Şen, Sena Yağmur
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Sena Yagmur Sen
Job Title
Araş.Gör.
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Main Affiliation
01. Yaşar Üniversitesi
Status
Former Staff
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Documents
4
Citations
17
Scholarly Output
6
Articles
0
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0/0
Supervised MSc Theses
1
Supervised PhD Theses
0
WoS Citation Count
11
Scopus Citation Count
105
Patents
0
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0
WoS Citations per Publication
1.83
Scopus Citations per Publication
17.50
Open Access Source
0
Supervised Theses
1
| Journal | Count |
|---|---|
| 2019 Innovations in Intelligent Systems and Applications Conference ASYU 2019 | 2 |
| Innovations in Intelligent Systems and Applications Conference (ASYU) | 2 |
| 2020 Innovations in Intelligent Systems and Applications Conference ASYU 2020 | 1 |
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6 results
Scholarly Output Search Results
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Conference Object ECG Arrhythmia Classification by Using Convolutional Neural Network and Spectrogram(Institute of Electrical and Electronics Engineers Inc., 2019) Sena Yagmur Sen; Nalan ǑzkurtIn this study the electrocardiography (ECG) arrhythmias have been classified by the proposed framework depend on deep neural networks in order to features information. The proposed approaches operates with a large volume of raw ECG time-series data and ECG signal spectrograms as inputs to a deep convolutional neural networks (CNN). Heartbeats are classified as normal (N) premature ventricular contractions (PVC) right bundle branch block (RBBB) rhythm by using ECG signals obtained from MIT-BIH arrhythmia database. The first approach is to directly use ECG time-series signals as input to CNN and in the second approach ECG signals are converted into time-frequency domain matrices and sent to CNN. The most appropriate parameters such as number of the layers size and number of the filters are optimized heuristically for fast and efficient operation of the CNN algorithm. The proposed system demonstrated high classification rate for the time-series data and spectrograms by using deep learning algorithms without standard feature extraction methods. Performance evaluation is based on the average sensitivity specificity and accuracy values. It is also worth to note that spectrogram increases the performance of classification since it extracts the useful time-frequency information of the signal. © 2020 Elsevier B.V. All rights reserved.Conference Object Citation - Scopus: 78Convolutional Neural Network Hyperparameter Tuning with Adam Optimizer for ECG Classification(Institute of Electrical and Electronics Engineers Inc., 2020) Sena Yagmur Sen; Nalan Ǒzkurt; Sen, Sena Yagmur; Ozkurt, NalanIn this research Adaptive Moment Estimation (Adam) optimization technique has been examined on ECG arrhythmia data that rely on deep neural networks. The proposed method indicates that Adam has great importance to solve deep learning problems. According to the proposed method the heartbeats are classified as normal (N) left bundle branch block (LBBB) and right bundle branch block (RBBB) considering the hyper-parameter tuning of the convolutional neural network (CNN). The heartbeats are transformed into spectrogram images and directly given into CNN without any feature extraction method but bounded with a specific frequency/time-resolution rate. The most important point of the study is the examination of the moment estimation coefficients of Adam optimizer such as first moment and second moments. Other tuned parameters are adaptive learning rate and epsilon value. The hyperparameters such as the learning rate and the moment estimation are investigated by grid search method. The effect of the parameters to validation loss were presented and analyzed as a result of this study. © 2020 Elsevier B.V. All rights reserved.Conference Object A Novel Face Identification Implementation for Class Attendance Monitoring(Institute of Electrical and Electronics Engineers Inc., 2019) Hayriye Donmez; Sena Yagmur Sen; Nedim Orta; Atakan Aylanc; Ibrahim Zincir; Donmez, Hayriye; Sen, Sena Yagmur; Zincir, Ibrahim; Orta, Nedim; Aylanc, AtakanFace identification has become more significant and relevant in the recent years. It is widely used for security purposes in enterprises and state-owned business since it has many advantages and benefits compared to other state of the art security applications. Previous face identification implementations inherited many different approaches and algorithms in order to overcome the challenges of recognizing an individual from a variety of angles and heights but none of them were completely successful. The main goal of this research is to demonstrate a novel face identification framework for an autonomous class attendance monitoring system implementing SIFT (Scale Invariant Feature Transform) algorithm. An image dataset generated with the participation of 20 volunteers that were photographed from a variety of different angles and heights was tested with the proposed system and achieved successful results in general with reasonable accuracy rates. © 2020 Elsevier B.V. All rights reserved.Master Thesis Kalp aritmilerinin evrişimsel sinir ağları ve spektrogram tabanlı yöntemle sınıflandırılması(2021) Şen, Sena Yağmur; Özkurt, NalanIn this thesis, the