Deep Learning-Based Prediction Models for the Detection of Vitamin D Deficiency and 25-Hydroxyvitamin D Levels Using Complete Blood Count Tests

Acı, Çiğdem | Acı, Mehmet

Vitamin D (VitD) is an essential nutrient that is critical for the well-being of both adults and children, and its deficiency is recognized as a precursor to several diseases. In previous studies, researchers have approached the problem of detecting vitamin D deficiency (VDD) as a single ”sufficient/deficient” classification problem using machine learning or statistics-based methods. The main objective of this paper is to predict a patient’s VitD status (i.e., sufficiency, insufficiency, or deficiency), severity of VDD (i.e., mild, moderate, or severe), and 25-hydroxyvitamin D (25(OH)D) level in a separate deep learning (DL)-based models. An original dataset consisting of complete blood count (CBC) tests from 907 patients, including 25(OH)D concentrations, collected from a public health la...

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 atten...

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

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

2025-12-22

Static Analysis of Malware Detection Using Machine Learning Algorithms

Vuran, Nisa | Acı, Mehmet

2024-12-26

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 Ne...

Detection of Malware by Static Analysis Using Machine Learning Methods

Vuran, Nisa | Acı, Mehmet

The increase in cyber-attacks has also started to threaten the use of internet and information technologies. This situation emphasizes the importance of detecting malicious software that is responsible for cyber-attacks. Nowadays, there are studies on the development of machine learning methods for malicious software detection. Malicious software detectors are the primary tools in defense against malicious software. The quality of such a detector is determined by the techniques it uses. Malware analysis methods such as machine learning, deep learning, and static and dynamic analysis are among these techniques. This study presents malware analysis and classification techniques. For malware detection, well-known algorithms for machine learning including such K-Nearest Neighbors, Naive Bayes,...

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

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

2025-12-22

Sağlık Görüntülerinin Sınıflandırılmasında Derin Nöral Bulanık Ağının Performansı

Vuran, Nisa | Acı, Mehmet | Korucu, Gizen Mutlu | Acı, Çiğdem

Çevrimiçi Aktif Öğrenme ile Geliştirilen Bulanık Sinirsel Sınıflandırıcı Analizi

Vuran, Nisa | Acı, Mehmet | Korucu, Gizen Mutlu | Acı, Çiğdem

Bulanık Kümeleme ve Destek Vektörleri ile Sinir Ağı Güçlendirme Uygulamaları

Vuran, Nisa | Acı, Mehmet | Korucu, Gizen Mutlu | Acı, Çiğdem

Bu çalışmada, sinir ağlarının kullanımında Regülerize Edilmiş Bulanık Kümeleme Sinir Ağı’nın (RFCNN) gürültülü ve uyumsuz veriler karşısında dayanıklılığının arttırılması amaçlanmıştır. Geleneksel sinir ağları, gürültülü verilerle çalışırken performans düşüşleri yaşamaktadır. Bu sorunu ele almak için, destek vektör tabanlı (Support Vector, SV) tabanlı Hiyerarşik Bulanık C-Ortalamalar (Hierarchical Fuzzy C-Means, HFCM) ve Bulanık C-Ortalamalar (Fuzzy C-Me-ans, FCM) kümeleme tekniklerini L2-norm düzenleme ile birleştirerek daha da-yanıklı bir model geliştirilmesi hedeflenmiştir. Çalışmada, SV tabanlı kümeleme tekniklerinin aykırı değerlerin etkilerini azaltarak modelin performansını iyileştiği gösterilmiştir. L2-norm düzenlemesi, modelin aşırı uyumunu önlemek ve varyans-önyargı dengesini sağ...

Image Generation Performance Analysis: A Comparative Study of GAN Models on MNIST Dataset

Vuran, Nisa | Elevi, Abdullah | Acı, Mehmet

2024-12-26

Mühendislik Alanında Gelişmeler

Korucu, Gizen Mutlu | Acı, Mehmet | Acı, Çiğdem

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