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Articles 331 - 360 of 3006
Full-Text Articles in Engineering
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 …
A Fuzzy Expert System For Predicting The Mortality Of Covid'19, Monika Mangla, Nonita Sharma, Poonam Mittal
A Fuzzy Expert System For Predicting The Mortality Of Covid'19, Monika Mangla, Nonita Sharma, Poonam Mittal
Turkish Journal of Electrical Engineering and Computer Sciences
The COVID-19 pandemic has had a widespread impact on health and economy across the globe. It is leading to a huge number of deaths per day. Few researchers have been attracted to analyzing the mortality rate of COVID-19 from various perspectives. During the research, it has become evident that these fatalities are not only caused by COVID19, but they are also affected by some other factors. The authors of this paper aim to encompass three important types of factors viz. risk factors, clinical factors, and miscellaneous factors that influence the mortality of COVID-19. This manuscript presents a rule-based model under …
Joint Optimization Of Target Wake Time Mechanism And Scheduling For Ieee802.11ax, Mehmet Karaca
Joint Optimization Of Target Wake Time Mechanism And Scheduling For Ieee802.11ax, Mehmet Karaca
Turkish Journal of Electrical Engineering and Computer Sciences
IEEE 802.11ax as the newest wireless local area networks (WLANs) standard brings enormous improvements in network throughput, coverage and energy efficiency in densely populated areas. Unlike previous IEEE 802.11 WLAN standards where power saving mechanisms have a limited capability and flexibility, 802.11ax comes with a different mechanism called target wake time (TWT) where stations (STAs) wake up only after each TWT interval and different STAs can wake up at different time instance depending on their application requirements. As an example, for a periodic data arrival occurring in IoT applications, STA can wake up by following the data period and go …
Information Retrieval-Based Bug Localization Approach With Adaptive Attributeweighting, Mustafa Erşahi̇n, Semi̇h Utku, Deni̇z Kilinç, Buket Erşahi̇n
Information Retrieval-Based Bug Localization Approach With Adaptive Attributeweighting, Mustafa Erşahi̇n, Semi̇h Utku, Deni̇z Kilinç, Buket Erşahi̇n
Turkish Journal of Electrical Engineering and Computer Sciences
Software quality assurance is one of the crucial factors for the success of software projects. Bug fixing has an essential role in software quality assurance, and bug localization (BL) is the first step of this process. BL is difficult and time-consuming since the developers should understand the flow, coding structure, and the logic of the program. Information retrieval-based bug localization (IRBL) uses the information of bug reports and source code to locate the section of code in which the bug occurs. It is difficult to apply other tools because of the diversity of software development languages, design patterns, and development …
In Depth Study Of Two Solutions For Common Mode Current Reduction In Six-Phasemachine Drive Inverters, Iman Abdoli, Alireza Lahooti Eshkevari, Ali Mosallanejad
In Depth Study Of Two Solutions For Common Mode Current Reduction In Six-Phasemachine Drive Inverters, Iman Abdoli, Alireza Lahooti Eshkevari, Ali Mosallanejad
Turkish Journal of Electrical Engineering and Computer Sciences
Common mode current (CMC) destroy machine bearings in the long run and increase electromagnetic interference. According to standards, the RMS value of CMC must be lower than 0.3A. Theories show that CMC is originated by applying zero states to the inverter. In this paper, the performance of two solutions in reducing CMC for six-phase machines is investigated. In the first solution, the traditional six-phase inverter is modified by adding two serial power switches on its input terminal. This method is an extended version of a method that has been presented for three-phase inverters, reduces CMC by optimizing the circuit structure. …
Novel Ofdm Transmission Scheme Using Generalized Prefix With Subcarrierindex Modulation, Yusuf Acar
Novel Ofdm Transmission Scheme Using Generalized Prefix With Subcarrierindex Modulation, Yusuf Acar
Turkish Journal of Electrical Engineering and Computer Sciences
The cyclic prefix (CP) is a prefix technique widely used in orthogonal frequency division multiplexing (OFDM) systems in order to eliminate the intersymbol interference (ISI) caused by the dispersion of wireless channels. However, CP reduces the number of symbols that can be transmitted in one OFDM symbol. Therefore, CP is one of the bottlenecks of OFDM systems limiting their spectral efficiency (SE). This limitation on the SE of the classical CP-based OFDM system is the main motivation for this work to introduce a novel method. In this paper, the design of a new CP structure, which is based on the …
