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Machine Learning

Author: Andrea Mechelli

Publisher: Academic Press

Published: 2019-11-14

Total Pages: 412

ISBN-13: 0128157402

Machine Learning is an area of artificial intelligence involving the development of algorithms to discover trends and patterns in existing data; this information can then be used to make predictions on new data. A growing number of researchers and clinicians are using machine learning methods to develop and validate tools for assisting the diagnosis and treatment of patients with brain disorders. Machine Learning: Methods and Applications to Brain Disorders provides an up-to-date overview of how these methods can be applied to brain disorders, including both psychiatric and neurological disease. This book is written for a non-technical audience, such as neuroscientists, psychologists, psychiatrists, neurologists and health care practitioners. Provides a non-technical introduction to machine learning and applications to brain disorders Includes a detailed description of the most commonly used machine learning algorithms as well as some novel and promising approaches Covers the main methodological challenges in the application of machine learning to brain disorders Provides a step-by-step tutorial for implementing a machine learning pipeline to neuroimaging data in Python

Artificial Intelligence Methods and Applications

Author: N G Bourbakis

Publisher: World Scientific

Published: 1992-12-31

Total Pages: 732

ISBN-13: 9814505293

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This volume is the first in a series which deals with the challenge of AI issues, gives updates of AI methods and applications, and promotes high quality new ideas, techniques and methodologies in AI. This volume contains articles by 38 specialists in various AI subfields covering theoretical and application issues. Contents:Introduction to Advanced Series on Artificial Intelligence (N G Bourbakis)Fundamental Methods for Horn Logic and Artificial Intelligence Applications (E Kounalis & P Marquis)Applications of Genetic Algorithms to Permutation Problems (F E Petry & B P Buckles)Extracting Procedural Knowledge from Software Systems Using Inductive Learning in the PM System (R G Reynolds et al.)Resource-Oriented Parallel Planning (S Lee & K Chung)Advanced Parsing Technology for Knowledge-Based Shells (J R Kipps)The Analysis and Synthesis of Intelligent Systems (W Arden)Document Image Analysis and Recognition (S N Srihari et al.)Signal Understanding: An Artificial Intelligence Approach to Modulation Classification (J E Whelchel et al.)and other papers Readership: Computer scientists, researchers and professionals in artificial intelligence. keywords:

Applications of Artificial Intelligence in Process Systems Engineering

Author: Jingzheng Ren

Publisher: Elsevier

Published: 2021-06-05

Total Pages: 542

ISBN-13: 012821743X

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Applications of Artificial Intelligence in Process Systems Engineering offers a broad perspective on the issues related to artificial intelligence technologies and their applications in chemical and process engineering. The book comprehensively introduces the methodology and applications of AI technologies in process systems engineering, making it an indispensable reference for researchers and students. As chemical processes and systems are usually non-linear and complex, thus making it challenging to apply AI methods and technologies, this book is an ideal resource on emerging areas such as cloud computing, big data, the industrial Internet of Things and deep learning. With process systems engineering's potential to become one of the driving forces for the development of AI technologies, this book covers all the right bases. Explains the concept of machine learning, deep learning and state-of-the-art intelligent algorithms Discusses AI-based applications in process modeling and simulation, process integration and optimization, process control, and fault detection and diagnosis Gives direction to future development trends of AI technologies in chemical and process engineering

Application of Artificial Intelligence to Assessment

Author: Hong Jiao

Publisher: IAP

Published: 2020-03-01

Total Pages: 223

ISBN-13: 1641139536

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The general theme of this book is to present the applications of artificial intelligence (AI) in test development. In particular, this book includes research and successful examples of using AI technology in automated item generation, automated test assembly, automated scoring, and computerized adaptive testing. By utilizing artificial intelligence, the efficiency of item development, test form construction, test delivery, and scoring could be dramatically increased. Chapters on automated item generation offer different perspectives related to generating a large number of items with controlled psychometric properties including the latest development of using machine learning methods. Automated scoring is illustrated for different types of assessments such as speaking and writing from both methodological aspects and practical considerations. Further, automated test assembly is elaborated for the conventional linear tests from both classical test theory and item response theory perspectives. Item pool design and assembly for the linear-on-the-fly tests elaborates more complications in practice when test security is a big concern. Finally, several chapters focus on computerized adaptive testing (CAT) at either item or module levels. CAT is further illustrated as an effective approach to increasing test-takers’ engagement in testing. In summary, the book includes both theoretical, methodological, and applied research and practices that serve as the foundation for future development. These chapters provide illustrations of efforts to automate the process of test development. While some of these automation processes have become common practices such as automated test assembly, automated scoring, and computerized adaptive testing, some others such as automated item generation calls for more research and exploration. When new AI methods are emerging and evolving, it is expected that researchers can expand and improve the methods for automating different steps in test development to enhance the automation features and practitioners can adopt quality automation procedures to improve assessment practices.

