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

Metin Zontul | Esra Kavalci Yilmaz | Oguz Kaynar | Ramazan Katirci

Makale | 2021 | ELSEVIER SCIENCE SA

In this study, chrome coatings were carried out using a Cr-III electroplating bath. The coated parts were classified depending on their appearance. A new approach was developed to classify the coated parts automatically using artificial intelligence methods. Mask RCNN and machine learning (ML) methods such as Multilayer Perceptron (MLP), Support Vector Classifier (SVC), Gaussian Process (GP), K-nearest Neighbors (KNN), XGBoost, and Random Forest Classifier (RFC) were used together. Mask RCNN was used to clean the coated parts from the redundant data. The extracted data were flattened and converted to the row vectors for use as input . . . in ML methods. ML algorithms were used to classify the coated parts as Pass and Fail. The classification accuracy was checked with the leave one out (loo) cross-validation method. RFC method gave the highest accuracy, 0.83, and F1 score, 0.88. The accuracy of Mask RCNN was checked using a dataset of separated validation images. It was observed that extracting the unnecessary data from the images increased the accuracy exceedingly. Moreover, the method exhibits a high potential to keep the parameters of the electroplating process under control Daha fazlası Daha az

Multi-Class Document Classification Based on Deep Neural Network and Word2Vec

Metin Zontul

Makale | 2022 | JOURNAL OF AERONAUTICS AND SPACE TECHNOLOGIES15 ( 1 ) , pp.59 - 65

With the increase in unstructured data, the importance of classification of text-based documents has increased. In particular, the classification of news texts and digital documentation provides easy access to the information sought. In this study, a large amount of news textual data was used. After the data set was preprocessed, Bag of Words (BoW), TF-IDF, Word2Vec and Doc2Vec word embedding methods were applied. In the classification phase, Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Deep Neural Network (DNN) algorithms were applied. As a result of the experimental studies, using the Word2Vec . . .method together with the DNN algorithm performed the best result. Yapısal olmayan verilerin artmasıyla birlikte metin tabanlı belgelerin sınıflandırılmasının önemi artmıştır. Özellikle haber metinlerinin sınıflandırılması ve dijital dokümantasyon, aranan bilgilere kolay erişim sağlar. Bu çalışmada, büyük miktarda metinsel haber verisi kullanılmıştır. Veri seti ön işlemeye tabi tutulduktan sonra, Bag of Words (BoW), TF-IDF, Word2Vec ve Doc2Vec kelime temsil yöntemleri uygulanmıştır. Sınıflandırma aşamasında Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM) ve Deep Neural Network (DNN) algoritmaları uygulanmıştır. Deneysel çalışmalar sonucunda DNN algoritması ile birlikte Word2Vec yönteminin kullanılması en iyi sonucu vermiştir Daha fazlası Daha az

A long short-term memory (LSTM)-based distributed denial of service (DDoS) detection and defense system design in public cloud network environment

HAKAN AYDIN

Makale | 2022 | Computers & Security , pp.59 - 65

The fact that cloud systems are under the increasing risks of cyber attacks has made the phenomenon of information security first a need and then a necessity for these systems. Distributed Denial of Service (DDoS) attacks can exploit, disrupt, change, prevent or damage cloud services. Accurate and timely detection and prevention of these attacks are very important in terms of ensuring information security. During the COVID-19 period, the increase in the use of information technologies and especially the internet has made cyber attacks a real concern. Deep learning (DL) has become widely used for the purpose of detecting and preventi . . .ng cyber attacks to provide information security. In this study, a Long Short-Term Memory (LSTM) based system (LSTM-CLOUD) which was designed for the detection and prevention of DDoS attacks in a public cloud network environment was proposed. The design of the system is based on a signature-based attack detection approach. The LSTM-CLOUD has two modules defined in the study: detection and defense. The function of the first module of the system was determined as detecting the occurrence of DDoS attacks with the LSTM DL model developed in this study with an accuracy rate of 99.83% on the CICDDoS2019 data set. The function of the second module was determined as activating the defense mechanism to protect the cloud systems when attacks are detected. The comparison results showed that our LSTM model had a performance as good as those in the previous studies conducted with different DL algorithms on the same and different datasets. The results obtained show the effectiveness of the LSTM model developed in this study in detecting the occurrence of attacks. (c) 2022 Elsevier Ltd. All rights reserved Daha fazlası Daha az

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