An Approach to Select the Investment Based on Bipolar Picture Fuzzy Sets
Abstract Motivated by bipolar fuzzy (%$B_{p}%$F) sets (%$B_{p}%$FSs) and picture fuzzy sets (%$P_{c}%$FSs), we introduced the aggregation operators for the novel extension of %$P_{c}%$FSs named as bipolar picture fuzzy sets (B%$P_{c}%$FSs). Firstly, various arithmetic rules, scores and accuracy func...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sindhu, M. Sarwar [verfasserIn] Rashid, Tabasam [verfasserIn] Kashif, Agha [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© Taiwan Fuzzy Systems Association 2021 |
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| Übergeordnetes Werk: |
Enthalten in: International journal of fuzzy systems - Taibei : Association, 2006, 23(2021), 7 vom: 29. März, Seite 2335-2347 |
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| Übergeordnetes Werk: |
volume:23 ; year:2021 ; number:7 ; day:29 ; month:03 ; pages:2335-2347 |
| Links: |
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| DOI / URN: |
10.1007/s40815-021-01072-3 |
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| Katalog-ID: |
SPR045349282 |
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| 100 | 1 | |a Sindhu, M. Sarwar |e verfasserin |4 aut | |
| 245 | 1 | 3 | |a An Approach to Select the Investment Based on Bipolar Picture Fuzzy Sets |
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| 520 | |a Abstract Motivated by bipolar fuzzy (%$B_{p}%$F) sets (%$B_{p}%$FSs) and picture fuzzy sets (%$P_{c}%$FSs), we introduced the aggregation operators for the novel extension of %$P_{c}%$FSs named as bipolar picture fuzzy sets (B%$P_{c}%$FSs). Firstly, various arithmetic rules, scores and accuracy functions of the B%$P_{c}%$FSs are presented. Secondly, some aggregation operators of B%$P_{c}%$FSs are constructed to accumulate the bipolar picture fuzzy (B%$P_{c}%$F) data. Thirdly, the features of these aggregation operators are penned and then based on these aggregation operators a multiple criteria decision-making (MCDM) approach is developed to resolve the vague data. In the end, an illustrated example of the investment of money is put forward to show the authenticity and efficacy of the suggested approach. Moreover, B%$P_{c}%$F-TOPSIS, B%$P_{c}%$F-VIKOR, and sensitivity analysis (SA) have been used to provide the strength and practicality of the proposed MCDM model. | ||
| 650 | 4 | |a Fuzzy sets |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Picture fuzzy sets |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Aggregation operators |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Bipolar fuzzy sets |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Linear programming model |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Rashid, Tabasam |e verfasserin |4 aut | |
| 700 | 1 | |a Kashif, Agha |e verfasserin |4 aut | |
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10.1007/s40815-021-01072-3 doi (DE-627)SPR045349282 (SPR)s40815-021-01072-3-e DE-627 ger DE-627 rakwb eng 510 ASE 31.78 bkl Sindhu, M. Sarwar verfasserin aut An Approach to Select the Investment Based on Bipolar Picture Fuzzy Sets 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Taiwan Fuzzy Systems Association 2021 Abstract Motivated by bipolar fuzzy (%$B_{p}%$F) sets (%$B_{p}%$FSs) and picture fuzzy sets (%$P_{c}%$FSs), we introduced the aggregation operators for the novel extension of %$P_{c}%$FSs named as bipolar picture fuzzy sets (B%$P_{c}%$FSs). Firstly, various arithmetic rules, scores and accuracy functions of the B%$P_{c}%$FSs are presented. Secondly, some aggregation operators of B%$P_{c}%$FSs are constructed to accumulate the bipolar picture fuzzy (B%$P_{c}%$F) data. Thirdly, the features of these aggregation operators are penned and then based on these aggregation operators a multiple criteria decision-making (MCDM) approach is developed to resolve the vague data. In the end, an illustrated example of the investment of money is put forward to show the authenticity and efficacy of the suggested approach. Moreover, B%$P_{c}%$F-TOPSIS, B%$P_{c}%$F-VIKOR, and sensitivity analysis (SA) have been used to provide the strength and practicality of the proposed MCDM model. Fuzzy sets (dpeaa)DE-He213 Picture fuzzy sets (dpeaa)DE-He213 Aggregation operators (dpeaa)DE-He213 Bipolar fuzzy sets (dpeaa)DE-He213 Linear programming model (dpeaa)DE-He213 Rashid, Tabasam verfasserin aut Kashif, Agha verfasserin aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 23(2021), 7 vom: 29. März, Seite 2335-2347 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:23 year:2021 number:7 day:29 month:03 pages:2335-2347 https://dx.doi.org/10.1007/s40815-021-01072-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.78 ASE AR 23 2021 7 29 03 2335-2347 |
