Improved Bonferroni mean operator to apprehend graph based data interconnections with application to the Hacker Attack system

The Bonferroni mean (BM) operator has extended the class of interrelationship handling fusion functions by modeling homogeneous pairwise interactions among data entities. This operator has been further extended to diverse directions with the intention of capturing dissimilar association that exists within data sets of different real-world systems like, social network systems, biological systems, etc. It has been observed that some of the existing forms of the BM operator pre-assumes a specific model of association among data entities during its formulation, which may not be feasible in many systems. In this study, an effort has been made to develop the framework of the BM operator by generalizing the structure of relationship patterns among data entities. Classical graphs are considered as a prominent tool for describing pairwise relations among data entities. Such consideration prompted the proposal of a systematized framework of an improved version of the BM operator, denoted by the f G BM operator, where the unconventional association among data entities are portrayed through different graphical patterns. The f G BM operator has been formulated in a way that the knowledge of interactional information depicted through graphs is embedded into its processing system with the aim of capturing precise interconnections among entities. The generalized variation of the f G BM operator has also been proposed by substituting the sub-components of the f G BM operator with other precise forms of the aggregation functions to provide an illustrative alignment, which is quite expressible and interpretable, and also facilitates modeling mandatory prerequisites of the decision systems. For an applicatory aspect, the proposed operators have been utilized over the Hacker attack system and have been presented with a numerical example. A detailed comprehensive analysis has been presented to demonstrate the efficiency of the proposed operators. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Hait, Swati Rani [verfasserIn] Dutta, Bapi [verfasserIn] Guha, Debashree [verfasserIn] Chakraborty, Debjani [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Graphical structure Aggregation operators Bonferroni mean Improved Bonferroni mean operator Generalized Bonferroni mean operator Hacker attack system

Übergeordnetes Werk:	Enthalten in: Information sciences - New York, NY : Elsevier Science Inc., 1968, 616, Seite 276-302
Übergeordnetes Werk:	volume:616 ; pages:276-302

DOI / URN:	10.1016/j.ins.2022.10.085

Katalog-ID:	ELV008843457

Internformat


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245	1	0	\|a Improved Bonferroni mean operator to apprehend graph based data interconnections with application to the Hacker Attack system
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520			\|a The Bonferroni mean (BM) operator has extended the class of interrelationship handling fusion functions by modeling homogeneous pairwise interactions among data entities. This operator has been further extended to diverse directions with the intention of capturing dissimilar association that exists within data sets of different real-world systems like, social network systems, biological systems, etc. It has been observed that some of the existing forms of the BM operator pre-assumes a specific model of association among data entities during its formulation, which may not be feasible in many systems. In this study, an effort has been made to develop the framework of the BM operator by generalizing the structure of relationship patterns among data entities. Classical graphs are considered as a prominent tool for describing pairwise relations among data entities. Such consideration prompted the proposal of a systematized framework of an improved version of the BM operator, denoted by the f G BM operator, where the unconventional association among data entities are portrayed through different graphical patterns. The f G BM operator has been formulated in a way that the knowledge of interactional information depicted through graphs is embedded into its processing system with the aim of capturing precise interconnections among entities. The generalized variation of the f G BM operator has also been proposed by substituting the sub-components of the f G BM operator with other precise forms of the aggregation functions to provide an illustrative alignment, which is quite expressible and interpretable, and also facilitates modeling mandatory prerequisites of the decision systems. For an applicatory aspect, the proposed operators have been utilized over the Hacker attack system and have been presented with a numerical example. A detailed comprehensive analysis has been presented to demonstrate the efficiency of the proposed operators.
