Dynamic Neutrosophic Cognitive Map With Improved Cuckoo Search Algorithm Dncm Icsa And Ensemble Classifier For Rheumatoid Arthritis
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Dynamic Neutrosophic Cognitive Map With Improved Cuckoo Search Algorithm Dncm Icsa And Ensemble Classifier For Rheumatoid Arthritis
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Author : B. Chithra
language : en
Publisher: Infinite Study
Release Date :
Dynamic Neutrosophic Cognitive Map With Improved Cuckoo Search Algorithm Dncm Icsa And Ensemble Classifier For Rheumatoid Arthritis written by B. Chithra and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with Mathematics categories.
Rheumatoid Arthritis (RA) falls under the group of chronic autoimmune diseases, which affects the joints and muscles, and can lead to considerable damage to the joint structure and their functionality. RA diagnosis much in early stages is quite critical in stopping the progression of the disease. In this technical work, Dynamic Neutrosophic Cognitive Map with Improved Cuckoo Search Algorithm (DNCM-ICSA) with ensemble classifier is introduced for obtaining the gene expression profile, which distinguishes between the persons affected with RA and probable subjects of control.
Handbook Of Research On The Applications Of Neutrosophic Sets Theory And Their Extensions In Education
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Author : Broumi, Said
language : en
Publisher: IGI Global
Release Date : 2023-06-09
Handbook Of Research On The Applications Of Neutrosophic Sets Theory And Their Extensions In Education written by Broumi, Said and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-09 with Mathematics categories.
Fuzzy sets have experienced multiple expansions since their conception to enhance their capacity to convey complex information. Intuitionistic fuzzy sets, image fuzzy sets, q-rung orthopair fuzzy sets, and neutrosophic sets are a few of these extensions. Researchers and academics have acquired a lot of information about their theories and methods for making decisions. However, only a small number of research findings discuss how neutrosophic sets theory and their extensions (NSTEs) are used in education. The Handbook of Research on the Applications of Neutrosophic Sets Theory and Their Extensions in Education implements fresh scientific approaches to enhance the quality of decisions under neutrosophic environments, particularly within education. Covering key topics such as data modeling, educational technologies, decision making, and learning management systems, this major reference work is ideal for instructional designers, researchers, academicians, scholars, practitioners, instructors, and students.
Ncmpy A Modelling Software For Neutrosophic Cognitive Maps Based On Python Package
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Author : Ilanthenral Kandasamy
language : en
Publisher: Infinite Study
Release Date :
Ncmpy A Modelling Software For Neutrosophic Cognitive Maps Based On Python Package written by Ilanthenral Kandasamy and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Cognitive Maps are a vital tool that can be used for knowledge representation and reasoning. Fuzzy Cognitive Maps (FCMs) are popular soft computing techniques used to model large and complex systems, and they can aid in explainable AI. FCMs, however, cannot model the indeterminacy that arises in a system due to various uncertainties. Neutrosophic Cognitive Maps (NCMs), upgraded FCMs that could model indeterminacy, was introduced to address this issue. NCMs are a generalization of FCMs, a field of cognitive science firmly based on neural networks. NCMs have been used to solve a wide range of problems. NCMs were introduced in 2002, and even after 20 years, NCMs do not have any supportive software or package, toolbox, or visualization software like FCMs. The main reason for the absence of dedicated software is due to the indeterminacy concept ‘I’ and how it has to be handled. This paper presents the dedicated Python package created for handling the functioning of NCMs. The modelling software presented in this paper aids in visualizing the NCMs as a signed digraph with indeterminacy that is directed signed neutrosophic graph. This package implements a sample case study using NCMs.
Soft Computing And Machine Learning
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Author : Mohd Anas Wajid
language : en
Publisher: CRC Press
Release Date : 2025-04-28
Soft Computing And Machine Learning written by Mohd Anas Wajid and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-28 with Computers categories.
This reference text covers the theory and applications of soft computing and machine learning and presents readers with the intelligent fuzzy and neutrosophic rules that require situations where classical modeling approaches cannot be utilized, such as when there is incomplete, unclear, or imprecise information at hand or inadequate data. It further illustrates topics such as image processing, and power system analysis. This book: Discusses soft computing techniques including fuzzy Logic, rough sets, neutrosophic sets, neural networks, generative adversarial networks, and evolutionary computation Examines novel and contemporary advances in the fields of soft computing, fuzzy computing, neutrosophic computing, and machine learning systems, as well as their applications in real life Serves as a comprehensive reference for applying machine learning and neutrosophic sets in real-world applications such as smart cities, healthcare, and the Internet of Things Covers topics such as image processing, bioinformatics, natural language processing, supply chain management, and cybernetics Illustrates classification of neutrosophic machine learning, neutrosophic reinforcement learning, and applications of neutrosophic machine learning in emerging industries The text is written for senior undergraduate students, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.
