Dominant Algorithms to Evaluate Artificial Intelligence: From the View of Throughput Model PDF Download

Author: Waymond Rodgers
Publisher: Bentham Science Publishers
ISBN: 9815049550
Category : Computers
Languages : en
Pages : 329

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Book Description
This book describes the Throughput Model methodology that can enable individuals and organizations to better identify, understand, and use algorithms to solve daily problems. The Throughput Model is a progressive model intended to advance the artificial intelligence (AI) field since it represents symbol manipulation in six algorithmic pathways that are theorized to mimic the essential pillars of human cognition, namely, perception, information, judgment, and decision choice. The six AI algorithmic pathways are (1) Expedient Algorithmic Pathway, (2) Ruling Algorithmic Guide Pathway, (3) Analytical Algorithmic Pathway, (4) Revisionist Algorithmic Pathway, (5) Value Driven Algorithmic Pathway, and (6) Global Perspective Algorithmic Pathway. As AI is increasingly employed for applications where decisions require explanations, the Throughput Model offers business professionals the means to look under the hood of AI and comprehend how those decisions are attained by organizations. Key Features: - Covers general concepts of Artificial intelligence and machine learning - Explains the importance of dominant AI algorithms for business and AI research - Provides information about 6 unique algorithmic pathways in the Throughput Model - Provides information to create a roadmap towards building architectures that combine the strengths of the symbolic approaches for analyzing big data - Explains how to understand the functions of an AI algorithm to solve problems and make good decisions - informs managers who are interested in employing ethical and trustworthiness features in systems. Dominant Algorithms to Evaluate Artificial Intelligence: From the view of Throughput Model is an informative reference for all professionals and scholars who are working on AI projects to solve a range of business and technical problems.

Author: Leslie G. Eldenburg
Publisher: John Wiley & Sons
ISBN: 1394263503
Category : Business & Economics
Languages : en
Pages : 820

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Book Description
The fifth edition of Management Accounting integrates fundamental technical aspects pertaining to cost management and management accounting and control with contemporary and evolving themes and challenges. This comprehensive approach offers students studying cost and management accounting a nuanced understanding of the discipline. Emphasizing practical learning, the textbook facilitates student comprehension through the application of cost and management accounting techniques across diverse organizational contexts. Each chapter concludes with a range of student tasks designed to reinforce understanding and foster critical thinking.

Author: Erik R. Ranschaert
Publisher: Springer
ISBN: 3319948784
Category : Medical
Languages : en
Pages : 369

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Book Description
This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.

Author: Alexander Heifetz
Publisher: Humana
ISBN: 9781071617892
Category : Medical
Languages : en
Pages : 0

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Book Description
This volume looks at applications of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in drug design. The chapters in this book describe how AI/ML/DL approaches can be applied to accelerate and revolutionize traditional drug design approaches such as: structure- and ligand-based, augmented and multi-objective de novo drug design, SAR and big data analysis, prediction of binding/activity, ADMET, pharmacokinetics and drug-target residence time, precision medicine and selection of favorable chemical synthetic routes. How broadly are these approaches applied and where do they maximally impact productivity today and potentially in the near future. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary software and tools, step-by-step, readily reproducible modeling protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and unique, Artificial Intelligence in Drug Design is a valuable resource for structural and molecular biologists, computational and medicinal chemists, pharmacologists and drug designers.

Author: Seyedali Mirjalili
Publisher: Springer
ISBN: 3030248356
Category : Technology & Engineering
Languages : en
Pages : 66

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Book Description
This book focuses on the most well-regarded and recent nature-inspired algorithms capable of solving optimization problems with multiple objectives. Firstly, it provides preliminaries and essential definitions in multi-objective problems and different paradigms to solve them. It then presents an in-depth explanations of the theory, literature review, and applications of several widely-used algorithms, such as Multi-objective Particle Swarm Optimizer, Multi-Objective Genetic Algorithm and Multi-objective GreyWolf Optimizer Due to the simplicity of the techniques and flexibility, readers from any field of study can employ them for solving multi-objective optimization problem. The book provides the source codes for all the proposed algorithms on a dedicated webpage.

Author: Nathan Brown
Publisher: Royal Society of Chemistry
ISBN: 1839160543
Category : Computers
Languages : en
Pages : 425

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Book Description
Following significant advances in deep learning and related areas interest in artificial intelligence (AI) has rapidly grown. In particular, the application of AI in drug discovery provides an opportunity to tackle challenges that previously have been difficult to solve, such as predicting properties, designing molecules and optimising synthetic routes. Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation. Providing a wealth of information from leading experts in the field this book is ideal for students, postgraduates and established researchers in both industry and academia.

Author: Dirk P. Kroese
Publisher: CRC Press
ISBN: 1000730778
Category : Business & Economics
Languages : en
Pages : 538

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Book Description
Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code

Author: David L. Poole
Publisher: Cambridge University Press
ISBN: 110719539X
Category : Computers
Languages : en
Pages : 821

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Book Description
Artificial Intelligence presents a practical guide to AI, including agents, machine learning and problem-solving simple and complex domains.

Author: Giuseppe Bonaccorso
Publisher: Packt Publishing Ltd
ISBN: 1785884514
Category : Computers
Languages : en
Pages : 352

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Book Description
Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide. Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation. Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide. Who This Book Is For This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here. What You Will Learn Acquaint yourself with important elements of Machine Learning Understand the feature selection and feature engineering process Assess performance and error trade-offs for Linear Regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector machines Implement clusters to a dataset Explore the concept of Natural Processing Language and Recommendation Systems Create a ML architecture from scratch. In Detail As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously. On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem. Style and approach An easy-to-follow, step-by-step guide that will help you get to grips with real -world applications of Algorithms for Machine Learning.

Author: Marco Dorigo
Publisher: MIT Press
ISBN: 9780262042192
Category : Computers
Languages : en
Pages : 324

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Book Description
An overview of the rapidly growing field of ant colony optimization that describes theoretical findings, the major algorithms, and current applications. The complex social behaviors of ants have been much studied by science, and computer scientists are now finding that these behavior patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behavior, the ability to find what computer scientists would call shortest paths, has become the field of ant colony optimization (ACO), the most successful and widely recognized algorithmic technique based on ant behavior. This book presents an overview of this rapidly growing field, from its theoretical inception to practical applications, including descriptions of many available ACO algorithms and their uses. The book first describes the translation of observed ant behavior into working optimization algorithms. The ant colony metaheuristic is then introduced and viewed in the general context of combinatorial optimization. This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings. The book surveys ACO applications now in use, including routing, assignment, scheduling, subset, machine learning, and bioinformatics problems. AntNet, an ACO algorithm designed for the network routing problem, is described in detail. The authors conclude by summarizing the progress in the field and outlining future research directions. Each chapter ends with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises. Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn how to implement ACO algorithms.