Master Degree / Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/11147/3008
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Master Thesis Estrus Detection in Cows With Deep Learning Techniques(01. Izmir Institute of Technology, 2024) Arıkan, İbrahim; Ayav, Tolga; Ayav, Tolga; Soygazi, Fatih; 03.04. Department of Computer Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyAccurately predicting the estrus period is essential for enhancing the efficiency and lowering the costs of artificial insemination in livestock, a crucial sector for global food production. Precisely identifying the estrus period is critical to avoid economic losses such as decreased milk production, delayed calf births, and loss of eligibility for government subsidies. Since the most obvious movement that needs to be detected during the fertilization period is mounting, it is important to detect this movement. Since manual detection of this movement is difficult and costly, automated methods were needed. Therefore, it is thought that deep learning-based methods can be applied to detect the mounting moment. The proposed method detects the estrus period using deep learning and XAI (Explainable Artificial Intelligence) techniques. Deep learning-based mounting detection is performed using CNN, ResNet, VGG-19 and YOLO-v5 models. The ResNet model in this proposed study detects mounting movement with 99% accuracy. Explainability of deep learning models describes features that aid in decision-making in detecting mounting motion. Grad-CAM and Gradient Inputs models, which are XAI techniques, are used for the black box behind the proposed models. The developed deep learning models reveal that they focus on the udder and back area of the cows during the decision-making phase. In addition, how successfully the Grad-CAM and Gradient Inputs models, which are the XAI models used for the explainability of the deep learning models trained in this study, performed the explanation process was measured by calculating the 'faithfulness', 'maximum sensitivity' and 'complexity' metrics.Master Thesis Enrichment of Turkish Question Answering Systems Using Knowledge Graphs(01. Izmir Institute of Technology, 2023) Çiftçi, Okan; Tekir, Selma; Tekir, Selma; Soygazi, Fatih; 03.04. Department of Computer Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyIn the era of digital communication, the ability to effectively process and interpret human language has become a key research area. Natural Language Processing (NLP) has emerged as a field that enables machines to better understand and analyze human language. One of the most important applications of NLP is the development of question answering systems, which are essential in various domains such as customer service, search engines, and chatbots. To answer incoming queries, question answering systems rely on knowledge graphs as a reliable source. This thesis proposes a Turkish Question Answering (TRQA) system that utilizes a knowledge graph. The research focuses on the automatic construction of a knowledge graph specific to the film industry, as well as the creation of a multi-hop question-answering dataset that can be queried from this graph. Building upon these constructions, we develop a deep learning based method for answering questions using the constructed knowledge graph. The constructed knowledge graph is compared with various knowledge graphs presented in the literature using DistMult, ComplEx and SimplE methods for the link prediction task. Additionally, the proposed question answering system is compared with the baseline study and compared with a generative large language model through quantitative and qualitative analyses.