Sultan Zavrak

Sultan Zavrak, Seyhmus Yilmaz

Email is one of the most widely used ways to communicate, with millions of people and businesses relying on it to communicate and share knowledge and information on a daily basis. Nevertheless, the rise in email users has occurred a dramatic increase in spam emails in recent years. Processing and managing emails properly for individuals and companies are getting increasingly difficult. This article proposes a novel technique for email spam detection that is based on a combination of convolutional neural networks, gated recurrent units, and attention mechanisms. During system training, the network is selectively focused on necessary parts of the email text. The usage of convolution layers to extract more meaningful, abstract, and generalizable features by hierarchical representation is the major contribution of this study. Additionally, this contribution incorporates cross-dataset evaluation, which enables the generation of more independent performance results from the model's training dataset. According to cross-dataset evaluation results, the proposed technique advances the results of the present attention-based techniques by utilizing temporal convolutions, which give us more flexible receptive field sizes are utilized. The suggested technique's findings are compared to those of state-of-the-art models and show that our approach outperforms them.

Sultan Zavrak

The advent of 2019 Coronavirus (COVID-19) has engendered a momentous global health crisis, necessitating the identification of the ailment in individuals through diverse diagnostic modalities. Radiological imaging, particularly the deployment of X-ray imaging, has been recognized as a pivotal instrument in the detection and characterization of COVID-19. Recent investigations have unveiled invaluable insights pertaining to the virus within X-ray images, instigating the exploration of methodologies aimed at augmenting diagnostic accuracy through the utilization of artificial intelligence (AI) techniques. The current research endeavor posits an innovative framework for the automated diagnosis of COVID-19, harnessing raw chest X-ray images, specifically by means of fine-tuning pre-trained Vision Transformer (ViT) models. The developed models were appraised in terms of their binary classification performance, discerning COVID-19 from Normal cases, as well as their ternary classification performance, discriminating COVID-19 from Pneumonia and Normal instances, and lastly, their quaternary classification performance, discriminating COVID-19 from Bacterial Pneumonia, Viral Pneumonia, and Normal conditions, employing distinct datasets. The proposed model evinced extraordinary precision, registering results of 99.92% and 99.84% for binary classification, 97.95% and 86.48% for ternary classification, and 86.81% for quaternary classification, respectively, on the respective datasets.

Seyhmus Yilmaz, Sultan Zavrak

In this study, we aimed to address the growing concern of trolling behavior on social media by developing and evaluating a set of model architectures for the automatic detection of troll tweets. Utilizing deep learning techniques and pre-trained word embedding methods such as BERT, ELMo, and GloVe, we evaluated the performance of each architecture using metrics such as classification accuracy, F1 score, AUC, and precision. Our results indicate that BERT and ELMo embedding methods performed better than the GloVe method, likely due to their ability to provide contextualized word embeddings that better capture the nuances and subtleties of language use in online social media. Additionally, we found that CNN and GRU encoders performed similarly in terms of F1 score and AUC, suggesting their effectiveness in extracting relevant information from input text. The best-performing method was found to be an ELMo-based architecture that employed a GRU classifier, with an AUC score of 0.929. This research highlights the importance of utilizing contextualized word embeddings and appropriate encoder methods in the task of troll tweet detection, which can assist social-based systems in improving their performance in identifying and addressing trolling behavior on their platforms.

Sultan Zavrak

The Model Context Protocol (MCP) has become a widely adopted interface for LLM agents to invoke external tools, yet learned monitoring of MCP tool-call traffic remains underexplored. In this article, MCPShield is presented as an attack detection framework for MCP tool-call traffic that encodes each agent session as a graph (tool calls as nodes, sequential and data-flow links as edges), enriches nodes with sentence-embedding features over arguments and responses, and classifies sessions as benign or attacked. Three GNN architectures (GAT, GCN, GraphSAGE), a no-graph MLP, and classical baselines (XGBoost, random forest, logistic regression, linear SVM) are evaluated, with the full architecture comparison conducted on RAS-Eval (task-stratified splits) and GraphSAGE retained as the GNN baseline on ATBench and a combined-source variant (both label-stratified). Three findings emerge. First, content-level features are essential: metadata-only detection plateaus around an AUROC of 0.64 regardless of architecture, while content embeddings push the AUROC above 0.89. Second, naive random-split evaluation inflates AUROC by up to 26 percentage points relative to task-disjoint splits, a memorization confound that prior agent-detection work has not addressed. Third, the detection signal resides primarily in the SBERT content embeddings: an AUROC of 0.975 was reached by tree ensembles on pooled embeddings, performing, for the most part, better than the neural architectures in the primary RAS-Eval setting including GNNs (0.917) and the MLP (0.896), and self-supervised pre-training does not deliver a label-efficiency advantage on this task.

Sultan Zavrak, Seyhmus Yilmaz

This paper presents a novel approach to accurately classify the hallmarks of cancer, which is a crucial task in cancer research. Our proposed method utilizes the Bidirectional Encoder Representations from Transformers (BERT) architecture, which has shown exceptional performance in various downstream applications. By applying transfer learning, we fine-tuned the pre-trained BERT model on a small corpus of biomedical text documents related to cancer. The outcomes of our experimental investigations demonstrate that our approach attains a noteworthy accuracy of 94.45%, surpassing almost all prior findings with a substantial increase of at least 8.04% as reported in the literature. These findings highlight the effectiveness of our proposed model in accurately classifying and comprehending text documents for cancer research, thus contributing significantly to the field. As cancer remains one of the top ten leading causes of death globally, our approach holds great promise in advancing cancer research and improving patient outcomes.