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Rahim M, Abosuliman SS, Alroobaea R, Shah K, Abdeljawad T. Cosine similarity and distance measures for p , q - quasirung orthopair fuzzy sets: Applications in investment decision-making. Heliyon 2024; 10:e32107. [PMID: 38961947 PMCID: PMC11219331 DOI: 10.1016/j.heliyon.2024.e32107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 05/26/2024] [Accepted: 05/28/2024] [Indexed: 07/05/2024] Open
Abstract
Similarity measures and distance measures are used in a variety of domains, such as data clustering, image processing, retrieval of information, and recognizing patterns, in order to measure the degree of similarity or divergence between elements or datasets. p , q - quasirung orthopair fuzzy ( p , q - QOF) sets are a novel improvement in fuzzy set theory that aims to properly manage data uncertainties. Unfortunately, there is a lack of research on similarity and distance measure between p , q - QOF sets. In this paper, we investigate different cosine similarity and distance measures between to p , q - quasirung orthopair fuzzy sets ( p , q - ROFSs). Firstly, the cosine similarity measure and the Euclidean distance measure for p , q - QOFSs are defined, followed by an exploration of their respective properties. Given that the cosine measure does not satisfy the similarity measure axiom, a method is presented for constructing alternative similarity measures for p , q - QOFSs. The structure is based on the suggested cosine similarity and Euclidean distance measures, which ensure adherence to the similarity measure axiom. Furthermore, we develop a cosine distance measure for p , q - QOFSs that connects similarity and distance measurements. We then apply this technique to decision-making, taking into account both geometric and algebraic perspectives. Finally, we present a practical example that demonstrates the proposed justification and efficacy of the proposed method, and we conclude with a comparison to existing approaches.
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Affiliation(s)
- Muhammad Rahim
- Department of Mathematics, Hazara University, Mansehra, 21300, PKP, Pakistan
| | - Shougi Suliman Abosuliman
- Department of Supply Chain and Maritime Business, Faculty of Maritime Studies, King Abdulaziz University, Jeddah, 21588, Saudi Arabia
| | - Roobaea Alroobaea
- Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif, 21944, Saudi Arabia
| | - Kamal Shah
- Department of Mathematics and Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia
- Department of Mathematics, University of Malakand, Chakdara, Dir(L), KPK, Pakistan
| | - Thabet Abdeljawad
- Department of Mathematics and Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia
- Department of Medical Research, China Medical University, Taichung, 40402, Taiwan
- Department of Mathematics and Applied Mathematics, School of Science and Technology, Sefako Makgatho Health Sciences University, Ga-Rankuwa, South Africa
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Nie J. A novel IFN-CSM-CoCoSo approach for multiple-attribute group decision-making with intuitionistic fuzzy sets: An application in assessing corporate social responsibility performance. Heliyon 2024; 10:e29207. [PMID: 38623234 PMCID: PMC11016726 DOI: 10.1016/j.heliyon.2024.e29207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 03/27/2024] [Accepted: 04/02/2024] [Indexed: 04/17/2024] Open
Abstract
With the rapid growth of the economy, enterprises have encountered a series of problems while pursuing economic benefits, such as food safety and environmental pollution issues, resource shortages and energy consumption issues, which affect the sustainable development of enterprises. Establishing a corporate performance evaluation system from the perspective of social responsibility, based on stakeholder theory and the importance of overall goals reflected in the weight of social responsibility indicators, is a very effective measure to achieve corporate social responsibility (CSR) goals through CSR motivation and stakeholders. The performance evaluation of CSR from the perspective of environmental accounting is a MAGDM. Recently, the CoCoSo technique and cosine similarity measure (CSM) technique was utilized to conduct the MAGDM. The intuitionistic fuzzy sets (IFSs) are utilized as a technique for conducting uncertain information during the performance evaluation of CSR from the perspective of environmental accounting. In this study, the intuitionistic fuzzy CoCoSo based on the CSM (IFN-CSM-CoCoSo) technique is built for MAGDM with IFSs. Finally, a numerical example for performance evaluation of CSR from the perspective of environmental accounting is conducted to verify the IFN-CSM-CoCoSo technique.
