Sürdürülebilir Kalkınma Amaçları

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Yazarlar

• Yildirim, Ozal(2)
• Acharya, U. Rajendra(1)
• Ay, Betul(1)
• Aydin, Galip(1)
• Baloglu, Ulas Baran(1)
• Benedicenti, L.(1)
• Demir, Yakup(1)
• Gunay, Mihriban(1)
• Kutlu, Banu(1)
• Liu, Z.(1)
• Puthankattil, Subha D.(1)
• Talo, Muhammed(1)
• Utku, Anıl(1)
• Utku, Esen Damla(1)

• BildiriAçık Erişim2025/r/munzur/1953
  Deep Learning-Based Approaches for Short-Term Residential Electricity Consumption Prediction: A Comparative Study of LSTM, CNN-LSTM, and CNN-GRU Models

  Gunay, Mihriban; Yildirim, Ozal; Demir, Yakup; Benedicenti, L.; Liu, Z.

  The rapid increase in the world population has increased electricity consumption and demand for electricity. In order for electricity distribution companies to determine electricity demand, household electricity consumption must be predicted. In this study, an open access dataset was used to predict individual household electricity consumption. For this purpose, deep learning-based LSTM, CNN-LSTM and CNN-GRU models were developed and a comparative analysis was performed. The performance of the models was evaluated using four evaluation metrics commonly used in prediction model performance evaluation. According to the results obtained, it was seen that the models used in the study were successful in predicting short-term electricity consumption. In particular, the CNNLSTM model was found to have a lower error rate compared to the other two models.

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  İncele
• MakaleYalnız künye2023Scopus SJRQ2/r/munzur/1573
  Deep learning based an effective hybrid model for water quality assessment

  Utku, Anıl; Utku, Esen Damla; Kutlu, Banu

  Water, which is very important for life and civilizations on Earth, has been a source of life for all living things. However, freshwater resources gradually decrease due to climate change, pollution, and population growth. Water pollution is the quality changes that occur due to various activities of people, change the chemical, biological, and physical properties of water, restrict or prevent its use, and disrupt ecological balances. The water quality criteria determined to keep this pollution under control ensure the safe use of water. Water quality observations have gained more importance today due to intense environmental concerns and water pollution. This situation revealed the necessity of water quality assessment for water source use. In this study, the CNN-LSTM-based hybrid model was proposed to assess water quality. The proposed model was compared with AdaBoost, DT, GNB, kNN, LGBM, RF, and LSTM according to accuracy, precision, recall, F-score, and AUC. The proposed model has 98.81% accuracy, 99.03% precision, 99.65% recall, 99.33% F-score, and 93% AUC. Practitioner Points: CNN-LSTM hybrid model was developed to water quality assessment. The proposed model is compared with established techniques like AdaBoost, DT, GNB, kNN, LGBM, RF, and LSTM. The developed model has 98.81% classification accuracy. Experimental results show that the developed model will ensure the use of safe water according to water quality criteria.

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  AtıfScopus7·WoS4·OpenAlex8

  İncele
• MakaleAçık Erişim2019Scopus SJRQ2/r/munzur/3201
  Automated Depression Detection Using Deep Representation and Sequence Learning with EEG Signals

  Ay, Betul; Yildirim, Ozal; Talo, Muhammed; Baloglu, Ulas Baran; Aydin, Galip; Puthankattil, Subha D.; Acharya, U. Rajendra

  Depression affects large number of people across the world today and it is considered as the global problem. It is a mood disorder which can be detected using electroencephalogram (EEG) signals. The manual detection of depression by analyzing the EEG signals requires lot of experience, tedious and time consuming. Hence, a fully automated depression diagnosis system developed using EEG signals will help the clinicians. Therefore, we propose a deep hybrid model developed using convolutional neural network (CNN) and long-short term memory (LSTM) architectures to detect depression using EEG signals. In the deep model, temporal properties of the signals are learned with CNN layers and the sequence learning process is provided through the LSTM layers. In this work, we have used EEG signals obtained from left and right hemispheres of the brain. Our work has provided 99.12% and 97.66% classification accuracies for the right and left hemisphere EEG signals respectively. Hence, we can conclude that the developed CNN-LSTM model is accurate and fast in detecting the depression using EEG signals. It can be employed in psychiatry wards of the hospitals to detect the depression using EEG signals accurately and thus aid the psychiatrists.

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  AtıfScopus279·OpenAlex291

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