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In this study, it is aimed to produce meaningful words in accordance with character-based Turkish grammar rules by using encoder-decoder and attention architecture, which are deep learning algorithms. The results of the developed model are compared with the results of LSTM and GRU models, which are other deep learning algorithms. It is seen that the language models created with LSTM and GRU models give similar results at 100 and 200 epoch values and at different threshold values of the temperature sampling method. Among these models, the GRU model gives the highest success value with 88.40% at 200 epochs and 0.5 temperature threshold value. The encoder-decoder and attention language model developed for this study gives the highest success value of 91.90% at 100 and 200 epoch values and at different threshold values of the temperature sampling method at 200 epoch and 0.5 temperature threshold value. At the end of the experiments, the encoder-decoder and attention architecture model showed an average of 2.83% more success than the LSTM model and an average of 0.19% more success than the GRU model. [ABSTRACT FROM AUTHOR]

After lung cancer, prostate cancer is the most common type of cancer in men. Nowadays, advanced prostate imaging is conducted by multiparametric prostate magnetic resonance imaging (MRI) performed by radiologists. Although many pathologies of the prostate can be visualized, the main purpose is to determine the probability of prostate cancer and to decide whether a biopsy is needed. This process includes the analysis of different series of Magnetic Resonance (MR) images, including T2-weighted images (T2W), diffuse-weighted images (DWI), and dynamic contrast enhanced images (DCE). However, firstly it is necessary to differentiate the prostate zones. Then, lesion scanning is performed in the relevant prostate zones. Finally, there is a need to perform prostate lesion scoring according to PI-RADS v2. Therefore, diagnosis of prostate cancer is a complex and long process. For this reason, decision support systems are needed for the diagnosis of prostate cancer. In this context, the main purpose of the study is to provide automatic segmentation of prostate regions. In the segmentation task, Mask R-CNN algorithm was used with T2W MR images of 15 patients. With the use of the ResNet-50 backbone model, the Mask R-CNN algorithm achieved the segmentation performance with a mAP50 value of 96.040. Finally, the trained model was integrated into the PACS system. Thanks to the integration, a ready-to-use in hospitals artificial intelligence-supported decision support system was developed. Thus, time was saved while reducing the workload on health employees. [ABSTRACT FROM AUTHOR]

Firms in the manufacturing sector need to constantly monitor their performance in order to maintain their development under competitive conditions in the market. In this study, eleven KPIs are determined to measure the production performance by taking into account the factory assets. The proposed system is designed in which the relevant KPIs are obtained via the instantaneous data received from the CNC machines in a production system. The main objective of this study is to measure and estimate production performance. In this way, it is aimed to provide a proactive approach for the assets whose performance is monitored by the decision-makers. LSTM and LightGBM models which are deep learning techniques are proposed for the estimation of performance indicators. The approximately three-month time series OEE (Overall Equipment Effectiveness) values of the sample CNC machine are used for estimation. The estimation performance of methods is compared over performance metrics (MSE, MAE, etc.). The results indicated that LightGBM outperforms LSTM for all performance metrics. [ABSTRACT FROM AUTHOR]

Thanks to the precision agriculture technologies developed in recent years, many processes such as irrigation, fertilization, spraying, weeding and harvesting of agricultural products can be done by autonomous systems. Especially in some plant species such as sunflower, when to apply these processes is largely decided according to the developmental stage of the plant. In this study, deep learning methods were used to classify the developmental stages of sunflower plants. Since the images taken with the drone are high resolution, each of them is divided into 6 equal parts, and then 8 classes are determined and the images belonging to each class are extracted. A data set consisting of 12800 images in total, 1600 in each class, was created. Six different deep learning models, namely AlexNet, InceptionV3, ResNet101, DenseNet121, MobileNet and Xception, were tested with Sgd, Adam and Rmsprop optimization methods and their performances were compared. In order to evaluate the success of the models correctly, the trained models were also tested on a second data set created with images taken from a different terrain and high success rates were obtained. In addition, a 7-class test set was created for images that could not be clearly determined at which stage the plant was in, and the success rates of the models were tested. It was observed that the success rate was very low for the images in the 7-8 intermediate class, and the filters used in the image processing techniques that would increase the success rate for this class were applied to the images, and the models were retrained and the results were evaluated. [ABSTRACT FROM AUTHOR]

