 Subjects -> SCIENCES: COMPREHENSIVE WORKS (Total: 374 journals)
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- Airborne imagery and lidar based 3D reconstruction using commercial drones
Authors: Koray AÇICI; Ömer Mert ERDAL, Alperen YILMAZ, Metehan UNAL, Gazi Erkan BOSTANCI, Mehmet Serdar GÜZEL
Abstract: In the study, the implementation of 3D reconstruction of buildings using drones is explained. In this project, Airsim was used as the simulation environment and images were obtained from the simulation environment using OpenCV and the Meshroom software was run on these images and modeling was done in the computer environment. For real-world studies, the engineering faculty in Ankara University 50. Yıl Campus was modeled using photogrammetry technique. In the last part, the results of different modelling algorithms were compared.
PubDate: Sat, 03 Jun 2023 00:00:00 +030
- Military camouflage classification with Mask R-CNN algorithm
Authors: İlkay KARATEPE; Vasif NABİYEV
Abstract: Camouflage, which is used as an art of hiding by living things in nature, started to be used in the military field in the 19th century with the widespread use of long-range firearms. When factors such as different nations, environment and climate are considered, we come across camouflages in various colors and patterns. Over time, the camouflage patterns adopted and used by countries or unions have become national identity. This study is on the classification and segmentation of camouflaged soldiers of 5 countries with deep learning. While the similarity of the camouflaged area with the background makes segmentation difficult, it becomes difficult to classify each camouflage pattern due to the cut of the fabric and the different locations of the pattern pieces on each soldier. There are different studies in the literature that are referred to as camouflage or pattern classification. The mentioned studies are in the form of segmentation of camouflaged object or classification of camouflaged objects of different types. Since the segmented and classified objects in this study are camouflaged soldiers, what is expected from the deep learning algorithm is to classify the objects mainly according to the camouflage pattern, not their outlines. In the study, 861 camouflaged soldier images were collected for 5 countries (Türkiye-Azerbaijan, USA, Russia, China, France) and polygonal labeling was made. Türkiye and Azerbaijan are considered a class as they have similar camouflages. For the solution of the problem, military camouflage classification was discussed with the Mask R-CNN algorithm, which is widely used today for object detection, segmentation and classification, and the importance of deep learning algorithms has been proven with such a difficult problem. The training resulted in 0.005219 classification loss and 0.03985 masking loss. The classification and segmentation success rate of the study is 95%.
PubDate: Sat, 03 Jun 2023 00:00:00 +030
- Ionization and phonon production by $^{10}$B ions in radiotherapy
applications
Authors: Fatih EKİNCİ
Abstract: The therapeutic use of heavy ions has received much attention due to their physical and radiobiological properties. Thanks to these features of heavy ion radiotherapy, radiation in tissues close to critical tissues can reduce LET while allowing an increase in LET in tumors. Selection of biomaterials closest to the tissue is critical to measure the accuracy of this LET transfer. The accuracy of LET and radiological features measured in phantoms created from biomaterials selected according to the characteristics of the target tissue is very important for human life. For this reason, the research of polymeric materials, which is the closest biomaterial to soft tissue and therefore phantom material, has increased recently. In this study, ionization to the polymeric biomaterials closest to the soft tissue in boron therapy application, and phonon release from all interactions were investigated and analyzed. This analysis was performed using MC-based TRIM simulation. In the analysis, the Bragg peak range closest to the soft tissue was 7.2% and PMMA was the phonon release from all interactions. It has been observed that the phonon production in phantoms results from ions on average 30% and recoils interactions 70%. The main novelty that this study will provide to the literature is to consider the phonon interactions as well as the ionization interactions. Thus, apart from proton and carbon, the most ideal polymeric biomaterial to be used instead of soft tissue was evaluated by calculating all interactions. Thus, it is aimed to determine the most ideal phantom material.