Innovative Computing And Communications
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Author : Aboul Ella Hassanien
language : en
Publisher: Springer Nature
Release Date : 2024-09-26
Innovative Computing And Communications written by Aboul Ella Hassanien and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-26 with Technology & Engineering categories.
This book includes high-quality research papers presented at the Seventh International Conference on Innovative Computing and Communication (ICICC 2024), which is held at the Shaheed Sukhdev College of Business Studies, University of Delhi, Delhi, India, on 16–17 February 2024. Introducing the innovative works of scientists, professors, research scholars, students, and industrial experts in the field of computing and communication, the book promotes the transformation of fundamental research into institutional and industrialized research and the conversion of applied exploration into real-time applications.
Computational Intelligence In Engineering And Project Management
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Author : Pedro Yobanis Piñero Pérez
language : en
Publisher: Springer Nature
Release Date : 2024-02-19
Computational Intelligence In Engineering And Project Management written by Pedro Yobanis Piñero Pérez and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-19 with Computers categories.
This book is dedicated to all those interested in the application of artificial intelligence in engineering and project management. Most of the jobs are focused on achieving agile project development. New algorithms that combine various computational intelligence techniques are applied in different areas of knowledge in project management. In this book, computational intelligence is presented as the branch of AI that encompasses various techniques aimed at simulating human tolerance in decision-making processes in environments with uncertainty and imprecision. Among the precursor techniques of computational intelligence are: evolutionary algorithms, artificial neural networks, fuzzy set theory, and fuzzy systems. However, other areas such as the rough set, linguistic data summary, natural language processing, the conversational systems, fuzzy cognitive maps, collective intelligence, the neutrosophic theory, and other fuzzy logic extensions are contributing to the application and extension of computational intelligence The book is organized into three parts, as shown below. The first part constitutes a critical review of computational intelligence in project management. The second part presents new computational intelligence techniques and their applications in project planning, control, and monitoring processes. In particular, the use of conversational systems and their applicability in the agile management of portfolio programs and projects stand out. Part three of the book exemplifies the use of computing techniques with words and other computational intelligence techniques for organizational decision-making. The authors of the book stand out for their extensive experience in the development of basic and applied applications of computational intelligence. The authors Janusz Kacprzyk, Pedro Y. Piñero Pérez, Rafael E. Bello Pérez, and Iliana Pérez Pupo have published several books associated with artificial intelligence and computational intelligence applied to projects. They continue working on fundamental-oriented and applied research on different artificial intelligence techniques to help with decision-making in different areas of knowledge. Authors would like to thank all the engineers, professors, and researchers without whose efforts this book could not have been written.
Deep Reinforcement Learning And Its Industrial Use Cases
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Author : Shubham Mahajan
language : en
Publisher: John Wiley & Sons
Release Date : 2024-10-29
Deep Reinforcement Learning And Its Industrial Use Cases written by Shubham Mahajan and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-29 with Computers categories.
This book serves as a bridge connecting the theoretical foundations of DRL with practical, actionable insights for implementing these technologies in a variety of industrial contexts, making it a valuable resource for professionals and enthusiasts at the forefront of technological innovation. Deep Reinforcement Learning (DRL) represents one of the most dynamic and impactful areas of research and development in the field of artificial intelligence. Bridging the gap between decision-making theory and powerful deep learning models, DRL has evolved from academic curiosity to a cornerstone technology driving innovation across numerous industries. Its core premise—enabling machines to learn optimal actions within complex environments through trial and error—has broad implications, from automating intricate decision processes to optimizing operations that were previously beyond the reach of traditional AI techniques. “Deep Reinforcement Learning and Its Industrial Use Cases: AI for Real-World Applications” is an essential guide for anyone eager to understand the nexus between cutting-edge artificial intelligence techniques and practical industrial applications. This book not only demystifies the complex theory behind deep reinforcement learning (DRL) but also provides a clear roadmap for implementing these advanced algorithms in a variety of industries to solve real-world problems. Through a careful blend of theoretical foundations, practical insights, and diverse case studies, the book offers a comprehensive look into how DRL is revolutionizing fields such as finance, healthcare, manufacturing, and more, by optimizing decisions in dynamic and uncertain environments. This book distills years of research and practical experience into accessible and actionable knowledge. Whether you’re an AI professional seeking to expand your toolkit, a business leader aiming to leverage AI for competitive advantage, or a student or academic researching the latest in AI applications, this book provides valuable insights and guidance. Beyond just exploring the successes of DRL, it critically examines challenges, pitfalls, and ethical considerations, preparing readers to not only implement DRL solutions but to do so responsibly and effectively. Audience The book will be read by researchers, postgraduate students, and industry engineers in machine learning and artificial intelligence, as well as those in business and industry seeking to understand how DRL can be applied to solve complex industry-specific challenges and improve operational efficiency.
