Text readability refers to the problem of measuring the difficulty level in reading a text. In other words, we are interested in how easily a written text can be understood by readers. Text readability score can be used in several application areas including preparing text materials for different educational levels. In this project, you will design and implement a text readability system for Turkish text. You will make use of linguistic properties of texts and use deep learning-based approaches.
Some of the related papers in this task are listed below:
Citation recommendation is the task of determining the most relevant citations for a given piece of text in a specific domain. Due to the large number of documents published continuously, it is not easy to find the relevant documents for a topic. In this project, you will design and implement a deep learning-based model for citation recommendation in the domain of scientific publications.
Some of the related papers in this task are listed below:
This project aims at carrying out performance evaluation of distributed streaming processing of applicaitons running on edge and cloud simultaneously. Methodology-wise students will first implement various use-cases on DSP simulators such as ECSNet++, at the second phase real world implementations using tools like Apache Storm will be required.
Machine learning and deep learning algorithms have led to good human activity recognition and behavior recognition results, such as stress, depression, and anxiety. We have examined the effects of wearable and survey data on students’ grades, stress, anxiety, and sleep. In this project, we want to add different modalities in the scope of the currently employed dataset, Nethealth, which will be communication-based information.
In this project, a blockchain based Token Rental system will be developed using Solidity smart contracts. The users will be able to (i) post token rental intervals (ii) search/explore posted token intervals and (iii) bid for token rental intervals. A web interface will also be developed for the rental system. The system will be deployed and tested on test chains of Avalanche, Polygon Matic , Binance Smart Chain and Hedera which support Solidity smart contracts.
We have been working mostly on behavioral analysis from wearables using machine learning and deep learning techniques. So far, we have applied our algorithms by not considering the personal effects on the results. This project will focus on personalizing the gathered results by applying transfer learning techniques.
In this project, tools will be developed that will collect and analyze transactions from the widely used Decentralized Exchange (DEX) smart contracts on major blockchains (such as Ethereum, Polygon Matic, Hedera, Avalanche and Binance Smart Chain). The project will develop (i) python based and (ii) web based tools that retrieve and extract transaction data and analyze it by forming transaction graphs.