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Affiliation(s)
- Jing Nie
- School of Accounting, Jilin Business and Technology College, Changchun, 130507, Jilin, China
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Ma H, Wang Y, Hao Z, Yu Y, Jia X, Li M, Chen L. Classification of Alzheimer's disease: application of a transfer learning deep Q-network method. Eur J Neurosci 2024; 59:2118-2127. [PMID: 38282277 DOI: 10.1111/ejn.16261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 12/25/2023] [Accepted: 01/08/2024] [Indexed: 01/30/2024]
Abstract
Early diagnosis is crucial to slowing the progression of Alzheimer's disease (AD), so it is urgent to find an effective diagnostic method for AD. This study intended to investigate whether the transfer learning approach of deep Q-network (DQN) could effectively distinguish AD patients using local metrics of resting-state functional magnetic resonance imaging (rs-fMRI) as features. This study included 1310 subjects from the Consortium for Reliability and Reproducibility (CoRR) and 50 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) GO/2. The amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF) and percent amplitude of fluctuation (PerAF) were extracted as features using the Power 264 atlas. Based on gender bias in AD, we searched for transferable similar parts between the CoRR feature matrix and the ADNI feature matrix, resulting in the CoRR similar feature matrix served as the source domain and the ADNI similar feature matrix served as the target domain. A DQN classifier was pre-trained in the source domain and transferred to the target domain. Finally, the transferred DQN classifier was used to classify AD and healthy controls (HC). A permutation test was performed. The DQN transfer learning achieved a classification accuracy of 86.66% (p < 0.01), recall of 83.33% and precision of 83.33%. The findings suggested that the transfer learning approach using DQN could be an effective way to distinguish AD from HC. It also revealed the potential value of local brain activity in AD clinical diagnosis.
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Affiliation(s)
- Huibin Ma
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
- Key Laboratory of Autonomous Intelligence and Information Processing in Heilongjiang Province, Jiamusi, China
| | - Yadan Wang
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
- Key Laboratory of Autonomous Intelligence and Information Processing in Heilongjiang Province, Jiamusi, China
| | - Zeqi Hao
- School of Psychology, Zhejiang Normal University, Jinhua, China
| | - Yang Yu
- Department of Psychiatry, the second affiliated hospital of Zhejiang University school of Medicine, Zhejiang, China
| | - Xize Jia
- Department of Radiology, Changshu No. 2 People's Hospital, The Affiliated Changshu Hospital of Xuzhou Medical University, Changshu, China
| | - Mengting Li
- School of Psychology, Zhejiang Normal University, Jinhua, China
| | - Lanfen Chen
- School of Medical Imaging, Weifang Medical University, Weifang, China
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Zafar A, Wajid B, Shabbir A, Gohar Awan F, Ahsan M, Ahmad S, Wajid I, Anwar F, Mazhar F. Unearthing Insights into Metabolic Syndrome by Linking Drugs, Targets, and Gene Expressions Using Similarity Measures and Graph Theory. Curr Comput Aided Drug Des 2024; 20:773-783. [PMID: 37592790 DOI: 10.2174/1573409920666230817101913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 06/13/2023] [Accepted: 07/05/2023] [Indexed: 08/19/2023]
Abstract
AIMS AND OBJECTIVES Metabolic syndrome (MetS) is a group of metabolic disorders that includes obesity in combination with at least any two of the following conditions, i.e., insulin resistance, high blood pressure, low HDL cholesterol, and high triglycerides level. Treatment of this syndrome is challenging because of the multiple interlinked factors that lead to increased risks of type-2 diabetes and cardiovascular diseases. This study aims to conduct extensive in silico analysis to (i) find central genes that play a pivotal role in MetS and (ii) propose suitable drugs for therapy. Our objective is to first create a drug-disease network and then identify novel genes in the drug-disease network with strong associations to drug targets, which can help in increasing the therapeutical effects of different drugs. In the future, these novel genes can be used to calculate drug synergy and propose new drugs for the effective treatment of MetS. METHODS For this purpose, we (i) investigated associated drugs and pathways for MetS, (ii) employed eight different similarity measures to construct eight gene regulatory networks, (iii) chose an optimal network, where a maximum number of drug targets were central, (iv) determined central genes exhibiting strong associations with these drug targets and associated disease-causing pathways, and lastly (v) employed these candidate genes to propose suitable drugs. RESULTS Our results indicated (i) a novel drug-disease network complex, with (ii) novel genes associated with MetS. CONCLUSION Our developed drug-disease network complex closely represents MetS with associated novel findings and markers for an improved understanding of the disease and suggested therapy.
