Yazar "Altun, Halis" seçeneğine göre listele
Listeleniyor 1 - 6 / 6
Sayfa Başına Sonuç
Sıralama seçenekleri
Yayın Digital health navigator: Preliminary work on a personal health assistant software for all health literacy level users in Turkey(İzmir Ekonomi Üniversitesi, 2023) Tokatlı, Nazlı; Kömür, Fatma Nur; Koçak, Muhammed Tayyip; Kırtay, Seda; Göztepeli, Gürkan; Gül, Beyza; Altun, HalisToday's digital health terminology is actually advanced med-ical technologies that include computer-assisted therapy, smartphone apps, and wearable technologies. These technologies offer significant po-tential for improving accees to immediate medical care, efficiency, clinical effectiveness, and personalization of many health problem therapies. In this paper. we will elobareate on the preliminary design steps of a per-sonalized health assistant application project (PHAS). The proposed ap-plication can be classified as a mobile health app and not a telemedicine application. The idea behind this application is to reduce the physicians' workload in hospitals while providing health care to the comm y with different health literacy levels by easily using the application when gen-eral assistance about any health issues or an overall health and wellness improvement is required.Yayın Edge detection method driven by knowledge-based neighborhood rules(The Taiwan Association of Engineering and Technology Innovation (TAETI), 2023) Çapkan, Yavuz; Altun, Halis; Fidan, Can BülentEdge detection is a fundamental process, and therefore there are still demands to improve its efficiency and computational complexity. This study proposes a knowledge-based edge detection method to meet this requirement by introducing a set of knowledge-based rules. The methodology to derive the rules is based on the observed continuity properties and the neighborhood characteristics of the edge pixels, which are expressed as simple arithmetical operations to improve computational complexity. The results show that the method has an advantage over the gradient-based methods in terms of performance and computational load. It is appropriately four times faster than Canny method and shows superior performance compared to the gradient-based methods in general. Furthermore, the proposed method provides robustness to effectively identify edges at the corners. Due to its light computational requirement and inherent parallelization properties, the method would be also suitable for hardware implementation on field-programmable gate arrays (FPGA).Yayın Guiding genetic search algorithm with ANN based fitness function: a case study using structured HOG descriptors for license plate detection(Springer, 2023) Muhammad, Jawad; Altun, HalisIn literature, various metaheuristic approaches such as Genetic Search Algorithm (GSA), has been adopted for finding the sub-optimal solution to a wide range of optimization problems. The main challenges in adopting GSA is the formulation of a proper fitness function which provides a measure of evaluating the generated candidate solutions, as the subsequent steps in the searching process would mainly be based on the quality of the previous and current solutions. As such, this is a highly crucial step in the successful application of GSA. However, in most of the applications, the construction of the suitable fitness function is difficult due to lack of analytical relations between the GSA parameters and the fitness of the solution. In this paper, a GSA approach of using shallow artificial neural network as a surrogate fitness function is proposed to alleviate such difficulties in the application of the GSA. The license plate detection problem is selected as a case study. For this problem, a new set of features which is called structured Histogram of Oriented Gradients (sHOG) is proposed in order to improve the overall performance of the license plate detection problem. The sHOG features were used to train the shallow ANN which assigns a degree of confidence score to the candidate regions and hence guide the GSA search to sub-optimal solution in the search space of a given input image. The performance of the proposed approach was evaluated on a private and public license plates datasets and results proves that it can archive an IOU detection rate of up to 98.74% on the private dataset and 91.66% cross database performance on the public dataset.Yayın Healthcare service accessibility path planner: Unveiling a new era of intelligent appointment management systems based on outpatient prioritizing(IEEE, 2023) Tokatlı, Nazlı; Koçak, Muhammed Tayyip; Kırtay, Seda; Göztepeli, Gürkan; Aktaş, İbrahim Serhat; Altun, HalisIn light of increased constraints on healthcare systems, particularly as a result of the pandemic, the importance of directing patients to the appropriate healthcare departments for individualized treatment based on their health conditions has been emphasized. Numerous healthcare institutions currently employ an online booking system that enables patients to schedule appointments. However, because patient requests are the main driving force behind this process, appointments with inappropriate departments or the bypassing of primary care facilities like general practice clinics frequently occur. Many studies proposed the use of AI-based chatbots and machine learning algorithms in healthcare systems to improve clinic operations, reduce patient wait times, and predict outpatient appointment no-show rates. This paper describes the conception and implementation steps of an innovative (mhealth app) that uses open AI tools to prioritize and classify outpatients based on their symptoms. Our AI-based appointment scheduling app will decide for the outpatient either to schedule appointments with primary care facilities or direct them to the appropriate healthcare department in hospitals only when absolutely necessary, thereby nurturing a more efficient, patient-centered healthcare service.Yayın MelanoTech: Development of a mobile application infrastructure for melanoma cancer diagnosis based on artificial intelligence technologies(IEEE, 2024) Tokatlı, Nazlı; Bilmez, Yakuphan; Göztepeli, Gürkan; Güler, Muhammed; Karan, Furkan; Altun, HalisThis preliminary work introduces MelanoTech, a mHealth application designed and implemented to offer a user-friendly and intuitive interface for the early diagnosis of melanoma, a kind of skin cancer with significant fatality rates [1]. The application demonstrates promising performance in segmentation and classification tasks by utilizing deep learning models with Generative Adversarial Networks (GANs) for data augmentation. MelanoTech achieves a comprehensive accuracy rate of 92%, with a segmentation model accuracy rate of 93% and a lesion detection accuracy rate of 90%. Finally, incorporating data augmentation approaches based on GANs resulted in a 5% enhancement in the model’s performance. These findings highlight the capacity of MelanoTech as a dependable tool for improving the early diagnosis of melanoma and decreasing the workload of physicians in Turkish public hospitals.Yayın Pedagogical classification of educational robots in pre-school teaching(2023) Gümüş, Muhammed Murat; Kayhan, Osman; Korkmaz, Özgen; Altun, Halis; Yılmaz, NihatThis study aims to create a rubric based on the pedagogical properties of educational robots for pre-school students and determine the compliance level with educational robot sets. In this sense, the study is considered a first and significant step toward selecting robots based on pedagogical-driven factors. For this aim, a mixed-method research design was employed. A qualitative method was used to create the rubric items, and the rubric development was also supported through a quantitative process by including expert opinions and ensuring content validity. Furthermore, a descriptive survey model, one of the quantitative designs, was used to examine the suitability of educational robots for the pre-school education level. As an outcome of this study, a rubric of four dimensions with 28 items related to the pedagogical features of educational robots in pre-school was created. Furthermore, widely used educational robots at the pre-school level, such as Kidoboto, Lego Wedo, Mbot, Lego Spike, Lego Ev3, and Matatalab, were evaluated by experts using the created rubric.
