Öneri̇len Konvolüsyon Si̇ni̇r Aği Yaklaşimi Kullanarak Elma Yapraği Hastaliklarinin Siniflandirilmasi

ÇETİNER, Halit

doi:10.21923/jesd.980629

Cited by 4 publications

(3 citation statements)

References 35 publications

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“…ANN, RNN and other artificial intelligence techniques are described in the literature and used for many applications across a wide range of fields (Çetin and Metlek, 2021; Çetiner, 2021; Çetiner and Çetiner, 2022; Metlek, 2022). In the study, deep learning architectures in artificial intelligence algorithms were used, and the results were evaluated.…”

Section: Methodsmentioning

confidence: 99%

A new proposal for the prediction of an aircraft engine fuel consumption: a novel CNN-BiLSTM deep neural network model

Metlek

2023

AEAT

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Purpose The purpose of this study is to develop and test a new deep learning model to predict aircraft fuel consumption. For this purpose, real data obtained from different landings and take-offs were used. As a result, a new hybrid convolutional neural network (CNN)-bi-directional long short term memory (BiLSTM) model was developed as intended. Design/methodology/approach The data used are divided into training and testing according to the k-fold 5 value. In this study, 13 different parameters were used together as input parameters. Fuel consumption was used as the output parameter. Thus, the effect of many input parameters on fuel flow was modeled simultaneously using the deep learning method in this study. In addition, the developed hybrid model was compared with the existing deep learning models long short term memory (LSTM) and BiLSTM. Findings In this study, when tested with LSTM, one of the existing deep learning models, values of 0.9162, 6.476, and 5.76 were obtained for R2, root mean square error (RMSE), and mean absolute percentage error (MAPE), respectively. For the BiLSTM model when tested, values of 0.9471, 5.847 and 4.62 were obtained for R2, RMSE and MAPE, respectively. In the proposed hybrid model when tested, values of 0.9743, 2.539 and 1.62 were obtained for R2, RMSE and MAPE, respectively. The results obtained according to the LSTM and BiLSTM models are much closer to the actual fuel consumption values. The error of the models used was verified against the actual fuel flow reports, and an average absolute percent error value of less than 2% was obtained. Originality/value In this study, a new hybrid CNN-BiLSTM model is proposed. The proposed model is trained and tested with real flight data for fuel consumption estimation. As a result of the test, it is seen that it gives much better results than the LSTM and BiLSTM methods found in the literature. For this reason, it can be used in many different engine types and applications in different fields, especially the turboprop engine used in the study. Because it can be applied to different engines than the engine type used in the study, it can be easily integrated into many simulation models.

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Section: Methodsmentioning

confidence: 99%

A new proposal for the prediction of an aircraft engine fuel consumption: a novel CNN-BiLSTM deep neural network model

Metlek

2023

AEAT

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“…The formula representing the most important layer of CNN structure is shown in Equation 5. After the convolution layer, layers such as batch normalization, dropout, density, and fully connected are applied in a certain order and successively [28]. The parameters and ordering of these layers can change the classification success rates.…”

Section: Cnnmentioning

confidence: 99%

CNN Ve LSTM Tabanli Hi̇bri̇t Bi̇r Deri̇n Öğrenme Modeli̇ İle Çok Eti̇ketli̇ Meti̇n Anali̇zi̇

ÇETİNER

2022

Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi

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In this article, it is aimed to categorize meaningful content from uncontrolled growing written social sharing data using natural language processing. Uncategorized data can disturb social sharing users with an increasing user network due to deprecating and negative content. For the stated reason, a hybrid model based on CNN and LSTM has been proposed to automatically classify all written social sharing content, both positive and negative, into defined target tags. With the proposed hybrid model, it is aimed at automatically classifying the content of the social sharing system into different categories by using the simplest embedding layer, keras. As a result of the experimental studies carried out, a better result was obtained than in the different studies in the literature using the same data set with the proposed method. The obtained performance results show that the proposed method can be applied to different multilabel text analysis problems.

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“…Önerilen CNN yöntemleri hem öznitelik çıkarmak için kullanılabileceği gibi hem de sigmoid ve softmax aktivasyon fonkisyonları ile sınıflandırma yapmak için de kullanılabilmektedir. Bunlara ek olarak istenirse, CNN yönteminden elde edilen öznitelikler klasik makine öğrenme sınıflandırma algoritmaları olan Support Vector Machines (SVM), Decision Tree (DT), K-Nearest Neighbour (KNN) ve Naïve Bayes (NB) ile de sınıflandırılabilmektedir [14][15][16].…”

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Konvolüsyonel Si̇ni̇r Aği Kullanilarak Sitma Hastaliği Siniflandirilmasi

Çetiner

2022

Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi

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Sıtma, sivrisinek ısırması sonucu oluşan bir hastalıktır. Sıtma hastalığını algılama, tanıma ve sınıflandırma gibi işlemler, Plasmodium parazitli hücrelerin mikroskobik olarak incelenmesine dayanmaktadır. Sıtma paraziti ile enfekte olmuş hücrelerin bilgisayar destekli olarak tanımlanması zorlu bir iştir. Bu zorlu işlemi kolaylaştırabilmek için son zamanlarda popüler hale gelen derin öğrenme yöntemleri ile kırmızı kan hücrelerinde sıtma parazitinin olup olmadığı tespit edilmeye çalışıldı. Bu hastalığın tanısını koyabilmek için gerçekleştirilen sınıflandırmada iki ayrı Konvolüsyonel Sinir Ağı (CNN) modeli geliştirilmiştir. Birinci modelde giriş katmanı olarak önceden eğitilmiş ResNet50V2 mimarisini temel alan derin öğrenme modeli verilmiştir. İkinci modelde ise 23 ağırlıklı katmandan oluşan yeni bir derin öğrenme modeli tasarlanmıştır. Önerilen modelleri eğitebilmek için toplam 27.558 ince kan hücresi görüntüsü kullanıldı. Önerilen modeller ile sıtma hastalığının sınıflandırılmasında %97.53 oranında doğruluk değerine ulaşılmıştır.

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Öneri̇len Konvolüsyon Si̇ni̇r Aği Yaklaşimi Kullanarak Elma Yapraği Hastaliklarinin Siniflandirilmasi

Cited by 4 publications

References 35 publications

A new proposal for the prediction of an aircraft engine fuel consumption: a novel CNN-BiLSTM deep neural network model

A new proposal for the prediction of an aircraft engine fuel consumption: a novel CNN-BiLSTM deep neural network model

CNN Ve LSTM Tabanli Hi̇bri̇t Bi̇r Deri̇n Öğrenme Modeli̇ İle Çok Eti̇ketli̇ Meti̇n Anali̇zi̇

Konvolüsyonel Si̇ni̇r Aği Kullanilarak Sitma Hastaliği Siniflandirilmasi

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