Artificial Intelligence In Project Management And Making Decisions
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Author : Pedro Y. Piñero Pérez
language : en
Publisher: Springer Nature
Release Date : 2022-04-07
Artificial Intelligence In Project Management And Making Decisions written by Pedro Y. Piñero Pérez and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-07 with Technology & Engineering categories.
This book presents new developments and advances in the theory, applications, and design methods of computational intelligence, integrated in various areas of project management and BIM environments. The chapters of the book span different soft computing techniques, such as: linguistic data summarization, fuzzy systems, evolutionary algorithms, estimation distribution algorithms, computing with words, augmented reality, and hybrid intelligence systems. In addition, different applications of the neutrosophic theory are presented for the treatment of uncertainty and indeterminacy in decision-making processes. Several chapters of the book constitute systematic reviews, useful for future investigations in the following topics: linguistic summarization of data, augmented reality, and the development of BIM technologies. It is a particularly interesting book for engineers, researchers, specialists, teachers, and students related to project management and the development of BIM technologies.
Neutrosophic Computing And Machine Learning Vol 22
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Author : Florentin Smarandache
language : es
Publisher: Infinite Study
Release Date : 2022-07-01
Neutrosophic Computing And Machine Learning Vol 22 written by Florentin Smarandache and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-01 with Mathematics categories.
Neutrosophic Computing and Machine Learning (NCML) es una revista académica que ha sido creada para publicaciones de estudios avanzados en neutrosofía, conjunto neutrosófico, lógica neutrosófica, probabilidad neutrosófica, estadística neutrosófica, enfoques neutrosóficos para el aprendizaje automático, etc. y sus aplicaciones en cualquier campo. Some papers in this issue: Florentin Smarandache. Introducción a los conjuntos IndetermSoft e IndetermHyperSoft; Jorge L Cárdenas Ponce, Jorge H Cárdenas Medina, Viviana Robles Ortiz, Jimena Mantilla García, Lisbeth Reales Chacón. H. Pylori y su Influencia negativa sobre el control glucémico del diabético tipo; Alejandro Sebastián Mayorga Garcés, Kathya Verónica Suaste Pazmiño, Carlos Omar Blacio Villa. Método multicriterio neutrosófico para clasificar las fuentes de información sobre el uso crónico de inhibidores de la bomba de protones; Diana Lorena Jordán Fiallos, Rodrigo Estalin Ramos Sánchez, Steban Alejandro Vaca Ortiz. Método multicriterio neutrosófico para la evaluación de la apendicitis aguda con malrotación intestinal; Elsy Labrada González, Lina Neri Spinosa Pire, María del Carmen Yabor Labrada. Método neutrosófico multicriterio para la evaluación de los factores de riesgo y aspectos clínicos de la bronconeumonía bacteriana en terapia intensiva; Juan Alberto Rojas Cárdenas, Edmundo Enrique Pino Andrade, Mesías Elías Machado Maliza. Proceso Analítico Jerárquico Neutrosófico para evaluar la garantía de motivación en un estado constitucional de derecho; José Israel Castillo González, Andrea Katherine Miranda Anchundia, Liset Camaño Carballo. Método neutrosófico para la recomendación en la recuperación del espacio biológico perdido en odontología restauradora con prótesis fija; Luis Alberto Ortiz Zambrano, Andrés Humberto Layedra Torres, Mónica Lorena Valle Villamarín. Método multicriterio neutrosófico para la evaluación de lesiones de la vía biliar en colecistectomía laparoscópica en el Hospital Luis Vernaza; Luis Alberto Ortiz Zambrano, Melissa Eliana Del Campo Zapata, Jeanneth Elizabeth Jami Carrera. Evaluación multicriterio neutrosófica sobre el tratamiento con bomba de presión negativa para las lesiones de partes blandas en los miembros en pacientes diabéticos; and many others.
Ensemble Methods In Data Mining
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Author : Giovanni Seni
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2010
Ensemble Methods In Data Mining written by Giovanni Seni and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Computers categories.
"Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges -- from investment timing to drug discovery, and fraud detection to recommendation systems -- where predictive accuracy is more vital than model interpretability. Ensembles are useful with all modeling algorithms, but this book focuses on decision trees to explain them most clearly. After describing trees and their strengths and weaknesses, the authors provide an overview of regularization -- today understood to be a key reason for the superior performance of modern ensembling algorithms. The book continues with a clear description of two recent developments: Importance Sampling (IS) and Rule Ensembles (RE). IS reveals classic ensemble methods -- bagging, random forests, and boosting -- to be special cases of a single algorithm, thereby showing how to improve their accuracy and speed. REs are linear rule models derived from decision tree ensembles. They are the most interpretable version of ensembles, which is essential to applications such as credit scoring and fault diagnosis. Lastly, the authors explain the paradox of how ensembles achieve greater accuracy on new data despite their (apparently much greater) complexity."--Publisher's website.