Automated evaluation of Cr-III coated parts using Mask RCNN and ML methods

Katırcı, Ramazan; Yılmaz, Esra Kavalcı; Kaynar, Oğuz; Zontul, Metin

doi:10.1016/j.surfcoat.2021.127571

Cited by 25 publications

(6 citation statements)

References 22 publications

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“…During the training process of RF, sample and special selections are made randomly. In this way, overfitting is prevented [34].…”

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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“…During the training process of RF, sample and special selections are made randomly. In this way, overfitting is prevented [34].…”

Section: Classification Algorithmsmentioning

confidence: 99%

Hyperparameter Tunning and Feature Selection Methods for Malware Detection

KAVALCI YILMAZ,

BAKIR

2024

Politeknik Dergisi

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“…ANN-based MLP is a feedforward neural network comprising a series of processing nodes arranged in a linear, feedforward configuration, with one or more fully integrated hidden level(s), input(s), and output(s). The biases and weights associated with the error are updated after the training is performed using backpropagation, as shown in Equation (1) [34,35].…”

Section: Modeling Proceduresmentioning

confidence: 99%

Assessing Cadmium Stress Resilience in Myrtle Genotypes Using Machine Learning Predictive Models: A Comparative In Vitro Analysis

Tütüncü,

Isak,

İzgü

et al. 2024

Horticulturae

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This study investigated the effects of cadmium (Cd) stress on the micropropagation and rooting dynamics of two myrtle (Myrtus communis L.) genotypes with different fruit colors under controlled in vitro conditions. We evaluated the response of these genotypes to varying concentrations of Cd (0, 100, 200, 300, 400, and 500 µM) to determine dose-dependent effects on plantlet multiplication and root formation. Our results demonstrate that the white-fruited (WF) genotype exhibits greater resilience than the black-fruited (BF) genotype across all concentrations, maintaining higher multiplication rates and shoot heights. For instance, the multiplication rate at 100 µM Cd was highest for WF at 6.73, whereas BF showed the lowest rate of 1.94 at 500 µM. Similarly, increasing Cd levels significantly impaired root length and the number of roots for both genotypes, illustrating the detrimental impact of Cd on root system development. Additionally, this study incorporated machine learning (ML) models to predict growth outcomes. The multilayer perceptron (MLP) model, including random forest (RF) and XGBoost, was used to analyze the data. The MLP model performed notably well, demonstrating the potential of advanced computational tools in accurately predicting plant responses to environmental stress. For example, the MLP model accurately predicted shoot height with an R2 value of 0.87 and root length with an R2 of 0.99, indicating high predictive accuracy. Overall, our findings provide significant insights into the genotypic differences in Cd tolerance and the utility of ML models in plant science. These results underscore the importance of developing targeted strategies to enhance plant resilience in contaminated environments.

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“…artificial intelligent approach [4,5] for DOE to achieve process optimization. When it comes to electroplating process use case, the application includes thickness prediction [6][7][8][9], chemical composition of coating [10], analysis of plating bath additives [11], microhardness prediction [12,13], coating appearance [14], prediction of tribological properties [15], and surface roughness [16]. For example, Shozib et al [11] explored various applications of machine learning in surface coating.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach for Process Optimization of Black Nickel Electroplating

SUN,

Tan,

Zhao

et al. 2024

Preprint

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Optimization of electroplating processes is crucial yet time-consuming. In an electroplating process like black nickel plating, achieving a consistent coating becomes challenging without boric acid, which serves as a pH buffer and widens the operational process window. Despite its benefits, the use of boric acid conflicts with the growing emphasis on sustainable and eco-friendly manufacturing. This complication intensifies the intricacy of process optimization. To tackle this challenge, this study employs machine learning techniques to gain insights into optimizing a boric acid-free black nickel plating process. Specifically, three machine learning models were developed to predict coating defects, coating colour, and coating mass. Drawing from the predictions of these models, an optimization algorithm is proposed to select process parameters ensuring a flawless coating with the desired colour and mass, which is validated by experimental result. The machine learning models were able to give accurate predictions for black nickel electroplating process which behaves in a non-linear manner. This research illustrates how machine learning can enhance the manufacturing industry, particularly in the surface finishing sector, by providing an effective approach to optimize processes in non-linear scenarios, thereby improving product quality and work productivity.

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Automated evaluation of Cr-III coated parts using Mask RCNN and ML methods

Cited by 25 publications

References 22 publications

Hyperparameter Tunning and Feature Selection Methods for Malware Detection

Hyperparameter Tunning and Feature Selection Methods for Malware Detection

Assessing Cadmium Stress Resilience in Myrtle Genotypes Using Machine Learning Predictive Models: A Comparative In Vitro Analysis

Machine Learning Approach for Process Optimization of Black Nickel Electroplating

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