Blended intelligence of FCA with FLC for knowledge representation from clustered data in medical analysis

Abstract

<span lang="EN-IN">Formal concept analysis is the process of data analysis mechanism with emergent attractiveness across various fields such as data mining, robotics, medical, big data and so on. FCA is helpful to generate the new learning ontology based techniques. In medical field, some growing kids are facing the problem of representing their knowledge from their gathered prior data which is in the form of unordered and insufficient clustered data which is not supporting them to take the right decision on right time for solving the uncertainty based questionnaires. In the approach of decision theory, many mathematical replicas such as probability-allocation, crisp set, and fuzzy based set theory were designed to deals with knowledge representation based difficulties along with their characteristic. This paper is proposing new ideological blended approach of FCA with FLC and described with major objectives: primarily the FCA analyzes the data based on relationships between the set of objects of prior-attributes and the set of attributes based prior-data, which the data is framed with data-units implicated composition which are formal statements of idea of human thinking with conversion of significant intelligible explanation. Suitable rules are generated to explore the relationship among the attributes and used the formal concept analysis from these suitable rules to explore better knowledge and most important factors affecting the decision making. Secondly how the FLC derive the fuzzification, rule-construction and defuzzification methods implicated for representing the accurate knowledge for uncertainty based questionnaires. Here the FCA is projected to expand the FCA based conception with help of the objective based item set notions considered as the target which is implicated with the expanded cardinalities along with its weights which is associated through the fuzzy based inference decision rules. This approach is more helpful for medical experts for knowing the range of patient’s memory deficiency also for people whose are facing knowledge explorer deficiency.</span>

An open computing language‐based parallel Brute Force algorithm for formal concept analysis on heterogeneous architectures

“…FCA research is important in various applications, such as data mining, robotics, and medicine. The scientific community carried out studies combining FCA and a Fuzzy Logic Controller (FLC), 25 to obtain responses from human action. This approach analyzes data that have relations between the set of objects and previous attributes of human thought ideas.…”

Arquitetura Heterogênea CPU+FPGA para Análise Formal de Conceitos

“…As pesquisas em FCA possuem grande importância em várias aplicações, como por exemplo em mineração de dados, robótica, medicina, etc. Sendo assim, como apresentado em [Neelima and Sarma 2019], uma nova abordagem baseada em FCA e Fuzzy Logic Controller (FLC)é estudada, para obter respostas de uma ação humana, em que, a parte de FCA analisa os dados de uma base que possuem relações entre o conjunto de objetos e atributos prévios de ideias de pensamentos humanos, e gera conceitos/regras para serem aplicados na FLC, construindo regras Fuzzy, que permitem a obtenção de respostas de uma ação posteriormente. Aárea de FCA tambémé uma boa alternativa para identificar estruturas conceituais em rede social, como pode ser visto em [Neto et al 2018a].…”

A New design of fuzzy logic controller optimized By PSO-SCSO applied To SFO-DTC induction motor drive

“…In recent years, the phenomenal development of digital electronics has demonstrated the merit of using fuzzy logic algorithms to control many complex systems. Nowadays, fuzzy logic controllers (FLCs) [5][6][7] have been widely used successfully in industrial control system applications, such as: Electrical engineering [8,9], Medical field [10], Renewable energy [11][12][13][14], Robotics [15][16][17]. These complex applications are nonlinear and do not require good knowledge of their exact mathematical models.…”