| spelling |
10.1007/s40815-021-01072-3 doi (DE-627)SPR045349282 (SPR)s40815-021-01072-3-e DE-627 ger DE-627 rakwb eng 510 ASE 31.78 bkl Sindhu, M. Sarwar verfasserin aut An Approach to Select the Investment Based on Bipolar Picture Fuzzy Sets 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Taiwan Fuzzy Systems Association 2021 Abstract Motivated by bipolar fuzzy (%$B_{p}%$F) sets (%$B_{p}%$FSs) and picture fuzzy sets (%$P_{c}%$FSs), we introduced the aggregation operators for the novel extension of %$P_{c}%$FSs named as bipolar picture fuzzy sets (B%$P_{c}%$FSs). Firstly, various arithmetic rules, scores and accuracy functions of the B%$P_{c}%$FSs are presented. Secondly, some aggregation operators of B%$P_{c}%$FSs are constructed to accumulate the bipolar picture fuzzy (B%$P_{c}%$F) data. Thirdly, the features of these aggregation operators are penned and then based on these aggregation operators a multiple criteria decision-making (MCDM) approach is developed to resolve the vague data. In the end, an illustrated example of the investment of money is put forward to show the authenticity and efficacy of the suggested approach. Moreover, B%$P_{c}%$F-TOPSIS, B%$P_{c}%$F-VIKOR, and sensitivity analysis (SA) have been used to provide the strength and practicality of the proposed MCDM model. Fuzzy sets (dpeaa)DE-He213 Picture fuzzy sets (dpeaa)DE-He213 Aggregation operators (dpeaa)DE-He213 Bipolar fuzzy sets (dpeaa)DE-He213 Linear programming model (dpeaa)DE-He213 Rashid, Tabasam verfasserin aut Kashif, Agha verfasserin aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 23(2021), 7 vom: 29. März, Seite 2335-2347 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:23 year:2021 number:7 day:29 month:03 pages:2335-2347 https://dx.doi.org/10.1007/s40815-021-01072-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.78 ASE AR 23 2021 7 29 03 2335-2347 |
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10.1007/s40815-021-01072-3 doi (DE-627)SPR045349282 (SPR)s40815-021-01072-3-e DE-627 ger DE-627 rakwb eng 510 ASE 31.78 bkl Sindhu, M. Sarwar verfasserin aut An Approach to Select the Investment Based on Bipolar Picture Fuzzy Sets 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Taiwan Fuzzy Systems Association 2021 Abstract Motivated by bipolar fuzzy (%$B_{p}%$F) sets (%$B_{p}%$FSs) and picture fuzzy sets (%$P_{c}%$FSs), we introduced the aggregation operators for the novel extension of %$P_{c}%$FSs named as bipolar picture fuzzy sets (B%$P_{c}%$FSs). Firstly, various arithmetic rules, scores and accuracy functions of the B%$P_{c}%$FSs are presented. Secondly, some aggregation operators of B%$P_{c}%$FSs are constructed to accumulate the bipolar picture fuzzy (B%$P_{c}%$F) data. Thirdly, the features of these aggregation operators are penned and then based on these aggregation operators a multiple criteria decision-making (MCDM) approach is developed to resolve the vague data. In the end, an illustrated example of the investment of money is put forward to show the authenticity and efficacy of the suggested approach. Moreover, B%$P_{c}%$F-TOPSIS, B%$P_{c}%$F-VIKOR, and sensitivity analysis (SA) have been used to provide the strength and practicality of the proposed MCDM model. Fuzzy sets (dpeaa)DE-He213 Picture fuzzy sets (dpeaa)DE-He213 Aggregation operators (dpeaa)DE-He213 Bipolar fuzzy sets (dpeaa)DE-He213 Linear programming model (dpeaa)DE-He213 Rashid, Tabasam verfasserin aut Kashif, Agha verfasserin aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 23(2021), 7 vom: 29. März, Seite 2335-2347 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:23 year:2021 number:7 day:29 month:03 pages:2335-2347 https://dx.doi.org/10.1007/s40815-021-01072-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.78 ASE AR 23 2021 7 29 03 2335-2347 |
