Interest point based face recognition using adaptive neuro fuzzy inference system

Rejeesh, M R

doi:10.1007/s11042-019-7577-5

Cited by 171 publications

(53 citation statements)

References 18 publications

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“…In any case, these days, the innovation of a delightful system named CS yields another way to deal with remake signals utilizing a base number of cases at a lower rate. CS likewise resolves image processing and computer representation issues [64][65][66][67][68].…”

Section: Signal and Image Reconstruction Methodsmentioning

confidence: 99%

Soft computing based compressive sensing techniques in signal processing: A comprehensive review

Mishra¹,

Jain

2020

Journal of Intelligent Systems

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In this modern world, a massive amount of data is processed and broadcasted daily. This includes the use of high energy, massive use of memory space, and increased power use. In a few applications, for example, image processing, signal processing, and possession of data signals, etc., the signals included can be viewed as light in a few spaces. The compressive sensing theory could be an appropriate contender to manage these limitations. “Compressive Sensing theory” preserves extremely helpful while signals are sparse or compressible. It very well may be utilized to recoup light or compressive signals with less estimation than customary strategies. Two issues must be addressed by CS: plan of the estimation framework and advancement of a proficient sparse recovery calculation. The essential intention of this work expects to audit a few ideas and utilizations of compressive sensing and to give an overview of the most significant sparse recovery calculations from every class. The exhibition of acquisition and reconstruction strategies is examined regarding the Compression Ratio, Reconstruction Accuracy, Mean Square Error, and so on.

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Section: Signal and Image Reconstruction Methodsmentioning

confidence: 99%

Soft computing based compressive sensing techniques in signal processing: A comprehensive review

Mishra¹,

Jain

2020

Journal of Intelligent Systems

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“…Adaptive and specific encoding methods have also been suggested with several distributed parallel algorithms. 20,21,22 Momenzadeh et al 23 proposed a hidden Markov model that integrates multiple features from the microarray dataset. In the hidden Markov model, the entropy along with the Wilcoxon test, characteristic curve, Bhattacharya distance, and t test was used for feature ranking purposes.…”

Section: Related Workmentioning

confidence: 99%

Cancer classification from microarray data for genomic disorder research using optimal discriminant independent component analysis and kernel extreme learning machine

Nguyen

Trần

et al. 2020

Numer Methods Biomed Eng

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One of the challenging tasks in the medicinal field is genomic disorder investigation and its classification from the microarray dataset. The microarray dataset reorganization and its classification is more complex and expensive in the biomedical research area due to the larger number of features in the microarray dataset. In this paper, we construct a hybrid feature selection method such as t test, Fisher ration, and Bayesian logistic regression to select genes and that reduce the time cost. Based on the features, the top‐ranked features are selected via the best hybrid rank method. Thereafter, the features are extracted using the modified firefly optimization‐based discriminant independent component analysis (MF‐DICA). Especially, the modified firefly optimization algorithm is capable of improving the search efficiency of DICA. From the high dimensional microarray dataset, MF‐DICA is used to obtain the best features within the entire search space. The kernel extreme learning machine classifies the gene features depending upon the most relevant class. Experimentally, six datasets namely Leukemia dataset, Diffuse Larger B‐cell Lymphomas, Lung cancer, Breast cancer, Prostate tumor, and Colon dataset are chosen to evaluate the performance of proposed approaches. Finally, the experimental data demonstrate that the proposed method is well suitable to classify the microarray data.

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“…For this purpose, soft computing techniques are used to track the GMPP optimally. The soft computing technique includes an artificial neural network (ANN) and fuzzy logic controller (FLC), 18,19 bat algorithm, firefly algorithm (FA), 17,20 particle swarm optimization (PSO), 21–23 Ant bee, 15 artificial bee colony (ABC), 24,25 and flower pollination algorithm (FPA). In the process of the PSO algorithm, to obtain the best solution, a velocity formula is utilized to converge the particles.…”

Section: Introductionmentioning

confidence: 99%

CCGPA‐MPPT: Cauchy preferential crossover‐based global pollination algorithm for MPPT in photovoltaic system

Sundararaj

Anoop

Dixit

et al. 2020

Progress in Photovoltaics

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176

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In general, the photovoltaic (PV) is considered as the best selection among renewable energy resources due to its nonpolluted operation and good flexibility condition. The PV system is affected because of the partial shading conditions (PSCs), which reduce the generated power. During steady-state operating conditions, there occurs a time delay in tracking the Global maximum power point (GMPP) and Local maximum power point (LMPP) under PSCs using the perturb and observe (P&O) method. In order to overcome such shortcomings, this paper proposed a hybrid algorithm with a P&O technique to improve the maximum power point tracking (MPPT) for the PV system under PSC. In addition to this, the P&O technique is utilized to achieve the LMPP in the first section, and the hybrid algorithm is utilized to achieve the GMPP in the second section. Here, the hybrid technique is the integration of Cauchy preferential crossover (CC) with the flower pollination algorithm (FPA). Furthermore, the exploitation ability of the FPA is enhanced by the CC, and the combined hybrid algorithm has the ability to produce the optimal duty cycle for the DC-DC boost converter for MPPT. Then the proposed method will be executed in MATLAB/Simulink model, and it is contrasted with the existing methods such as CC, current sensorless (CS), and FPA, respectively. The experimental results and analysis reveal that the proposed approach provides better performances when compared with several other metaheuristic algorithms. K E Y W O R D S crossover, DC to DC boost converter, global maximum power point, global pollination, local maximum power point, photovoltaic, PSC 1 | INTRODUCTION Due to the rapid growth and expansion in business as well as the consistently increasing refinement of existing ways of living, the world supply energy is exposed to a huge strain. This wonder creates uncertainties over energy security as well as environmental sustainability. 1,2 Besides, the problem of climatic changes and it is necessary to reduce carbon impressions has added to the solid force for organizations and countries to put resources into elective energy sources, especially renewable energy (RE). Solar energy is a standout among the most vital RE sources, rather than regular unrenewable assets, for example, fuel and coal. 3 Solar energy is spotless, limitless, and free. The primary utilization of photovoltaic (PV) frameworks is said to be a stand-alone (military, street and residential lighting, electric vehicles space applications, and water pumping). Despite these

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Interest point based face recognition using adaptive neuro fuzzy inference system

Cited by 171 publications

References 18 publications

Soft computing based compressive sensing techniques in signal processing: A comprehensive review

Soft computing based compressive sensing techniques in signal processing: A comprehensive review

Cancer classification from microarray data for genomic disorder research using optimal discriminant independent component analysis and kernel extreme learning machine

CCGPA‐MPPT: Cauchy preferential crossover‐based global pollination algorithm for MPPT in photovoltaic system

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