650		4	\|a Graphical structure
650		4	\|a Aggregation operators
650		4	\|a Bonferroni mean
650		4	\|a Improved Bonferroni mean operator
650		4	\|a Generalized Bonferroni mean operator
650		4	\|a Hacker attack system
700	1		\|a Dutta, Bapi \|e verfasserin \|4 aut
700	1		\|a Guha, Debashree \|e verfasserin \|4 aut
700	1		\|a Chakraborty, Debjani \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Information sciences \|d New York, NY : Elsevier Science Inc., 1968 \|g 616, Seite 276-302 \|h Online-Ressource \|w (DE-627)271175850 \|w (DE-600)1478990-5 \|w (DE-576)078412293 \|x 0020-0255 \|7 nnns
773	1	8	\|g volume:616 \|g pages:276-302
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912			\|a GBV_ILN_62
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912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
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912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2003
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912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
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912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4251
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912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
936	b	k	\|a 54.00 \|j Informatik: Allgemeines
936	b	k	\|a 53.71 \|j Theoretische Nachrichtentechnik
951			\|a AR
952			\|d 616 \|h 276-302

Indexfelder

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publishDate	2022
allfields	10.1016/j.ins.2022.10.085 doi (DE-627)ELV008843457 (ELSEVIER)S0020-0255(22)01205-1 DE-627 ger DE-627 rda eng 070 004 DE-600 LING DE-30 fid 54.00 bkl 53.71 bkl Hait, Swati Rani verfasserin aut Improved Bonferroni mean operator to apprehend graph based data interconnections with application to the Hacker Attack system 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Bonferroni mean (BM) operator has extended the class of interrelationship handling fusion functions by modeling homogeneous pairwise interactions among data entities. This operator has been further extended to diverse directions with the intention of capturing dissimilar association that exists within data sets of different real-world systems like, social network systems, biological systems, etc. It has been observed that some of the existing forms of the BM operator pre-assumes a specific model of association among data entities during its formulation, which may not be feasible in many systems. In this study, an effort has been made to develop the framework of the BM operator by generalizing the structure of relationship patterns among data entities. Classical graphs are considered as a prominent tool for describing pairwise relations among data entities. Such consideration prompted the proposal of a systematized framework of an improved version of the BM operator, denoted by the f G BM operator, where the unconventional association among data entities are portrayed through different graphical patterns. The f G BM operator has been formulated in a way that the knowledge of interactional information depicted through graphs is embedded into its processing system with the aim of capturing precise interconnections among entities. The generalized variation of the f G BM operator has also been proposed by substituting the sub-components of the f G BM operator with other precise forms of the aggregation functions to provide an illustrative alignment, which is quite expressible and interpretable, and also facilitates modeling mandatory prerequisites of the decision systems. For an applicatory aspect, the proposed operators have been utilized over the Hacker attack system and have been presented with a numerical example. A detailed comprehensive analysis has been presented to demonstrate the efficiency of the proposed operators. Graphical structure Aggregation operators Bonferroni mean Improved Bonferroni mean operator Generalized Bonferroni mean operator Hacker attack system Dutta, Bapi verfasserin aut Guha, Debashree verfasserin aut Chakraborty, Debjani verfasserin aut Enthalten in Information sciences New York, NY : Elsevier Science Inc., 1968 616, Seite 276-302 Online-Ressource (DE-627)271175850 (DE-600)1478990-5 (DE-576)078412293 0020-0255 nnns volume:616 pages:276-302 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-LING SSG-OPC-BBI GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines 53.71 Theoretische Nachrichtentechnik AR 616 276-302
spelling	10.1016/j.ins.2022.10.085 doi (DE-627)ELV008843457 (ELSEVIER)S0020-0255(22)01205-1 DE-627 ger DE-627 rda eng 070 004 DE-600 LING DE-30 fid 54.00 bkl 53.71 bkl Hait, Swati Rani verfasserin aut Improved Bonferroni mean operator to apprehend graph based data interconnections with application to the Hacker Attack system 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Bonferroni mean (BM) operator has extended the class of interrelationship handling fusion functions by modeling homogeneous pairwise interactions among data entities. This operator has been further extended to diverse directions with the intention of capturing dissimilar association that exists within data sets of different real-world systems like, social network systems, biological systems, etc. It has been observed that some of the existing forms of the BM operator pre-assumes a specific model of association among data entities during its formulation, which