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 …
Analysis And Simulation Of Efficiency Optimized Ipm Drives In Constant Torqueregion With Reduced Computational Burden, Mi̇kai̇l Koç, Selçuk Emi̇roğlu, Bünyami̇n Tamyürek
Analysis And Simulation Of Efficiency Optimized Ipm Drives In Constant Torqueregion With Reduced Computational Burden, Mi̇kai̇l Koç, Selçuk Emi̇roğlu, Bünyami̇n Tamyürek
Turkish Journal of Electrical Engineering and Computer Sciences
Moving from internal combustion engine based towards electric based transportation is crucial for wide societies as they facilitate the use of green energy technologies such as wind and solar. Interior mounted permanent magnet (IPM) machines, also known as salient brushless alternating current (AC) machines, are commonly employed in traction applications as they have superior features, such as high efficiency operation, high torque, and power densities. The efficiency optimization in IPM drives is achieved by obtaining and operating at accurate and unique current angle for a certain electromagnetic torque demand. In conventional drives, the optimum current angle is obtained by online …
Control Synthesis For Parametric Timed Automata Under Reachability, Ebru Aydin Göl
Control Synthesis For Parametric Timed Automata Under Reachability, Ebru Aydin Göl
Turkish Journal of Electrical Engineering and Computer Sciences
Timed automata is a fundamental modeling formalism for real-time systems. During the design of such real-time systems, often the system information is incomplete, and design choices can vary. These uncertainties can be integrated to the model via parameters and labelled transitions. Then, the design can be completed by tuning the parameters and restricting the transitions via controller synthesis. These problems, namely parameter synthesis and controller synthesis, are studied separately in the literature. Herein, these are combined to generate an automaton satisfying the given specification by both parameter tuning and controller synthesis, thus exploring all design choices. First, it is shown …
A Novel Hybrid Global Optimization Algorithm Having Training Strategy: Hybridtaguchi-Vortex Search Algorithm, Mustafa Saka, Meli̇h Çoban, İbrahi̇m Eke, Suleyman Sungur Tezcan, Müslüm Cengi̇z Taplamacioğlu
A Novel Hybrid Global Optimization Algorithm Having Training Strategy: Hybridtaguchi-Vortex Search Algorithm, Mustafa Saka, Meli̇h Çoban, İbrahi̇m Eke, Suleyman Sungur Tezcan, Müslüm Cengi̇z Taplamacioğlu
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, a novel hybrid Taguchi-vortex search algorithm (HTVS) is proposed for solving global optimization problems. Taguchi orthogonal approximation and vortex search algorithm (VS) are hybridized in presenting method. In HTVS, orthogonal arrays in the Taguchi method are trained and obtained better solutions are used to find global optima in VS. Thus, HTVS has better relation between exploration and exploitation, and it exhibits more powerful approximation to find global optimum value. Proposed HTVS algorithm is applied to sixteen well-known benchmark optimization test functions with different dimensions. The results are compared with the Taguchi orthogonal array approximation (TOAA), vortex search …
Performance Improvement Speed Control Of Ipmsm Drive Based On Nonlinearcurrent Control, Muhammad Usama, Jaehong Kim
Performance Improvement Speed Control Of Ipmsm Drive Based On Nonlinearcurrent Control, Muhammad Usama, Jaehong Kim
Turkish Journal of Electrical Engineering and Computer Sciences
Recently model predictive control (MPC) scheme emerges as an efficient current control technique for dynamic performance of motor drives. For excellent dynamic performance, maximum torque per ampere (MTPA) control technique is utilized to achieve maximum torque while using minimum current constrain in contrast to conventional qaxis current control. Model predictive current control (MPCC) scheme alongside MTPA control is employed to replace the traditional constant gain proportional-integral (PI) current control and a nonlinear hysteresis current (HC) control schemes. The PI and hysteresis current controller offers satisfactory performance at ideal conditions but, with variable speed and load conditions, these control schemes cause …
