The prediction of the ZnNi thickness and Ni % of ZnNi alloy electroplating using a machine learning method

Katırcı, Ramazan; Aktas, H.; Zontul, Metin

doi:10.1080/00202967.2021.1898183

Cited by 15 publications

(6 citation statements)

References 19 publications

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“…The booster was chosen as a tree model (called gbtree). This gives a better fit for nonlinear dependencies than the linear booster (gblinear) [58,59]. When considering a series of external factors affecting the quality of the images, it is expected that most of them will have a nonlinear effect.…”

Section: Methodsmentioning

confidence: 99%

Application of the XBoost Regressor for an A Priori Prediction of UAV Image Quality

Sekrecka

2021

Remote Sensing

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In general, the quality of imagery from Unmanned Aerial Vehicles (UAVs) is evaluated after the flight, and then a decision is made on the further value and use of the acquired data. In this paper, an a priori (preflight) image quality prediction methodology is proposed to estimate the preflight image quality and to avoid unfavourable flights, which is extremely important from a time and cost management point of view. The XBoost Regressor model and cross-validation were used for machine learning of the model and image quality prediction. The model was learned on a rich database of real-world images acquired from UAVs under conditions varying in both sensor type, UAV type, exposure parameters, weather, topography, and land cover. Radiometric quality indices (SNR, Entropy, PIQE, NIQE, BRISQUE, and NRPBM) were calculated for each image to train and test the model and to assess the accuracy of image quality prediction. Different variants of preflight parameter knowledge were considered in the study. The proposed methodology offers the possibility of predicting image quality with high accuracy. The correlation coefficient between the actual and predicted image quality, depending on the number of parameters known a priori, ranged from 0.90 to 0.96. The methodology was designed for data acquired from a UAV. Similar prediction accuracy is expected for other low-altitude or close-range photogrammetric data.

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Section: Methodsmentioning

confidence: 99%

Application of the XBoost Regressor for an A Priori Prediction of UAV Image Quality

Sekrecka

2021

Remote Sensing

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“…Individual classification models (trees) are created and each model is trained by the previous tree. Thanks to these features, xGboost offers better results compared to other boost algorithms [37].…”

Section: Classification Algorithmsmentioning

confidence: 99%

Hyperparameter Tunning and Feature Selection Methods for Malware Detection

KAVALCI YILMAZ,

BAKIR

2024

Politeknik Dergisi

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Smartphones have started to take an essential place in every aspect of our lives with the developing technology. All kinds of transactions, from daily routine work to business meetings, payments, and personal transactions, started to be done via smartphones. Therefore, there is a significant amount of very important user information stored in these devices which makes them a target for malware developers. For these reasons, machine learning (ML) methods have been used to detect malicious software on android devices quickly and reliably. In this study, a machine learning-based Android malware detection system has been developed, optimized, and tested. To this end, firstly, the data in the dataset has been balanced with 3 different methods namely SMOTE, SMOTETomek and ClusterCentroids. Afterward, the obtained results have been tried to be optimized by using different feature selection approaches including mRMR, Mutual Information, Select From Model, and Select k Best. Finally, the most two successful methods from the five tested ML algorithms (i.e. RF, SVM, LR, XGBoost, and ETC) have been tuned using GridSearch, Random Search, and Bayesian Optimization algorithms in order to investigate the effects of hyperparameter tuning on the performance of ML algorithms.

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“…[5] Due to the influence of various factors, the data obtained from the study fluctuates. The reasons for the fluctuations can be divided into two categories, one is uncontrollable random factors, and the other is controllable factors that affect the results in the research [6].…”

Section: Feature Extractionmentioning

confidence: 99%

An Analysis of the Authenticity of Financial Data of Listed Companies Based on Vector Machines

Gong

Zheng²,

Hou

et al. 2022

Advances in Transdisciplinary Engineering

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Driven by the benefits of listing at a high premium, financial fraud in listed companies has become a widespread problem worldwide. In the face of economic interests, even developed countries with relatively complete laws and policies cannot completely eliminate financial fraud. The financial fraud incidents of listed companies have seriously disrupted the normal order of my country’s capital market. Therefore, the specific financial data of listed companies are analyzed to obtain the influence of financial indicators on the identification of financial fraud and to determine whether there is financial fraud in listed companies.

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The prediction of the ZnNi thickness and Ni % of ZnNi alloy electroplating using a machine learning method

Cited by 15 publications

References 19 publications

Application of the XBoost Regressor for an A Priori Prediction of UAV Image Quality

Application of the XBoost Regressor for an A Priori Prediction of UAV Image Quality

Hyperparameter Tunning and Feature Selection Methods for Malware Detection

An Analysis of the Authenticity of Financial Data of Listed Companies Based on Vector Machines

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