PubMed İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8650
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Article Critical Data-Based Incremental Cooperative Communication for Wireless Body Area Network(MDPI AG, 2018) Al-Mishmish, Hameed; Rahim, Hasliza A.; Alkhayyat, Ahmed; Ahmad, R. Badlishah; Hammood, Dalal A.; Abbasi, Qammer H.Article An Experimental Investigation of the Split-Attention Effect on Endoscopic Surgical Performance(Wolters Kluwer Medknow Publications, 2025) Ozcelik, Erol; Cagiltay, Nergiz Ercil; Topalli, DamlaIntroduction:Surgeons in the operating theatre frequently need to split their attention, such as when switching between the monitor and radiological images during endoscopic surgery. This split attention can lead to cognitive overload, potentially impacting performance. Despite this, limited research has been conducted on how split attention affects surgical outcomes.Patients and Methods:This study examines the impact of split attention on surgical performance in a simulation-based training environment with two conditions: A far-condition (where information sources were spaced farther apart) and a near-condition (where sources were positioned closer together). A total of 53 participants (13 experienced surgical residents and 40 beginners) completed ten trials in each condition.Results:The results indicated that split attention led to diminished performance in beginners but not in residents. These findings suggest that expertise plays a crucial role in managing cognitive load for surgeons.Conclusion:The study highlights the need to develop training curricula that promote the automation of surgical skills through practice, allowing surgeons to allocate more cognitive resources effectively.Article Citation - Scopus: 15A Novel Steganography Method for Binary and Color Halftone Images(PeerJ Inc., 2022) Sümer, Emre; Çiftci, EfeDigital steganography is the science of establishing hidden communication on electronics; the aim is to transmit a secret message to a particular recipient using unsuspicious carriers such as digital images, documents, and audio files with the help of specific hiding methods. This article proposes a novel steganography method that can hide plaintext payloads on digital halftone images. The proposed method distributes the secret message over multiple output copies and scatters parts of the message randomly within each output copy for increased security. A payload extraction algorithm, where plain carrier is not required, is implemented and presented as well. Results gained from conducted objective and subjective tests prove that the proposed steganography method is secure and can hide large payloads.Article Citation - Scopus: 3A New Parallel Multi-Objective Harris Hawk Algorithm for Predicting the Mortality of COVID-19 Patients(PeerJ Inc., 2023) Dokeroglu, TanselHarris' Hawk Optimization (HHO) is a novel metaheuristic inspired by the collective hunting behaviors of hawks. This technique employs the flight patterns of hawks to produce (near)-optimal solutions, enhanced with feature selection, for challenging classification problems. In this study, we propose a new parallel multi-objective HHO algorithm for predicting the mortality risk of COVID-19 patients based on their symptoms. There are two objectives in this optimization problem: to reduce the number of features while increasing the accuracy of the predictions. We conduct comprehensive experiments on a recent real-world COVID-19 dataset from Kaggle. An augmented version of the COVID-19 dataset is also generated and experimentally shown to improve the quality of the solutions. Significant improvements are observed compared to existing state-of-the-art metaheuristic wrapper algorithms. We report better classification results with feature selection than when using the entire set of features. During experiments, a 98.15% prediction accuracy with a 45% reduction is achieved in the number of features. We successfully obtained new best solutions for this COVID-19 dataset.Article Citation - Scopus: 1Randomised Comparison Between Navigation and Non-Navigation Camera Control Performance in a Surgical Simulation Task Using a Haptic Device Interface(Wolters Kluwer Medknow Publications, 2026) Cagiltay, Nergiz Ercil; Topalli, Damla; Tuner, Emre; Berker, MustafaIntroduction:Navigation skills for controlling the camera in the surgical field are critical for many minimally invasive surgery (MIS) procedures. Currently, endoscopes lack integrated navigation aids, making camera control a challenging task. This experimental study aims to investigate the effect of navigation guidance on the performance of beginners.Patients and Methods:A custom