Area-Delay Efficient Radix-4 8×8 Booth Multiplier For Dsp Applications, Subodh Singhal, Sujit Patel, Anurag Mahajan, Gaurav Saxena
Area-Delay Efficient Radix-4 8×8 Booth Multiplier For Dsp Applications, Subodh Singhal, Sujit Patel, Anurag Mahajan, Gaurav Saxena
Turkish Journal of Electrical Engineering and Computer Sciences
Booth multiplier is the key component in portable very large-scale integration (VSLI) systems enabled with signal and image processing applications. The area, delay, and energy are the major constraints in these systems. Therefore, in this paper, a detailed analysis of the state-of-the-art Booth multiplier architecture and its various internal units are presented to find the scope of optimization. Based on the finding of analysis, optimized new binary to 2's complement (B2C), Booth encoder-cum-selector type-1 and type-2, and partial product addition units are proposed. Furthermore, using these optimized units, an efficient parallel radix-4 8×8 Booth multiplier architecture is proposed. The simulation …
Image Forgery Detection Based On Fusion Of Lightweight Deep Learning Models, Amit Doegar, Srinidhi Hiriyannaiah, Siddesh Gaddadevara Matt, Srinivasa Krishnarajanagar Gopaliyengar, Maitreyee Dutta
Image Forgery Detection Based On Fusion Of Lightweight Deep Learning Models, Amit Doegar, Srinidhi Hiriyannaiah, Siddesh Gaddadevara Matt, Srinivasa Krishnarajanagar Gopaliyengar, Maitreyee Dutta
Turkish Journal of Electrical Engineering and Computer Sciences
Image forgery detection is one of the key challenges in various real time applications, social media and online information platforms. The conventional methods of detection based on the traces of image manipulations are limited to the scope of predefined assumptions like hand-crafted features, size and contrast. In this paper, we propose a fusion based decision approach for image forgery detection. The fusion of decision is based on the lightweight deep learning models namely SqueezeNet, MobileNetV2 and ShuffleNet. The fusion decision system is implemented in two phases. First, the pretrained weights of the lightweight deep learning models are used to evaluate …
Zero Knowledge Based Data Deduplication Using In-Line Block Matching Protocolfor Secure Cloud Storage, Vivekrabinson Kanagamani, Muneeswaran Karuppiah
Zero Knowledge Based Data Deduplication Using In-Line Block Matching Protocolfor Secure Cloud Storage, Vivekrabinson Kanagamani, Muneeswaran Karuppiah
Turkish Journal of Electrical Engineering and Computer Sciences
In the area of cloud computing, data deduplication enables the cloud server to store a single copy of data by eliminating redundant files to improve storage and network efficiency. Proof-of-ownership (PoW) is a cryptographic function that verifies the user who really owns the data. Most of the existing schemes have tried to solve the deduplication problem by providing the same encryption key for identical data. However, these schemes suffer from dynamic changes in ownership management. In this paper, we propose an in-line block matching (IBM) protocol based on zero-knowledge proof for deduplication with dynamic ownership management, which eliminates the unauthorized …
Legendre-Wavelet Embedded Neurofuzzy Feedback Linearization Based Controlscheme For Phevs Charging Station In A Microgrid, Muhammad Awais, Laiq Khan, Saghir Ahmad, Sidra Mumtaz, Rabiah Badar, Shafaat Ullah
Legendre-Wavelet Embedded Neurofuzzy Feedback Linearization Based Controlscheme For Phevs Charging Station In A Microgrid, Muhammad Awais, Laiq Khan, Saghir Ahmad, Sidra Mumtaz, Rabiah Badar, Shafaat Ullah
Turkish Journal of Electrical Engineering and Computer Sciences
The immense emergence of plug-in hybrid electric vehicles (PHEVs) is envisioned in the future. The rapid proliferation of PHEVs and their charging triggers intense surges in the load during load peak hours. A sophisticated controlled charging station is developed for PHEVs to alleviate grid load during peak demand hours. A novel feedback linearization embedded full recurrent adaptive NeuroFuzzy Legendre wavelet control (FBL-FRANF-Leg-WC) technique is employed to control the charging of PHEVs. The antecedent part of the NeuroFuzzy framework is based on recurrent Gaussian membership function while the consequent part comprises of recurrent Legendre wavelet. The charging station is integrated into …
A Linear Programming Approach To Multiple Instance Learning, Emel Şeyma Küçükaşci, Mustafa Gökçe Baydoğan, Zeki̇ Caner Taşkin
A Linear Programming Approach To Multiple Instance Learning, Emel Şeyma Küçükaşci, Mustafa Gökçe Baydoğan, Zeki̇ Caner Taşkin
