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Predictive Analytics With Neural Networks Using Matlab
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Book Synopsis PREDICTIVE ANALYTICS with NEURAL NETWORKS Using MATLAB by : Cesar Perez Lopez
Download or read book PREDICTIVE ANALYTICS with NEURAL NETWORKS Using MATLAB written by Cesar Perez Lopez and published by CESAR PEREZ. This book was released on 2020-09-06 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events. Different work fields with neural networks and predictive analytics techniques are listed below: The multilayer perceptron (MLP), A radial basis function (RBF), Support vector machines (SVM), Fit regression models with neural networks, Time series neural networks, Hopfield and linear neural networks, Generalized regression and LVQ neural networks, Adaptative linear filters and non linear problems
Download or read book MATLAB Deep Learning written by Phil Kim and published by Apress. This book was released on 2017-06-15 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book. With this book, you'll be able to tackle some of today's real world big data, smart bots, and other complex data problems. You’ll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage. What You'll Learn Use MATLAB for deep learning Discover neural networks and multi-layer neural networks Work with convolution and pooling layers Build a MNIST example with these layers Who This Book Is For Those who want to learn deep learning using MATLAB. Some MATLAB experience may be useful.
Book Synopsis MATLAB for Machine Learning by : Giuseppe Ciaburro
Download or read book MATLAB for Machine Learning written by Giuseppe Ciaburro and published by Packt Publishing Ltd. This book was released on 2017-08-28 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Extract patterns and knowledge from your data in easy way using MATLAB About This Book Get your first steps into machine learning with the help of this easy-to-follow guide Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB Understand how your data works and identify hidden layers in the data with the power of machine learning. Who This Book Is For This book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well. What You Will Learn Learn the introductory concepts of machine learning. Discover different ways to transform data using SAS XPORT, import and export tools, Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data. Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment. Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures. Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox. Learn feature selection and extraction for dimensionality reduction leading to improved performance. In Detail MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners. You'll start by getting your system ready with t he MATLAB environment for machine learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB. Style and approach The book takes a very comprehensive approach to enhance your understanding of machine learning using MATLAB. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work.
Book Synopsis Predictive Analytics using MATLAB(R) for Biomedical Applications by : L. Ashok Kumar
Download or read book Predictive Analytics using MATLAB(R) for Biomedical Applications written by L. Ashok Kumar and published by Elsevier. This book was released on 2024-10-03 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: Predictive Analytics using MATLAB(R) for Biomedical Applications is a comprehensive and practical guide for biomedical engineers, data scientists, and researchers on how to use predictive analytics techniques in MATLAB(R) for solving real-world biomedical problems. The book offers a technical overview of various predictive analytics methods and covers the utilization of MATLAB(R) for implementing these techniques. It includes several case studies that demonstrate how predictive analytics can be applied to real-world biomedical problems, such as predicting disease progression, analyzing medical imaging data, and optimizing treatment outcomes.With a plethora of examples and exercises, this book is the ultimate tool for reinforcing one's knowledge and skills. - Covers various predictive analytics methods, including regression analysis, time series analysis, and machine learning algorithms, providing readers with a comprehensive understanding of the field - Provides a hands-on approach to learning predictive analytics, with a focus on practical applications in biomedical engineering - Includes several case studies that demonstrate the practical application of predictive analytics in real-world biomedical problems, such as disease progression prediction, medical imaging analysis, and treatment optimization
Download or read book Big Data Analytics written by C. Perez and published by CESAR PEREZ. This book was released on 2020-05-31 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big data analytics is the process of collecting, organizing and analyzing large sets of data (called big data) to discover patterns and other useful information. Big data analytics can help organizations to better understand the information contained within the data and will also help identify the data that is most important to the business and future business decisions. Analysts working with big data basically want the knowledge that comes from analyzing the data.To analyze such a large volume of data, big data analytics is typically performed using specialized software tools and applications for predictive analytics, data mining, text mining, forecasting and data optimization. Collectively these processes are separate but highly integrated functions of high-performance analytics. Using big data tools and software enables an organization to process extremely large volumes of data that a business has collected to determine which data is relevant and can be analyzed to drive better business decisions in the future. Among all these tools highlights MATLAB. MATLAB implements various toolboxes for working on big data analytics, such as Statistics Toolbox and Neural Network Toolbox (Deep Learning Toolbox for version 18) . This book develops the work capabilities of MATLAB with Neural Networks and Big Data.
