ISBN: 9786205521199 - soft computing techniques for breast cancer detection (8 resultados)

Condición: New.

Taschenbuch. Condición: Neu. Soft computing Techniques for Breast Cancer Detection | Srinivasa Rao P (u. a.) | Taschenbuch | Englisch | 2023 | Scholars' Press | EAN 9786205521199 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. …

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Soft Computing (SC) has emerged as a versatile tool for solving complex computational problems across various fields. SC leverages human-like recognition and learning capabilities to provide innovative solutions to real-world challenges. In an era of data explosion, effective data processing requires selecting key attributes for predictive modelling, leading to the demand for feature subset selection. Feature subset selection is a challenging NP-Hard problem, with various methods categorized into filter, wrapper, and embedded approaches. Metaheuristic algorithms, known for global search capabilities, have been harnessed for feature selection to maximize classification accuracy. With a focus on medical applications, this study explores computer-aided diagnosis, where population-based feature selection methods enhance classification accuracy by reducing analysis time. The research introduces two novel metaheuristic methods, Separated Enemy Driven Dragon Algorithm (SEDDA) and Fitness-based Crow Search Algorithm (FSCA), and compares them with established techniques. 92 pp. Englisch.…

Condición: New. Print on Demand.

Condición: New. PRINT ON DEMAND.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Soft Computing (SC) has emerged as a versatile tool for solving complex computational problems across various fields. SC leverages human-like recognition and learning capabilities to provide innovative solutions to real-world challenges. In an era of data e.…

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Soft Computing (SC) has emerged as a versatile tool for solving complex computational problems across various fields. SC leverages human-like recognition and learning capabilities to provide innovative solutions to real-world challenges. In an era of data explosion, effective data processing requires selecting key attributes for predictive modelling, leading to the demand for feature subset selection. Feature subset selection is a challenging NP-Hard problem, with various methods categorized into filter, wrapper, and embedded approaches. Metaheuristic algorithms, known for global search capabilities, have been harnessed for feature selection to maximize classification accuracy. With a focus on medical applications, this study explores computer-aided diagnosis, where population-based feature selection methods enhance classification accuracy by reducing analysis time. The research introduces two novel metaheuristic methods, Separated Enemy Driven Dragon Algorithm (SEDDA) and Fitness-based Crow Search Algorithm (FSCA), and compares them with established techniques.…

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Soft Computing (SC) has emerged as a versatile tool for solving complex computational problems across various fields. SC leverages human-like recognition and learning capabilities to provide innovative solutions to real-world challenges. In an era of data explosion, effective data processing requires selecting key attributes for predictive modelling, leading to the demand for feature subset selection. Feature subset selection is a challenging NP-Hard problem, with various methods categorized into filter, wrapper, and embedded approaches. Metaheuristic algorithms, known for global search capabilities, have been harnessed for feature selection to maximize classification accuracy. With a focus on medical applications, this study explores computer-aided diagnosis, where population-based feature selection methods enhance classification accuracy by reducing analysis time. The research introduces two novel metaheuristic methods, Separated Enemy Driven Dragon Algorithm (SEDDA) and Fitness-based Crow Search Algorithm (FSCA), and compares them with established techniques.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 92 pp. Englisch.…