scholarly journals Enhancing Recommendation Quality of Content-based Filtering through Collaborative Predictions and Fuzzy Similarity Measures

2012 ◽  
Vol 38 ◽  
pp. 939-944 ◽  
Author(s):  
Vibhor Kant ◽  
Kamal K. Bharadwaj
Keyword(s):  
Similarity Measures ◽  
Fuzzy Similarity ◽  
Content Based Filtering

scholarly journals Fuzzy logic in air pollution: Revisited

2014 ◽  
Vol 2 ◽  
pp. 1-5
Author(s):  
A. Deshpande
Keyword(s):  
Air Pollution ◽  
Fuzzy Logic ◽  
Air Quality ◽  
Epistemic Uncertainty ◽  
Similarity Measures ◽  
Epidemiological Studies ◽  
Fuzzy Similarity ◽  
Parametric Data ◽  
Numeric Data ◽  
Pollution Exposure

In everyday life and field, people mostly deal with concepts that involve factors that defy classification into crisp sets. The decisions people usually make are perceptions without rigorous analysis of numeric data. Like in other field of studies, there may exist imprecision in air quality parametric data collected and in the perception made by air quality experts in defining these parameters in linguistic terms such as: very good, good, poor. This is the reason why over the past few decades, soft computing tools such as fuzzy logic based methods, neural networks, and genetic algorithms have had significant and growing impacts to deal with aleatory as well as epistemic uncertainty in air quality related issues. This paper has highlighted mathematical preliminaries of air pollution studies like Similarity Measures (Cosine Amplitude Method), Fuzzy to Crisp Conversion (Alpha cut method), Fuzzy c Mean Clustering, Zadeh-Deshpande (ZD) Approach and linguistic description of air quality. Similarly, the applications of fuzzy similarity measures and fuzzy c mean clustering with defined possibility (- cut) levels in case air pollution studies for Delhi, India have been reflected. Though the approach of using fuzzy logic in pollution studies are not of common practice, the comprehensive approach that involves air pollution exposure surveys, toxicological data, and epidemiological studies coupled with fuzzy modeling will go a long way toward resolving some of the divisiveness and controversy in the current regulatory paradigm.

Novel Similarity Measures, Entropy of Intuitionistic Fuzzy Sets and their Application in Software Quality Evaluation

2021 ◽  
Author(s):  
Xuan Thao Nguyen ◽  
Shuo Yan Chou
Keyword(s):  
Fuzzy Sets ◽  
Similarity Measures ◽  
Intuitionistic Fuzzy Sets ◽  
Entropy Measure ◽  
Uncertain Information ◽  
Membership Functions ◽  
Software Projects ◽  
Intuitionistic Fuzzy ◽  
Software Quality Evaluation

Abstract Intuitionistic fuzzy sets (IFSs), including member and nonmember functions, have many applications in managing uncertain information. The similarity measures of IFSs proposed to represent the similarity between different types of sensitive fuzzy information. However, some existing similarity measures do not meet the axioms of similarity. Moreover, in some cases, they could not be applied appropriately. In this study, we proposed some novel similarity measures of IFSs constructed by combining the exponential function of membership functions and the negative function of non-membership functions. In this paper, we also proposed a new entropy measure as a stepping stone to calculate the weights of the criteria in the proposed multi-criteria decision making (MCDM) model. The similarity measures used to rank alternatives in the model. Finally, we used this MCDM model to evaluate the quality of software projects.

