Fuzzy Logic Decision Research Articles

Using Fuzzy Logic Decision Support System to Predict the Lifted Weight for Students at Weightlifting Class

Using Fuzzy Logic Decision Support System to Predict the Lifted Weight for Students at Weightlifting Class

Using Fuzzy Logic Decision Support System to Predict the Lifted Weight for Students at Weightlifting Class

Using Fuzzy Logic Decision Support System to Predict the Lifted Weight for Students at Weightlifting Class

Using Fuzzy Logic Decision Support System to Predict the Lifted Weight for Students at Weightlifting Class

Using Fuzzy Logic Decision Support System to Predict the Lifted Weight for Students at Weightlifting Class

Using Fuzzy Logic Decision Support System to Predict the Lifted Weight for Students at Weightlifting Class

Using Fuzzy Logic Decision Support System to Predict the Lifted Weight for Students at Weightlifting Class

Integrating a fuzzy-logic decision support system with bridge information modelling and cost estimation at conceptual design stage of concrete box-girder bridges

Integrating 3D bridge information modelling (BrIM) with construction technologies had inspired many researchers for the past decade. In this study, research objectives are intended to demonstrate the viability of integrating a 3D computer-aided design (3D-CAD) model with a structural analysis application and bridge cost estimation framework without compromising interoperability matters. An integrated model that relates a fuzzy logic decision support system with cost estimation for concrete box-girder bridges is presented. Model development methodology comprises an integrated preliminary cost estimation system (IPCES), and complex quality functions and deployment of a multi-criteria decision making (MCDM) approach. An actual case project is used to validate and illustrate model corresponding estimating capabilities. The proposed model is engineered to enhance existing techniques implemented by bridge stakeholders and designers to prepare cost estimates at the conceptual design stage by taking into consideration box-girder bridge project site preparations, substructure, and superstructure. The proposed model is anticipated to be of major significance to designers and its contribution resides into the integration of BrIM technologies with cost estimation approaches.

Modeling of Intelligent System Thinking in Complex Adaptive Systems

The use of proposed technology of Modeling of Intelligent Systems Thinking in Complex Adaptive systems is oriented on use in autonomous, intelligent Fuzzy Control and Information Complex Adaptive systems, that operate in real-time, fuzzy conditions, heterogeneous subject areas and multilingual communications, there the situations are unknown in advance, fuzzy structured and not clearly regulated. The technology consists in extracting of relevant data, information and knowledge from texts (speech) in various natural languages, within dissimilar subject areas for implementation of fuzzy control of data, information and knowledge in light of representing them in knowledge bases and organizing on basis of them the processes of Reasoning and Systems Thinking under uncertainty and in Fuzzy Environment. The technology is formalized using Fuzzy Logic, Situational Control theories, Linguistics, Artificial Intelligence, Knowledge base technologies and is defined by methods of a) Situational Control of fuzzy data, information and knowledge, b) Knowledge Representation, Generalization and Explanation, c) Modeling of Fuzzy Logic Inference, Decision making and Fuzzy Control, d) Intelligent Fuzzy Reasoning and Systems Thinking.

Comparative Study of Multimodal Biometric Recognition by Fusion of Iris and Fingerprint

This research investigates the comparative performance from three different approaches for multimodal recognition of combined iris and fingerprints: classical sum rule, weighted sum rule, and fuzzy logic method. The scores from the different biometric traits of iris and fingerprint are fused at the matching score and the decision levels. The scores combination approach is used after normalization of both scores using the min-max rule. Our experimental results suggest that the fuzzy logic method for the matching scores combinations at the decision level is the best followed by the classical weighted sum rule and the classical sum rule in order. The performance evaluation of each method is reported in terms of matching time, error rates, and accuracy after doing exhaustive tests on the public CASIA-Iris databases V1 and V2 and the FVC 2004 fingerprint database. Experimental results prior to fusion and after fusion are presented followed by their comparison with related works in the current literature. The fusion by fuzzy logic decision mimics the human reasoning in a soft and simple way and gives enhanced results.

