Master Degree / Yüksek Lisans Tezleri

Permanent URI for this collectionhttps://hdl.handle.net/11147/3008

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Department: search.filters.department.Thesis (Master)--İzmir Institute of Technology, Computer EngineeringSubject: search.filters.subject.Artificial intelligence

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Now showing 1 - 6 of 6
  • Master Thesis
    Estrus Detection in Cows With Deep Learning Techniques
    (01. Izmir Institute of Technology, 2024) Arıkan, İbrahim; Ayav, Tolga; Soygazi, Fatih
    Accurately 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
    Combining Persona and Argument in Dialogue
    (2024) Güzel, Şükrü; Tekir, Selma
    The increasing popularity of personalized dialogue systems has gained momentum as people's desire for human-like interaction grows. This thesis aims to increase persona-consistent responses in personalized dialogue systems. A data augmentation method was used to enhance the persona consistency of dialogue systems. This technique utilized Large Language Models' few-shot learning capabilities to add counterfactual sentences to the dialogue. GPT 3.5 and Llama 2 models were used to generate counterfactual sentences using the few-shot prompting method. The augmentation method was applied to every dialogue in the PersonaChat dataset that did not originally contain a counterfactual sentence. Evaluation using the state-of-the-art personalized dialogue generation study showed that the persona-consistency results of the dataset augmented with the GPT 3.5 model showed better performance when assessed using metrics.
  • Master Thesis
    Predicting Software Size From Requirements Written in Natural Language: a Generative Ai Approach
    (01. Izmir Institute of Technology, 2024) Kennouche, Dhıa Eddıne; Demirörs, Onur
    In project management, software size measurement represents a critical process aimed at visualizing a project. This quantification is pursued independently of the specific technologies or technical decisions adopted during the project's development phase. Among the various methodologies employed for this purpose, the COSMIC Functional Size Measurement (FSM) and Event Points are used to facilitate such assessments. These methodologies are instrumental in offering a standardized approach for measuring software size, yet they inherently demand a considerable amount of manual effort. Furthermore, these methods require the manual extraction of Objects of Interest and Event Names, adding to the labor-intensive nature of the process. In response to these challenges, this thesis implements a suite of Artificial Intelligence (AI)-based methods that have dramatically transformed the measurement process. These innovative approaches encompass the creation of a Regression Model that predicts software sizes with remarkable accuracy, a Summarization Model that automates the extraction of Event Names, and a finely tuned Large Language Model (LLM) that generates Objects of Interest with a significant precision. The adoption of these AI-driven techniques has proven to be highly successful, substantially minimizing the manual effort traditionally required in software size measurement and thereby greatly enhancing both efficiency and reliability of estimation practices. Together, these AI-based methodologies represent a significant advancement in software size measurements, offering a more streamlined and efficient approach. By reducing the reliance on manual processes, these methods not only enhance the accuracy and reliability of measurements but also contribute to a more agile project management environment.
  • Master Thesis
    Recognition of Counterfactual Statements in Turkish
    (01. Izmir Institute of Technology, 2023) Acar, Ali; Tekir, Selma
    Counterfactual statements describe an event that did not happen or cannot happen, and optionally the consequence of this event if it would happen. Counterfactual statements are the building blocks of human thought processes as people constantly reflect upon past happenings and consider their future implications. Counterfactual reasoning is essential for machine intelligence and explainable artificial intelligence studies. Detecting counterfactuals automatically with machine learning algorithms is very crucial for these areas. This thesis presents the development of the first-ever Turkish counterfactual detection dataset. It presents a comprehensive classification baseline and expands the scope of counterfactual detection to include the Turkish language.
  • Master Thesis
    Classification of Contradictory Opinions in Text Using Deep Learning Methods
    (01. Izmir Institute of Technology, 2020) Oğul, İskender Ülgen; Tekir, Selma
    Natural language inference (NLI) problem aims to ensure consistency as well as accuracy of propositions while making sense of natural language. Natural language inference aims to classify the relationship between two given sentences as contradiction, entailment or neutrality. To accomplish the classification task, sentences or words must be translated into mathematical representations called vectors or embedding. Vectorization of a sentence is as important as the complexity of the classification model. In this study, both pre-trained (Glove, Fasttext, Word2Vec) and contextual word embedding methods (BERT) were used for comparison and acquire the best result. One of the natural language processing tasks NLI, is highly complex and requires solutions. Conventional machine learning methods are insufficient to carry out natural language processing solutions. Therefore, more advanced solutions are required. This study used deep learning methods to perform the classification task. Unlike conventional machine learning approaches, deep learning approaches reduce errors while increasing accuracy by repeating the data many times. Opinion sentences have complex grammatical structures that are difficult to classify. This study used Decomposable Attention and Enhanced LSTM for natural language inference to perform NLI classification task. Using the advanced LSTM deep learning method and Bert contextual vectors for natural language extraction on the SNLI dataset, an accuracy result 88.0% very close state of the art result 92.1% was obtained. In order to show the usability of the developed solution in different NLI tasks, an accuracy of 80.02% was obtained in the studies performed on the MNLI data set.
  • Master Thesis
    A Language Modeling Approach To Detect Bias
    (Izmir Institute of Technology, 2020) Atik, Ceren; Tekir, Selma
    Technology is developing day by day and is involved in every area of our lives. Technological innovations such as artificial intelligence can strengthen social biases that already exist in society, regardless of the developers' intentions. Therefore, researchers should be aware of this ethical issue. In this thesis, the effect of gender bias, which is one of the social biases, on occupation classification is investigated. For this, a new dataset was created by collecting obituaries from the New York Times website and they were handled in two different versions, with and without gender indicators. Since occupation and gender are independent variables, gender indicators should not have an impact on the occupation prediction of models. In this context, in order to investigate gender bias on occupation estimation, a model in which occupation and gender are learned together is evaluated as well as models that make only occupation classification are evaluated. The results obtained from models state that gender bias has a role in classification occupation.