Hi!, I am Mucahit

Yes, I can say that some of it belongs to me. I want to post some of the pictures I took here as a background. After all, we need to spread the beautiful things ;)

I can say that they are from all over the world's music that I like.

Dependency parsing, a syntactic analysis approach, aims to identify the relations of the words that form the sentence. It is used as a fundamental component that enhances or improves the performance of many natural language processing (NLP) problems such as machine translation, information extraction, semantic analysis, question answering, and so on. While transition-based dependency parsing techniques offer a rich feature set and effective computational cost, their parsing performance falls behind graph-based dependency parsing approaches owing to error propagation. On the other side, the key problem with graph-based parsing is determining how to broaden the collection of features in order to improve performance. Some previous researches have attempted to expand the feature scope of graph-based algorithms, but their proposed methods require sophisticated components or computationally expensive inference approaches. In this research, we provide an alternate technique for increasing the efficiency of the graph-based dependency parser by equipping a bi-affine layer with augmented global and local features. Our experimental outcomes show that the proposed enhancements increase dependency parsing performance. Furthermore, we outperformed the current State of Art studies in both Turkish and English, attaining 82.60% and 76.33% in Turkish, and 93.27% and 91.34% in English, unlabeled (UAS) and labeled attachment scores (LAS) respectively. Ph.D. A. Cüneyd Tantuğ supervised this study. You can find more detail in our article.

My master's thesis was on automatic classification of assistance tickets using artificial intelligence techniques, and even auto-replying some of them. The loss or minimization of physical face-to-face interaction between customers and staff affects the customers’ satisfaction negatively. In the current competitive world, organizations and companies need to improve online support systems by increasing the quality of the support services, responding quickly and minimizing procedural steps. Further, in an organization, better allocation and efficient management of valuable support resources have critical importance to reduce costs. Many support systems in use are not effective in meeting the pointed demands of the organizations. In this study, automated assignment of the issue tickets to the related person or unit in the support staff is carried out by using an online active learning technique. We intend to minimize manual efforts and human errors while improving customers’ satisfaction and service quality. The obtained results provide better allocation and effective usage of the valuable support resources for the needs of organizations. Ph.D. A. Cüneyd Tantuğ was the supervisor in this study.

The Hscode (Harmonized System code) is a standardized numerical technique for categorizing traded items. It was generated by the World Customs Organization (WCO) and is widely used for identifying items in international trade. Each HS code usually has six digits long, however several nations add extra digits for categorization. In this project using linguistic models and vector databases an assistant tool has been developed to suggest product specialized HScode for Turkiye.

This project is designed to empower contact centers and customer service teams by enhancing their support capabilities. Using advanced speech-to-text and speaker diarization algorithms, the system transcribes calls, identifying and segmenting individual speakers for greater clarity and analysis. Key features include automated topic recognition, which detects the main subjects of conversation and keyword alerting, allowing the system to flag pre-defined terms that might indicate customer sentiment, urgency, or compliance issues. By leveraging natural language processing (NLP) and machine learning, this project not only helps agents respond more promptly but also facilitates post-call analysis, identifying areas for improvement. As a result, it contributes to improved customer satisfaction, optimized operational efficiency, and the continuous upskilling of customer service agents.

The automatic comprehension of a document's content is critical for accelerating the official process and minimizing error-prone human labor. This study employs a self-attention based multi-modal neural network design. Text content that is obtained by OCR (Optical Character Recognition) of the document and visual partitioning of matching parts of the document are both utilized to assign a label to each component (token, logo, signature, etc.) in the document. To create the training set, the open source Label Studio Platform (https://labelstud.io/) is employed. The developed algorithm can accurately extract layouts with a 95% F1 score. This project is overseen by Türkiye's Ministry of Industry.

Keeping up with current legislation and news regarding a specific topic and communicating it to relevance customers is a job that requires serious time and effort. This project aims to make this job autonomous. Relevant content was accessed through web-scraping methods and the relevance of the content was determined with NLP methods. Selenium, Large Language Models, Flask API, SMTP technologies are exploited in this project.

It can properly identify over a hundred different types of documents with a 97% success rate owing to the created artificial intelligence-supported archive management system. The document's class is determined using both optical character recognition (OCR) and document visual pictures.

Yes. Error propagation problem is seen in many artificial intelligence solutions that contain sequential decisions. The transition-based dependency parsing method is subject to error propagation due to its greedy nature. Some recent studies showed that reinforcement learning can be used to deal with error propagation. I apply it on transition-based dependency parsing methods. The goal of this research is to create solutions that are resistant to error propagation by utilizing reinforcement learning techniques, which is an advanced machine learning method. Ph.D. A. Cüneyd Tantuğ is the supervisor in this study.

Artificial intelligence approaches increase human life quality in a variety of ways. In this project, an assistant is created to find relevant keywords with the supplied query inside patient notes in order to improve the treatment process of physicians. The proposed approach can discover 87 percent of relevant terms annotated by human experts in the NBME Clinical Patient Notes dataset.

Baseline correction is a pre-processing technique that is used to deal with data from sensors such as breath flow volume. In this work, the iterative averaging approach for determining baseline is extended to be used for a developed wearable piezoelectric airflow transducer sensor. Ph.D. Yi Li supervised this study.

In this study, all phases of activity recognition; design, data acquisitions and model development are handled. The essential requirements to design a sensor-based activity recognition system; measurement frequency, number of subjects, location of sensors, and sensor types are explored. Data acquisition procedures and collected data are introduced. Different feature domains; spectrogram, wavelet and time and deep learning models; CNN, LSTM and hybrids of them are examined. Comparable recognition performances against state-of-arts are obtained. This study has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 644268. Ph.D. Yi Li and Ph.D. Senem Kurşun Bahadır supervised this study.