Multiple Targets Classification and Fuzzy Logic Decision Fusion in Wireless Sensor Networks

Multiple Targets Classification and Fuzzy Logic Decision Fusion in Wireless Sensor Networks

Multiple Targets Classification and Fuzzy Logic Decision Fusion in Wireless Sensor Networks

Multiple Targets Classification and Fuzzy Logic Decision Fusion in Wireless Sensor Networks

Landslide Susceptibility Mapping for Poulrood Earth Fill Dam Reservoir (The Comparison of Two Methods) Iran, Guilan Province

The purpose of this study is to produce the Landslide susceptibility zonation map for Poulrood earth fill dam reservoir with using fuzzy muti criteria decision analysis and fuzzy logic methods. To obtain the Fuzzy relations for producing the susceptibility map, a landslide inventory database is obtained by both field surveys and desk studies. To produce susceptibility map with User define and Gaussian functions then landslide related parameters such as lithology, distance from faults, distance from drainage, slope angle, slope aspect, slope curvature, distance from road, NDVI, earth quick horizontal acceleration and Precipitation were used in the landslide susceptibility analysis. The results obtained in this study showed that the frequency ratio can be used as a simple tool in assessment of landslide susceptibility when a sufficient number of data were obtained. The prediction performance of the susceptibility map is checked by considering quality sum amount ( ). According to landslide susceptibility index for different parameters, it was found that the aspect, lithology and drainage system are the most effective parameters in the study area. With investigation get values of quality sum index( ), landslide density for two types of function in two methods(fuzzy logic and Fuzzy multi criteria decision analysis) and attention to good work of operator Gamma in landslide susceptibility mapping, produced map with Gaussian function and Gamma0.7 operator (fuzzy logic method) has topmost quality.

Site selection for wastewater treatment plant using integrated fuzzy logic and multicriteria decision model: A case study in Kahak, Iran

One of the environmental issues in urban planning is finding a suitable site for constructing infrastructures such as water and wastewater treatment plants. There are numerous factors to be considered for this purpose, which make decision-making a complex task. We used an integrated fuzzy logic and multicriteria decision model to select a suitable site for establishing wastewater treatment plant in Kahak, Iran. We used super decision software and a geographic information system (GIS) for scoring the parameters. The western part of Kahak was found to be a suitable place for constructing municipal wastewater treatment plant. Our findings indicated that decision makers and policy makers would be able to achieve better results concerning the most suitable location for wastewater treatment plant easily through combining these two models.

Development of a New Approach to Aid in Visual Identification of Murine iPS Colonies Using a Fuzzy Logic Decision Support System

The a priori identification of induced pluripotent stem cells remains a challenge. Being able to quickly identify the most embryonic stem cell-similar induced pluripotent stem cells when validating results could help to reduce costs and save time. In this context, tools based on non-classic logic can be useful in creating aid-systems based on visual criteria. True colonies when viewed at 100x magnification have been found to have the following 3 characteristics: a high degree of border delineation, a more uniform texture, and the absence of a cracked texture. These visual criteria were used for fuzzy logic modeling. We investigated the possibility of predicting the presence of alkaline phosphatase activity, typical of true induced pluripotent stem cell colonies, after 25 individuals, with varying degrees of experience in working with murine iPS cells, categorized the images of 136 colonies based on visual criteria. Intriguingly, the performance evaluation by area under the ROC curve (16 individuals with satisfactory performance), Spearman correlation (all statistically significant), and Cohen's Kappa agreement analysis (all statistically significant) demonstrates that the discriminatory capacity of different evaluators are similar, even those who have never cultivated cells. Thus, we report on a new system to facilitate visual identification of murine- induced pluripotent stem cell colonies that can be useful for staff training and opens the possibility of exploring visual characteristics of induced pluripotent stem cell colonies with their functional peculiarities. The fuzzy model has been integrated as a web-based tool named “2see-iPS” which is freely accessed at http://genetica.incor.usp.br/2seeips/.