Artificial Intelligence Methods For Software Engineering

Author: Meir Kalech

Publisher: World Scientific

Published: 2021-06-15

Total Pages: 457

ISBN-13: 9811239932

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Software is an integral part of our lives today. Modern software systems are highly complex and often pose new challenges in different aspects of Software Engineering (SE).Artificial Intelligence (AI) is a growing field in computer science that has been proven effective in applying and developing AI techniques to address various SE challenges.This unique compendium covers applications of state-of-the-art AI techniques to the key areas of SE (design, development, debugging, testing, etc).All the materials presented are up-to-date. This reference text will benefit researchers, academics, professionals, and postgraduate students in AI, machine learning and software engineering.Related Link(s)

The Application of Artificial Intelligence

Author: Zoltán Somogyi

Publisher: Springer Nature

Published: 2021-03-11

Total Pages: 448

ISBN-13: 3030600327

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This book presents a unique, understandable view of machine learning using many practical examples and access to free professional software and open source code. The user-friendly software can immediately be used to apply everything you learn in the book without the need for programming. After an introduction to machine learning and artificial intelligence, the chapters in Part II present deeper explanations of machine learning algorithms, performance evaluation of machine learning models, and how to consider data in machine learning environments. In Part III the author explains automatic speech recognition, and in Part IV biometrics recognition, face- and speaker-recognition. By Part V the author can then explain machine learning by example, he offers cases from real-world applications, problems, and techniques, such as anomaly detection and root cause analyses, business process improvement, detecting and predicting diseases, recommendation AI, several engineering applications, predictive maintenance, automatically classifying datasets, dimensionality reduction, and image recognition. Finally, in Part VI he offers a detailed explanation of the AI-TOOLKIT, software he developed that allows the reader to test and study the examples in the book and the application of machine learning in professional environments. The author introduces core machine learning concepts and supports these with practical examples of their use, so professionals will appreciate his approach and use the book for self-study. It will also be useful as a supplementary resource for advanced undergraduate and graduate courses on machine learning and artificial intelligence.

Artificial Intelligence: Methods and Applications

Author: Aristidis Likas

Publisher: Springer

Published: 2014-04-18

Total Pages: 657

ISBN-13: 3319070649

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This book constitutes the proceedings of the 8th Hellenic Conference on Artificial Intelligence, SETN 2014, held in Ioannina, Greece, in May 2014. There are 34 regular papers out of 60 submissions, in addition 5 submissions were accepted as short papers and 15 papers were accepted for four special sessions. They deal with emergent topics of artificial intelligence and come from the SETN main conference as well as from the following special sessions on action languages: theory and practice; computational intelligence techniques for bio signal Analysis and evaluation; game artificial intelligence; multimodal recommendation systems and their applications to tourism.

Artificial Intelligence Applications for Health Care

Author: Mitul Kumar Ahirwal

Publisher: CRC Press

Published: 2022-04-15

Total Pages: 332

ISBN-13: 1000570304

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This book takes an interdisciplinary approach by covering topics on health care and artificial intelligence. Data sets related to biomedical signals (ECG, EEG, EMG) and images (X-rays, MRI, CT) are explored, analyzed, and processed through different computation intelligence methods. Applications of computational intelligence techniques like artificial and deep neural networks, swarm optimization, expert systems, decision support systems, clustering, and classification techniques on medial datasets are explained. Survey of medical signals, medial images, and computation intelligence methods are also provided in this book. Key Features Covers computational Intelligence techniques like artificial neural networks, deep neural networks, and optimization algorithms for Healthcare systems Provides easy understanding for concepts like signal and image filtering techniques Includes discussion over data preprocessing and classification problems Details studies with medical signal (ECG, EEG, EMG) and image (X-ray, FMRI, CT) datasets Describes evolution parameters such as accuracy, precision, and recall etc. This book is aimed at researchers and graduate students in medical signal and image processing, machine and deep learning, and healthcare technologies.

Artificial Intelligence Methods in the Environmental Sciences

Author: Sue Ellen Haupt

Publisher: Springer Science & Business Media

Published: 2008-11-28

Total Pages: 418

ISBN-13: 1402091192

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How can environmental scientists and engineers use the increasing amount of available data to enhance our understanding of planet Earth, its systems and processes? This book describes various potential approaches based on artificial intelligence (AI) techniques, including neural networks, decision trees, genetic algorithms and fuzzy logic. Part I contains a series of tutorials describing the methods and the important considerations in applying them. In Part II, many practical examples illustrate the power of these techniques on actual environmental problems. International experts bring to life ways to apply AI to problems in the environmental sciences. While one culture entwines ideas with a thread, another links them with a red line. Thus, a “red thread“ ties the book together, weaving a tapestry that pictures the ‘natural’ data-driven AI methods in the light of the more traditional modeling techniques, and demonstrating the power of these data-based methods.

Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics

Author: Yi Pan

Publisher: John Wiley & Sons

Published: 2013-11-12

Total Pages: 534

ISBN-13: 1118345789

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Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics An in-depth look at the latest research, methods, and applications in the field of protein bioinformatics This book presents the latest developments in protein bioinformatics, introducing for the first time cutting-edge research results alongside novel algorithmic and AI methods for the analysis of protein data. In one complete, self-contained volume, Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics addresses key challenges facing both computer scientists and biologists, arming readers with tools and techniques for analyzing and interpreting protein data and solving a variety of biological problems. Featuring a collection of authoritative articles by leaders in the field, this work focuses on the analysis of protein sequences, structures, and interaction networks using both traditional algorithms and AI methods. It also examines, in great detail, data preparation, simulation, experiments, evaluation methods, and applications. Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics: Highlights protein analysis applications such as protein-related drug activity comparison Incorporates salient case studies illustrating how to apply the methods outlined in the book Tackles the complex relationship between proteins from a systems biology point of view Relates the topic to other emerging technologies such as data mining and visualization Includes many tables and illustrations demonstrating concepts and performance figures Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics is an essential reference for bioinformatics specialists in research and industry, and for anyone wishing to better understand the rich field of protein bioinformatics.