Turkish Journal of Electrical Engineering and Computer Sciences
Multiple instance learning (MIL) aims to classify objects with complex structures and covers a wide range of real-world data mining applications. In MIL, objects are represented by a bag of instances instead of a single instance, and class labels are provided only for the bags. Some of the earlier MIL methods focus on solving MIL problem under the standard MIL assumption, which requires at least one positive instance in positive bags and all remaining instances are negative. This study proposes a linear programming framework to learn instance level contributions to bag label without emposing the standart assumption. Each instance of …
Wavelet-Based Super Resolution Using Pansharpened Multispectral Images, Vi̇ldan Atalay Aydin, Hassan Foroosh
Wavelet-Based Super Resolution Using Pansharpened Multispectral Images, Vi̇ldan Atalay Aydin, Hassan Foroosh
Turkish Journal of Electrical Engineering and Computer Sciences
Several remote sensing applications require high-spatial-high-spectral resolution multispectral (MS) images. However, most MS sensors provide low-spatial-high-spectral resolution MS images together with high-spatial-low-spectral resolution panchromatic (PAN) bands. In order to increase the spatial resolution of MS bands to the resolution of PAN images and to obtain high-spatial/spectral resolution MS bands, either MS and PAN images are fused (i.e., pansharpening) or super resolution (SR) is performed using MS bands only. Nevertheless, existing methods do not utilize the available temporal and spatial information together. In this paper, we propose a multiframe SR algorithm using high-spatial/spectral resolution MS images (i.e., pansharpened), taking advantage of …
Classification Of Neonatal Jaundice In Mobile Application With Noninvasive Imageprocessing Methods, Firat Hardalaç, Mustafa Aydin, Uğurhan Kutbay, Kubi̇lay Ayturan, Anil Akyel, Ati̇ka Çağlar, Bo Hai̇, Fati̇h Mert
Classification Of Neonatal Jaundice In Mobile Application With Noninvasive Imageprocessing Methods, Firat Hardalaç, Mustafa Aydin, Uğurhan Kutbay, Kubi̇lay Ayturan, Anil Akyel, Ati̇ka Çağlar, Bo Hai̇, Fati̇h Mert
Turkish Journal of Electrical Engineering and Computer Sciences
This study aims a mobile support system to aid health care professionals in hospitals or in regions far away from hospitals to utilize noninvasive image processing methods for classification of neonatal jaundice. A considerably low processing cost is aimed to be attained by developing an algorithm that could work on a mobile device with low-end camera and processor capabilities within this study. In this context, an algorithm with low cost is developed performing detection of most meaningful parameters by a multiple input single output regression model and correlation.The advantage of the proposed method is that it can estimate bilirubin with …
Analyzing Students' Experience In Programming With Computational Thinkingthrough Competitive, Physical, And Tactile Games: The Quadrilateral Methodapproach, M Ahsan Habib, Raja Jamilah Raja Yusof, Siti Salwah Salim, Asmiza Abdul Sani, Hazrina Sofian, Aishah Abu Bakar
Analyzing Students' Experience In Programming With Computational Thinkingthrough Competitive, Physical, And Tactile Games: The Quadrilateral Methodapproach, M Ahsan Habib, Raja Jamilah Raja Yusof, Siti Salwah Salim, Asmiza Abdul Sani, Hazrina Sofian, Aishah Abu Bakar
Turkish Journal of Electrical Engineering and Computer Sciences
The lack of computational thinking (CT) skills can be one of the reasons why students find themselves having difficulties in writing a good program. Therefore, understanding how CT skills can be developed is essential. This research explores how CT skills can be developed for programming through competitive, physical, and tactile games. The CT elements in this research focus on four major programming concepts, which are decomposition, pattern recognition, abstraction, and algorithmic thinking. We have conducted game activities through several algorithms that include sorting, swapping, and graph algorithms and analyzed how the game affects the student experience (SX) in understanding the …
Exploring The Attention Process Differentiation Of Attention Deficit Hyperactivity Disorder (Adhd) Symptomatic Adults Using Artificial Intelligence Onelectroencephalography (Eeg) Signals, Gökhan Güney, Esra Kisacik, Canan Kalaycioğlu, Görkem Saygili
Exploring The Attention Process Differentiation Of Attention Deficit Hyperactivity Disorder (Adhd) Symptomatic Adults Using Artificial Intelligence Onelectroencephalography (Eeg) Signals, Gökhan Güney, Esra Kisacik, Canan Kalaycioğlu, Görkem Saygili