may not be feasible in many systems. In this study, an effort has been made to develop the framework of the BM operator by generalizing the structure of relationship patterns among data entities. Classical graphs are considered as a prominent tool for describing pairwise relations among data entities. Such consideration prompted the proposal of a systematized framework of an improved version of the BM operator, denoted by the f G BM operator, where the unconventional association among data entities are portrayed through different graphical patterns. The f G BM operator has been formulated in a way that the knowledge of interactional information depicted through graphs is embedded into its processing system with the aim of capturing precise interconnections among entities. The generalized variation of the f G BM operator has also been proposed by substituting the sub-components of the f G BM operator with other precise forms of the aggregation functions to provide an illustrative alignment, which is quite expressible and interpretable, and also facilitates modeling mandatory prerequisites of the decision systems. For an applicatory aspect, the proposed operators have been utilized over the Hacker attack system and have been presented with a numerical example. A detailed comprehensive analysis has been presented to demonstrate the efficiency of the proposed operators. Graphical structure Aggregation operators Bonferroni mean Improved Bonferroni mean operator Generalized Bonferroni mean operator Hacker attack system Dutta, Bapi verfasserin aut Guha, Debashree verfasserin aut Chakraborty, Debjani verfasserin aut Enthalten in Information sciences New York, NY : Elsevier Science Inc., 1968 616, Seite 276-302 Online-Ressource (DE-627)271175850 (DE-600)1478990-5 (DE-576)078412293 0020-0255 nnns volume:616 pages:276-302 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-LING SSG-OPC-BBI GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines 53.71 Theoretische Nachrichtentechnik AR 616 276-302
allfields_unstemmed	10.1016/j.ins.2022.10.085 doi (DE-627)ELV008843457 (ELSEVIER)S0020-0255(22)01205-1 DE-627 ger DE-627 rda eng 070 004 DE-600 LING DE-30 fid 54.00 bkl 53.71 bkl Hait, Swati Rani verfasserin aut Improved Bonferroni mean operator to apprehend graph based data interconnections with application to the Hacker Attack system 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Bonferroni mean (BM) operator has extended the class of interrelationship handling fusion functions by modeling homogeneous pairwise interactions among data entities. This operator has been further extended to diverse directions with the intention of capturing dissimilar association that exists within data sets of different real-world systems like, social network systems, biological systems, etc. It has been observed that some of the existing forms of the BM operator pre-assumes a specific model of association among data entities during its formulation, which may not be feasible in many systems. In this study, an effort has been made to develop the framework of the BM operator by generalizing the structure of relationship patterns among data entities. Classical graphs are considered as a prominent tool for describing pairwise relations among data entities. Such consideration prompted the proposal of a systematized framework of an improved version of the BM operator, denoted by the f G BM operator, where the unconventional association among data entities are portrayed through different graphical patterns. The f G BM operator has been formulated in a way that the knowledge of interactional information depicted through graphs is embedded into its processing system with the aim of capturing precise interconnections among entities. The generalized variation of the f G BM operator has also been proposed by substituting the sub-components of the f G BM operator with other precise forms of the aggregation functions to provide an illustrative alignment, which is quite expressible and interpretable, and also facilitates modeling mandatory prerequisites of the decision systems. For an applicatory aspect, the proposed operators have been utilized over the Hacker attack system and have been presented with a numerical example. A detailed comprehensive analysis has been presented to demonstrate the efficiency of the proposed operators. Graphical structure Aggregation operators Bonferroni mean Improved Bonferroni mean operator Generalized Bonferroni mean operator Hacker attack system Dutta, Bapi verfasserin aut Guha, Debashree verfasserin aut Chakraborty, Debjani verfasserin aut Enthalten in Information sciences New York, NY : Elsevier Science Inc., 1968 616, Seite 276-302 Online-Ressource (DE-627)271175850 (DE-600)1478990-5 (DE-576)078412293 0020-0255 nnns volume:616 pages:276-302 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-LING SSG-OPC-BBI GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines 53.71 Theoretische Nachrichtentechnik AR 616 276-302
