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Öğe An Analysis of the Characteristics of Verified Twitter Users(2019) Kabakus, Abdullah Talha; Şimşek, MehmetTwitter, the most popular microblog, contains a large variety of users as a result of its huge popularity. Twittermanually verifies the accounts which are deemed worthy of public interest. As a natural consequence of beingverified, users trust these verified accounts since they represent legitimate users, and are managed by authorizedusers. To the best of our knowledge, Twitter has never revealed the requirements of being verified. In this study,in order to shed light on the characteristics of verified Twitter users, a software, which is based on Pythonprogramming language that utilizes a recent dataset, which consists of 297,798 verified Twitter users, wasimplemented within the scope of this study. The characteristics of verified Twitter users such as being public, andhaving a customized profile were revealed as a result of the analysis of the utilized dataset.Öğe An analysis of the professional preferences and choices of computer engineering students(Wiley, 2020) Kabakus, Abdullah Talha; Senturk, ArafatThe revelation of the preferences and choices of students is critical for understanding which areas of the discipline they aim. Especially for final-year undergraduate students, job prospects are an important concern. In this study, the graduation project choices of the final-year undergraduate students in the department of computer engineering were investigated in light of their impact on both the industry and academia. A total of 1,693 course grades of 94 final-year undergraduate students were retrieved from the Student Information System. These course grades were utilized as the features of the employed machine learning algorithms alongside the features that were deduced from them. In addition, the popularities of the graduation project topics on both GitHub and IEEE were investigated to reveal their impact on the industry and academia. To this end, we proposed an experimental study, using 14 machine learning techniques, that predicts the topics of the graduation projects that the final-year undergraduate students chose. According to the experimental results, the accuracy of the proposed model was calculated to be as high as 100 % when it was utilized with the Bayes Net, Kleene Star, or J48 algorithm. This experimental result confirms the efficiency of the proposed model. Finally, the insights gained from the data are discussed to shed light on the reasons for the choices of the graduation projects as well as their relationships with the courses.Öğe A Decade of Progress: A Systematic Literature Review on the Integration of AI in Software Engineering Phases and Activities (2013-2023)(Ieee-Inst Electrical Electronics Engineers Inc, 2024) Durrani, Usman Khan; Akpinar, Mustafa; Adak, Muhammed Fatih; Kabakus, Abdullah Talha; Ozturk, Muhammed Maruf; Saleh, MohammedThe synergy between software engineering (SE) and artificial intelligence (AI) catalyzes software development, as numerous recent studies illustrate an intensified intersection between these domains. This systematic literature review examines the integration of AI techniques or methodologies across SE phases and related activities spanning from 2013 to 2023, resulting in the selection of 110 research papers. Investigating the profound influence of AI techniques, including machine learning, deep learning, natural language processing, optimization algorithms, and expert systems, across various SE phases-such as planning, requirement engineering, design, development, testing, deployment, and maintenance-is the focal point of this study. Notably, the extensive adoption of machine learning and deep learning algorithms in the development and testing phases has enhanced software quality through defect prediction, code recommendation, and vulnerability detection initiatives. Furthermore, natural language processing's role in automating requirements classification and sentiment analysis has streamlined SE practices. Optimization algorithms have also demonstrated efficacy in refining SE activities such as feature location and software repair action predictions, augmenting precision and efficiency in maintenance endeavors. Prospective research emphasizes the imperative of interpretable AI models and the exploration of novel AI paradigms, including explainable AI and reinforcement learning, to promote ethical and efficient software development practices. This paper fills the gap identified in AI techniques dedicated to improving SE phases. The review concludes that AI in SE is revolutionizing the discipline, enhancing software quality, efficiency, and innovation, with ongoing efforts targeting the mitigation of identified limitations and the augmentation of AI capabilities for intelligent and dependable SE.