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Affiliation(s)
- Alwaz Zafar
- Ibn Sina Research & Development Division, Sabz-Qalam, Lahore, 54000, Pakistan
| | - Bilal Wajid
- Ibn Sina Research & Development Division, Sabz-Qalam, Lahore, 54000, Pakistan
- Department of Electrical Engineering, University of Engineering and Technology, Lahore, 54000, Pakistan
| | - Ans Shabbir
- Ibn Sina Research & Development Division, Sabz-Qalam, Lahore, 54000, Pakistan
| | - Fahim Gohar Awan
- Department of Electrical Engineering, University of Engineering and Technology, Lahore, 54000, Pakistan
| | - Momina Ahsan
- Ibn Sina Research & Development Division, Sabz-Qalam, Lahore, 54000, Pakistan
| | - Sarfraz Ahmad
- Ibn Sina Research & Development Division, Sabz-Qalam, Lahore, 54000, Pakistan
| | - Imran Wajid
- Ibn Sina Research & Development Division, Sabz-Qalam, Lahore, 54000, Pakistan
- Department of Social Sciences, Istanbul Commerce University, Istanbul, Turkey
| | - Faria Anwar
- Outpatient Department, Mayo Hospital, Lahore, 54000, Pakistan
| | - Fazeelat Mazhar
- Department of Biomedical, Electrical and System Engineering, University of Bologna, Cesena Campus, Bologna, Italy
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Alahmadi RA, Ganie AH, Al-Qudah Y, Khalaf MM, Ganie AH. Multi-attribute decision-making based on novel Fermatean fuzzy similarity measure and entropy measure. GRANULAR COMPUTING 2023; 8:1-21. [PMID: 38625150 PMCID: PMC10068732 DOI: 10.1007/s41066-023-00378-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 03/13/2023] [Indexed: 04/05/2023]
Abstract
To deal with situations involving uncertainty, Fermatean fuzzy sets are more effective than Pythagorean fuzzy sets, intuitionistic fuzzy sets, and fuzzy sets. Applications for fuzzy similarity measures can be found in a wide range of fields, including clustering analysis, classification issues, medical diagnosis, etc. The computation of the weights of the criteria in a multi-criteria decision-making problem heavily relies on fuzzy entropy measurements. In this paper, we employ t-conorms to suggest various Fermatean fuzzy similarity measures. We have also discussed all of their interesting characteristics. Using the suggested similarity measurements, we have created some new entropy measures for Fermatean fuzzy sets. By using numerical comparison and linguistic hedging, we have established the superiority of the suggested similarity metrics and entropy measures over the existing measures in the Fermatean fuzzy environment. The usefulness of the proposed Fermatean fuzzy similarity measurements is shown by pattern analysis. Last but not least, a novel multi-attribute decision-making approach is described that tackles a significant flaw in the order preference by similarity to the ideal solution, a conventional approach to decision-making, in a Fermatean fuzzy environment.
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Affiliation(s)
- Reham A. Alahmadi
- Basic Sciences Department, College of Science and Theoretical Studies, Saudi Electronic University, PO Box 93499, Riyadh, 11673 Kingdom of Saudi Arabia
| | - Abdul Haseeb Ganie
- Department of Mathematics, National Institute of Technology, Warangal, Telangana 506004 India
| | - Yousef Al-Qudah
- Department of Mathematics, Faculty of Arts and Science, Amman Arab University, Amman, 11953 Jordan
| | - Mohammed M. Khalaf
- Department of Mathematics, Higher Institute of Engineering and Technology, King Marriott, P.O. Box 3135, Egypt, Egypt
| | - Abdul Hamid Ganie
- Basic Sciences Department, College of Science and Theoretical Studies, Saudi Electronic University, PO Box 93499, Riyadh, 11673 Kingdom of Saudi Arabia
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