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Protein Yapı Tahmini için Derin Öğrenme Modellerinin Geliştirilmesi
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2022) Görmez, Yasin; Aydın, Zafer
The three-dimensional structure of a protein provides important clues about the function of that protein. Although there have been many studies on protein structure prediction, the problem has still not been solved completely. As it is very difficult to predict the three-dimensional structure of a protein directly, predictions of structural properties of proteins such as secondary structure, solvent accessibility, and torsion angles are carried out first, which are later used as inputs to more elaborate structure estimation tasks. In this thesis, novel deep learning models have been developed by using convolutional neural networks (CNN), graph convolutional networks (GCN) and long-short-term memory (LSTM) recurrent neural networks to predict secondary structure, solvent accessibility and torsion angles of proteins. A rich feature set formed by using PSI-BLAST, HHBlits, physicochemical properties, structural profile matrices, AA index values, and graphs representing the relationship between amino acids were used as inputs to the models. In the first study, a deep learning model was developed by using CNN and GCN layers for secondary structure prediction. In the second study, LSTM layers were added to the first model, which was extended to make solvent accessibility and torsion angle predictions as well using the multi-task learning approach. In both studies, graphs were generated using neighborhood relations between amino acids. In the last study, a novel U-net-based model was designed for secondary structure prediction using CNN, GCN, and LSTM layers. The graph matrices used as input to GCN layers were obtained by using protein contact map prediction. All models were trained, optimized and tested on benchmark data sets. Improvements were obtained in accuracy as compared to the state-of-the-art
Zamansal Bilgiden Faydalanarak Videodan Orman Yangınlarının Erken Tespiti
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2022) Taş, Merve; Taşdemir, Kasım; Aydın, Zafer
Forest fires are considered as the major threats to lives, properties and to the integrity of the ecosystem around the world. In most cases, the fire damage can be reduced, when the initial signs of the fire are detected in a timely manner. Since smoke is considered as the first visual sign of fire, detection of smoke is vital. Hence, a successfully designed smoke detection system is essentially critical in the early detection of smoke for outdoor environments. The existing smoke detection methods suffer from high false alarm rates and cannot accurately detect smoke in hazy environments. To address these problems, this thesis is focused on smoke detection model at an early stage that utilizes deep learning (DL) based techniques for outdoor locations. This work contributes mainly to four aspects of smoke detection: (1) new datasets preparation for three smoke detection tasks classification, detection-segmentation, and video classification, (2) utilizing transfer learning to detect the smoke on the relatively small dataset, (3) image dehazing process that includes removing the haze from the dataset images to enhance the system performance, (4) designing a novel hybrid video classification model by combining the two DL based video classification structures. This work will be a resourceful reference for researchers working in the fields of forest fire or smoke detection studies at an early stage. The experiments, research findings, and enhanced performance of the smoke detection system provide a source of information about smoke detection. Current studies can be utilized to further improve the design of efficient and reliable fire safety models. Keywords: Deep Learning, Spatio-Temporal Information, Forest Fire Early Detection, Smoke Detection, Image Dehazing

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