Öğe EMPOWERING SELF-DETECTION: A GRAPHICAL USER INTERFACE POWERED BY MACHINE LEARNING FOR EARLY DIAGNOSIS OF ALZHEIMER'S DISEASE(2024) Kabakus, Abdullah Talha; Erdoğmuş, PakizeAlzheimer’s Disease (AD) is one of the most, if not the most, devastating neurodegenerative diseases that are incurable and progressive. Early diagnosis of AD comes with many promises in terms of medicine, sociology, and economics. Despite the existence of numerous studies that aim for early diagnosis of AD, to the best of our knowledge, there is not a publicly available tool that lets end-users assess AD. To address this gap, we propose a Graphical User Interface (GUI) powered by Machine Learning (ML) that makes self-assessment of AD possible – without any input from medical experts. The developed GUI lets end-users enter various information considering both commonly used features for the diagnosis of AD and the questions available in the gold standard screening tool for the diagnosis of AD, namely the Mini-Mental State Exam. In addition to employing 11 traditional ML algorithms, we propose a novel 1-dimensional (1D) Convolutional Neural Network (CNN). All ML models were trained on a gold standard dataset that comprised 373 records from three subject classes as follows: (i) non- demented, (ii) demented, and (iii) converted. Once the end- user enters the required input through the developed GUI, the previously trained ML model assesses the diagnosis of AD through this input in a couple of seconds. According to the experimental results, the proposed novel 1D CNN outperformed the state-of-the-art by obtaining an accuracy as high as 95,3% on the used gold standard dataset.Öğe An Experimental Performance Comparison of Widely Used Face Detection Tools(Ediciones Univ Salamanca, 2019) Kabakus, Abdullah TalhaFace detection is the task of detecting faces on photos, videos as well as the streaming data such as a webcam. Face detection, which is a specific type of general-purpose object detection, is a key prerequisite for many other artificial intelligence tasks such as face verification, face tagging and retrieval, and face tracking. In addition to that, convolutional neural nowadays, face detection is commonly used in daily routines such as social media, and network camera software of smartphones. As a result of this necessity, several face detection tools have been proposed. In this study, an experimental performance comparison of well-known face detection tools in terms of (1) accuracy, and (2) elapsed time of detection, which has become even more critical criteria especially when the face detection mechanism is utilized for a real-time system, is proposed. As a result of this experimental study, it is aimed that shed light on the much-concerned query which face detection tool provides the best performance?. In addition to that, this study succeeds in showing that convolutional neural networks achieve great accuracy for face detection.Öğe EXPLORING THE EFFECTIVENESS OF PRE-TRAINED TRANSFORMER MODELS FOR TURKISH QUESTION ANSWERING(2025) Kabakus, Abdullah TalhaRecent advancements in Natural Language Processing (NLP) and Artificial Intelligence (AI) have been propelled by the emergence of Transformer-based Large Language Models (LLMs), which have demonstrated outstanding performance across various tasks, including Question Answering (QA). However, the adoption and performance of these models in low-resource and morphologically rich languages like Turkish remain underexplored. This study addresses this gap by systematically evaluating several state-of-the-art Transformer-based LLMs on a curated, gold-standard Turkish QA dataset. The models evaluated include BERTurk, XLM-RoBERTa, ELECTRA-Turkish, DistilBERT, and T5-Small, with a focus on their ability to handle the unique linguistic challenges posed by Turkish. The experimental results indicate that the BERTurk model outperforms other models, achieving an F1-score of 0.8144, an Exact Match of 0.6351, and a BLEU score of 0.4035. The study highlights the importance of language-specific pre-training and the need for further research to improve the performance of LLMs in low-resource languages. The findings provide valuable insights for future efforts in enhancing Turkish NLP resources and advancing QA systems in underrepresented linguistic contexts.