The effects on classifier performance of 2D discrete wavelet transform analysis and whale optimization algorithm for recognition of power quality disturbances

Karasu, Seçkin; Saraç, Zehra

doi:10.1016/j.cogsys.2022.05.001

Cited by 14 publications

(4 citation statements)

References 27 publications

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“…By setting standards, weights, and dynamic cooperative combination forms, the causes of athletes' injuries are classified, and finally, the regression results are obtained [ 24 ]. At the same time, in order to avoid the excessive error of multiple regression, the residual analysis method is integrated to find out the causes of athletes' injuries and put forward reasonable prevention measures [ 25 ]. MATLAB simulation results show that the accuracy of the proposed diagnosis algorithm is more than 90%, the convergence time is less than 10 minutes, and the overall convergence is good, converging at 10 iterations, which is significantly better than other algorithms.…”

Section: Discussionmentioning

confidence: 99%

[Retracted] Research on Injury Causes and Prevention Effect of College Rowing Athletes Based on Multiple Regression and Residual Algorithm

2022

Journal of Environmental and Public Health

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Rowing competition in colleges and universities is an international competition, and it is also a favorite competition for college students. However, in the course of rowing competition, the stability of athletes’ injuries often occurs, which is difficult to solve effectively. Aiming at the problem that the loss of athletes in rowing competition in colleges and universities cannot be accurately prevented, this paper puts forward a multiple regression prevention effect model and makes a comprehensive analysis combined with complex reasons. Through the integration of multiple regression and residual analysis, we can better find out the influencing factors, aiming at finding out the causes of athletes’ injuries and putting forward corresponding countermeasures. First of all, analyze the causes of loss, establish a framework of injury prevention for college rowers, and the overall diagnosis framework is reasonable. Then, according to the “University Rowing Prevention and Control Standards” divided into various prevention measures, through the comprehensive prevention and control measure mechanism to get the cause of injury, finally, the optimal combination of various control measures forms a control system. The results of MATLAB show that the combination of multiple regression and residual analysis can improve the accuracy of athletes’ injury prevention and treatment, make the accuracy more than 90%, shorten the diagnosis time less than 10 minutes, and meet the requirements of athletes’ injury diagnosis under normal rowing competition.

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Section: Discussionmentioning

confidence: 99%

[Retracted] Research on Injury Causes and Prevention Effect of College Rowing Athletes Based on Multiple Regression and Residual Algorithm

2022

Journal of Environmental and Public Health

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“…Half of the extracted features consist of the mean, standard deviation, and entropy values of MRV, which is the maximum row vector of the

image, and MCV, which is the maximum column vector of the

image. Thus, the sensitivity of the proposed plant leaf disease classification model to the changes in the rows and columns of the

image matrix was increased [ 41 ].…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence-Based Robust Hybrid Algorithm Design and Implementation for Real-Time Detection of Plant Diseases in Agricultural Environments

Yağ

Altan

2022

Biology

174

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The early detection and prevention of plant diseases that are an important cause of famine and food insecurity worldwide are very important for increasing agricultural product productivity. Not only the early detection of the plant disease but also the determination of its type play a critical role in determining the appropriate treatment. The fact that visual inspection, which is frequently used in determining plant disease and types, is tiring and prone to human error, necessitated the development of algorithms that can automatically classify plant disease with high accuracy and low computational cost. In this study, a new hybrid plant leaf disease classification model with high accuracy and low computational complexity, consisting of the wrapper approach, including the flower pollination algorithm (FPA) and support vector machine (SVM), and a convolutional neural network (CNN) classifier, is developed with a wrapper-based feature selection approach using metaheuristic optimization techniques. The features of the image dataset consisting of apple, grape, and tomato plants have been extracted by a two-dimensional discrete wavelet transform (2D-DWT) using wavelet families such as biorthogonal, Coiflets, Daubechies, Fejer–Korovkin, and symlets. Features that keep classifier performance high for each family are selected by the wrapper approach, consisting of the population-based metaheuristics FPA and SVM. The performance of the proposed optimization algorithm is compared with the particle swarm optimization (PSO) algorithm. Afterwards, the classification performance is obtained by using the lowest number of features that can keep the classification performance high for the CNN classifier. The CNN classifier with a single layer of classification without a feature extraction layer is used to minimize the complexity of the model and to deal with the model hyperparameter problem. The obtained model is embedded in the NVIDIA Jetson Nano developer kit on the unmanned aerial vehicle (UAV), and real-time classification tests are performed on apple, grape, and tomato plants. The experimental results obtained show that the proposed model classifies the specified plant leaf diseases in real time with high accuracy. Moreover, it is concluded that the robust hybrid classification model, which is created by selecting the lowest number of features with the optimization algorithm with low computational complexity, can classify plant leaf diseases in real time with precision.

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“…This article will analyze the types of faults studied by researchers, including LG (line-to-ground) and SLG (single-line-to-ground fault), which are short circuits of one phase-to-ground that occur due to a flashover between the phase conductor and the ground (pole) [27] and Travers or ground wire on SUTM. This disturbance is temporary, there is no permanent damage at the point of disturbance.…”

Section: Introductionmentioning

confidence: 99%

Disturbance Modeling on Transmission Lines Using Wavelet Transform Method: A Review

Amalia

Hasanah²,

Warmi³

et al. 2023

AJEEET

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This paper introduces a number of methods for identifying disturbances on transmission lines and provides algorithms for classifying various disturbance types. working with the discrete wavelets transform (DWT) and various embedded systems techniques using a simulator to carry out digital simulations of system trials using the controlled technique, the nature of disturbance in distinct sub-systems is determined. The analysis of transmission lines using the wavelet transform is the main topic of this study, along with techniques for transmission line protection. These factors include channel capacity, channel length, simulation software, mother wavelet type, parameters, sampling frequency, and type of disturbance analysis.

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The effects on classifier performance of 2D discrete wavelet transform analysis and whale optimization algorithm for recognition of power quality disturbances

Cited by 14 publications

References 27 publications

[Retracted] Research on Injury Causes and Prevention Effect of College Rowing Athletes Based on Multiple Regression and Residual Algorithm

[Retracted] Research on Injury Causes and Prevention Effect of College Rowing Athletes Based on Multiple Regression and Residual Algorithm

Artificial Intelligence-Based Robust Hybrid Algorithm Design and Implementation for Real-Time Detection of Plant Diseases in Agricultural Environments

Disturbance Modeling on Transmission Lines Using Wavelet Transform Method: A Review

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