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10.1007/s40815-021-01072-3 doi (DE-627)SPR045349282 (SPR)s40815-021-01072-3-e DE-627 ger DE-627 rakwb eng 510 ASE 31.78 bkl Sindhu, M. Sarwar verfasserin aut An Approach to Select the Investment Based on Bipolar Picture Fuzzy Sets 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Taiwan Fuzzy Systems Association 2021 Abstract Motivated by bipolar fuzzy (%$B_{p}%$F) sets (%$B_{p}%$FSs) and picture fuzzy sets (%$P_{c}%$FSs), we introduced the aggregation operators for the novel extension of %$P_{c}%$FSs named as bipolar picture fuzzy sets (B%$P_{c}%$FSs). Firstly, various arithmetic rules, scores and accuracy functions of the B%$P_{c}%$FSs are presented. Secondly, some aggregation operators of B%$P_{c}%$FSs are constructed to accumulate the bipolar picture fuzzy (B%$P_{c}%$F) data. Thirdly, the features of these aggregation operators are penned and then based on these aggregation operators a multiple criteria decision-making (MCDM) approach is developed to resolve the vague data. In the end, an illustrated example of the investment of money is put forward to show the authenticity and efficacy of the suggested approach. Moreover, B%$P_{c}%$F-TOPSIS, B%$P_{c}%$F-VIKOR, and sensitivity analysis (SA) have been used to provide the strength and practicality of the proposed MCDM model. Fuzzy sets (dpeaa)DE-He213 Picture fuzzy sets (dpeaa)DE-He213 Aggregation operators (dpeaa)DE-He213 Bipolar fuzzy sets (dpeaa)DE-He213 Linear programming model (dpeaa)DE-He213 Rashid, Tabasam verfasserin aut Kashif, Agha verfasserin aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 23(2021), 7 vom: 29. März, Seite 2335-2347 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:23 year:2021 number:7 day:29 month:03 pages:2335-2347 https://dx.doi.org/10.1007/s40815-021-01072-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.78 ASE AR 23 2021 7 29 03 2335-2347 |
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10.1007/s40815-021-01072-3 doi (DE-627)SPR045349282 (SPR)s40815-021-01072-3-e DE-627 ger DE-627 rakwb eng 510 ASE 31.78 bkl Sindhu, M. Sarwar verfasserin aut An Approach to Select the Investment Based on Bipolar Picture Fuzzy Sets 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Taiwan Fuzzy Systems Association 2021 Abstract Motivated by bipolar fuzzy (%$B_{p}%$F) sets (%$B_{p}%$FSs) and picture fuzzy sets (%$P_{c}%$FSs), we introduced the aggregation operators for the novel extension of %$P_{c}%$FSs named as bipolar picture fuzzy sets (B%$P_{c}%$FSs). Firstly, various arithmetic rules, scores and accuracy functions of the B%$P_{c}%$FSs are presented. Secondly, some aggregation operators of B%$P_{c}%$FSs are constructed to accumulate the bipolar picture fuzzy (B%$P_{c}%$F) data. Thirdly, the features of these aggregation operators are penned and then based on these aggregation operators a multiple criteria decision-making (MCDM) approach is developed to resolve the vague data. In the end, an illustrated example of the investment of money is put forward to show the authenticity and efficacy of the suggested approach. Moreover, B%$P_{c}%$F-TOPSIS, B%$P_{c}%$F-VIKOR, and sensitivity analysis (SA) have been used to provide the strength and practicality of the proposed MCDM model. Fuzzy sets (dpeaa)DE-He213 Picture fuzzy sets (dpeaa)DE-He213 Aggregation operators (dpeaa)DE-He213 Bipolar fuzzy sets (dpeaa)DE-He213 Linear programming model (dpeaa)DE-He213 Rashid, Tabasam verfasserin aut Kashif, Agha verfasserin aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 23(2021), 7 vom: 29. März, Seite 2335-2347 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:23 year:2021 number:7 day:29 month:03 pages:2335-2347 https://dx.doi.org/10.1007/s40815-021-01072-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.78 ASE AR 23 2021 7 29 03 2335-2347 |
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Sindhu, M. Sarwar @@aut@@ Rashid, Tabasam @@aut@@ Kashif, Agha @@aut@@ |
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Sindhu, M. Sarwar |
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Sindhu, M. Sarwar ddc 510 bkl 31.78 misc Fuzzy sets misc Picture fuzzy sets misc Aggregation operators misc Bipolar fuzzy sets misc Linear programming model An Approach to Select the Investment Based on Bipolar Picture Fuzzy Sets |
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510 ASE 31.78 bkl An Approach to Select the Investment Based on Bipolar Picture Fuzzy Sets Fuzzy sets (dpeaa)DE-He213 Picture fuzzy sets (dpeaa)DE-He213 Aggregation operators (dpeaa)DE-He213 Bipolar fuzzy sets (dpeaa)DE-He213 Linear programming model (dpeaa)DE-He213 |
