Intuitionistic fuzzy solid assignment problems: a software-based approach

Kumar, P. Senthil

doi:10.1007/s13198-019-00794-w

Cited by 42 publications

(8 citation statements)

References 41 publications

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“…In addition, the fuzzy set-based optimization algorithm [70] has received much attention in recent years and has been successfully applied to solid assignment [71,72] and transportation [73] problems, and it can also be considered a focal method for future studies on high-order SNP epistatic interaction detection. In addition, it is also very important to develop an effective scoring function for seeking clues to guide the SISA to locate the positions of potential SNP interactions.…”

Section: Discussionmentioning

confidence: 99%

MTHSA-DHEI: multitasking harmony search algorithm for detecting high-order SNP epistatic interactions

Tuo

Liu

et al. 2022

Complex Intell. Syst.

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Genome-wide association studies have succeeded in identifying genetic variants associated with complex diseases, but the findings have not been well interpreted biologically. Although it is widely accepted that epistatic interactions of high-order single nucleotide polymorphisms (SNPs) [(1) Single nucleotide polymorphisms (SNP) are mainly deoxyribonucleic acid (DNA) sequence polymorphisms caused by variants at a single nucleotide at the genome level. They are the most common type of heritable variation in humans.] are important causes of complex diseases, the combinatorial explosion of millions of SNPs and multiple tests impose a large computational burden. Moreover, it is extremely challenging to correctly distinguish high-order SNP epistatic interactions from other high-order SNP combinations due to small sample sizes. In this study, a multitasking harmony search algorithm (MTHSA-DHEI) is proposed for detecting high-order epistatic interactions [(2) In classical genetics, if genes X1 and X2 are mutated and each mutation by itself produces a unique disease status (phenotype) but the mutations together cause the same disease status as the gene X1 mutation, gene X1 is epistatic and gene X2 is hypostatic, and gene X1 has an epistatic effect (main effect) on disease status. In this work, a high-order epistatic interaction occurs when two or more SNP loci have a joint influence on disease status.], with the goal of simultaneously detecting multiple types of high-order (k1-order, k2-order, …, kn-order) SNP epistatic interactions. Unified coding is adopted for multiple tasks, and four complementary association evaluation functions are employed to improve the capability of discriminating the high-order SNP epistatic interactions. We compare the proposed MTHSA-DHEI method with four excellent methods for detecting high-order SNP interactions for 8 high-orderepistatic interaction models with no marginal effect (EINMEs) and 12 epistatic interaction models with marginal effects (EIMEs) (*) and implement the MTHSA-DHEI algorithm with a real dataset: age-related macular degeneration (AMD). The experimental results indicate that MTHSA-DHEI has power and an F1-score exceeding 90% for all EIMEs and five EINMEs and reduces the computational time by more than 90%. It can efficiently perform multiple high-order detection tasks for high-order epistatic interactions and improve the discrimination ability for diverse epistasis models.

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Section: Discussionmentioning

confidence: 99%

MTHSA-DHEI: multitasking harmony search algorithm for detecting high-order SNP epistatic interactions

Tuo

Liu

et al. 2022

Complex Intell. Syst.

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“…Nasir et al [18] used the complex intuitionistic FR with the application of cyber security and cybercrime in the oil and gas industries. Kumar et al [19] defined the intuitionistic fuzzy solid assignment difficulties. Kumar et al [20] discussed the method for solving type-1 and type-3 fuzzy transportation harms.…”

Section: Introductionmentioning

confidence: 99%

A new approach to investigate the effects of artificial neural networks based on bipolar complex spherical fuzzy information

Gwak

Garg

Jan

et al. 2023

Complex Intell. Syst.

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Artificial neural network is revolutionizing business and everyday life, bringing us to the next level in artificial intelligence. It has a unique ability to extract meaning from complex data to find patterns and detect trends that are too convoluted for the human brain. This paper analyzes the artificial neural network impact on different computational organizations by using the innovative structure of bipolar complex spherical fuzzy relation which is any subset of the Cartesian product of two bipolar complex spherical fuzzy sets. This notion has a comprehensive structure that consists of membership grade, abstinence grade, and non-membership grade. Furthermore, various kinds of bipolar complex spherical fuzzy relation with suitable examples are given and some authentic results also have been proved. These newly defined structures are used to investigate the impact of artificial neural network work on a variety of organizations. The innovative framework is also compared with the existing structure in the field of fuzzy set theory to prove its superiority.

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“…Control chart is one of the most useful tools in Statistical Process Control since control charts play a key role in detecting the assignable cause(s) [1]. Other effective way to mitigate the incidence of false alarm rate and to increase the control chart sensitivity includes the fuzzy logic scheme [2][3][4][5][6], which combines the probability and fuzzy set theories for enabling inference of process state based on fuzzified sensitivity criteria. When the quality characteristics can only be classified into two possible outcomes, for instance, "Yes or No, " "Good or Bad, " "Conforming or Nonconforming, " and "Defective or Non-defective, " it is not possible to monitor the process using the variable control charts, such as the X, s, and R charts.…”

Section: Introductionmentioning

confidence: 99%

Optimal statistical design of the double sampling np chart based on expected median run length

Tuh

Kon

Chua

et al. 2022

Front. Appl. Math. Stat.

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Double sampling (DS) control charts are widely regarded as an effective process monitoring tool owing to their remarkable properties, such as the ability to detect small and moderate process shifts efficiently with the reduced sample size. Since the shape of the run length distribution is highly right-skewed for the process small shift size and becomes almost symmetric when the process shift size is large, the use of median run length (MRL) as a performance measure is therefore more representative. Existing works on the DS np chart construction were performed by taking an approach that the shift size of the process fraction nonconforming is assumed to be known. However, the shift size of the fraction nonconforming is usually unknown by the quality practitioners in practice. Herein, to address this issue, the expected median run length (EMRL) has been suggested as a performance measure for the unknown shift size. This paper suggests an optimal design procedure for the DS np chart based on the EMRL criterion. An example is provided to illustrate the construction of the EMRL-based DS np chart. The DS np chart is compared with a competing chart based on the EMRL criterion. Findings obtained reveal that when the shift size is unknown, the EMRL is an alternative performance measure for the DS np chart, with greater sensitivity observed for the DS np chart in contrast to the standard np chart for detecting a wide range of shifts.

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Intuitionistic fuzzy solid assignment problems: a software-based approach

Cited by 42 publications

References 41 publications

MTHSA-DHEI: multitasking harmony search algorithm for detecting high-order SNP epistatic interactions

MTHSA-DHEI: multitasking harmony search algorithm for detecting high-order SNP epistatic interactions

A new approach to investigate the effects of artificial neural networks based on bipolar complex spherical fuzzy information

Optimal statistical design of the double sampling np chart based on expected median run length

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