Sürdürülebilir Kalkınma Amaçları

• 1

Yazarlar

• Kerre, EE(2)
• Aydin, Nezir(1)
• Baloglu, Ulas Baran(1)
• Bozbura, FT(1)
• Bölükbas, Ufuk(1)
• Celik, Erkan(1)
• Gumus, Alev Taskin(1)
• Güneri, Ali Fuat(1)
• Kahraman, C(1)
• Koehl, L(1)
• Lu, J(1)
• Martinez, L(1)
• Yildirim, Ozal(1)
• Zeng, X(1)
• Çelik, Erkan(1)

• MakaleYalnız künye2019Scopus SJRQ4/r/munzur/4542
  CONVOLUTIONAL LONG-SHORT TERM MEMORY NETWORKS MODEL FOR LONG DURATION EEG SIGNAL CLASSIFICATION

  Baloglu, Ulas Baran; Yildirim, Ozal

  Background and objective: Deep learning structures have recently achieved remarkable success in the field of machine learning. Convolutional neural networks (CNN) in image processing and long-short term memory (LSTM) in the time-series analysis are commonly used deep learning algorithms. Healthcare applications of deep learning algorithms provide important contributions for computer-aided diagnosis research. In this study, convolutional long-short term memory (CLSTM) network was used for automatic classification of EEG signals and automatic seizure detection. Methods: A new nine-layer deep network model consisting of convolutional and LSTM layers was designed. The signals processed in the convolutional layers were given as an input to the LSTM network whose outputs were processed in densely connected neural network layers. The EEG data is appropriate for a model having 1-D convolution layers. A bidirectional model was employed in the LSTM layer. Results: Bonn University EEG database with five different datasets was used for experimental studies. In this database, each dataset contains 23.6 s duration 100 single channel EEG segments which consist of 4097 dimensional samples (173.61 Hz). Eight two-class and three three-class clinical scenarios were examined. When the experimental results were evaluated, it was seen that the proposed model had high accuracy on both binary and ternary classification tasks. Conclusions: The proposed end-to-end learning structure showed a good performance without using any hand-crafted feature extraction or shallow classifiers to detect the seizures. The model does not require filtering, and also automatically learns to filter the input as well. As a result, the proposed model can process long duration EEG signals without applying segmentation, and can detect epileptic seizures automatically by using the correlation of ictal and interictal signals of raw data.

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  AtıfWoS17·OpenAlex19

  İncele
• BildiriYalnız künye2016/r/munzur/4022
  AN INTUITIONISTIC FUZZY APPROACH FOR EVALUATING SERVICE QUALITY OF PUBLIC TRANSPORTATION SYSTEMS

  Celik, Erkan; Gumus, Alev Taskin; Aydin, Nezir; Zeng, X; Lu, J; Kerre, EE; Martinez, L; Koehl, L

  Service quality evaluation has become a main area of interest for practitioners, managers, researchers and policy makers, who have inclined on the passengers' service. In this paper, intuitionistic fuzzy TOPSIS approach is applied to evaluate service quality of rail transit systems of Istanbul. A total of 6553 surveys are conducted and analyzed in seven rail lines with respect to 20 service qualities.

  AtıfWoS2

  İncele
• BildiriYalnız künye2012/r/munzur/3943
  PERFORMANCE EVALUATION OF TURKISH RETAIL FIRMS USING THE FUZZY AHP, PROMETHEE, ELECTRE AND VIKOR METHODS

  Bölükbas, Ufuk; Çelik, Erkan; Güneri, Ali Fuat; Kahraman, C; Kerre, EE; Bozbura, FT

  The performance evaluation of firms determines capabilities of firms to compete in the sector and has a critical importance for the development of the sector. In this study, a fuzzy multi-criteria decision model is proposed to evaluate the performances of retail firms. The eight Turkish retail firms are analyzed and firms are evaluated in terms of ten different financial ratios in six separate groups. Fuzzy Analytic Hierarchy Process (FAHP), Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), Elimination Et Choix Traduisant la Realit (ELECTRE) and Multicriteria Optimization and Compromise Solution (VIKOR) methods are integrated in the proposed model. A hierarchical model of financial performance evaluation is structured by the decision makers. FAHP is used to determine the weights of the criteria. These criteria weights and financial ratio values are input to the PROMETHEE, ELECTRE and VIKOR method to rank the firms. The rankings of methods are compared to each other's in the conclusion.

  AtıfWoS1

  İncele