Browsing by Subject "Makine öğrenmesi"

Now showing 1 - 5 of 5

Open Access
Automatic selection of compiler optimizations by machine learning
(IEEE - Institute of Electrical and Electronics Engineers, 2023-08-28) Peker, Melih; Öztürk, Özcan; Yıldırım, S.; Uluyağmur Öztürk, M.
Many widely used telecommunications applications have extremely long run times. Therefore, faster and more efficient execution of these codes on the same hardware is important in critical telecommunication applications such as base stations. Compilers greatly affect the properties of the executable program to be created. It is possible to change properties such as compilation speed, execution time, power consumption and code size using compiler flags. This study aims to find the set of flags that will provide the shortest run time among hundreds of compiler flag combinations in GCC using code flow analysis, loop analysis and machine learning methods without running the program.
Open Access
Detection of cardiac arrhythmia using autonomic nervous system, Gaussian mixture model and artificial neural network
(Institute of Electrical and Electronics Engineers, 2020) Terzi, Merve Begüm; Arıkan, Orhan
In this study, a new technique which detects anomalies in skin sympathetic nerve activity (SKNA) by using state-of-the-art signal processing and machine learning methods is developed to perform the robust detection of cardiac arrhythmia (CA). For this purpose, a signal processing technique that simultaneously obtains SKNA and ECG from wideband recordings on MIT-BIH database is developed. By using preprocessed data, a novel feature extraction technique which obtains SKNA features that are critical for the reliable detection of CA is developed. By using extracted features, a supervised learning technique based on artificial neural network (ANN) and an unsupervised learning technique based on Gaussian mixture model (GMM) are developed to perform the robust detection of SKNA anomalies. A Neyman-Pearson type of approach is developed to perform the robust detection of outliers that correspond to CA. The performance results of the proposed technique over MIT-BIH database showed that the technique provides highly reliable detection of CA by performing the robust detection of SKNA anomalies. Therefore, in cases where the diagnostic information of ECG is not sufficient for the reliable diagnosis of CA, the proposed technique can provide early diagnosis of the disease, which can lead to a significant reduction in the mortality rates of cardiovascular diseases.
Open Access
Detection of myocardial infarction using autonomic nervous system, Gaussian mixture model and artificial neural network
(Institute of Electrical and Electronics Engineers, 2020) Terzi, Merve Begüm; Arıkan, Orhan
In this study, a new technique which detects anomalies in skin sympathetic nerve activity (SKNA) and ECG by using state-of- the-art signal processing and machine learning methods is developed to perform the robust detection of myocardial infarction (MI). For this purpose, a signal processing technique that simultaneously obtains SKNA and ECG from wideband recordings on PTB-EKG database is developed. By using preprocessed data, a novel feature extraction technique which obtains SKNA features that are critical for the reliable detection of MI is developed. By using extracted features, a supervised learning technique based on artificial neural network (ANN) and an unsupervised learning technique based on Gaussian mixture model (GMM) are developed to perform the robust detection of SKNA anomalies. A Neyman-Pearson type of approach is developed to perform the robust detection of outliers that correspond to MI. The performance results of the proposed technique over PTB-EKG database showed that the technique provides highly reliable detection of MI by performing the robust detection of SKNA anomalies. Therefore, in cases where the diagnostic information of ECG is not sufficient for the reliable diagnosis of MI, the proposed technique can provide early diagnosis of the disease, which can lead to a significant reduction in the mortality rates of cardiovascular diseases.
Open Access
Predicting outcomes of the court of cassation of Turkey with recurrent neural networks
(IEEE, 2022-08-29) Öztürk, Ceyhun E.; Özçelik, Ş. Barış; Koç, Aykut
Natural Language Processing (NLP) based approaches have recently become very popular for studies in legal domain. In this work, the outcomes of the cases of the Court of Cassation of Turkey were predicted with the use of Deep Learning models. These models are GRU, LSTM and BiLSTM which are variants of the recurrent neural network. Models saw only fact description parts of the case decision texts during training. Firstly, the models were trained with the word embeddings that were created from the texts from daily language. Then, the models were trained with the word embeddings that were created from downloaded legal cases from Turkish courts. The results of the experiments on the models are given in a comparative and detailed manner. It is observed based on this study and the past studies that the outcomes of the Court of Cassation can be predicted with higher accuracy than most of the courts that were investigated in previous legal NLP studies. The model which is best at predicting decisions is GRU. The GRU model achieves 96.8% accuracy in the decision prediction task.
Open Access
Wind power prediction using machine learning and deep learning algorithms
(IEEE - Institute of Electrical and Electronics Engineers, 2023-08-28) Şimşek, Ecem; Güngör, Ayşemüge; Karavelioğlu, Öykü; Yerli, Mustafa Tolga
In this study, it has been tried to predict the wind power generation values in a long-term period by using a dataset containing the wind power generation values of 10 zones using machine learning and deep learning methods. In this context, the importance of accurately predicting renewable energy production was emphasized by associating it with machine learning and deep learning methods. The methods to be used in the study were selected based on the literature review and the characteristics of the time series datasets. Since the dataset includes the basic wind components, a detailed feature analysis was performed, and the dataset was enriched with the newly added features. The hyperparameters of the utilized models were optimized for all regions in the dataset separately and the models were run with these hyperparameters. The results of the models were evaluated with different error metrics and compared with each other, and the models with the lowest error scores were determined.