PubDate: Sat, 03 Jun 2023 00:00:00 +030
- A comprehensive computational cost analysis for state-of-the-art visual
slam methods for autonomous mapping
Authors: Ömer Faruk YANIK; Hakki Alparslan ILGIN
Abstract: It is important to solve the autonomous mapping problem with high accuracy using limited energy resources in an environment without prior knowledge and/or signal. Visual Simultaneous Localization and Mapping (SLAM) deals with the problem of determining the position and orientation of an autonomous vehicle or robot with various on-board sensors, and simultaneously creating a map of environment with low energy consumption. However visual SLAM methods require high processing performance for real-time operations. Also, processing capability of the hardware is limited by the power constraints. Therefore, it is necessary to compare the processing load and power consumption of visual SLAM methods for autonomous vehicles or robots. For visual SLAM methods, although there are different comparison studies, there is no comprehensive computational cost analysis covering different datasets and important parameters including absolute trajectory error, RAM Usage, CPU load, GPU load, with total power consumption. In this paper, ORB-SLAM2, Direct Sparse Odometry (DSO), and DSO with Loop Closure (LDSO), which are state of the art visual SLAM methods, are compared. Besides the performance of these methods, energy consumption and resource usage are evaluated allowing the selection of the appropriate SLAM method.
PubDate: Sat, 03 Jun 2023 00:00:00 +030
- Ontology development for web services to be used within the scope of
remote monitoring project
Authors: Kader GÜRCÜOĞLU; Tunç Durmuş MEDENİ, İhsan Tolga MEDENİ, Mehmet Serdar GÜZEL, Halil ARSLAN
Abstract: In today’s society, as the digital transformation has become widespread rapidly; information technologies also started to develop themselves quickly along with this prevalence. This rapid development and transformation bring about new and different requirements. Situations like reuse of the information, the ability to integrate the obtained information and sharing it, among others, have pushed Semantic Web to the forefront, especially scientifically. Semantic Web, which provides the communication of a machine with other machines, gets a great attention especially in today’s digital age. Probably because of this, significant works have been done on ontology development method and ontology based systems started to be advanced over the last decade. Ontology development methods or ontology based systems play a key role in the integrity, being shared and management of the data. Ontology development method, which is of vital importance in reusability and expandability of real time monitoring systems, is in a position to be accommodable to many architectural systems like Deep Learning architecture at the same time.
PubDate: Sat, 03 Jun 2023 00:00:00 +030
- Disease prognosis using machine learning algorithms based on new clinical
dataset
Authors: Melike ÇOLAK; Talya TÜMER SİVRİ, Nergis PERVAN AKMAN, Ali BERKOL, Yahya EKİCİ
Abstract: Today, artificial intelligence-based solutions are produced to facilitate human life in almost every field. The healthcare sector is one of the sectors which took advantage of these solutions. Due to reasons such as the world’s ever-expanding population, ongoing epidemics, and the emergence of new disease types, it is becoming increasingly difficult for a patient to benefit from health services quickly and to make an accurate diagnosis. At this juncture, artificial intelligence reduces the patient density in hospitals, enables patients to access accurate information, and allows medical students to practice by seeing new cases. In this study, a new and reliable dataset was created with disease information obtained from various sources under the supervision of a specialist medical doctor. Then, new patient histories were added to the dataset used in the previous study, the experiments were repeated with the same algorithms, and the accuracy score comparison was presented. The created dataset includes 2006 unique patient histories, 358 symptoms, and 141 diseases and we think it will be a valuable dataset for researchers who make developments using machine learning in the field of healthcare. Various machine learning algorithms have been used in the training process to predict diseases belonging to different branches of medicine, such as diabetes, bronchial asthma, and covid. Besides, Support Vector Machine, Naive Bayes, K-Nearest Neighbors, Multilayer Perceptron, Decision Tree, and Random Forest algorithms, we also studied popular boosting algorithms such as XGBoost and LightGBM. All algorithms were validated with cross-validation and performance comparisons were made with different performance metrics such as accuracy, precision, recall, and f1-score. It is also the first study to achievean accuracy score of 99.33% with a dataset that involves a greater number of diseases than the datasets used in the studies examined.
PubDate: Sat, 03 Jun 2023 00:00:00 +030
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Open Access journal
