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47 articles for “fuzzy measures”
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Image Segmentation Using Credibility Critical Values
Abstract: In this paper, a method of creating fuzzy data matrix (a matrix whose elements are fuzzy variables) representing an image is proposed. Further, a study on image segmentation carried out on the basis of such a matrix is carried out. This work considers three methods of crisp conversion available in credibility theory. Their performances have been evaluated under k-means clustering and rough k-means clustering with the help of a mammogram …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 2, Issue 2, 2015 · pp. 46–55 Read article
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Level Control of Tank System: Comparative Approach
Abstract: Level measurement and control and monitoring are important for many products and processes in a diverse range of industries including manufacturing, storage and service industries. In this paper liquid level in the cylindrical tank is to be controlled at desired value. The composite controllers of proportional integral controller and proportional integral derivative controller was designed using Ziegler Nichols tuning for the cylinderical tank system. The fuzzy logic controller was implemented …
Published in Journal of Control & Instrumentation · Vol. 6, Issue 2, 2015 · pp. 9–12 Read article
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Bland-Altman Analysis for Evaluating AHP-based Wildlife Habitat Suitability Models
Abstract: The paper targets in establishing the potential of Bland-Altman analysis integrated with geospatial approach in habitat suitability ecological studies for species conservation. Assessing agreement between two measurement methods can be done by using Bland-Altman plots and calculating limits of agreement and hence compares two assay methods. This method is tried in this study for evaluating suitable habitats for tiger. The study incorporates Geographic Information System – Multi-Criteria Decision Analysis (GIS-MCDA) …
Published in Research & Reviews : Journal of Space Science & Technology · Vol. 4, Issue 2, 2015 · pp. 11–18 Read article
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Computational Intelligence and Neuro-Fuzzy Modelling of Polymer Composites: A Critical Review of Performance Prediction and Optimization
Abstract: The increased variety in polymer matrices, reinforcements, fillers, and processing parameters has led to the need to better understand the structure-property, process-property relationships in order to accurately predict and optimize the performance of polymer composites. This paper reviews the applications of computational intelligence methods in polymer composites, with special focus on artificial neural networks, adaptive neuro-fuzzy inference systems, machine learning techniques, and hybrid optimization. The literature is analyzed based on …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Alzheimer's Diseases Risk Level Classifications Using Chaos Technique
Abstract: This paper aim to classify Alzheimer's diseases risk level determining Dementia and Epilepsy which is a collective term used to describe the problems that people with various underlying brain disorders or damage can have with their memory, language and thinking. Alzheimer disease is the best known and most common disorder under the umbrella of Dementia and Epilepsy. Brain functions are measured in the hope of determining and differentiating certain functional …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 2, Issue 2, 2015 · pp. 36–46 Read article
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Short-Term Load Demand Forecasting using Chaos Theory and ANFIS
Abstract: In the electrical power sector, forecasting of load demand is an important process for effective planning of future expansion and periodical operations including unit commitments, fuel scheduling, short-term maintenance, security assessments, reducing spinning reserve, reliability analysis etc. Accurate load predictions are also necessary to utilize the electrical energy efficiently and to minimize the conflicts between the demand and supply of electricity. As electric load pattern of a region is very …
Published in Trends in Electrical Engineering · Vol. 6, Issue 2, 2016 · pp. 50–57 Read article
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Fuzzy C-Means Clustering for Effective Segmentation and Classification of Brain Tumors in MRI Scans
Abstract: The paper discusses the importance of detecting and classifying brain tumors via MRI for effective treatment. It proposes a framework utilizing the Fuzzy C-means clustering algorithm for segmentation, demonstrating improved performance through real dataset validation. The model is trained on a large, annotated MRI dataset to identify and classify different tumor types, enabling machine learning-based classification into benign and malignant tumors. The MATLAB-based solution automates brain tumor feature extraction, aiding …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 2, 2024 · pp. 23–28 Read article