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
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
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
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.