• An effective colorectal polyp classification for histopathological images based on supervised contrastive learning 

  Yengec-Tasdemir,Sena Busra; Aydin,Zafer; Akay,Ebru; Doğan,Serkan; Yilmaz,Bulent (Elsevier, 2024)

  Early detection of colon adenomatous polyps is pivotal in reducing colon cancer risk. In this context, accurately distinguishing between adenomatous polyp subtypes, especially tubular and tubulovillous, from hyperplasti ...

• Integrating Biological Domain Knowledge with Machine Learning for Identifying Colorectal-Cancer-Associated Microbial Enzymes in Metagenomic Data 

  Bakir-Gungor, Burcu; Ersoz, Nur Sebnem; Yousef, Malik (MDPI, 2025)

  Advances in metagenomics have revolutionized our ability to elucidate links between the microbiome and human diseases. Colorectal cancer (CRC), a leading cause of cancer-related mortality worldwide, has been associated ...

• CompreCity: Accelerating the Traveling Salesman Problem on GPU with data compression 

  Yalcin, Salih; Usul, Hamdi Burak; Yalcin, Gulay (ELSEVIER, 2025)

  Traveling Salesman Problem (TSP) is one of the significant problems in computer science which tries to find the shortest path for a salesman who needs to visit a set of cities and it is involved in many computing problems ...

• Network intrusion detection based on machine learning strategies: performance comparisons on imbalanced wired, wireless, and software-defined networking (SDN) network traffics 

  Hacilar, Hilal; Aydin, Zafer; Gungor, Vehbi Cagri (TÜBİTAK Academic Journals, 2024)

  The rapid growth of computer networks emphasizes the urgency of addressing security issues. Organizations rely on network intrusion detection systems (NIDSs) to protect sensitive data from unauthorized access and theft. ...

• CSA-DE-LR: enhancing cardiovascular disease diagnosis with a novel hybrid machine learning approach 

  Dedeturk, Beyhan Adanur; Dedeturk, Bilge Kagan; Bakir-Gungor, Burcu (PEERJ INC, 2024)

  Cardiovascular diseases (CVD) are a leading cause of mortality globally, necessitating the development of efficient diagnostic tools. Machine learning (ML) and metaheuristic algorithms have become prevalent in addressing ...

• Can artificial intelligence algorithms recognize knee arthroplasty implants from X-ray radiographs? 

  Gölgelioğlu, Fatih; Aşkın, Aydoğan; Gündoğdu, Mehmet Cihat; Uzun, Mehmet Fatih; Dedeturk, Bilge Kagan; Yalın, Mustafa (MediHealth Academy, 2023)

  Aims: This study aimed to investigate the use of a convolutional neural network (CNN) deep learning approach to accurately identify total knee arthroplasty (TKA) implants from X-ray radiographs. Methods: This retrospective ...

• Machine Learning based Network Intrusion Detection with Hybrid Frequent Item Set Mining 

  Fırat, Murat; Bakal, Gokhan; Akbas, Ayhan (GAZİ ÜNİVERSİTESİ, 2024)

  With the development and expansion of computer networks day by day and the diversity of software developed, the damage that possible attacks can cause is increasing beyond the predictions. Intrusion Detection Systems ...

• Review of feature selection approaches based on grouping of features 

  Kuzudisli,Cihan; Gungor-Bakır, Burcu; Bulut, Nurten; Qaqish, Behjat; Yousef, Malik (PeerJ, 2023)

  With the rapid development in technology, large amounts of high-dimensional data have been generated. This high dimensionality including redundancy and irrelevancy poses a great challenge in data analysis and decision ...

• Graph-based Biomedical Knowledge Discovery 

  Altuner, Osman; Bakir-Gungor, Burcu; Bakal, Gokhan (IEEE, 2024)

  Dijitalleşme süreci tüm dünyada oldukça yüksek bir hızla ilerlemektedir. Bu durum günümüz yaşantısında bir çok kolaylık sağladığı gibi ortaya çıkan devasa dijital verilerin analizi ve işlenmesi gibi bir problemi de ...

• Aguhyper: a hyperledger-based electronic health record management framework 

  Dedeturk, Beyhan Adanur; Bakir-Gungor, Burcu (PEERJ INC, 2024)

  The increasing importance of healthcare records, particularly given the emergence of new diseases, emphasizes the need for secure electronic storage and dissemination. With these records dispersed across diverse healthcare ...

