Mehmet Acı Mehmet Acı MÜHENDİSLİK FAKÜLTESİ BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ BİLGİSAYAR YAZILIMI ANABİLİM DALI
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Morphological and structural complexity analysis of low-resource English-Turkish language pair using neural machine translation models

Acı, Mehmet | Vuran Sarı, Nisa | Acı, Çiğdem

Neural machine translation (NMT) has achieved remarkable success in high-resource language pairs; however, its effectiveness for morphologically rich and low-resource languages like Turkish remains underexplored. As a highly agglutinative and morphologically complex language with limited high-quality parallel data, Turkish serves as a representative case for evaluating NMT systems on low-resource and linguistically challenging settings. Its structural divergence from English makes it a critical testbed for assessing tokenization strategies, attention mechanisms, and model generalizability in neural translation. This study investigates the comparative performance of two prominent NMT paradigms—the Transformer architecture, and recurrent-based sequence-to-sequence (Seq2Seq) models with attention for both English-to-Turkish and Turkish-to-English translation. The models are evaluated under various configurations, including different tokenization strategies (Byte Pair Encoding (BPE) vs. Word Tokenization), attention mechanisms (Bahdanau and an exploratory hybrid mechanism combining Bahdanau and Scaled Dot-Product attention), and architectural depths (layer count and attention head number). Extensive experiments using automatic metrics such as BiLingual Evaluation Understudy (BLEU), M...

Comparative Analysis of Deep Learning, Transfer Learning and Hybrid Models for Phising Detection

Vuran Sarı, Nisa | Acı, Mehmet | Sayar, Elif

2025-12-22

A hybrid CNN-GRU model with XAI-Driven interpretability using LIME and SHAP for static analysis in malware detection

Sarı, Nisa Vuran | Acı, Mehmet

The increasing sophistication of evolving malware types and attack techniques has rendered traditional antivirus solutions inadequate, particularly in mitigating zero-day threats. To address this challenge, Machine Learning (ML) and Deep Learning (DL)-based approaches have been developed, demonstrating significant efficacy and high accuracy in malware classification. However, the black box nature of these models raises significant concerns in terms of transparency and interpretability. This study presents a comprehensive evaluation of Ensemble Learning and Deep Learning methods for static analysis-based malware classification, which allows joint analysis of Application Programming Interface (API) calls and Dynamic Link Library (DLL) data. In the study, a specially designed Convolutional Neural Network (CNN)-Gated Recurrent Units (GRU)-3 model is trained using a tailored dataset consisting of malicious and secure software. In order to better understand the model’s performance, feature importance analysis was performed using SHapley additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) Explainable Artificial Intelligence (XAI) techniques and the reliability of model decisions was increased. The proposed model was compared with DL models such as CNN...

Enhanced Multi-Class Driver Injury Severity Prediction Using a Hybrid Deep Learning and Random Forest Approach

Çiğdem İnan Acı | Gizen Mutlu | Murat Ozen | Mehmet Acı

Predicting driver injury severity and identifying factors influencing crash outcomes are crucial for developing effective traffic safety measures. This study focuses on estimating driver injury severity (uninjured, injured, or killed) and determining critical factors affecting crash outcomes. A hybrid framework combining Deep Neural Networks (DNNs) and Random Forest (RF) is proposed, where a DNN extracts features and RF performs the final classification, leveraging ensemble methods. The results were compared with those of well-known methods (e.g., kNN, XGBoost), with the hybrid approach achieving the best performance (0.92 accuracy, 0.89 F1-macro, 0.91 F1-micro scores) in predicting injury severity. The results showed that crash type, vehicle type, driver fault, intersection type, season, time, and road type had the greatest impact, while factors like pavement condition and driver gender had minimal influence. To the best of our knowledge, this is the first study to combine DNN-based feature extraction with RF classification in the context of traffic injury severity prediction. The framework offers a new approach for drivers and policymakers, providing a deeper understanding of driver injury severity prediction and its underlying factors.

Performance Comparison of Machine Learning and Deep Learning Algorithms in Malware Classification

Vuran Sarı, Nisa | Santur, Umut Fikri | Acı, Mehmet

2025-12-22

Windows Malware Detection via Enhanced Graph Representations with Node2Vec and Graph Attention Network

Nisa Vuran Sarı | Mehmet Acı | Çiğdem İnan Acı

As malware has become increasingly complex, advanced techniques have emerged to improve traditional detection systems. The increasing complexity of malware poses significant challenges in cybersecurity due to the inability of existing methods to understand detailed and contextual relationships in modern software behavior. Therefore, developing innovative detection frameworks that can effectively analyze and interpret these complex patterns has become critical. This work presents a novel framework integrating API call sequences and DLL information into a unified, graph-based representation to analyze malware behavior comprehensively. The proposed model generates initial embeddings using Node2Vec, which uses a random walk approach to understand structural relationships between nodes. Graph Attention Network (GAT) then enhances these initial embeddings, which utilizes attention mechanisms to incorporate contextual dependencies and enhance semantic representations. Finally, the enhanced embeddings are classified using Convolutional Neural Network (CNN) and Gated Recurrent Units (GRU)s, a custom hybrid CNN-GRU-3 deep learning-based model capable of effectively modeling sequential patterns. The dual role of GAT as a classifier and feature extractor is also analyzed to evaluate its impa...