Analyzing multilingual knowledge innovation in patents

In the process of analyzing knowledge innovation, it is necessary to identify the existing boundaries of knowledge so as to determine whether knowledge is new – outside these boundaries. For a patent to be granted, all aspects of the patent request must be studied to determine the patent innovation. Knowledge innovation for patent requests depends on analyzing current state of the art in multiple languages. Currently the process is usually limited to the languages and search terms the patent seeker knows. The paper describes a model for representing the patent request by a set of concepts related to a multilingual knowledge ontology. The search for patent knowledge is based on Fuzzy Logic Decision Support and allows a multilingual search. The model was analyzed using a twofold approach: a total of 104,296 patents from the United States Patent and Trademark Office were used to analyze the patent extraction process, and patents from the Korean, US, and Chinese patent offices were used in the analysis of the multilingual decision process. The results display high recall and precision and suggest that increasing the number of languages used only has minor effects on the model results.

Developing a Fuzzy Logic Decision Support System for Strategic Planning in Industrial Organizations

Internal – External (IE), Strategic Position and Action Evaluation (SPACE), Boston Consulting Group (BCG), and Grand Strategy matrices are important tools in generating and evaluating alternative output strategies which is the most important step in the formulation stage in strategic management process. Managers need to make difficult and important decisions on the basis of imprecise information and incomplete knowledge and select the right strategic position or alternative output strategies. Managers found it difficult to make the right decision when the values that resulted from the internal and external audits of an organization's environment fall in the middle or on the sharp edges in these matrices. It is found that each matrix depends on some factors that represent the internal and external environment of an organization and neglect the others. A systematic approach that incorporates fuzzy set theory in conjunction with strategic planning is proposed to assist managers in reaching the best alternative output set of strategies. The four matrices are combined by relations in order to integrate more than two fuzzy factors in selecting the best alternative output strategies. This make the suggested strategic plan robust and help organizations with limited resources to neglect alternative output strategies that do not depend on the four matrices jointly. The implementation of the developed Fuzzy Logic Decision Support System (FLDSS) is done using MATLAB fuzzy logic tools and the relations between these matrices are entered as rules in the Graphical User Interface (GUI) screens. The model was validated by a case study on a Jordanian company, and the results were satisfactory.

Clinical Outcome Prediction in Aneurysmal Subarachnoid Hemorrhage Using Bayesian Neural Networks with Fuzzy Logic Inferences

Objective. The novel clinical prediction approach of Bayesian neural networks with fuzzy logic inferences is created and applied to derive prognostic decision rules in cerebral aneurysmal subarachnoid hemorrhage (aSAH). Methods. The approach of Bayesian neural networks with fuzzy logic inferences was applied to data from five trials of Tirilazad for aneurysmal subarachnoid hemorrhage (3551 patients). Results. Bayesian meta-analyses of observational studies on aSAH prognostic factors gave generalizable posterior distributions of population mean log odd ratios (ORs). Similar trends were noted in Bayesian and linear regression ORs. Significant outcome predictors include normal motor response, cerebral infarction, history of myocardial infarction, cerebral edema, history of diabetes mellitus, fever on day 8, prior subarachnoid hemorrhage, admission angiographic vasospasm, neurological grade, intraventricular hemorrhage, ruptured aneurysm size, history of hypertension, vasospasm day, age and mean arterial pressure. Heteroscedasticity was present in the nontransformed dataset. Artificial neural networks found nonlinear relationships with 11 hidden variables in 1 layer, using the multilayer perceptron model. Fuzzy logic decision rules (centroid defuzzification technique) denoted cut-off points for poor prognosis at greater than 2.5 clusters. Discussion. This aSAH prognostic system makes use of existing knowledge, recognizes unknown areas, incorporates one's clinical reasoning, and compensates for uncertainty in prognostication.