Turkish Journal of Electrical Engineering and Computer Sciences
Attention deficit and hyperactivity disorder (ADHD) onset in childhood and its symptoms can last up till adulthood. Recently, electroencephalography (EEG) has emerged as a tool to investigate the neurophysiological connection of ADHD and the brain. In this study, we investigated the differentiation of attention process of healthy subjects with or without ADHD symptoms under visual continuous performance test (VCPT). In our experiments, artificial neural network (ANN) algorithm achieved 98.4% classification accuracy with 0.98 sensitivity when P2 event related potential (ERP) was used. Additionally, our experimental results showed that fronto-central channels were the most contributing. Overall, we conclude that the attention …
An Admm-Based Incentive Approach For Cooperative Data Analysis In Edgecomputing, Weiwei Fang, Xue Wang, Qingli Wang, Yi Ding
An Admm-Based Incentive Approach For Cooperative Data Analysis In Edgecomputing, Weiwei Fang, Xue Wang, Qingli Wang, Yi Ding
Turkish Journal of Electrical Engineering and Computer Sciences
Edge computing is a new paradigm that provides data processing capabilities at the network edge. In view of the uneven data distribution and the constrained onboard resource, an edge device often needs to call for a number of neighboring devices as followers to cooperate on data analysis tasks. However, these followers may be rational and selfish, having their private optimization objectives such as energy efficiency. Therefore, the leader device needs to incentivize the followers to achieve a certain global objective, e.g., maximizing task accomplishment, rather than their own objectives. In this paper, we model the aforementioned challenges in edge computing …
Privacy Preserving Hybrid Recommender System Based On Deep Learning, Sangeetha Selvaraj, Sudha Sadasivam Gangadharan
Privacy Preserving Hybrid Recommender System Based On Deep Learning, Sangeetha Selvaraj, Sudha Sadasivam Gangadharan
Turkish Journal of Electrical Engineering and Computer Sciences
Deep learning models are widely being used to provide relevant recommendations in hybrid recommender systems. These hybrid systems combine the advantages of both content based and collaborative filtering approaches. However, these learning systems hamper the user privacy and disclose sensitive information. This paper proposes a privacy preserving deep learning based hybrid recommender system. In hybrid deep neural network, user?s side information such as age, location, occupation, zip code along with user rating is embedded and provided as input. These embedding?s pose a severe threat to individual privacy. In order to eliminate this breach of privacy, we have proposed a private …
Prediction Of Long-Term Physical Properties Of Low Density Polyethylene (Ldpe)Cable Insulation Materials By Artificial Neural Network Modeling Approach Underenvironmental Constraints, Ferhat Slimani, Abdallah Hedir, Mustapha Moudoud, Ali̇ Durmuş, Mounir Amir, Mohamed Megherbi
Prediction Of Long-Term Physical Properties Of Low Density Polyethylene (Ldpe)Cable Insulation Materials By Artificial Neural Network Modeling Approach Underenvironmental Constraints, Ferhat Slimani, Abdallah Hedir, Mustapha Moudoud, Ali̇ Durmuş, Mounir Amir, Mohamed Megherbi
Turkish Journal of Electrical Engineering and Computer Sciences
This study quantifies long-term physical properties of low density polyethylene (LDPE) cables insulations exposed to environmental constraints such as UV radiation and temperature via both experimental measurements and mathematical modeling approach. For this purpose, tensile test and electrical breakdown test were carried out to determine elongation at break, tensile strength, and dielectric strength of unaged and aged specimens, respectively. Experimental results showed that both UV and temperature exposures affected the LDPE properties, significantly. A supervised artificial neural network (ANN) trained by the Levenberg?Marquardt algorithm was designed for predicting the long-term characteristics of specimens and also for minimizing the experimental procedures. …
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 …
Simultaneous Feedforward Online Command Rate Limiter Filters For Existingcontrollers, Gali̇p Serdar Tombul
Simultaneous Feedforward Online Command Rate Limiter Filters For Existingcontrollers, Gali̇p Serdar Tombul
Turkish Journal of Electrical Engineering and Computer Sciences
