Evolutionary Machine Learning Techniques: Algorithms and Applications, Mirjalili Seyedali, Faris Hossam, Aljarah Ibrahim
Автор: Raymond Chiong; Thomas Weise; Zbigniew Michalewicz Название: Variants of Evolutionary Algorithms for Real-World Applications ISBN: 3642440584 ISBN-13(EAN): 9783642440588 Издательство: Springer Рейтинг: Цена: 20896.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book offers a comprehensive discussion of the ways that Evolutionary Algorithms can be adapted to real-world problems such as production process planning, inventory system and supply chain network optimisation, soil properties analysis and more.
Описание: Addresses the complex realm of machine learning and its applications for solving various real-world problems in a variety of disciplines, such as manufacturing, business, information retrieval, and security. This premier reference source is essential for professors, researchers, and students in artificial intelligence as well as computer science and engineering.
Описание: This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics.
Автор: Ernesto Sanchez; Giovanni Squillero; Alberto Tonda Название: Industrial Applications of Evolutionary Algorithms ISBN: 364244346X ISBN-13(EAN): 9783642443466 Издательство: Springer Рейтинг: Цена: 15672.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Ideal as a reference both for experienced users and novices, this publication combines a thorough introduction to evolutionary computation with details of its application to real-world problems and advice on tackling a wealth of issues in its implementation.
Описание: Chapter 1. Introduction: Optimization and Metaheuristics Algorithms.- Chapter 2. Metaheuristics Paradigms for Renewable Energy Systems: Advances in Optimization Algorithms.- Chapter 3. Tackling Power Quality Issues using Metaheuristics.- Chapter 4. Meta-Heuristic application in Suppression of Noise.- Chapter 5. A review on Genetic Algorithm and its application in Power system Engineering.- Chapter 6. Different Variants of Particle Swarm Optimization Algorithms and its Application: A Review.- Chapter 7. Application of Metaheuristics in Power Electronics.- Chapter 8. Cuckoo Search Algorithm: A Review of Recent Variants and Engineering Applications.- Chapter 9. Energy Management System for Hybrid Energy System: Renewable Integration, modeling & optimization, control aspects and conceptual framework.- Chapter 10. Recent Advances and Application of Metaheuristic Algorithms: A survey (2014-2020).
Автор: Seyedali Mirjalili; Hossam Faris; Ibrahim Aljarah Название: Evolutionary Machine Learning Techniques ISBN: 9813299894 ISBN-13(EAN): 9789813299894 Издательство: Springer Рейтинг: Цена: 25155.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy networks, modular neural networks, physical neural networks, and deep neural networks. The book provides essential definitions, literature reviews, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the pros and cons of classical training algorithms. It features a range of proven and recent nature-inspired algorithms used to train different types of artificial neural networks, including genetic algorithm, ant colony optimization, particle swarm optimization, grey wolf optimizer, whale optimization algorithm, ant lion optimizer, moth flame algorithm, dragonfly algorithm, salp swarm algorithm, multi-verse optimizer, and sine cosine algorithm. The book also covers applications of the improved artificial neural networks to solve classification, clustering, prediction and regression problems in diverse fields.
Автор: Rik Das, Siddhartha Bhattacharyya, Sudarshan Nandy Название: Machine Learning Applications: Emerging Trends ISBN: 3110608537 ISBN-13(EAN): 9783110608533 Издательство: Walter de Gruyter Цена: 18586.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
The publication is attempted to address emerging trends in machine learning applications. Recent trends in information identification have identified huge scope in applying machine learning techniques for gaining meaningful insights. Random growth of unstructured data poses new research challenges to handle this huge source of information. Efficient designing of machine learning techniques is the need of the hour. Recent literature in machine learning has emphasized on single technique of information identification. Huge scope exists in developing hybrid machine learning models with reduced computational complexity for enhanced accuracy of information identification. This book will focus on techniques to reduce feature dimension for designing light weight techniques for real time identification and decision fusion. Key Findings of the book will be the use of machine learning in daily lives and the applications of it to improve livelihood. However, it will not be able to cover the entire domain in machine learning in its limited scope. This book is going to benefit the research scholars, entrepreneurs and interdisciplinary approaches to find new ways of applications in machine learning and thus will have novel research contributions. The lightweight techniques can be well used in real time which will add value to practice.
Описание: Artificial intelligence is at the forefront of research and implementation in many industries including healthcare and agriculture. Whether it's detecting disease or generating algorithms, deep learning techniques are advancing exponentially. Researchers and professionals need a platform in which they can keep up with machine learning trends and their developments in the real world.