computer-based simulation environment was developed for this study, featuring two conditions - one with navigation guidance and one without - focussed on a camera-cleaning task. Participants (64 beginners) were randomly assigned to one of these groups and used two haptic devices to simulate the endoscope and surgical tools.Results:Participants in the guided condition performed significantly better than those in the unguided condition. Notably, female participants completed the task in significantly less time under the guided condition compared to the unguided one.Conclusion:These findings suggest that incorporating navigation aids into endoscope interfaces could improve user performance, especially for beginners. Medical device manufacturers should consider adding navigation features to enhance usability. In addition, simulation-based instructional systems should integrate navigation aids to better support surgical training.Article Citation - WoS: 2Comparing Hand-Based and Controller-Based Interactions in Virtual Reality Learning: Effects on Presence and Interaction Performance(PeerJ Inc, 2025) Saran, MuratVirtual reality (VR) holds significant promise for enhancing science education by providing immersive and interactive learning experiences. However, the optimal interaction modality within educational VR environments remains an open question. This study investigates the impact of hand-based vs. controller-based interaction on sixth-grade students' sense of presence and interaction performance in a VR science laboratory simulation. Fifty-four sixth-grade students were randomly assigned to either a hand-based interaction group or a controller-based interaction group. Participants completed three interactive science experiments (solar system, electrical circuits, and force/energy) within a virtual laboratory environment designed to mimic their school's physical lab. Presence was assessed using a validated Turkish adaptation of the Presence Questionnaire (PQ), while interaction performance was evaluated using a structured observation form completed by a school teacher. Independent samples t-tests and Mann-Whitney U tests were used to compare the presence and performance scores between the groups. Supplementary analyses explored the effects of gender and prior VR experience. Contrary to expectations, no significant differences were found in either presence (t(49.4) = -0.01, p = 0.992) or interaction performance (t(52) = -1.30, p = 0.199) between the hand-based and controller-based interaction groups. Both interaction modalities yielded comparable levels of self-reported presence and observed performance. However, an unexpected finding emerged regarding performance. A supplementary analysis revealed a significant main effect of gender on performance scores (F(1, 50) = 4.844, p = 0.032), independent of interaction type. Specifically, males demonstrated significantly higher performance than females. This study suggests that, for sixth-grade students engaging in these specific VR science simulations, hand-based and controller-based interactions are equally effective in terms of fostering presence and supporting interaction performance. These findings have practical implications for the design and implementation of VR learning environments, particularly in resource-constrained settings where the reduced maintenance and hygiene concerns associated with hand-based interaction may be advantageous.Article Citation - WoS: 6Citation - Scopus: 6Modeling the Transmission Dynamics of Middle Eastern Respiratory Syndrome Coronavirus with the Impact of Media Coverage(Elsevier, 2021) Fatima, BiBi; Alqudah, Manar A.; Zaman, Gul; Jarad, Fahd; Abdeljawad, ThabetMiddle East respiratory syndrome coronavirus has been persistent in the Middle East region since 2012. In this paper, we propose a deterministic mathematical model to investigate the effect of media coverage on the transmission and control of Middle Eastern respiratory syndrome coronavirus disease. In order to do this we develop model formulation. Basic reproduction number R-0 will be calculated from the model to assess the transmissibility of the (MERS-CoV). We discuss the existence of backward bifurcation for some range of parameters. We also show stability of the model to figure out the stability condition and impact of media coverage. We show a special case of the model for which the endemic equilibrium is globally asymptotically stable. Finally all the theoretical results will be verified with the help of numerical simulation for easy understanding.Article Detection and Classification of Femoral Neck Fractures From Plain Pelvic X-Rays Using Deep Learning and Machine Learning Methods(Turkish