Öğe GitHubNet: Understanding the Characteristics of GitHub Network(Springer, 2020) Kabakus, Abdullah TalhaWeb 2.0 technologies have not only raised microblogs, but also social software development and collaboration platforms. GitHub is the most popular software development platform that provides social collaboration. Within the scope of this study, a novel graph-based analysis model is proposed which targets to reveal (1) the characteristics of the GitHub in order to shed light on social software development in general, and (2) the most popular programming languages, repositories, and developers in order to shed light on the trending software development technologies. To this end, a subset of the GitHub network, which contains 84, 737 developers and 209, 100 repositories, was collected through the GitHub API and stored on a graph database namely neo4j to be later analyzed. The result of the analysis shows that (1) the connections in GitHub are not mutually linked, (2) JavaScript, Python, and Java are currently the most popular three programming languages, (3) You-Dont-Know-JS, oh-my-zsh, and public-apis are the most popular three repositories, and (4) TarrySingh (Tarry Singh), indrajithbandara (Indrajith Bandara), and rootsongjc (Jimmy Song) are the most popular three developers. Furthermore, the proposed novel analysis model can be easily applied to other social networks.Öğe A novel bidirectional long short-term memory model with multi-head attention for accurate language detection(Gazi Univ, Fac Engineering Architecture, 2025) Toklu, Sinan; Kabakus, Abdullah TalhaLanguage detection, one of the most important elements used in natural language processing, is used extensively in various applications such as machine translation, sentiment analysis, and information retrieval. Thanks to language detection, communication between people in many different countries is possible. In addition, human-animal interaction can also be carried out in this area. In this paper, a novel Bidirectional Long Short-Term Memory model with Multi-Head Attention mechanism is proposed to accurately classify text into 17 languages, namely Arabic, Danish, Dutch, English, French, German, Greek, Hindi, Italian, Kannada, Malayalam, Portuguese, Russian, Spanish, Swedish, Tamil, and Turkish. A publicly available dataset consisting of 10,337 texts written in the above-mentioned languages is utilized to train and evaluate the proposed model. The proposed novel model achieved an extraordinary accuracy, precision, recall, and F1-score of 99.9%, outperforming the state-of-the-art baseline models. In particular, the proposed model demonstrated perfect precision (100%) for 15 languages, namely Arabic, Dutch, English, French, German, Greek, Hindi, Italian, Kannada, Malayalam, Portuguese, Russian, Swedish, Tamil, and Turkish. This research highlights the effectiveness of deep learning techniques in language detection, providing promising avenues for further advances in the field of multilingual text processing.Öğe A novel handwritten Turkish letter recognition model based on convolutional neural network(Wiley, 2021) Kabakus, Abdullah Talha; Erdogmus, PakizeConvolutional neural networks have provided state-of-the-art solutions for many subfields of computer vision. While there exist many studies in the literature for several languages, studies for handwritten Turkish character recognition lack in the research field. To this end, we propose a novel handwritten Turkish letter recognition model based on a convolutional neural network. Since, to the best of our knowledge, there do not exist any publicly available handwritten Turkish letters datasets, we constructed a handwritten Turkish letters dataset that consists of 25,875 samples. To compare the performance of the proposed model with the related work, three state-of-the-art models, namely, VGG19, InceptionV3, and Xception, were utilized through the transfer learning technique. When these models were evaluated on the handwritten Turkish letter dataset, the proposed model's accuracy was calculated as high as 96.07% which was higher than the benchmark models. To measure the generalization ability of the proposed model, it was evaluated on a gold standard dataset, namely, EMNIST, and has achieved an accuracy of 80.54% which was higher than the benchmark models. Finally, the proposed model was trained and evaluated on the EMNIST dataset and it has achieved an accuracy of 94.61% which outperformed the related work.Öğe A Novel Sketch Recognition Model based on Convolutional Neural Networks(Ieee, 2020) Kabakus, Abdullah TalhaDeep neural networks have been widely used for visual recognition tasks based on real images as they have proven their efficiency. Unlike real images, sketches exhibit a high level of abstraction as they lack the rich features that the real images contain such as various colors, backgrounds, and environmental detail. Despite all of these shortages and being drawn with just a few strokes, they are still meaningful enough to encompass an appropriate level of meaning. The efficiency of deep neural networks on sketch recognition has been relatively less studied compared to the visual recognition of real images. To experiment with the efficiency of deep neural networks on sketch recognition, a novel sketch recognition model based on Convolutional Neural Networks is proposed in this study. The proposed model consisted of 21 layers and was tuned in an automated manner to find out the best-optimized model. In order to reveal the proposed model's efficiency in terms of predicting the classes of the given sketches, the model was evaluated on a gold standard sketch dataset, namely, Quick, Draw!. According to the experimental result, the proposed model's accuracy was calculated as high as 89.53% which outperformed the related work on the same dataset. The key findings that were obtained during the conducted experiments were discussed to shed light on future studies.