Название: Machine Learning and Mechanics Based Soft Computing ApplicationsАвтор: Thi Dieu Linh Nguyen, Joan Lu
Издательство: Springer
Серия: Studies in Computational Intelligence
Год: 2023
Страниц: 323
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB
This book highlights recent advances in the area of Machine Learning and robotics-based soft computing applications. The book covers various Artificial Intelligence, Machine Learning, and mechanics, a mix of mechanical computational engineering work. The current computing era has a huge market/potential for Machine Learning, robotics, and soft computing techniques and their applications. With this in view, the book shares latest research and cutting-edge applications useful for professionals and researchers in these areas.
In recent years, as some of the key drivers Industry 4.0, the enabling technologies including Artificial Intelligence, Big Data, Cloud Computing, Internet of Things, block chain, robotics, and Digital Twin have been widely attracting a lot of attention from researchers and practitioners. In addition, the development of soft computing techniques has enabled researchers to create more fidelity data-driven models and provide innovative solutions to real-world problems.
This book provides the latest research findings in the emerging technologies with special focus on soft computing intelligent techniques and applications in various fields of engineering. Starting from Artificial Intelligence to mechanics, robotics and how technology is helping in management is covered very selectively. We believe that the collection of these research works will be proved helping to give a support edge to many research problems.
In the chapter "Heuristic Methods Solving Markowitz Mean-Variance Portfolio Optimization Problem", we introduce two heuristic methods for solving Markowitz mean-variance portfolio optimization problem with cardinality constraints and bounding on variables: genetic algorithm (GA) and heuristic branching (HB) with some proposed improvements. There are exact methods for solving the problem: outer approximation, branch-and-bound, etc.
This study ("Context-Based and Collaboration-Based Product Recommendation Approaches for a Clothes Online Sale") proposes a recommendation system for a Clothes Online Sale system based on analyzing context-based and collaboration-based methods. Each type was divided into memory-based and model-based approaches. The results give the same product, but the cosine distance of the Word2vec + IDF algorithm is the lowest. We have also deployed algorithms including the K-nearest neighbor’s algorithm (KNN), singular value decomposition (SVD), non-negative matrix factorization (NMF), and matrix factorization (MF) for the comparison. The method is evaluated on Amazon women’s clothing, including 50,046 samples and six features. We proposed a content-based memory-based method using Word2vec + IDF and a collaboration-based model-based method using the SVD algorithm with the result of RSME as 1.268 to deploy on the sales system.
In this study ("Particle Swarm Optimization for Acceleration Tracking Control of an Actuator System"), a platform of a fluid-power actuator system with a combination of electro-hydraulic and pneumatic for acceleration tracking control is proposed. Furthermore, a control strategy is provided to obtain high-performance results in controlling the piston's motion. Here, the particle swarm optimization (PSO), a computational method, is appropriately utilized for selecting the parameters of the classical proportional integral derivative (PID) control. The tracking errors are eliminated without the challenge of the tuning process, and the control performance is further enhanced.
The book shares the highlights about the:
• 6G discussion about futuristic approach
• Artificial Intelligence and management
• Machine Learnings and Industry 4.0
• Mathematics and computational drives’ applications
• Mechanics and robotics applications
• Soft computing applications
The book is a comprehensive resource and will be beneficial for students, engineers, researchers, and practitioners who want to explore intelligent computing algorithms as well as harness different smart and cutting-edge technologies in solving current challenging engineering problems.
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