The Handbook of Research on Applications and Implementations of Machine Learning Techniques provides innovative insights into the multi-disciplinary applications of machine learning algorithms for data analytics. The content within this publication examines disease identification, neural networks, and language support. It is designed for IT professionals, developers, data analysts, technology specialists, R&D professionals, industrialists, practitioners, researchers, academicians, and students seeking research on deep learning procedures and their enactments in the fields of medicine, engineering, and computer science.
Delve into the world of real-world financial applications using deep learning, artificial intelligence, and production-grade data feeds and technology with Python
Key Features
Understand how to obtain financial data via Quandl or internal systems
Automate commercial banking using artificial intelligence and Python programs
Implement various artificial intelligence models to make personal banking easy
Book Description
Remodeling your outlook on banking begins with keeping up to date with the latest and most effective approaches, such as artificial intelligence (AI). Hands-On Artificial Intelligence for Banking is a practical guide that will help you advance in your career in the banking domain. The book will demonstrate AI implementation to make your banking services smoother, more cost-efficient, and accessible to clients, focusing on both the client- and server-side uses of AI.
You'll begin by understanding the importance of artificial intelligence, while also gaining insights into the recent AI revolution in the banking industry. Next, you'll get hands-on machine learning experience, exploring how to use time series analysis and reinforcement learning to automate client procurements and banking and finance decisions. After this, you'll progress to learning about mechanizing capital market decisions, using automated portfolio management systems and predicting the future of investment banking. In addition to this, you'll explore concepts such as building personal wealth advisors and mass customization of client lifetime wealth. Finally, you'll get to grips with some real-world AI considerations in the field of banking.
By the end of this book, you'll be equipped with the skills you need to navigate the finance domain by leveraging the power of AI.
What you will learn
Automate commercial bank pricing with reinforcement learning
Perform technical analysis using convolutional layers in Keras
Use natural language processing (NLP) for predicting market responses and visualizing them using graph databases
Deploy a robot advisor to manage your personal finances via Open Bank API
Sense market needs using sentiment analysis for algorithmic marketing
Explore AI adoption in banking using practical examples
Understand how to obtain financial data from commercial, open, and internal sources
Who this book is for
This is one of the most useful artificial intelligence books for machine learning engineers, data engineers, and data scientists working in the finance industry who are looking to implement AI in their business applications. The book will also help entrepreneurs, venture capitalists, investment bankers, and wealth managers who want to understand the importance of AI in finance and banking and how it can help them solve different problems related to these domains. Prior experience in the financial markets or banking domain, and working knowledge of the Python programming language are a must.
Автор: Das, S.K., Das, S.P., Dey, N., Hassanien, A.-E. Название: Machine Learning Algorithms for Industrial Applications ISBN: 3030506401 ISBN-13(EAN): 9783030506407 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book explores several problems and their solutions regarding data analysis and prediction for industrial applications. Machine learning is a prominent topic in modern industries: its influence can be felt in many aspects of everyday life, as the world rapidly embraces big data and data analytics.
Описание: This book constitutes the refereed proceedings of the First Symposium on Machine Learning and Metaheuristics Algorithms, and Applications, held in Trivandrum, India, in December 2019. The 17 full papers and 6 short papers presented in this volume were thoroughly reviewed and selected from 53 qualified submissions.
This book is intended for academic and industrial developers, exploring and developing applications in the area of big data and machine learning, including those that are solving technology requirements, evaluation of methodology advances and algorithm demonstrations.
The intent of this book is to provide awareness of algorithms used for machine learning and big data in the academic and professional community. The 17 chapters are divided into 5 sections: Theoretical Fundamentals; Big Data and Pattern Recognition; Machine Learning: Algorithms & Applications; Machine Learning's Next Frontier and Hands-On and Case Study. While it dwells on the foundations of machine learning and big data as a part of analytics, it also focuses on contemporary topics for research and development. In this regard, the book covers machine learning algorithms and their modern applications in developing automated systems.
Subjects covered in detail include:
Mathematical foundations of machine learning with various examples.
An empirical study of supervised learning algorithms like Naпve Bayes, KNN and semi-supervised learning algorithms viz. S3VM, Graph-Based, Multiview.
Precise study on unsupervised learning algorithms like GMM, K-mean clustering, Dritchlet process mixture model, X-means and Reinforcement learning algorithm with Q learning, R learning, TD learning, SARSA Learning, and so forth.
Hands-on machine leaning open source tools viz. Apache Mahout, H2O.
Case studies for readers to analyze the prescribed cases and present their solutions or interpretations with intrusion detection in MANETS using machine learning.
Showcase on novel user-cases: Implications of Electronic Governance as well as Pragmatic Study of BD/ML technologies for agriculture, healthcare, social media, industry, banking, insurance and so on.
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