A Feature Selection-Based Method for an Ontological Enrichment Process in Geographic Knowledge Modelling

2017 ◽  
pp. 407-426
Author(s):  
Mohamed Farah ◽  
Hafedh Nefzi ◽  
Imed Riadh Farah
Keyword(s):  
Feature Selection ◽  
Similarity Measures ◽  
Geographic Information ◽  
Information Representation ◽  
Ontology Alignment ◽  
Research Projects ◽  
Selection Methods ◽  
Geographic Knowledge ◽  
Enrichment Process

Nowadays, geographic information becomes too complex and abundant, thus recent research projects have been undertaken to make it manageable and exploitable. Ontologies are considered as a valuable support for geographic information representation. Building geographic ontologies could be viewed as an enrichment process. Alignment of concepts coming from different ontologies is central to the enrichment process and deeply affects the quality of the resulting ontology. The alignment of ontologies is based on using similarity measures. In the literature, there are many models for ontology alignment that mainly differ with respect to the similarity measures they use and the way they are combined. Most of the alignment methods do not deal with the problem of correlation between similarity measures. In this chapter, we address this issue to better decide which similarity measures we should consider to better assess the true similarity between concepts. Our proposal consists of using feature selection methods, in order to select a reduced set of relevant similarity measures.

scholarly journals The effect of similarity measures on the quality of query clusters

2004 ◽  
Vol 30 (5) ◽  
pp. 396-407 ◽  
Author(s):  
Lin Fu ◽  
Dion Hoe-Lian Goh ◽  
Schubert Shou-Boon Foo
Keyword(s):  
Similarity Measures

scholarly journals Construction of Quality Indicators Based on Pre-Established Goals: Application to a Colombian Public University

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1075
Author(s):  
Vicente Liern ◽  
Sandra E. Parada-Rico ◽  
Olga Blasco-Blasco
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Gender Inequality ◽  
Multiple Criteria Decision Making ◽  
Similarity Measures ◽  
Public University ◽  
Strategic Decisions ◽  
Economic Disparity

This study creates indicators of adequacy and excellence based on multiple-criteria decision-making (MCDM) methods and fuzzy logic. The calculation of indicators presents two main difficulties: The nature of the data (numerical, interval, and linguistic values are mixed) and the objective of each criterion (which does not have to reach either the maximum or the minimum). A method is proposed, based on similarity measures with predetermined ideals, that is capable of overcoming these difficulties to provide easy-to-interpret information about the quality of the alternatives. To illustrate the usefulness of this proposed method, it has been applied to data collected from students across nine semesters at the Bucaramanga campus of the Industrial University of Santander in Colombia. This case study demonstrates that the proposed method can facilitate strategic decisions at an institution and open the way for the establishment of action policies regarding gender inequality and economic disparity, among other things.

Efficacy of Fuzzy-Stat Modelling in Classification of Gynaecologists and Patients

2016 ◽  
Vol 25 (2) ◽  
pp. 147-157 ◽  
Author(s):  
Anjali Sardesai ◽  
Vilas Kharat ◽  
Pradip Sambarey ◽  
Ashok Deshpande
Keyword(s):  
Medical Science ◽  
Similarity Measures ◽  
Disease Diagnosis ◽  
Diagnostic Process ◽  
Mathematical Framework ◽  
Domain Experts ◽  
Binary Logic ◽  
Fuzzy Similarity ◽  
Medical Diagnostic ◽  
Inference Systems

AbstractFuzzy logic-based inference systems depend on the domain experts’ perceptions, which are intrinsically imprecise/vague/fuzzy. The perceptions of more than one expert are needed in the decision-making process. Therefore, there is a need to study the similarity between the experts using a mathematical framework. Classical mathematical models simulating the medical diagnostic process are usually either logical or probabilistic, wherein the concept of partial belief is not considered. Except in a few cases, binary logic is too unrealistic to apply to medical diagnosis. Another important factor in medical science is the patient-symptom relationship, which influences the disease diagnosis. In summary, the following two issues stand out: (i) Do experts agree with one another in arriving at the same diagnostic labels? (ii) Based on the symptom-patient relationship, can patients be classified? The authors have tried to explore the possibility of using fuzzy similarity measures and also Gower’s coefficient in classifying gynaecologists and patients. The comparative evaluation infers that the efficacy of two-valued binary logic-based Gower’s coefficient is low.

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