main objective is to classify heart rhythms which were acquired from MIT-BIH Arrhythmia Database using Convolutional Neural Networks (CNN) architecture. The classified heartbeats are normal sinus rhythm, premature ventricular contraction (PVC), left bundle branch block (LBBB), and right bundle branch block (RBBB). Two main studies are described as follows; CNN Classification of Time-Series vs. Spectrogram Study and Spectrogram-CNN-Hyperparameter Tuning with Adam Optimization Algorithm Study. In CNN Classification of Time-Series vs. Spectrogram study, the classification of arrhythmias was carried out via using both raw signals and Short-Time Fourier Transform (STFT) in order to analyze the characteristic of the heartbeat signals. In time-domain classification, normal sinus rhythm, PVC and RBBB heart signals were used as a 1-D vector. The large number of electrocardiogram (ECG) time-series signals were classified with CNN by using their raw form. With the aid of STFT, Hamming window was applied in order to obtain the information from the signal. The normal sinus rhythm, PVC, and RBBB heart signals which are in the time domain were transformed into the time-frequency domain with the help of STFT. The STFT provided the acquisition of spectrograms from heart signals, and these spectrograms were classified with CNN through using as their RGB image form. The proposed CNN Classification of Time-Series vs. Spectrogram study demonstrated the high accomplishment rates in the deep learning approach, and according to accuracy, sensitivity and specificity terms, and also showed to better than traditional feature extraction methods. In Spectrogram-CNN-Hyperparameter Tuning with Adam Optimization Algorithm study, heartbeat signals were examined by tuning CNN hyperparameters. The ECG heart signals which are normal sinus rhythm, LBBB, and RBBB were transformed into their corresponding spectrograms in order to acquire characteristics of the heart signals. These spectrograms were restricted with a particular time/frequency resolution rate, and the suitable time/frequency resolution rate was identified heuristically. Adam was selected as an optimization algorithm of the deep learning network in order to train the ECG spectrograms. The tuned hyperparameters were the learning rate, gradient decay factor and squared gradient decay factor of the Adam algorithm. The identification of hyperparameters was performed by using the grid search method in order to compare the results. The effect of the tuning process according to learning rate and the moment estimation coefficients were represented as their validation loss graphs. The proposed Spectrogram-CNN-Hyperparameter Tuning with Adam Optimization Algorithm study yielded great achievement rates with respect to accuracy, sensitivity and specificity terms.Conference Object A Novel Face Identification Implementation for Class Attendance Monitoring(IEEE, 2019) Hayriye Donmez; Sena Yagmur Sen; Nedim Orta; Atakan Aylanc; Ibrahim ZincirFace identification has become more significant and relevant in the recent years. It is widely used for security purposes in enterprises and state-owned business since it has many advantages and benefits compared to other state of the art security applications. Previous face identification implementations inherited many different approaches and algorithms in order to overcome the challenges of recognizing an individual from a variety of angles and heights but none of them were completely successful. The main goal of this research is to demonstrate a novel face identification framework for an autonomous class attendance monitoring system implementing SIFT (Scale Invariant Feature Transform) algorithm. An image dataset generated with the participation of 20 volunteers that were photographed from a variety of different angles and heights was tested with the proposed system and achieved successful results in general with reasonable accuracy rates.Conference Object Citation - WoS: 11Citation - Scopus: 27ECG Arrhythmia Classification By Using Convolutional Neural Network And Spectrogram(IEEE, 2019) Sena Yagmur Sen; Nalan Ozkurt; Sen, Sena Yagmur; Ozkurt, NalanIn this study the electrocardiography (ECG) arrhythmias have been classified by the proposed framework depend on deep neural networks in order to features information. The proposed approaches operates with a large volume of raw ECG time-series data and ECG signal spectrograms as inputs to a deep convolutional neural networks (CNN). Heartbeats are classified as normal ( N) premature ventricular contractions (PVC) right bundle branch block (RBBB) rhythm by using ECG signals obtained from MIT-BIH arrhythmia database. The first approach is to directly use ECG time-series signals as input to CNN and in the second approach ECG signals are converted into time-frequency domain matrices and sent to CNN. The most appropriate parameters such as number of the layers size and number of the filters are optimized heuristically for fast and efficient operation of the CNN algorithm. The proposed system demonstrated high classification rate for the time-series data and spectrograms by using deep learning algorithms without standard feature extraction methods. Performance evaluation is based on the average sensitivity specificity and accuracy values. It is also worth to note that spectrogram increases the performance of classification since it extracts the useful time-frequency information of the signal.