Book Synopsis Modeling Solar Radiation at the Earth's Surface by : Viorel Badescu
Download or read book Modeling Solar Radiation at the Earth's Surface written by Viorel Badescu and published by Springer Science & Business Media. This book was released on 2008-02-01 with total page 537 pages. Available in PDF, EPUB and Kindle. Book excerpt: Solar radiation data is important for a wide range of applications, e.g. in engineering, agriculture, health sector, and in many fields of the natural sciences. A few examples showing the diversity of applications may include: architecture and building design, e.g. air conditioning and cooling systems; solar heating system design and use; solar power generation; evaporation and irrigation; calculation of water requirements for crops; monitoring plant growth and disease control; skin cancer research.
Book Synopsis Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering by : Shahab Araghinejad
Download or read book Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering written by Shahab Araghinejad and published by Springer Science & Business Media. This book was released on 2013-11-26 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: “Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering” provides a systematic account of major concepts and methodologies for data-driven models and presents a unified framework that makes the subject more accessible to and applicable for researchers and practitioners. It integrates important theories and applications of data-driven models and uses them to deal with a wide range of problems in the field of water resources and environmental engineering such as hydrological forecasting, flood analysis, water quality monitoring, regionalizing climatic data, and general function approximation. The book presents the statistical-based models including basic statistical analysis, nonparametric and logistic regression methods, time series analysis and modeling, and support vector machines. It also deals with the analysis and modeling based on artificial intelligence techniques including static and dynamic neural networks, statistical neural networks, fuzzy inference systems, and fuzzy regression. The book also discusses hybrid models as well as multi-model data fusion to wrap up the covered models and techniques. The source files of relatively simple and advanced programs demonstrating how to use the models are presented together with practical advice on how to best apply them. The programs, which have been developed using the MATLAB® unified platform, can be found on extras.springer.com. The main audience of this book includes graduate students in water resources engineering, environmental engineering, agricultural engineering, and natural resources engineering. This book may be adapted for use as a senior undergraduate and graduate textbook by focusing on selected topics. Alternatively, it may also be used as a valuable resource book for practicing engineers, consulting engineers, scientists and others involved in water resources and environmental engineering.
Book Synopsis The Perceptron by : Frank Rosenblatt
Download or read book The Perceptron written by Frank Rosenblatt and published by . This book was released on 1958 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author :J. Smith Publisher :Createspace Independent Publishing Platform ISBN 13 :9781545455784 Total Pages :268 pages Book Rating :4.4/5 (557 download)
Book Synopsis Predictive Analytics With Matlab Regression and Neural Networks by : J. Smith
Download or read book Predictive Analytics With Matlab Regression and Neural Networks written by J. Smith and published by Createspace Independent Publishing Platform. This book was released on 2017-04-18 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavior patterns. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future. For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs. The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions. This books develops the more important predictive models like Regression Models, Generalized Regression Models, Discrete Choice Models, Logit and Probit Models, Support Vector Machine Regression, Gaussian Process Regresion, Regression Trees, Regression Models with Neural Networks and Neural Networks Time Series Prediction.
Book Synopsis Machine Learning with Neural Networks Using MATLAB by : J. Smith
Download or read book Machine Learning with Neural Networks Using MATLAB written by J. Smith and published by Createspace Independent Publishing Platform. This book was released on 2017-02-27 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to produce reliable, repeatable decisions and results" and uncover "hidden insights" through learning from historical relationships and trends in the data. MATLAB has the tool Neural Network Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, dynamic system modeling and control and most machine learning techniques. The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox. The more important features are the following: -Deep learning, including convolutional neural networks and autoencoders -Parallel computing and GPU support for accelerating training (with Parallel Computing Toolbox) -Supervised learning algorithms, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and recurrent neural network (RNN) -Unsupervised learning algorithms, including self-organizing maps and competitive layers -Apps for data-fitting, pattern recognition, and clustering -Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance -Simulink(R) blocks for building and evaluating neural networks and for control systems applications
Book Synopsis Proceedings of Data Analytics and Management by : Abhishek Swaroop
Download or read book Proceedings of Data Analytics and Management written by Abhishek Swaroop and published by Springer Nature. This book was released on 2024-01-29 with total page 550 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes original unpublished contributions presented at the International Conference on Data Analytics and Management (ICDAM 2023), held at London Metropolitan University, London, UK, during June 2023. The book covers the topics in data analytics, data management, big data, computational intelligence, and communication networks. The book presents innovative work by leading academics, researchers, and experts from industry which is useful for young researchers and students. The book is divided into four volumes.
