Optimization machine learning fuzzy (32 résultats)

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Buch. Etat : Neu. Computational Optimization | Machine Learning and Fuzzy Systems | Narinder Kaur (u. a.) | Buch | Mathematical Methods in the Digital Age | XII | Englisch | 2026 | Walter de Gruyter | EAN 9783119143769 | Verantwortliche Person für die EU: Walter de Gruyter GmbH, De Gruyter GmbH, Genthiner Str. 13, 10785 Berlin, productsafety[at]degruyterbrill[dot]com | Anbieter: preigu. …

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Etat : Sehr gut. Zustand: Sehr gut | Seiten: 317 | Sprache: Englisch | Produktart: Bücher | Data science, quantitative analysis, computer optimisation, and machine learning are all explored in this book. Insightful mathematical analysis of ML models is presented in this work. Various fields, including healthcare, banking, and e-commerce, are covered in this book through case studies. To choose features, hyperparameters, and models, the latter shows how optimisation can be implemented. Practical insights on the assessment and improvement of machine learning models in real-world situations, such as e-commerce, healthcare, banking, and agriculture, are included. Professionals in the fields of research, engineering, mathematics, and computer science are the target audience for this book. Through the integration of theoretical principles with practical applications, the usage of computational optimisation techniques in real-time settings is to be enhanced. …

Buch. Etat : Neu. Neuware - Data science, quantitative analysis, computer optimisation, and machine learning are all explored in this book. Insightful mathematical analysis of ML models is presented in this work. Various fields, including healthcare, banking, and e-commerce, are covered in this book through case studies. To choose features, hyperparameters, and models, the latter shows how optimisation can be implemented. Practical insights on the assessment and improvement of machine learning models in real-world situations, such as e-commerce, healthcare, banking, and agriculture, are included. Professionals in the fields of research, engineering, mathematics, and computer science are the target audience for this book. Through the integration of theoretical principles withpractical applications, the usage of computational optimisation techniques in real-time settings is to be enhanced.…

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Taschenbuch. Etat : Neu. A Renewable Energy Selection Model for Sustainable Development | Integrating Hesitant Fuzzy Logic and Machine Learning for Renewable Energy Optimization | Virendra Singh Rana (u. a.) | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786208437046 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.…

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Achieving SDG 7, universal access to affordable, reliable, and clean energy, is vital for tackling the energy crisis and ensuring sustainable development. In Uttarakhand, despite rich renewable potential, energy access is hindered by challenging terrain, ecological sensitivity, and infrastructure gaps. This research proposes a hybrid decision-making framework combining Machine Learning (ML) with Hesitant Fuzzy Multi-Criteria Decision-Making (MCDM) to identify the best renewable energy options for the region. Five alternatives, solar PV, solar thermal, CSP, mini & small hydropower, and bioenergy, were chosen based on resources and expert input. Using bibliometric analysis in R and the Nominal Group Technique (NGT), criteria were set. The Hesitant Fuzzy AHP assigned weights, while H-FTOPSIS ranked options. Logistic regression enhanced prediction accuracy, and sensitivity analysis tested model stability. Results show solar PV as the most viable choice. The framework supports strategic, evidence-based energy planning for Uttarakhand and offers a scalable, adaptable method for similar regions worldwide.…

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Optimization, machine learning, and fuzzy logic are fundamental in the field of computational intelligence, each contributing to solving complex problems across various domains. Optimization techniques focus on finding the best solutions to problems by improving efficiency and minimizing resources. Machine learning enables systems to learn from data, making predictions or decisions without being programmed. Fuzzy logic deals with uncertainty and imprecision, allowing for flexible decision-making processes. Together, these theories, algorithms, and applications solve challenges in fields such as engineering, finance, and healthcare, where traditional methods often fall short. The continued application and exploration of these disciplines may unveil new possibilities for advanced problem-solving and intelligent systems. Optimization, Machine Learning, and Fuzzy Logic: Theory, Algorithms, and Applications explores optimization techniques, fuzzy logic, and their integration with machine learning. It covers fundamental concepts, mathematical foundations, algorithms, and applications, providing a holistic understanding of these domains. This book covers topics such as disease detection, deep learning, and text analysis, and is a useful resource for engineers, data scientists, medical professionals, academicians, and researchers.…