Optimization Machine Learning Fuzzy Logic (19 results)

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Taschenbuch. Condition: 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. Condition: 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. Condition: 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.…

Buch. Condition: 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.…

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