allfieldsGer	10.1016/j.ins.2022.10.085 doi (DE-627)ELV008843457 (ELSEVIER)S0020-0255(22)01205-1 DE-627 ger DE-627 rda eng 070 004 DE-600 LING DE-30 fid 54.00 bkl 53.71 bkl Hait, Swati Rani verfasserin aut Improved Bonferroni mean operator to apprehend graph based data interconnections with application to the Hacker Attack system 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Bonferroni mean (BM) operator has extended the class of interrelationship handling fusion functions by modeling homogeneous pairwise interactions among data entities. This operator has been further extended to diverse directions with the intention of capturing dissimilar association that exists within data sets of different real-world systems like, social network systems, biological systems, etc. It has been observed that some of the existing forms of the BM operator pre-assumes a specific model of association among data entities during its formulation, which may not be feasible in many systems. In this study, an effort has been made to develop the framework of the BM operator by generalizing the structure of relationship patterns among data entities. Classical graphs are considered as a prominent tool for describing pairwise relations among data entities. Such consideration prompted the proposal of a systematized framework of an improved version of the BM operator, denoted by the f G BM operator, where the unconventional association among data entities are portrayed through different graphical patterns. The f G BM operator has been formulated in a way that the knowledge of interactional information depicted through graphs is embedded into its processing system with the aim of capturing precise interconnections among entities. The generalized variation of the f G BM operator has also been proposed by substituting the sub-components of the f G BM operator with other precise forms of the aggregation functions to provide an illustrative alignment, which is quite expressible and interpretable, and also facilitates modeling mandatory prerequisites of the decision systems. For an applicatory aspect, the proposed operators have been utilized over the Hacker attack system and have been presented with a numerical example. A detailed comprehensive analysis has been presented to demonstrate the efficiency of the proposed operators. Graphical structure Aggregation operators Bonferroni mean Improved Bonferroni mean operator Generalized Bonferroni mean operator Hacker attack system Dutta, Bapi verfasserin aut Guha, Debashree verfasserin aut Chakraborty, Debjani verfasserin aut Enthalten in Information sciences New York, NY : Elsevier Science Inc., 1968 616, Seite 276-302 Online-Ressource (DE-627)271175850 (DE-600)1478990-5 (DE-576)078412293 0020-0255 nnns volume:616 pages:276-302 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-LING SSG-OPC-BBI GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines 53.71 Theoretische Nachrichtentechnik AR 616 276-302
allfieldsSound	10.1016/j.ins.2022.10.085 doi (DE-627)ELV008843457 (ELSEVIER)S0020-0255(22)01205-1 DE-627 ger DE-627 rda eng 070 004 DE-600 LING DE-30 fid 54.00 bkl 53.71 bkl Hait, Swati Rani verfasserin aut Improved Bonferroni mean operator to apprehend graph based data interconnections with application to the Hacker Attack system 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Bonferroni mean (BM) operator has extended the class of interrelationship handling fusion functions by modeling homogeneous pairwise interactions among data entities. This operator has been further extended to diverse directions with the intention of capturing dissimilar association that exists within data sets of different real-world systems like, social network systems, biological systems, etc. It has been observed that some of the existing forms of the BM operator pre-assumes a specific model of association among data entities during its formulation, which may not be feasible in many systems. In this study, an effort has been made to develop the framework of the BM operator by generalizing the structure of relationship patterns among data entities. Classical graphs are considered as a prominent tool for describing pairwise relations among data entities. Such consideration prompted the proposal of a systematized framework of an improved version of the BM operator, denoted by the f G BM operator, where the unconventional association among data entities are portrayed through different graphical patterns. The f G BM operator has been formulated in a way that the knowledge of interactional information depicted through graphs is embedded into its processing system with the aim of capturing precise interconnections among entities. The generalized variation of the f G BM operator has also been proposed by substituting the sub-components of the f G BM operator with other precise forms of the aggregation functions to provide an illustrative alignment, which is quite expressible and interpretable, and also facilitates modeling mandatory prerequisites of the decision systems. For an applicatory aspect, the proposed operators have been utilized over the Hacker attack system and have been presented with a numerical example. A detailed comprehensive analysis has been presented to demonstrate the efficiency of the proposed operators. Graphical structure Aggregation operators Bonferroni mean Improved Bonferroni mean operator Generalized Bonferroni mean operator Hacker attack system Dutta, Bapi verfasserin aut Guha, Debashree verfasserin aut Chakraborty, Debjani verfasserin aut Enthalten in Information sciences New York, NY : Elsevier Science Inc., 1968 616, Seite 276-302 Online-Ressource (DE-627)271175850 (DE-600)1478990-5 (DE-576)078412293 0020-0255 nnns volume:616 pages:276-302 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-LING SSG-OPC-BBI GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines 53.71 Theoretische Nachrichtentechnik AR 616 276-302
