activity
20202026
most citedA Deep Reinforcement Learning Approach for the Meal Delivery Problem

62 citations · 107 across the 26 of their papers we have counts for

collaborators

30 papers

cs.AI2026

Forecasting Intraday USD/CAD Exchange Rate with News-Derived Monetary-Policy Signals

Maya Kodeih, Aliaa Alnaggar, Mucahit Cevik

Monetary-policy announcements and central-bank communications play a central role in foreign exchange markets, yet their qualitative, unstructured form makes their forecasting valu…

cs.AI2023

Assessing the impact of emergency department short stay units using length-of-stay prediction and discrete event simulation

Mucahit Cevik, Can Kavaklioglu, Fahad Razak +2

Accurately predicting hospital length-of-stay at the time a patient is admitted to hospital may help guide clinical decision making and resource allocation. In this study we aim to…

cs.SE2023★ 2 cited

Few-shot learning for sentence pair classification and its applications in software engineering

Robert Kraig Helmeczi, Mucahit Cevik, Savas Yıldırım

Few-shot learning-the ability to train models with access to limited data-has become increasingly popular in the natural language processing (NLP) domain, as large language models…

cs.SE2023

Data Augmentation for Conflict and Duplicate Detection in Software Engineering Sentence Pairs

Garima Malik, Mucahit Cevik, Ayşe Başar

This paper explores the use of text data augmentation techniques to enhance conflict and duplicate detection in software engineering tasks through sentence pair classification. The…

q-fin.ST2023★ 2 cited

Explaining Exchange Rate Forecasts with Macroeconomic Fundamentals Using Interpretive Machine Learning

Davood Pirayesh Neghab, Mucahit Cevik, M. I. M. Wahab

The complexity and ambiguity of financial and economic systems, along with frequent changes in the economic environment, have made it difficult to make precise predictions that are…

cs.LG2023

DANLIP: Deep Autoregressive Networks for Locally Interpretable Probabilistic Forecasting

Ozan Ozyegen, Juyoung Wang, Mucahit Cevik

Despite the high performance of neural network-based time series forecasting methods, the inherent challenge in explaining their predictions has limited their applicability in cert…