Assoc Trauma Emergency Surgery, 2025) Sevinc, Huseyin Fatih; Ureten, Kemal; Karadeniz, Talha; Gultekin, Gokhan KorayBackground: Femoral neck fractures are a serious health concern, particularly among the elderly. The aim of this study is to diagnose and classify femoral neck fractures from plain pelvic X-rays using deep learning and machine learning algorithms, and to compare the performance of these methods. Methods: The study was conducted on a total of 598 plain pelvic X-ray images, including 296 patients with femoral neck fractures and 302 individuals without femoral neck fractures. Initially, transfer learning was applied using pre-trained deep learning models: VGG-16, ResNet-50, and MobileNetv2. Results: The pre-trained VGG-16 network demonstrated slightly better performance than ResNet-50 and MobileNetV2 for detecting and classifying femoral neck fractures. Using the VGG-16 model, the following results were obtained: 95.6% accuracy, 95.5% sensitivity, 93.3% specificity, 95.7% precision, 95.5% F1 Score, a Cohen's kappa of 0.91, and the Receiver Operating Characteristic (ROC) curve of 0.99. Subsequently, features extracted from the convolution layers of VGG-16 were classified using common machine learning algorithms. Among these, the k-nearest neighbor (k-NN) algorithm outperformed the others and exceeded the accuracy of the VGG-16 model by 1%. Conclusion: Successful results were obtained using deep learning and machine learning methods for the detection and classification of femoral neck fractures. The model can be further improved through multi-center studies. The proposed model may be especially useful for physicians working in emergency departments and for those not having sufficient experience in evaluating plain pelvic radiographs.Article Citation - WoS: 10Citation - Scopus: 11Low-Diameter Topic-Based Pub/Sub Overlay Network Construction With Minimum Maximum Node Degree(Peerj inc, 2021) Yumusak, Semih; Layazali, Sina; Oztoprak, Kasim; Hassanpour, RezaIn the construction of effective and scalable overlay networks, publish/subscribe (pub/sub) network designers prefer to keep the diameter and maximum node degree of the network low. However, existing algorithms are not capable of simultaneously decreasing the maximum node degree and the network diameter. To address this issue in an overlay network with various topics, we present herein a heuristic algorithm, called the constant-diameter minimum-maximum degree (CD-MAX), which decreases the maximum node degree and maintains the diameter of the overlay network at two as the highest. The proposed algorithm based on the greedy merge algorithm selects the node with the minimum number of neighbors. The output of the CD-MAX algorithm is enhanced by applying a refinement stage through the CD-MAX-Ref algorithm, which further improves the maximum node degrees. The numerical results of the algorithm simulation indicate that the CD-MAX and CD-MAX-Ref algorithms improve the maximum node-degree by up to 64% and run up to four times faster than similar algorithms.Article Citation - WoS: 2Citation - Scopus: 6Fast Binary Logistic Regression(Peerj inc, 2025) Saran, Nurdan Ayse; Nar, FatihThis study presents a novel numerical approach that improves the training efficiency of binary logistic regression, a popular statistical model in the machine learning community. Our method achieves training times an order of magnitude faster than traditional logistic regression by employing a novel Soft-Plus approximation, which enables reformulation of logistic regression parameter estimation into matrix-vector form. We also adopt the L-f-norm penalty, which allows using fractional norms, including the L-2-norm, L-1-norm, and L-0-norm, to regularize the model parameters. We put L-f-norm formulation in matrix-vector form, providing flexibility to include or exclude penalization of the intercept term when applying regularization. Furthermore, to address the common problem of collinear features, we apply singular value decomposition (SVD), resulting in a low-rank representation commonly used to reduce computational complexity while preserving essential features and mitigating noise. Moreover, our approach incorporates a randomized SVD alongside a newly developed SVD with row reduction (SVD-RR) method, which aims to manage datasets with many rows and features efficiently. This computational efficiency is crucial in developing a generalized model that requires repeated training over various parameters to balance bias and variance. We also demonstrate the effectiveness of our fast binary logistic regression (FBLR) method on various datasets from the OpenML repository in addition to synthetic datasets.