNDs Fluorescent Nanodiamonds is an important working resource for a broad range of scientists and engineers in industry and academia. It will also be a welcome reference for instructors in chemistry, physics, materials science, biology and related fields.

Machine Learning for Signal Processing
Machine Learning for Signal Processing

Author: Max A. Little

Publisher: Oxford University Press, USA

Published: 2019

Total Pages: 378

ISBN-13: 0198714939

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Describes in detail the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy. Builds up concepts gradually so that the ideas and algorithms can be implemented in practical software applications.

Mobile Computing and Sustainable Informatics
Mobile Computing and Sustainable Informatics

Author: Subarna Shakya

Publisher: Springer Nature

Published: 2023-05-26

Total Pages: 792

ISBN-13: 9819908353

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This book gathers selected high-quality research papers presented at International Conference on Mobile Computing and Sustainable Informatics (ICMCSI 2022) organized by Pulchowk Campus, Institute of Engineering, Tribhuvan University, Nepal, during January 11–12, 2023. The book discusses recent developments in mobile communication technologies ranging from mobile edge computing devices to personalized, embedded, and sustainable applications. The book covers vital topics like mobile networks, computing models, algorithms, sustainable models, and advanced informatics that support the symbiosis of mobile computing and sustainable informatics.

Financial Signal Processing and Machine Learning
Financial Signal Processing and Machine Learning

Author: Ali N. Akansu

Publisher: John Wiley & Sons

Published: 2016-04-21

Total Pages: 312

ISBN-13: 1118745639

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The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analysis through temporal-causal modeling, and large-scale copula-based approaches. Key features: Highlights signal processing and machine learning as key approaches to quantitative finance. Offers advanced mathematical tools for high-dimensional portfolio construction, monitoring, and post-trade analysis problems. Presents portfolio theory, sparse learning and compressed sensing, sparsity methods for investment portfolios. including eigen-portfolios, model return, momentum, mean reversion and non-Gaussian data-driven risk measures with real-world applications of these techniques. Includes contributions from leading researchers and practitioners in both the signal and information processing communities, and the quantitative finance community.

Integrated Circuit and System Design. Power and Timing Modeling, Optimization and Simulation
Integrated Circuit and System Design. Power and Timing Modeling, Optimization and Simulation

Author: Lars Svensson

Publisher: Springer Science & Business Media

Published: 2009-02-13

Total Pages: 474

ISBN-13: 3540959475

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This book constitutes the thoroughly refereed post-conference proceedings of 18th International Workshop on Power and Timing Modeling, Optimization and Simulation, PATMOS 2008, featuring Integrated Circuit and System Design, held in Lisbon, Portugal during September 10-12, 2008. The 31 revised full papers and 10 revised poster papers presented together with 3 invited talks and 4 papers from a special session on reconfigurable architectures were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on low-leakage and subthreshold circuits, low-power methods and models, arithmetic and memories, variability and statistical timing, synchronization and interconnect, power supplies and switching noise, low-power circuits; reconfigurable architectures, circuits and methods, power and delay modeling, as well as power optimizations addressing reconfigurable architectures.

Machine Learning Algorithms for Signal and Image Processing
Machine Learning Algorithms for Signal and Image Processing

Author: Suman Lata Tripathi

Publisher: John Wiley & Sons

Published: 2022-12-01

Total Pages: 516

ISBN-13: 1119861829

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Enables readers to understand the fundamental concepts of machine and deep learning techniques with interactive, real-life applications within signal and image processing Machine Learning Algorithms for Signal and Image Processing aids the reader in designing and developing real-world applications using advances in machine learning to aid and enhance speech signal processing, image processing, computer vision, biomedical signal processing, adaptive filtering, and text processing. It includes signal processing techniques applied for pre-processing, feature extraction, source separation, or data decompositions to achieve machine learning tasks. Written by well-qualified authors and contributed to by a team of experts within the field, the work covers a wide range of important topics, such as: Speech recognition, image reconstruction, object classification and detection, and text processing Healthcare monitoring, biomedical systems, and green energy How various machine and deep learning techniques can improve accuracy, precision rate recall rate, and processing time Real applications and examples, including smart sign language recognition, fake news detection in social media, structural damage prediction, and epileptic seizure detection Professionals within the field of signal and image processing seeking to adapt their work further will find immense value in this easy-to-understand yet extremely comprehensive reference work. It is also a worthy resource for students and researchers in related fields who are looking to thoroughly understand the historical and recent developments that have been made in the field.