Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 11 of 11
Full-Text Articles in Physical Sciences and Mathematics
A Novel Method For Soc Estimation Of Li-Ion Batteries Using A Hybrid Machinelearning Technique, Eymen İpek, Murat Yilmaz
A Novel Method For Soc Estimation Of Li-Ion Batteries Using A Hybrid Machinelearning Technique, Eymen İpek, Murat Yilmaz
Turkish Journal of Electrical Engineering and Computer Sciences
The battery system is one of the key components of electric vehicles (EV) which has brought groundbreaking technologies. Since modern EVs have mostly Li-ion batteries, they need to be monitored and controlled to achieve safe and high-performance operation. Particularly, the battery management system (BMS) uses complex processing systems that perform measurements, estimation of the battery states, and protection of the system. State of charge (SOC) estimation is a major part of these processes which defines remaining capacity in the battery until the next charging operation as a proportion to the total battery capacity. Since SOC is not a parameter that …
An Improved Version Of Multi-View K-Nearest Neighbors (Mvknn) For Multipleview Learning, Eli̇fe Öztürk Kiyak, Derya Bi̇rant, Kökten Ulaş Bi̇rant
An Improved Version Of Multi-View K-Nearest Neighbors (Mvknn) For Multipleview Learning, Eli̇fe Öztürk Kiyak, Derya Bi̇rant, Kökten Ulaş Bi̇rant
Turkish Journal of Electrical Engineering and Computer Sciences
Multi-view learning (MVL) is a special type of machine learning that utilizes more than one views, where views include various descriptions of a given sample. Traditionally, classification algorithms such as k-nearest neighbors (KNN) are designed for learning from single-view data. However, many real-world applications involve datasets with multiple views and each view may contain different and partly independent information, which makes the traditional single-view classification approaches ineffective. Therefore, this article proposes an improved MVL algorithm, called multi-view k-nearest neighbors (MVKNN), based on the existing KNN algorithm. The experimental results conducted in this research show that a significant improvement is achieved …
Determining And Evaluating New Store Locations Using Remote Sensing Andmachine Learning, Berkan Höke, Zeynep Zerri̇n Turgay, Cem Ünsalan, Hande Küçükaydin
Determining And Evaluating New Store Locations Using Remote Sensing Andmachine Learning, Berkan Höke, Zeynep Zerri̇n Turgay, Cem Ünsalan, Hande Küçükaydin
Turkish Journal of Electrical Engineering and Computer Sciences
Decision making for store locations is crucial for retail companies as the profit depends on the location. The key point for correct store location is profit approximation, which is highly dependent on population of the corresponding region, and hence, the volume of the residential area. Thus, estimating building volumes provides insight about the revenue if a new store is about to be opened there. Remote sensing through stereo/tri-stereo satellite images provides wide area coverage as well as adequate resolution for three dimensional reconstruction for volume estimation. We reconstruct 3D map of corresponding region with the help of semiglobal matching and …
A New Approach: Semisupervised Ordinal Classification, Ferda Ünal, Derya Bi̇rant, Özlem Şeker
A New Approach: Semisupervised Ordinal Classification, Ferda Ünal, Derya Bi̇rant, Özlem Şeker
Turkish Journal of Electrical Engineering and Computer Sciences
Semisupervised learning is a type of machine learning technique that constructs a classifier by learning from a small collection of labeled samples and a large collection of unlabeled ones. Although some progress has been made in this research area, the existing semisupervised methods provide a nominal classification task. However, semisupervised learning for ordinal classification is yet to be explored. To bridge the gap, this study combines two concepts ?semisupervised learning? and "ordinal classification" for the categorical class labels for the first time and introduces a new concept of "semisupervised ordinal classification". This paper proposes a new algorithm for semisupervised learning …
Ordered Physical Human Activity Recognition Based On Ordinal Classification, Duygu Bağci Daş, Derya Bi̇rant
Ordered Physical Human Activity Recognition Based On Ordinal Classification, Duygu Bağci Daş, Derya Bi̇rant
Turkish Journal of Electrical Engineering and Computer Sciences
Human activity recognition (HAR) is a critical process for applications that focus on the classification of human physical activities such as jogging, walking, downstairs, and upstairs. Ordinal classification (OC) is a special type of supervised multi-class classification in which an inherent ordering among the classes exists, such as low, medium, and high. This study combines these two concepts and introduces an approach to ?human activity recognition based on ordinal classification? (HAROC). In the proposed approach, ordinal classification is applied to human activity recognition where the physical activities can be ordered by using their signals? band power values. This is the …
Deep Learning For Turkish Makam Music Composition, İsmai̇l Hakki Parlak, Yalçin Çebi̇, Ci̇han Işikhan, Derya Bi̇rant
Deep Learning For Turkish Makam Music Composition, İsmai̇l Hakki Parlak, Yalçin Çebi̇, Ci̇han Işikhan, Derya Bi̇rant
