Use of Evolutionary Algorithms for Detection of Fatal Diseases via DNA Micro-array Classification: A Review

Jena, Junali Jasmine; Satapathy, Suresh Chandra

doi:10.1007/978-981-15-5397-4_65

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…In backward search while a function is deleted it can't be reselected and in ahead search when a characteristic is chosen, it cannot be deleted [22]. The wrapper version [23] makes use of a predictive accuracy of a special getting to know algorithm to determine the fine of selected functions. This approach is computationally pricey to run massive datasets having huge wide variety of capabilities.…”

Section: The Classical Filter Techniques Are Correlation Characterist...mentioning

confidence: 99%

EapGAFS: Microarray Dataset for Ensemble Classification for Diseases Prediction

Krishna

Rajarajeswari

2022

IJRITCC

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Microarray data stores the measured expression levels of thousands of genes simultaneously which helps the researchers to get insight into the biological and prognostic information. Cancer is a deadly disease that develops over time and involves the uncontrolled division of body cells. In cancer, many genes are responsible for cell growth and division. But different kinds of cancer are caused by a different set of genes. So to be able to better understand, diagnose and treat cancer, it is essential to know which of the genes in the cancer cells are working abnormally. The advances in data mining, machine learning, soft computing, and pattern recognition have addressed the challenges posed by the researchers to develop computationally effective models to identify the new class of disease and develop diagnostic or therapeutic targets. This paper proposed an Ensemble Aprior Gentic Algorithm Feature Selection (EapGAFS) for microarray dataset classification. The proposed algorithm comprises of the genetic algorithm implemented with aprior learning for the microarray attributes classification. The proposed EapGAFS uses the rule set mining in the genetic algorithm for the microarray dataset processing. Through framed rule set the proposed model extract the attribute features in the dataset. Finally, with the ensemble classifier model the microarray dataset were classified for the processing. The performance of the proposed EapGAFS is conventional classifiers for the collected microarray dataset of the breast cancer, Hepatities, diabeties, and bupa. The comparative analysis of the proposed EapGAFS with the conventional classifier expressed that the proposed EapGAFS exhibits improved performance in the microarray dataset classification. The performance of the proposed EapGAFS is improved ~4 – 6% than the conventional classifiers such as Adaboost and ensemble.

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Section: The Classical Filter Techniques Are Correlation Characterist...mentioning

confidence: 99%

EapGAFS: Microarray Dataset for Ensemble Classification for Diseases Prediction

Krishna

Rajarajeswari

2022

IJRITCC

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“…Younas et al introduced EA to solve the sensor selection problem in Internet of Thing (IoT) systems 37 . There have been many studies on EAs for gene selection and detection of fatal diseases 38 – 41 . EAs were also used for reservoir operation 42 , dynamic optimization of biodiesel production process 43 , vehicle route planning 44 , traffic signal control 45 , assembly line balancing 46 , MPPT of hybrid renewable energy system 47 , etc.…”

Section: Introductionmentioning

confidence: 99%

Maximum power point tracking of PEMFC based on hybrid artificial bee colony algorithm with fuzzy control

2022

Sci Rep

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Maximum power point tracking (MPPT) is an effective method to improve the power generation efficiency and power supply quality of a proton exchange membrane fuel cell (PEMFC). Due to the inherent nonlinear characteristics of PEMFC, conventional MPPT methods are often difficult to achieve a satisfactory control effect. Considering this, artificial bee colony algorithm combining fuzzy control (ABC-fuzzy) was proposed to construct a MPPT control scheme for PEMFC. The global optimization ability of ABC algorithm was used to approach the maximum power point of PEMFC and solve the problem of falling into local optimization, and fuzzy control was used to eliminate the problems of large overshoot and slow convergence speed of ABC algorithm. The testing results show that compared with perturb & observe algorithm, conductance increment and ABC methods, ABC-fuzzy method can make PEMFC obtain greater output power, faster regulation speed, smaller steady-state error, less oscillation and stronger anti-interference ability. The MPPT scheme based on ABC-fuzzy can effectively realize the maximum power output of PEMFC, and plays an important role in improving the service life and power supply efficiency of PEMFC.

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Intelligent drone-assisted robust lightweight multi-factor authentication for military zone surveillance in the 6G era

Deebak

Hwang

2023

Computer Networks

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Use of Evolutionary Algorithms for Detection of Fatal Diseases via DNA Micro-array Classification: A Review

Cited by 3 publications

References 32 publications

EapGAFS: Microarray Dataset for Ensemble Classification for Diseases Prediction

EapGAFS: Microarray Dataset for Ensemble Classification for Diseases Prediction

Maximum power point tracking of PEMFC based on hybrid artificial bee colony algorithm with fuzzy control

Intelligent drone-assisted robust lightweight multi-factor authentication for military zone surveillance in the 6G era

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