Öğe The promise of convolutional neural networks for the early diagnosis of the Alzheimer?s disease(Pergamon-Elsevier Science Ltd, 2023) Erdogmus, Pakize; Kabakus, Abdullah TalhaAlzheimer's Disease (AD) is one of the most devastating neurologic disorders, if not the most, as there is no cure for this disease, and its symptoms eventually become severe enough to interfere with daily tasks. The early diagnosis of AD, which might be up to 8 years before the onset of dementia symptoms, comes with many promises. To this end, we propose a novel Convolutional Neural Network (CNN) as a cheap, fast, yet accurate solution. First, a gold-standard dataset, namely DARWIN, that was proposed for the detection of AD through handwriting and consisted of 1D features, was used to generate the 2D features, which were yielded into the proposed novel model. Then, the proposed novel model was trained and evaluated on this dataset. According to the experimental result, the proposed novel model obtained an accuracy as high as 90.4%, which was higher than the accuracies obtained by the state-of-the-art baselines, which covered a total of 17 widely-used classifiers.Öğe PyFER: A Facial Expression Recognizer Based on Convolutional Neural Networks(Ieee-Inst Electrical Electronics Engineers Inc, 2020) Kabakus, Abdullah TalhaFacial expression recognition (FER), one of the most trending research areas of the Human-Machine Interaction, is the task of detecting emotions by analyzing facial expressions and this analysis plays a critical role as it conveys the clearest information regarding the emotions of people. Despite the fact that the traditional machine learning algorithms produce high accuracies for similar tasks, they lack to detect emotions of faces, which are captured in a spontaneous manner (a.k.a. in the wild) or in different poses or environmental conditions. In this article, a novel convolutional neural network architecture, namely, PyFER, is proposed to address the FER problem, of which the efficiency was revealed thanks to the experiments conducted on a widely-used benchmark dataset. According to the experimental results, the accuracy of PyFER was calculated to be as high as 96.3% on a de-facto standard dataset, namely, CK+, and all facial expressions, except for happiness, were correctly detected by PyFER, which is encouraging for future studies. 16.67% of the images that actually represented the facial expression happiness were misdetected as the facial expression fear. The experimental results confirmed that the proposed neural network architecture is fast enough to be integrated into real-time FER applications as it was able to complete the analysis of a given photo for an average of 12.8 milliseconds, which is in the tolerable limit to latency for real-time applications.Öğe A transfer learning-based deep learning approach for automated COVID-19 diagnosis with audio data(Tubitak Scientific & Technical Research Council Turkey, 2021) Akgun, Devrim; Kabakus, Abdullah Talha; Senturk, Zehra Karapinar; Senturk, Arafat; Kucukkulahli, EnverThe COVID-19 pandemic has caused millions of deaths and changed daily life globally. Countries have declared a half or full lockdown to prevent the spread of COVID-19. According to medical doctors, as many people as possible should be tested to identify their status, and corresponding actions then should be taken for COVID-19 positive cases. Despite the clear necessity of these medical tests, many countries are still struggling to acquire them. This fact clearly indicates the necessity of a large-scale, cheap, fast, and accurate alternative prescreening tool that can be used for the diagnosis of COVID-19 while waiting for the medical tests. To this end, a novel end-to-end transfer learning-based deep learning approach that uses only a given cough sound for the diagnosis of COVID-19 was proposed in this study. The proposed models employed various pretrained deep neural networks, namely, VGG19, ResNet50V2, DenseNet121, and MobileNet, via the transfer learning technique. Then, these models were evaluated on a gold standard dataset, namely, Cambridge data. According to the experimental result, the proposed model, which employed the MobileNet via the transfer learning technique, provided the best accuracy, 86.42%, and outperformed the state-of-the-art. Thus, the proposed model has the potential to provide automated COVID-19 diagnosis in an easily applicable and fast yet accurate way.