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, we introduce a new deep-learning-based system that can compose structured Turkish makam music (TMM) in the symbolic domain. Presented artificial TMM composer (ATMMC) takes eight initial notes from a human user and completes the rest of the piece. The backbone of the composer system consists of multilayered long short-term memory (LSTM) networks. ATMMC can create pieces in Hicaz and Nihavent makams in Şarkı form, which can be viewed and played with Mus2, a notation software for microtonal music. Statistical analysis shows that pieces composed by ATMMC are approximately 84% similar to training data. ATMMC is an open-source …
A Case Study On Player Selection And Team Formation In Football With Machinelearning, Di̇dem Abi̇di̇n
A Case Study On Player Selection And Team Formation In Football With Machinelearning, Di̇dem Abi̇di̇n
Turkish Journal of Electrical Engineering and Computer Sciences
Machine learning has been widely used in different domains to extract information from raw data. Sports is one of the popular domains for researchers to work on recently. Although score prediction for matches is the most preferred application area for artificial intelligence, player selection, and team formation is also an application area worth working on. There are some studies in the literature about player selection and team formation which are examined in this study. The study has two important contributions: First one is to apply seven different machine learning algorithms on our dataset to find the best player combination for …
Improved Online Sequential Extreme Learning Machine: Os-Celm, Olcay Tosun, Recep Eryi̇ği̇t
Improved Online Sequential Extreme Learning Machine: Os-Celm, Olcay Tosun, Recep Eryi̇ği̇t
Turkish Journal of Electrical Engineering and Computer Sciences
Online learning methods (OLM) have been gaining traction as a solution to classification problems because of rapid renewal and fast growth in volume of available data. ELM-based sequential learning (OS-ELM) is one of the most frequently used online learning methodologies partly due to fast training algorithm but suffers from inefficient use of its hidden layers due to the random assignment of the parameters of those layers. In this study, we propose an improved online learning model called online sequential constrained extreme learning machine (OS-CELM), which replaces the random assignment of those parameters with better generalization performance using the CELM method …
Deep-Learning-Based Spraying Area Recognition System Forunmanned-Aerial-Vehicle-Based Sprayers, Shahbaz Khan, Muhammad Tufail, Muhammad Tahir Khan, Zubair Ahmed Khan, Shahzad Anwer
Deep-Learning-Based Spraying Area Recognition System Forunmanned-Aerial-Vehicle-Based Sprayers, Shahbaz Khan, Muhammad Tufail, Muhammad Tahir Khan, Zubair Ahmed Khan, Shahzad Anwer
Turkish Journal of Electrical Engineering and Computer Sciences
Unmanned aerial vehicle (UAV)-based spraying system employing machine learning techniques is a recent advancement in precision agriculture for precise spraying, promoting saving chemicals (pesticide/herbicide), and enhancing their effectiveness. This study aims to develop an efficient deep learning system for UAV-based sprayers, which has the capability to accurately recognize spraying areas. A deep learning system is proposed and developed incorporating a faster region-based convolutional neural network (R-CNN) for the imagery collected. In order to develop a classifier for identifying spraying areas from nonspraying areas, four different agriculture croplands and orchards were considered. All the experiments were performed in agriculture fields through …
Swft: Subbands Wavelet For Local Features Transform Descriptor For Cornealdiseases Diagnosis, Samer Al-Salihi, Sezgi̇n Aydin, Nebras Hussein
Swft: Subbands Wavelet For Local Features Transform Descriptor For Cornealdiseases Diagnosis, Samer Al-Salihi, Sezgi̇n Aydin, Nebras Hussein
Turkish Journal of Electrical Engineering and Computer Sciences
Human cornea is the front see-through shield of the eye. It refracts light onto the retina to induce vision.Therefore, any defect in the cornea may lead to vision disturbance. This deficiency is estimated by sets of topographicalimages measured, and assessed by an ophthalmologist. Consequently, an important priority is the early and accuratediagnosis of diseases that may affect corneal integrity through the use of machine learning algorithms. Images producedby a Pentacam device can be subjected to rotation or some distortion during acquisition; therefore, accurate diagnosisrequires the use of local features in the image. Accordingly, a new algorithm called subbands wavelet for …
A New Classification Method For Encrypted Internet Traffic Using Machine Learning, Mesut Uğurlu, İbrahi̇m Alper Doğru, Recep Si̇nan Arslan
A New Classification Method For Encrypted Internet Traffic Using Machine Learning, Mesut Uğurlu, İbrahi̇m Alper Doğru, Recep Si̇nan Arslan
Turkish Journal of Electrical Engineering and Computer Sciences
The rate of internet usage in the world is over 62% and this rate is increasing day by day. With this increase, it becomes important to ensure the confidentiality of the information in the traffic flowing over the internet. Encryption algorithms and protocols are used for this purpose. This situation, which is beneficial for normal users, is also used by attackers to hide. Cyber attackers or hackers gain the ability to bypass security precautions such as IDS/IPS and antivirus systems with using encrypted traffic. Since payload analysis cannot be performed without deciphering the encrypted traffic, existing commercial security solutions fall …