Book Synopsis Big Data Analytics for Cyber-Physical System in Smart City by : Mohammed Atiquzzaman
Download or read book Big Data Analytics for Cyber-Physical System in Smart City written by Mohammed Atiquzzaman and published by Springer Nature. This book was released on 2020-01-11 with total page 2049 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers a selection of peer-reviewed papers presented at the first Big Data Analytics for Cyber-Physical System in Smart City (BDCPS 2019) conference, held in Shengyang, China, on 28–29 December 2019. The contributions, prepared by an international team of scientists and engineers, cover the latest advances made in the field of machine learning, and big data analytics methods and approaches for the data-driven co-design of communication, computing, and control for smart cities. Given its scope, it offers a valuable resource for all researchers and professionals interested in big data, smart cities, and cyber-physical systems.
Book Synopsis Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing by : Y. A. Liu
Download or read book Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing written by Y. A. Liu and published by John Wiley & Sons. This book was released on 2023-07-25 with total page 1027 pages. Available in PDF, EPUB and Kindle. Book excerpt: Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing Detailed resource on the “Why,” “What,” and “How” of integrated process modeling, advanced control and data analytics explained via hands-on examples and workshops for optimizing polyolefin manufacturing. Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing discusses, as well as demonstrates, the optimization of polyolefin production by covering topics from polymer process modeling and advanced process control to data analytics and machine learning, and sustainable design and industrial practice. The text also covers practical problems, handling of real data streams, developing the right level of detail, and tuning models to the available data, among other topics, to allow for easy translation of concepts into practice. Written by two highly qualified authors, Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing includes information on: Segment-based modeling of polymer processes; selection of thermodynamic methods; estimation of physical properties for polymer process modeling Reactor modeling, convergence tips and data-fit tool; free radical polymerization (LDPE, EVA and PS), Ziegler-Natta polymerization (HDPE, PP, LLPDE, and EPDM) and ionic polymerization (SBS rubber) Improved polymer process operability and control through steady-state and dynamic simulation models Model-predictive control of polyolefin processes and applications of multivariate statistics and machine learning to optimizing polyolefin manufacturing Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing enables readers to make full use of advanced computer models and latest data analytics and machine learning tools for optimizing polyolefin manufacturing, making it an essential resource for undergraduate and graduate students, researchers, and new and experienced engineers involved in the polyolefin industry.
Book Synopsis Proceedings of Data Analytics and Management by : Deepak Gupta
Download or read book Proceedings of Data Analytics and Management written by Deepak Gupta and published by Springer Nature. This book was released on 2022-01-04 with total page 822 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes original unpublished contributions presented at the International Conference on Data Analytics and Management (ICDAM 2021), held at Jan Wyzykowski University, Poland, during June 2021. The book covers the topics in data analytics, data management, big data, computational intelligence, and communication networks. The book presents innovative work by leading academics, researchers, and experts from industry which is useful for young researchers and students.
Download or read book Big Data Analytics written by C. Perez and published by CESAR PEREZ. This book was released on 2020-05-31 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. MATLAB has the tool Neural Network Toolbox (Deep Learning Toolbox from version 18) that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control.The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Big Data tools (Parallel Computing Toolbox). Unsupervised learning algorithms, including self-organizing maps and competitive layers-Apps for data-fitting, pattern recognition, and clustering-Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance. his book develops cluster analysis and pattern recognition
Book Synopsis Deep Learning Applications, Volume 2 by : M. Arif Wani
Download or read book Deep Learning Applications, Volume 2 written by M. Arif Wani and published by Springer. This book was released on 2020-12-14 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents selected papers from the 18th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2019). It focuses on deep learning networks and their application in domains such as healthcare, security and threat detection, fault diagnosis and accident analysis, and robotic control in industrial environments, and highlights novel ways of using deep neural networks to solve real-world problems. Also offering insights into deep learning architectures and algorithms, it is an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.
Book Synopsis XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019 by : Jorge Henriques
Download or read book XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019 written by Jorge Henriques and published by Springer Nature. This book was released on 2019-09-24 with total page 2058 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers the proceedings of MEDICON 2019 – the XV Mediterranean Conference on Medical and Biological Engineering and Computing – which was held in September 26-28, 2019, in Coimbra, Portugal. A special emphasis has been given to practical findings, techniques and methods, aimed at fostering an effective patient empowerment, i.e. to position the patient at the heart of the health system and encourages them to be actively involved in managing their own healthcare needs. The book reports on research and development in electrical engineering, computing, data science and instrumentation, and on many topics at the interface between those disciplines. It provides academics and professionals with extensive knowledge on cutting-edge techniques and tools for detection, prevention, treatment and management of diseases. A special emphasis is given to effective advances, as well as new directions and challenges towards improving healthcare through holistic patient empowerment.