Application of fuzzy analysis with the energy condition of bioactive points to the prediction and diagnosis of gastrointestinal tract diseases

For many diseases, new methods and models for their prediction, diagnosis and analysis are sought. We apply mathematical models for the interaction of the internal and biologically active points of meridian structures and can thereby specify diseases for which reflex diagnostics and reflex therapy methods are the most effective; amongst these are diseases of the gastrointestinal tract. The use of fuzzy logic decision-making yields good results for the prediction and early diagnosis of such diseases, depending on the reaction energy of biologically active points (acupuncture points).

Explaining Risk Attitude in Framing Tasks by Regulatory Focus: A Verbal Protocol Analysis and a Simulation Using Fuzzy Logic

We investigate the role of salient regulatory focus for risk attitude in framed gambles. In Experiment 1 we measured regulatory focus by collecting verbal protocols and found that people avoided risk under prevention focus and preferred risk under promotion focus. In Experiment 2 the same result was found when measuring people's regulatory focus with a questionnaire. Finally, the questionnaire data were used as inputs for simulating the choices of our participants by a fuzzy-logic decision generator. The findings show that regulatory focus has a strong effect on risk attitude and that risk attitude in framing tasks can be successfully modeled as some form of fuzzy processing.

A tool for site prioritisation of marine protected areas under data poor conditions

Abstract A challenge of managing data poor fisheries lies in overcoming uncertainties associated with a lack of information on biological and socio-economic conditions. This paper focuses on site prioritisation for marine protected areas, a process that usually imposes moderate to heavy demands on data, time, local expertise, and funds. A fuzzy logic decision support tool for zoning marine areas that is suitable for use in data poor conditions is developed. This tool, the Protected Area Suitability Index (PASI), assesses the suitability of sites for protection based on fishers’ preferences for that site and the site’s conservation value. Only eight input attributes are required to run the PASI, which operates on a series of heuristic rules to estimate a site suitability score that ranges from 0 to 10, where 10 indicates that a site is highly suitable for being protected from fishing. Sensitivity analysis reveals that the PASI is relatively robust, and produces reliable results even as the system is degraded by the loss of data. Eighteen actively fished sites and 11 sites that are closed to fishing are assessed to evaluate how well the PASI is able to distinguish differences between sites. The estimated scores are significantly different (p

A Novel Method for Automatic Identification of Motion Artifact Beats in ECG Recordings

This paper presents a novel method for automatic identification of motion artifact beats in ECG recordings. The proposed method is based on the ECG complexes clustering, fuzzy logic and multi-parameters decision. Firstly, eight simulated datasets with different signal-to-noise ratio (SNR) were built for identification experiments. Results show that the identification sensitivity of our method is sensitive to SNR levels and acts like a low-pass filter that matches the cardiologists' recognition, while the Norm FP rate and PVB FP rate keep significantly low regardless of SNR. Furthermore, a simulated dataset including random durations of motion activities superimposed segments and two clinical datasets acquired from two different commercial recorders were adopted for the evaluation of accuracy and robustness. The overall identification results on these datasets were: sensitivity >94.69%, Norm FP rate <0.60% and PVB FP rate <2.65%. All the results were obtained without any manual threshold adjustment according to the priori information, thus dissolving the drawbacks of previous published methods. Additionally, the total cost time of our method applied to 24 h recordings is less than 1 s, which is extremely suitable in the situation of magnanimity data in long-term ECG recordings.

Off-line control of the postprandial glycemia in type 1 diabetes patients by a fuzzy logic decision support

The target of this paper is to describe the use of fuzzy techniques in the development of a decision support system that allows the optimization of postprandial glycemia in type 1 diabetes patients taking into account the kind of meal taken by patients, the preprandial glycemia and the insulin resistance (the response of the body to insulin dose injection therapy). The decision support system can, in many cases, provide patients with the correct number of rapid insulin units that must be assumed to assure an optimal glycemic profile, keeping the blood glucose level close to the homeostatic condition, several hours after the meal.