It has become imperative to monitor the data-driven industrial systems of today's technology before potential failures occur. Predictive maintenance predicts these failures before they occur and takes the necessary action to prevent malfunctions from occurring. In this study a comparative predictive maintenance application which is based on machine and deep learning is realized. Logistic Regression, Naive Bayes Classifier, Decision Tree, Support Vector Machine, Random Forest, and K-Nearest Neighborhood are used as the classical machine learning methods while Long Short-Term Memory and Gated Recurrent Unit are used as the deep learning architectures. The performances of the methods are examined on the Predictive Maintenance dataset from UCI Machine Learning Repository for fault type detection and the results are presented comparatively in terms of metrics in detail. In the experimental studies, fault type detection is handled separately in the form of multiple and binary classification problems. In the solution of the multiclassification problem, the highest accuracy among the machine learning methods is obtained by Random Forest method with 98.26%, while the accuracy value obtained with both deep learning architectures is 97.51%. In the solution of the binary classification problem, after the data balancing, the highest accuracy among the machine learning methods is obtained by Random Forest method with 95.03%, while the highest accuracy among the deep learning architectures is obtained by Gated Recurrent Unit architecture as 93.03%. [ABSTRACT FROM AUTHOR]

Nowadays, the Internet is a structure that people can access easily and at the same time produce content easily and without control. In parallel with this situation, the ability of extract information from the raw data that makes up big data has become more complex. The fact that the headlines of the contents contain uncontrolled and misleading elements makes it difficult to reach the right information. The headlines of the contents are important for people to reach the information they want in their limited time. In this study, it is aimed to produce headlines suitable for the content instead of headlines that may be misleading for news. For this purpose, an application that produces headlines for Turkish news with deep learning method has been developed. SuDer news corpus is used as dataset. For the training of the model, it is aimed to obtain more humanoid results in the production of news headlines by using the Transformer architecture, which is frequently preferred in natural language studies today and the abstract summarization method. In this study, in order to compare the performance of the Transformer model, models are prepared and trained with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures. At the end of 25 epochs of training with LSTM, GRU and Transformer architectures on the corpus, the values of loss are 1.03, 0.55 and 2.49 respectively. In the experiments performed on the validation data, measurements are made with ROUGE-1, ROUGE-2 and ROUGE-L metrics. As a result of the measurements, it is observed that the Transformer architecture is partially good, based on the metric values produced. In addition, when the headlines produced with these architectures are examined, it is observed that the headline obtained with the Transformer architecture produce headlines that are partially more suitable for the news content compared to other architectures. [ABSTRACT FROM AUTHOR]

In this study, the Deep Learning technique was combined with the Uncertainty Quantification scale to determine whether patients have cancer or not, and if cancer is present, to classify it as benign or malignant using a dataset of 663,549 lung tomography data. The reliability of this model was also investigated. 80% of the mentioned images were used for training, 10% for testing, and 10% for validation. Two methods were proposed for this purpose. In the first method, measurements were made without using Uncertainty Quantification, and the accuracy rate for the ResNet50 model was found to be 0.965, while the accuracy rate for the AlexNet model was calculated as 0.837. Additionally, the accuracy rate for the Inception v3 model was found to be 0.849, and for the VGG16 model, it was 0.826. Firstly, the accuracy rate of the 4-Layer Convolutional Neural Network model was calculated as 0.971. In the second method, measurements obtained using Uncertainty Quantification (UQ) resulted in an accuracy rate of 0.89 for the Bayesian Neural Networks ResNet50 model. Furthermore, the accuracy rate for the Bayesian Neural Networks AlexNet model was 0.86, for the Inception v3 model it was 0.91, and for the VGG16 model, it was 0.77. The study has innovative features such as the development of a new model called the 4-Layer Convolutional Neural Network and the use of uncertainty quantification methods. These results demonstrate that the performance of specific neural network models can be enhanced by considering uncertainty. [ABSTRACT FROM AUTHOR]