language	English
source	Enthalten in Information sciences 616, Seite 276-302 volume:616 pages:276-302
sourceStr	Enthalten in Information sciences 616, Seite 276-302 volume:616 pages:276-302
format_phy_str_mv	Article
bklname	Informatik: Allgemeines Theoretische Nachrichtentechnik
institution	findex.gbv.de
topic_facet	Graphical structure Aggregation operators Bonferroni mean Improved Bonferroni mean operator Generalized Bonferroni mean operator Hacker attack system
dewey-raw	070
isfreeaccess_bool	false
container_title	Information sciences
authorswithroles_txt_mv	Hait, Swati Rani @@aut@@ Dutta, Bapi @@aut@@ Guha, Debashree @@aut@@ Chakraborty, Debjani @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	271175850
dewey-sort	270
id	ELV008843457
language_de	englisch
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author	Hait, Swati Rani
spellingShingle	Hait, Swati Rani ddc 070 fid LING bkl 54.00 bkl 53.71 misc Graphical structure misc Aggregation operators misc Bonferroni mean misc Improved Bonferroni mean operator misc Generalized Bonferroni mean operator misc Hacker attack system Improved Bonferroni mean operator to apprehend graph based data interconnections with application to the Hacker Attack system
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topic_title	070 004 DE-600 LING DE-30 fid 54.00 bkl 53.71 bkl Improved Bonferroni mean operator to apprehend graph based data interconnections with application to the Hacker Attack system Graphical structure Aggregation operators Bonferroni mean Improved Bonferroni mean operator Generalized Bonferroni mean operator Hacker attack system
topic	ddc 070 fid LING bkl 54.00 bkl 53.71 misc Graphical structure misc Aggregation operators misc Bonferroni mean misc Improved Bonferroni mean operator misc Generalized Bonferroni mean operator misc Hacker attack system
topic_unstemmed	ddc 070 fid LING bkl 54.00 bkl 53.71 misc Graphical structure misc Aggregation operators misc Bonferroni mean misc Improved Bonferroni mean operator misc Generalized Bonferroni mean operator misc Hacker attack system
topic_browse	ddc 070 fid LING bkl 54.00 bkl 53.71 misc Graphical structure misc Aggregation operators misc Bonferroni mean misc Improved Bonferroni mean operator misc Generalized Bonferroni mean operator misc Hacker attack system
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title	Improved Bonferroni mean operator to apprehend graph based data interconnections with application to the Hacker Attack system
ctrlnum	(DE-627)ELV008843457 (ELSEVIER)S0020-0255(22)01205-1
title_full	Improved Bonferroni mean operator to apprehend graph based data interconnections with application to the Hacker Attack system
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author_browse	Hait, Swati Rani Dutta, Bapi Guha, Debashree Chakraborty, Debjani
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author-letter	Hait, Swati Rani
doi_str_mv	10.1016/j.ins.2022.10.085
dewey-full	070 004
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title_sort	improved bonferroni mean operator to apprehend graph based data interconnections with application to the hacker attack system
title_auth	Improved Bonferroni mean operator to apprehend graph based data interconnections with application to the Hacker Attack system
abstract	The Bonferroni mean (BM) operator has extended the class of interrelationship handling fusion functions by modeling homogeneous pairwise interactions among data entities. This operator has been further extended to diverse directions with the intention of capturing dissimilar association that exists within data sets of different real-world systems like, social network systems, biological systems, etc. It has been observed that some of the existing forms of the BM operator pre-assumes a specific model of association among data entities during its formulation, which may not be feasible in many systems. In this study, an effort has been made to develop the framework of the BM operator by generalizing the structure of relationship patterns among data entities. Classical graphs are considered as a prominent tool for describing pairwise relations among data entities. Such consideration prompted the proposal of a systematized framework of an improved version of the BM operator, denoted by the f G BM operator, where the unconventional association among data entities are portrayed through different graphical patterns. The f G BM operator has been formulated in a way that the knowledge of interactional information depicted through graphs is embedded into its processing system with the aim of capturing precise interconnections among entities. The generalized variation of the f G BM operator has also been proposed by substituting the sub-components of the f G BM operator with other precise forms of the aggregation functions to provide an illustrative alignment, which is quite expressible and interpretable, and also facilitates modeling mandatory prerequisites of the decision systems. For an applicatory aspect, the proposed operators have been utilized over the Hacker attack system and have been presented with a numerical example. A detailed comprehensive analysis has been presented to demonstrate the efficiency of the proposed operators.
