Optimal design of type-2 fuzzy systems for diabetes classification based on genetic algorithms

Melín, Patricia; Sánchez, Daniela

doi:10.3233/his-210004

Cited by 10 publications

(10 citation statements)

References 36 publications

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“…In DFO algorithm, the position vector of population is shown as, 22

{F}_{ad}^t=\left[{F}_{a1}^t,{F}_{a2}^t,\dots, {F}_{aD}^t\right],\kern1em a\in \left\{1,2,\dots, N\right\},

where,

a

represents the individual flies,

t

represents present time step,

D

is the solution space dimensionality, and

N

denotes the population size. The computed fuzzy matches are considered as the population space.…”

Section: Optimal Selection Of Best Matches Using Dfo Algorithmmentioning

confidence: 99%

“…The application of meta‐heuristic algorithm in many fields is an emerging research nowadays. The most conventionally used algorithms are Genetic algorithm (GA), 22 Particle swarm optimization algorithm (PSO), 23 ant colony algorithm (ACO), 24 bio‐geography based optimization (BBO), 25 ant lion optimizer 26 and so on. Each algorithm varies in their working process based on the individual biological behavior and lifecycles.…”

Section: Introductionmentioning

confidence: 99%

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Novel algorithm machine translation for language translation tool

Jayasakthi Velmurugan,

Sumathy,

Pradeep

2024

Computational Intelligence

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Fuzzy matching techniques are the presently used methods in translating the words. Neural machine translation and statistical machine translation are the methods used in MT. In machine translator tool, the strategy employed for translation needs to handle large amount of datasets and therefore the performance in retrieving correct matching output can be affected. In order to improve the matching score of MT, the advanced techniques can be presented by modifying the existing fuzzy based translator and neural machine translator. The conventional process of modifying architectures and encoding schemes are tedious process. Similarly, the preprocessing of datasets also involves more time consumption and memory utilization. In this article, a new spider web based searching enhanced translation is presented to be employed with the neural machine translator. The proposed scheme enables deep searching of available dataset to detect the accurate matching result. In addition, the quality of translation is improved by presenting an optimal selection scheme for using the sentence matches in source augmentation. The matches retrieved using various matching scores are applied to an optimization algorithm. The source augmentation using optimal retrieved matches increases the translation quality. Further, the selection of optimal match combination helps to reduce time requirement, since it is not necessary to test all retrieved matches in finding target sentence. The performance of translation is validated by measuring the quality of translation using BLEU and METEOR scores. These two scores can be achieved for the TA‐EN language pairs in different configurations of about 92% and 86%, correspondingly. The results are evaluated and compared with other available NMT methods to validate the work.

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“…In DFO algorithm, the position vector of population is shown as, 22

{F}_{ad}^t=\left[{F}_{a1}^t,{F}_{a2}^t,\dots, {F}_{aD}^t\right],\kern1em a\in \left\{1,2,\dots, N\right\},

where,

a

represents the individual flies,

t

represents present time step,

D

is the solution space dimensionality, and

N

denotes the population size. The computed fuzzy matches are considered as the population space.…”

Section: Optimal Selection Of Best Matches Using Dfo Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Novel algorithm machine translation for language translation tool

Jayasakthi Velmurugan,

Sumathy,

Pradeep

2024

Computational Intelligence

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“…In Ref. [31], the design of Interval Type-2 FIS and its optimization using a GA is proposed, and the results achieved prove the effectiveness of these kinds of fuzzy systems over the Type-1 FIS applied to the PIMA Indian Diabetes dataset using the same five attributes. For both work the instances were divided into two sets: design and testing.…”

Section: Introductionmentioning

confidence: 97%

Interval Type-3 Fuzzy Inference System Design for Medical Classification Using Genetic Algorithms

Melin,

Sánchez,

Castillo

2023

Axioms

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An essential aspect of healthcare is receiving an appropriate and opportune disease diagnosis. In recent years, there has been enormous progress in combining artificial intelligence to help professionals perform these tasks. The design of interval Type-3 fuzzy inference systems (IT3FIS) for medical classification is proposed in this work. This work proposed a genetic algorithm (GA) for the IT3FIS design where the fuzzy inputs correspond to attributes relational to a particular disease. This optimization allows us to find some main fuzzy inference systems (FIS) parameters, such as membership function (MF) parameters and the fuzzy if-then rules. As a comparison against the proposed method, the results achieved in this work are compared with Type-1 fuzzy inference systems (T1FIS), Interval Type-2 fuzzy inference systems (IT2FIS), and General Type-2 fuzzy inference systems (GT2FIS) using medical datasets such as Haberman’s Survival, Cryotherapy, Immunotherapy, PIMA Indian Diabetes, Indian Liver, and Breast Cancer Coimbra dataset, which achieved 75.30, 87.13, 82.04, 77.76, 71.86, and 71.06, respectively. Also, cross-validation tests were performed. Instances established as design sets are used to design the fuzzy inference systems, the optimization technique seeks to reduce the classification error using this set, and finally, the testing set allows the validation of the real performance of the FIS.

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“…The application of meta-heuristic algorithm in many fields is an emerging research nowadays. The most conventionally used algorithms are Genetic algorithm (GA) [26], Particle swarm optimization algorithm (PSO) [27], ant colony algorithm (ACO) [28], bio-geography based optimization (BBO) [29], ant lion optimizer [30] and so on. Each algorithm varies in their working process based on the individual biological behaviour and lifecycles.…”

Section: Introductionmentioning

confidence: 99%

Deep Searching Based Optimized Machine Translation for Language Translation Tool

Velmurugan¹,

Sumathy²,

Pradeep

2022

Preprint

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Fuzzy matching techniques are the presently used methods in translating the words. Neural machine translation and statistical machine translation are the methods used in MT. In machine translator tool, the strategy employed for translation needs to handle large amount of datasets and therefore the performance in retrieving correct matching output can be affected. In order to improve the matching score of MT, the advanced techniques can be presented by modifying the existing fuzzy based translator and neural machine translator. The conventional process of modifying architectures and encoding schemes are tedious process. Similarly the preprocessing of datasets also involves more time consumption and memory utilization. In this paper, a new spider web based searching enhanced translation is presented to be employed with the neural machine translator. The proposed scheme enables deep searching of available dataset to detect the accurate matching result. In addition, the quality of translation is improved by presenting an optimal selection scheme for using the sentence matches in source augmentation. The matches retrieved using various matching scores are applied to an optimization algorithm. The source augmentation using optimal retrieved matches increases the translation quality. Further, the selection of optimal match combination helps to reduce time requirement, since it is not necessary to test all retrieved matches in finding target sentence. The performance of translation is validated by measuring the quality of translation using BLEU score. The results are evaluated and compared with other available NMT methods to validate the work.

show abstract

Optimal design of type-2 fuzzy systems for diabetes classification based on genetic algorithms

Cited by 10 publications

References 36 publications

Novel algorithm machine translation for language translation tool

Novel algorithm machine translation for language translation tool

Interval Type-3 Fuzzy Inference System Design for Medical Classification Using Genetic Algorithms

Deep Searching Based Optimized Machine Translation for Language Translation Tool

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