Algorithm for T-spherical fuzzy MADM based on associated immediate probability interactive geometric aggregation operators

2021 ◽  
Author(s):  
Muhammad Munir ◽  
Tahir Mahmood ◽  
Azmat Hussain
Keyword(s):  
Aggregation Operators ◽  
Fuzzy Madm

scholarly journals Methods for MADM with Picture Fuzzy Muirhead Mean Operators and Their Application for Evaluating the Financial Investment Risk

Symmetry ◽  
2018 ◽  
Vol 11 (1) ◽  
pp. 6 ◽  
Author(s):  
Rui Wang ◽  
Jie Wang ◽  
Hui Gao ◽  
Guiwu Wei
Keyword(s):  
Decision Making ◽  
Fuzzy Numbers ◽  
Multiple Attribute Decision Making ◽  
Aggregation Operators ◽  
Investment Risk ◽  
Financial Investment ◽  
Multiple Attribute ◽  
Fuzzy Aggregation ◽  
Fuzzy Madm ◽  
Decision Making Model

In this article, we study multiple attribute decision-making (MADM) problems with picture fuzzy numbers (PFNs) information. Afterwards, we adopt a Muirhead mean (MM) operator, a weighted MM (WMM) operator, a dual MM (DMM) operator, and a weighted DMM (WDMM) operator to define some picture fuzzy aggregation operators, including the picture fuzzy MM (PFMM) operator, the picture fuzzy WMM (PFWMM) operator, the picture fuzzy DMM (PFDMM) operator, and the picture fuzzy WDMM (PFWDMM) operator. Of course, the precious merits of these defined operators are investigated. Moreover, we have adopted the PFWMM and PFWDMM operators to build a decision-making model to handle picture fuzzy MADM problems. In the end, we take a concrete instance of appraising a financial investment risk to demonstrate our defined model and to verify its accuracy and scientific merit.

Green Supplier Evaluation and Selection Using Interval 2-Tuple Linguistic Hybrid Aggregation Operators

Informatica ◽  
2018 ◽  
Vol 29 (4) ◽  
pp. 801-824 ◽  
Author(s):  
Xue-Guo Xu ◽  
Hua Shi ◽  
Feng-Bao Cui ◽  
Mei-Yun Quan
Keyword(s):  
Aggregation Operators ◽  
Supplier Evaluation

PERANCANGAN PENENTUAN MATERI KURSUS MENGGUNAKAN FUZZY MADM DENGAN METODE SIMPLE ADDITTIVE WEIGHTING

2019 ◽  
Vol 24 (1) ◽  
pp. 24-36
Author(s):  
Elyna Fazriyati
Keyword(s):  
Multiple Attribute ◽  
Fuzzy Madm ◽  
Simple Additive Weighting

Kursus merupakan salah satu sarana penunjang bagi setiap mahasiswa untuk menambah dan mengasah kemampuan serta pengetahuan dalam belajar. Terutama bagi jurusan teknik informatika, manajemen informatika dan sistem informasi yang memerlukan praktik dalam kegiatan belajar sehari-hari. Kursus yang diselenggarakan untuk ketiga jurusan tersebut terdiri dari 3 tingkatan, yaitu fundamental, beginner dan intermediate yang disesuaikan dengan semester yang sedang dijalani oleh mahasiswa. Materi tingkat beginner dan intermediate merupakan cabang dari kursus materi tingkat fundamental yang berjumlah 12 materi. Proses penjadwalan kursus untuk ketiga jurusan tersebut seringkali terdapat kendala berupa penentuan materi beginner yang merupakan cabang dari materi fundamental, yang masing-masing materi fundamental memiliki 2 cabang untuk tingkat beginner sehingga mahasiswa memiliki 4 cabang materi beginner sebagai pilihan yang di ambil dari materi fundamental yang telah diikuti pada tingkat 1, yaitu semester 1 dan 2. Hal ini menyebabkan pengelolaan data untuk penentuan materi dilakukan secara acak tanpa melihat prioritas tertinggi dari mahasiswa dan kriteria pendukung lainnya. Maka untuk menyelesaikan masalah yang terjadi, dibuat perancangan pembangunan sistem pendukung keputusan untuk menentukan materi tingkat beginner dengan alternatif terbaik berdasarkan dengan kriteria yang telah ditentukan akan menggunakan metode Fuzzy Multiple Attribute Decission Making (FMADM) dengan metode Simple Additive Weighting (SAW).

