A model agnostic perturbation-based explanation approach is proposed for the spellchecker of Turkish “de/da” spelling errors.
Our problem is rare disease detection which can be posed as a few- shot classification problem from machine learning point of view.
We design and implement a flexible, easy-to-use, high performance, GPU-accelerated nanonetworking simulation library.
We developed a security suit that finds SSL/TLS vulnerabilities in networks and SQL injection vulnerabilities in websites.
This project is created for capturing the network flow between a mobile application and a REST API.
This project implements and tests the performance of nested-trie indexing approach for autocompletion of prefix-abbreviated inputs.
Security vulnerabilities of state-of-art deep learning model for time series (LSTM) are exposed for regression and classification by adversarial attacks: Basic Iterat
Bilgisayar Mühendisliği Bölümü, Boğaziçi Üniversitesi, 34342 Bebek, İstanbul, Türkiye
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