arXiv subjects

Uğur Ünal

Publications and source records attributed to Uğur Ünal.

3 recordsLinked to original sources

arXiv2026

V-Doped Niobate Nanosheets for Enhanced Photocatalytic Activity

V-doped [Ca$_{2}$Nb$_{3-x}$V$_{x}$O$_{10}$]$^{-}$ (x = 0, 0.15, 0.3, 0.6, 0.75) nanosheets were produced by solid state reaction followed by protonation and exfoliation by tetrabutylammonium ions. 2D nanosheets have improved photocatalytic activity due to their small thickness that reduces electron-hole recombination, larger surface area that increases photocatalytic sites, and decreased band gap from added V. V-doping has caused the wide band gap of 3.54 eV of undoped nanosheets to decrease to 2.60-2.88 eV range. 10 vol% methanol was used as a hole scavenger in the hydrogen evolution reactions, and 3 wt% Pt was deposited on nanosheets as a co-catalyst. Under a full-spectrum Xe light, [Ca$_{2}$Nb$_{2.7}$V$_{0.3}$O$_{10}$]$^{-}$ has produced 4.7 times the H$_{2}$ yield as undoped nanosheets with a 11.3 mmol/g/h production rate, [Ca$_{2}$Nb$_{2.7}$V$_{0.3}$O$_{10}$]$^{-}$ with Pt co-catalyst has produced 2.9 times the H$_{2}$ yield as undoped Pt-loaded nanosheets.

cond-mat.mtrl-sci↗

arXiv2022

An Ensemble of Pre-trained Transformer Models For Imbalanced Multiclass Malware Classification

Classification of malware families is crucial for a comprehensive understanding of how they can infect devices, computers, or systems. Thus, malware identification enables security researchers and incident responders to take precautions against malware and accelerate mitigation. API call sequences made by malware are widely utilized features by machine and deep learning models for malware classification as these sequences represent the behavior of malware. However, traditional machine and deep learning models remain incapable of capturing sequence relationships between API calls. On the other hand, the transformer-based models process sequences as a whole and learn relationships between API calls due to multi-head attention mechanisms and positional embeddings. Our experiments demonstrate that the transformer model with one transformer block layer surpassed the widely used base architecture, LSTM. Moreover, BERT or CANINE, pre-trained transformer models, outperformed in classifying highly imbalanced malware families according to evaluation metrics, F1-score, and AUC score. Furthermore, the proposed bagging-based random transformer forest (RTF), an ensemble of BERT or CANINE, has reached the state-of-the-art evaluation scores on three out of four datasets, particularly state-of-the-art F1-score of 0.6149 on one of the commonly used benchmark dataset.

cs.CR↗

arXiv2020

Random CapsNet Forest Model for Imbalanced Malware Type Classification Task

Behavior of a malware varies with respect to malware types. Therefore,knowing type of a malware affects strategies of system protection softwares. Many malware type classification models empowered by machine and deep learning achieve superior accuracies to predict malware types.Machine learning based models need to do heavy feature engineering and feature engineering is dominantly effecting performance of models.On the other hand, deep learning based models require less feature engineering than machine learning based models. However, traditional deep learning architectures and components cause very complex and data sensitive models. Capsule network architecture minimizes this complexity and data sensitivity unlike classical convolutional neural network architectures. This paper proposes an ensemble capsule network model based on bootstrap aggregating technique. The proposed method are tested on two malware datasets, whose the-state-of-the-art results are well-known.

cs.CR↗