Time Aggregation Operators for Multi-label Audio Event Detection

2018 ◽  
Author(s):  
Pankaj Joshi ◽  
Digvijaysingh Gautam ◽  
Ganesh Ramakrishnan ◽  
Preethi Jyothi
Keyword(s):  
Event Detection ◽  
Aggregation Operators ◽  
Time Aggregation ◽  
Audio Event

scholarly journals Efficient local locking for massively multithreaded in-memory hash-based operators

2021 ◽  
Author(s):  
Bashar Romanous ◽  
Skyler Windh ◽  
Ildar Absalyamov ◽  
Prerna Budhkar ◽  
Robert Halstead ◽  
...  
Keyword(s):  
Relational Databases ◽  
Aggregation Operators ◽  
Main Memory ◽  
Paradigm Shifts ◽  
Multithreaded Processors ◽  
Cache Hierarchies ◽  
Processor Architectures ◽  
Spatial Locality ◽  
Content Addressable Memories ◽  
Multi Core Processor

AbstractThe join and group-by aggregation are two memory intensive operators that are affecting the performance of relational databases. Hashing is a common approach used to implement both operators. Recent paradigm shifts in multi-core processor architectures have reinvigorated research into how the join and group-by aggregation operators can leverage these advances. However, the poor spatial locality of the hashing approach has hindered performance on multi-core processor architectures which rely on using large cache hierarchies for latency mitigation. Multithreaded architectures can better cope with poor spatial locality by masking memory latency with many outstanding requests. Nevertheless, the number of parallel threads, even in the most advanced multithreaded processors, such as UltraSPARC, is not enough to fully cover the main memory access latency. In this paper, we explore the hardware re-configurability of FPGAs to enable deeper execution pipelines that maintain hundreds (instead of tens) of outstanding memory requests across four FPGAs-drastically increasing concurrency and throughput. We present two end-to-end in-memory accelerators for the join and group-by aggregation operators using FPGAs. Both accelerators use massive multithreading to mask long memory delays of traversing linked-list data structures, while concurrently managing hundreds of thread states across four FPGAs locally. We explore how content addressable memories can be intermixed within our multithreaded designs to act as a synchronizing cache, which enforces locks and merges jobs together before they are written to memory. Throughput results for our hash-join operator accelerator show a speedup between 2$$\times $$ × and 3.4$$\times $$ × over the best multi-core approaches with comparable memory bandwidths on uniform and skewed datasets. The accelerator for the hash-based group-by aggregation operator demonstrates that leveraging CAMs achieves average speedup of 3.3$$\times $$ × with a best case of 9.4$$\times $$ × in terms of throughput over CPU implementations across five types of data distributions.

scholarly journals Approach to Multi-Attribute Decision-Making Methods for Performance Evaluation Process Using Interval-Valued T-Spherical Fuzzy Hamacher Aggregation Information

Axioms ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 145
Author(s):  
Yun Jin ◽  
Zareena Kousar ◽  
Kifayat Ullah ◽  
Tahir Mahmood ◽  
Nimet Yapici Pehlivan ◽  
...  
Keyword(s):  
Decision Making ◽  
Fuzzy Set ◽  
Evaluation Process ◽  
Aggregation Operators ◽  
Weighted Averaging ◽  
Membership Degree ◽  
Operational Laws ◽  
Multi Attribute Decision Making ◽  
Interval Valued ◽  
Do So

Interval-valued T-spherical fuzzy set (IVTSFS) handles uncertain and vague information by discussing their membership degree (MD), abstinence degree (AD), non-membership degree (NMD), and refusal degree (RD). MD, AD, NMD, and RD are defined in terms of closed subintervals of that reduce information loss compared to the T-spherical fuzzy set (TSFS), which takes crisp values from intervals; hence, some information may be lost. The purpose of this manuscript is to develop some Hamacher aggregation operators (HAOs) in the environment of IVTSFSs. To do so, some Hamacher operational laws based on Hamacher t-norms (HTNs) and Hamacher t-conorms (HTCNs) are introduced. Using Hamacher operational laws, we develop some aggregation operators (AOs), including an interval-valued T-spherical fuzzy Hamacher (IVTSFH) weighted averaging (IVTSFHWA) operator, an IVTSFH-ordered weighted averaging (IVTSFHOWA) operator, an IVTSFH hybrid averaging (IVTSFHHA) operator, an IVTSFH-weighted geometric (IVTSFHWG) operator, an IVTSFH-ordered weighted geometric (IVTSFHOWG) operator, and an IVTSFH hybrid geometric (IVTSFHHG) operator. The validation of the newly developed HAOs is investigated, and their basic properties are examined. In view of some restrictions, the generalization and proposed HAOs are shown, and a multi-attribute decision-making (MADM) procedure is explored based on the HAOs, which are further exemplified. Finally, a comparative analysis of the proposed work is also discussed with previous literature to show the superiority of our work.

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