• Document Classification with Contextually Enriched Word Embeddings 

  Mahmood, Raad Saadi; Bakal, Gokhan; Akbas, Ayhan (Bajece (İstanbul Teknik Ünv), 2024)

  The text classification task has a wide range of application domains for distinct purposes, such as the classification of articles, social media posts, and sentiments. As a natural language processing application, machine ...

• A Comparative Analysis of Convolutional Neural Network Architectures for Binary Image Classification: A Case Study in Skin Cancer Detection 

  Korkut, Şerife Gül; Kocabaş, Hatice; Kurban, Rifat (Giresun Üniversitesi, 2024)

  In this study, a comprehensive comparative analysis of Convolutional Neural Network (CNN) architectures for binary image classification is presented with a particular focus on the benefits of transfer learning. The ...

• MULTILEVEL THRESHOLDING FOR BRAIN MR IMAGE SEGMENTATION USING SWARM-BASED OPTIMIZATION ALGORITHMS 

  Toprak, Ahmet Nusret; Şahin, Ömür; Kurban, Rifat (Kahramanmaraş Sütçü İmam Üniversitesi, 2024)

  Image segmentation, the process of dividing an image into various sets of pixels called segments, is an essential technique in image processing. Image segmentation reduces the complexity of the image and makes it easier ...

• ENHANCING DEEP LEARNING PERFORMANCE THROUGH A GENETIC ALGORITHM-ENHANCED APPROACH: FOCUSING ON LSTM 

  Şen, Tarık Üveys; Bakal, Gokhan (Kahramanmaraş Sütçü İmam Üniversitesi, 2024)

  Deep learning has shown remarkable success in various applications, such as image classification, natural language processing, and speech recognition. However, training deep neural networks is challenging due to their ...

• Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network 

  Hacilar, Hilal; Dedeturk, Bilge Kagan; Bakir-Gungor, Burcu; Gungor, Vehbi Cagr (PEERJ INC, 2024)

  Cyberattacks are increasingly becoming more complex, which makes intrusion detection extremely difficult. Several intrusion detection approaches have been developed in the literature and utilized to tackle computer security ...

• An optimal concentric circular antenna array design using atomic orbital search for communication systems 

  Durmus, Ali; Yildirim, Zafer; Kurban, Rifat; Karakose, Ercan (Walter de Gruyter GmbH, 2024)

  In this study, optimum radiation patterns of Concentric Circular Antenna Arrays (CCAAs) are obtained by using the Atomic Orbital Search (AOS) algorithm for communication spectrum. Communication systems stands as a nascent ...

• Ensemble Feature Selection for Clustering Damage Modes in Carbon Fiber-Reinforced Polymer Sandwich Composites Using Acoustic Emission 

  Gulsen, Abdulkadir; Kolukisa, Burak; Caliskan, Umut; Bakir-Gungor, Burcu; Gungor, Vehbi Cagri (John Wiley and Sons Inc, 2024)

  Acoustic emission (AE) serves as a noninvasive technique for real-time structural health monitoring, capturing the stress waves produced by the formation and growth of cracks within a material. This study presents a novel ...

• CCPred: Global and population-specific colorectal cancer prediction and metagenomic biomarker identification at different molecular levels using machine learning techniques 

  Bakir-Gungor, Burcu; Temiz, Mustafa; Inal, Yasin; Cicekyurt, Emre; Yousef, Malik (ELSEVIER, 2024)

  Colorectal cancer (CRC) ranks as the third most common cancer globally and the second leading cause of cancer-related deaths. Recent research highlights the pivotal role of the gut microbiota in CRC development and ...

• Lifetime maximization of IoT-enabled smart grid applications using error control strategies 

  Tekin, Nazli; Dedeturk, Bilge Kagan; Gungor, Vehbi Cagri (ELSEVIER, 2024)

  Recently, with the advancement of Internet of Things (IoT) technology, IoT-enabled Smart Grid (SG) applications have gained tremendous popularity. Ensuring reliable communication in IoT-based SG applications is challenging ...

• EdgeBus: Co-Simulation based resource management for heterogeneous mobile edge computing environments 

  Ali, Babar; Golec, Muhammed; Gill, Sukhpal Singh; Wu, Huaming; Cuadrado, Felix; Uhlig, Steve (ELSEVIER, 2024)

  Kubernetes has revolutionized traditional monolithic Internet of Things (IoT) applications into lightweight, decentralized, and independent microservices, thus becoming the de facto standard in the realm of container ...

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