abstractGer	The Bonferroni mean (BM) operator has extended the class of interrelationship handling fusion functions by modeling homogeneous pairwise interactions among data entities. This operator has been further extended to diverse directions with the intention of capturing dissimilar association that exists within data sets of different real-world systems like, social network systems, biological systems, etc. It has been observed that some of the existing forms of the BM operator pre-assumes a specific model of association among data entities during its formulation, which may not be feasible in many systems. In this study, an effort has been made to develop the framework of the BM operator by generalizing the structure of relationship patterns among data entities. Classical graphs are considered as a prominent tool for describing pairwise relations among data entities. Such consideration prompted the proposal of a systematized framework of an improved version of the BM operator, denoted by the f G BM operator, where the unconventional association among data entities are portrayed through different graphical patterns. The f G BM operator has been formulated in a way that the knowledge of interactional information depicted through graphs is embedded into its processing system with the aim of capturing precise interconnections among entities. The generalized variation of the f G BM operator has also been proposed by substituting the sub-components of the f G BM operator with other precise forms of the aggregation functions to provide an illustrative alignment, which is quite expressible and interpretable, and also facilitates modeling mandatory prerequisites of the decision systems. For an applicatory aspect, the proposed operators have been utilized over the Hacker attack system and have been presented with a numerical example. A detailed comprehensive analysis has been presented to demonstrate the efficiency of the proposed operators.
abstract_unstemmed	The Bonferroni mean (BM) operator has extended the class of interrelationship handling fusion functions by modeling homogeneous pairwise interactions among data entities. This operator has been further extended to diverse directions with the intention of capturing dissimilar association that exists within data sets of different real-world systems like, social network systems, biological systems, etc. It has been observed that some of the existing forms of the BM operator pre-assumes a specific model of association among data entities during its formulation, which may not be feasible in many systems. In this study, an effort has been made to develop the framework of the BM operator by generalizing the structure of relationship patterns among data entities. Classical graphs are considered as a prominent tool for describing pairwise relations among data entities. Such consideration prompted the proposal of a systematized framework of an improved version of the BM operator, denoted by the f G BM operator, where the unconventional association among data entities are portrayed through different graphical patterns. The f G BM operator has been formulated in a way that the knowledge of interactional information depicted through graphs is embedded into its processing system with the aim of capturing precise interconnections among entities. The generalized variation of the f G BM operator has also been proposed by substituting the sub-components of the f G BM operator with other precise forms of the aggregation functions to provide an illustrative alignment, which is quite expressible and interpretable, and also facilitates modeling mandatory prerequisites of the decision systems. For an applicatory aspect, the proposed operators have been utilized over the Hacker attack system and have been presented with a numerical example. A detailed comprehensive analysis has been presented to demonstrate the efficiency of the proposed operators.
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title_short	Improved Bonferroni mean operator to apprehend graph based data interconnections with application to the Hacker Attack system
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author2	Dutta, Bapi Guha, Debashree Chakraborty, Debjani
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up_date	2024-07-06T21:05:45.637Z
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Improved Bonferroni mean operator to apprehend graph based data interconnections with application to the Hacker Attack system

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