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ddc 510 bkl 31.78 misc Fuzzy sets misc Picture fuzzy sets misc Aggregation operators misc Bipolar fuzzy sets misc Linear programming model |
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ddc 510 bkl 31.78 misc Fuzzy sets misc Picture fuzzy sets misc Aggregation operators misc Bipolar fuzzy sets misc Linear programming model |
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An Approach to Select the Investment Based on Bipolar Picture Fuzzy Sets |
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approach to select the investment based on bipolar picture fuzzy sets |
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An Approach to Select the Investment Based on Bipolar Picture Fuzzy Sets |
| abstract |
Abstract Motivated by bipolar fuzzy (%$B_{p}%$F) sets (%$B_{p}%$FSs) and picture fuzzy sets (%$P_{c}%$FSs), we introduced the aggregation operators for the novel extension of %$P_{c}%$FSs named as bipolar picture fuzzy sets (B%$P_{c}%$FSs). Firstly, various arithmetic rules, scores and accuracy functions of the B%$P_{c}%$FSs are presented. Secondly, some aggregation operators of B%$P_{c}%$FSs are constructed to accumulate the bipolar picture fuzzy (B%$P_{c}%$F) data. Thirdly, the features of these aggregation operators are penned and then based on these aggregation operators a multiple criteria decision-making (MCDM) approach is developed to resolve the vague data. In the end, an illustrated example of the investment of money is put forward to show the authenticity and efficacy of the suggested approach. Moreover, B%$P_{c}%$F-TOPSIS, B%$P_{c}%$F-VIKOR, and sensitivity analysis (SA) have been used to provide the strength and practicality of the proposed MCDM model. © Taiwan Fuzzy Systems Association 2021 |
| abstractGer |
Abstract Motivated by bipolar fuzzy (%$B_{p}%$F) sets (%$B_{p}%$FSs) and picture fuzzy sets (%$P_{c}%$FSs), we introduced the aggregation operators for the novel extension of %$P_{c}%$FSs named as bipolar picture fuzzy sets (B%$P_{c}%$FSs). Firstly, various arithmetic rules, scores and accuracy functions of the B%$P_{c}%$FSs are presented. Secondly, some aggregation operators of B%$P_{c}%$FSs are constructed to accumulate the bipolar picture fuzzy (B%$P_{c}%$F) data. Thirdly, the features of these aggregation operators are penned and then based on these aggregation operators a multiple criteria decision-making (MCDM) approach is developed to resolve the vague data. In the end, an illustrated example of the investment of money is put forward to show the authenticity and efficacy of the suggested approach. Moreover, B%$P_{c}%$F-TOPSIS, B%$P_{c}%$F-VIKOR, and sensitivity analysis (SA) have been used to provide the strength and practicality of the proposed MCDM model. © Taiwan Fuzzy Systems Association 2021 |
| abstract_unstemmed |
Abstract Motivated by bipolar fuzzy (%$B_{p}%$F) sets (%$B_{p}%$FSs) and picture fuzzy sets (%$P_{c}%$FSs), we introduced the aggregation operators for the novel extension of %$P_{c}%$FSs named as bipolar picture fuzzy sets (B%$P_{c}%$FSs). Firstly, various arithmetic rules, scores and accuracy functions of the B%$P_{c}%$FSs are presented. Secondly, some aggregation operators of B%$P_{c}%$FSs are constructed to accumulate the bipolar picture fuzzy (B%$P_{c}%$F) data. Thirdly, the features of these aggregation operators are penned and then based on these aggregation operators a multiple criteria decision-making (MCDM) approach is developed to resolve the vague data. In the end, an illustrated example of the investment of money is put forward to show the authenticity and efficacy of the suggested approach. Moreover, B%$P_{c}%$F-TOPSIS, B%$P_{c}%$F-VIKOR, and sensitivity analysis (SA) have been used to provide the strength and practicality of the proposed MCDM model. © Taiwan Fuzzy Systems Association 2021 |
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An Approach to Select the Investment Based on Bipolar Picture Fuzzy Sets |
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https://dx.doi.org/10.1007/s40815-021-01072-3 |
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Rashid, Tabasam Kashif, Agha |
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Rashid, Tabasam Kashif, Agha |
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| doi_str |
10.1007/s40815-021-01072-3 |
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2024-07-03T15:25:27.675Z |
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|
| score |
7.400529 |