Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8651

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Author: search.filters.author.Baleanu, DumitruPublisher: search.filters.publisher.Elsevier Sci Ltd

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    The Hesitant Pythagorean Fuzzy Electre Iii: an Adaptable Recycling Method for Plastic Materials (Vol 291, 125281, 2021)
    (Elsevier Sci Ltd, 2024) Geetha, Selvaraj; Narayanamoorthy, Samayan; Kureethara, Joseph Varghese; Baleanu, Dumitru; Kang, Daekook
  • Article
    Citation - WoS: 28
    Citation - Scopus: 33
    The Hesitant Pythagorean Fuzzy Electre Iii: an Adaptable Recycling Method for Plastic Materials
    (Elsevier Sci Ltd, 2021) Narayanamoorthy, Samayan; Kureethara, Joseph Varghese; Baleanu, Dumitru; Kang, Daekook; Geetha, Selvaraj
    In this research article, introduce a novel decision making method called HPF-ELECTRE method by extending the ELECTRE III (ELimination and Choice Expressing REality) method with HPF (Hesitant Pythagorean Fuzzy) set. The efficiency of the new method is testing in the plastic recycling problem. One of the most hazardous domestic materials is plastic. The low biodegradability nature of plastic is a serious threat to the environment and to human life. Plastic is a synthetic chemical that do not belong to the natural world. Owing to the non-biodegradability, the only way to deal with this modern-world problem is recycling. Finding a suitable recycling method for disposing and recycling plastic materials is a major research issue. Propose the HPF-ELECTRE III method to find out the adaptable recycling method for plastics materials. The outranking in HPF-ELECTRE III method expand on concordance and discordance acceptability value values. Established method is an effective tool for decision making problems. (c) 2020 Elsevier Ltd. All rights reserved.
  • Article
    Citation - WoS: 8
    Citation - Scopus: 10
    Early Anomaly Prediction in Breast Thermogram by Hybrid Model Consisting of Superpixel Segmentation, Sparse Feature Descriptors and Extreme Learning Machine Classifier
    (Elsevier Sci Ltd, 2021) Sharma, Janki Ballabh; Maheshwari, Ranjan; Baleanu, Dumitru; Sharma, Ritam
    The breast thermograms can be used to detect location, physiological condition and vascular state of anomalous breast tissues. Most of the schemes reported in literature use breast tissues as region of interest (ROI) for feature extraction and breast anomaly detection. This paper presents a two-level hybrid method for breast thermogram anomaly detection. In the first stage, suspected region-based ROI segmentation model is developed. For this, thermally adaptive superpixels with spatial and temperature coherency are generated by applying linear iterative clustering on pre-processed breast thermograms. Different temperature regions are integrated by clustering superpixels. In the proposed method first and second highest temperature regions are considered as ROI to cover maximum anomalous regions which also make it robust against pseudo colouring. In second stage, shearlet transform is employed on the segmented ROI to obtain co-occurrence matrix-based feature descriptors. The problem of large coefficients in shearlet decomposition is overcome by selecting effective features using kernel principal component analysis technique. Extreme Learning Machine classifier is employed on a dataset of thermograms to classify the normal and anomalous thermogram. The obtained performance parameters demonstrate the classification accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F-1 score and area under curve of 95%, 93.33%, 96.66%, 96.55%, 93.54%, 94.91% and 95.11%, respectively. The efficacy of the proposed method is also verified by comparing the results; hence, it can be used for early anomaly detection.
  • Article
    Citation - WoS: 95
    Citation - Scopus: 109
    A Novel Assessment of Bio-Medical Waste Disposal Methods Using Integrating Weighting Approach and Hesitant Fuzzy Moosra
    (Elsevier Sci Ltd, 2020) Annapoorani, V.; Kang, Daekook; Baleanu, Dumitru; Jeon, Jeonghwan; Kureethara, Joseph Varghese; Ramya, L.; Narayanamoorthy, S.
    Bio-medical waste (BMW) management is highly important precaution for human health and environmental concern. There are several disposal treatment followed by medical practitioners in medical waste management. Here, a few disposal treatment is considered to be an alternatives. When assessing, it is necessary to evaluate and assume that all disposal treatment methods are safe and hygienic. In this way, every alternative assessment is evaluated based on the social acceptance, technology and operation, environmental protection, cost, noise and health risk. Finally the best alternative is chosen. When BMW is disposed and we select the best treatment method in BMW management, it can lead to multi-criteria decision making (MCDM) processes related to uncertain critical assessments. When making a decision, the decision makers having some hesitation to give their suggestions. Therefore, here we use hesitant MCDM method. In today's practice we have choose five methods of BMW disposal methods used in the medical world and we have its alternatives. One of these alternative is sorted by six criteria weights for selecting the best method. The main aim of this research paper is propose a new methodology of hesitant fuzzy weight finding technique, it is named as Hesitant Fuzzy Subjective and Objective Weight Integrated Approach (HF-SOWIA) and also propose a new hesitant fuzzy rank finding methodology, it is named as Hesitant Fuzzy Multi-Objective Optimization on the basis of Simple Ratio Analysis (HF-MOOSRA). After evaluation, the result shows that autoclaving is the best alternative for BMW disposal treatment methods. Furthermore, sensitivity analysis is make in order to observe the difference of alternative ranking when the importance of subjective and objective weights changes. (C) 2020 Elsevier Ltd. All rights reserved.
  • Article
    Citation - WoS: 21
    Citation - Scopus: 24
    Exploration of Dual Solutions for an Enhanced Cross Liquid Flow Past a Moving Wedge Under the Significant Impacts of Activation Energy and Chemical Reaction
    (Elsevier Sci Ltd, 2020) Zaib, A.; Baleanu, Dumitru; Sheikholeslami, M.; Wakif, Abderrahim; Khan, Umair
    The mathematical modeling and numerical simulation are conferred to offer the novel perception of binary chemical reaction with an activation energy aspect on magneto flow comprising Cross liquid inspired by a moving wedge. The influences of Soret and Dufour are also presented. The similarity procedure is utilized to modify the leading PDEs into a non-linear system of ODEs and then analyzed through a significant technique namely bvp4c based on the collocation method. The impacts of varying distinct parameters under the temperature and the velocity distribution are explored and discussed with the support of the graphs. The outcomes indicate that the multiple results are attained for a specific amount of shrinking/stretching constraint. Furthermore, the Weissenberg number reduces the skin factor and speed up the heat and mass transport rate in the lower and upper branch solutions. Also, an assessment of current results with earlier published literature is made in the limiting case.