One of the biggest challenges in controller design for a mechatronics system is the actuator limitations. Either response time of the actuator or the input constraints creates limits for the controller performance and stability. In this study a novel feedforward online rate limiter scheme for arbitrary input signals is introduced by taking velocity, acceleration and jerk constraints into account, and it is investigated that how the control effort and system response is affected by the demand signal's rate of change limitations. A fin actuation system for a guided missile is given as an example where the demand signal comes from …
Line-Of-Sight Rate Construction For A Roll-Pitch Gimbal Via A Virtualpitch-Yaw Gimbal, Oğuzhan Çi̇fdalöz
Line-Of-Sight Rate Construction For A Roll-Pitch Gimbal Via A Virtualpitch-Yaw Gimbal, Oğuzhan Çi̇fdalöz
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, a method to construct the line of sight rate of a target with a roll-pitch gimbal and tracker is described. Construction of line-of-sight rate is performed via utilizing a virtual pitch-yaw gimbal. Kinematics of both the roll-pitch and pitch-yaw gimbals are described. A dynamical model for the roll-pitch gimbal is developed, and a nested control structure is designed to control the angular rates and line of sight angles. A kinematic model of the tracker is developed and a tracker controller is designed to keep the target in the field of view. Conversion equations between roll-pitch and pitch-yaw …
Learning Prototypes For Multiple Instance Learning, Özgür Emre Si̇vri̇kaya, Mert Yüksekgönül, Mustafa Gökçe Baydoğan
Learning Prototypes For Multiple Instance Learning, Özgür Emre Si̇vri̇kaya, Mert Yüksekgönül, Mustafa Gökçe Baydoğan
Turkish Journal of Electrical Engineering and Computer Sciences
Multiple instance learning (MIL) is a weakly supervised learning method that works on the labeled bag of instances data. A prototypical network is a popular embedding approach in MIL. They overcome the common problems that other MIL approaches may have to deal with including dimensionality, loss of instance-level information, and complexity. They demonstrate competitive performance in classification. This work proposes a simple model that provides a permutation invariant prototype generator from a given MIL data set. We aim to find out prototypes in the feature space to map the collection of instances (i.e. bags) to a distance feature space and …
A New Distributed Anomaly Detection Approach For Log Ids Management Based Ondeep Learning, Murat Koca, Muhammed Ali̇ Aydin, Ahmet Sertbaş, Abdül Hali̇m Zai̇m
A New Distributed Anomaly Detection Approach For Log Ids Management Based Ondeep Learning, Murat Koca, Muhammed Ali̇ Aydin, Ahmet Sertbaş, Abdül Hali̇m Zai̇m
Turkish Journal of Electrical Engineering and Computer Sciences
Today, with the rapid increase of data, the security of big data has become more important than ever for managers. However, traditional infrastructure systems cannot cope with increasingly big data that is created like an avalanche. In addition, as the existing database systems increase licensing costs per transaction, organizations using information technologies are shifting to free and open source solutions. For this reason, we propose an anomaly attack detection model on Apache Hadoop distributed file system (HDFS), which stands out in open source big data analytics, and Apache Spark, which stands out with its speed performance in analysis to reduce …
Design And Analysis Of A Truncated Elliptical-Shaped Chipless Rfid Tag, Ameer Taimour Khan, Yassin Abdullah, Sidra Farhat, Wasim Nawaz, Usman Rauf
Design And Analysis Of A Truncated Elliptical-Shaped Chipless Rfid Tag, Ameer Taimour Khan, Yassin Abdullah, Sidra Farhat, Wasim Nawaz, Usman Rauf
Turkish Journal of Electrical Engineering and Computer Sciences
This article presents a novel polarization-insensitive chipless radio frequency identification tag having an encoding capacity of 11 bits. The proposed resonator design comprises discontinuous arc slots forming truncated elliptically shape offering 1:1 slot to bit correspondence with suppressed unwanted harmonic resonances. Electromagnetic performance analysis of the proposed tag design is done over an ungrounded Rogers RT duroid® 5880 laminate. The overall tag design covers a footprint of 15 × 15 × 0.508 mm3 offering convincingly appreciable bit density of 4.88 bits/cm2 . The realized tags are analyzed for real-world electromagnetic performance resulting in an agreement between measured and computed results. …
Sleep Staging With Deep Structured Neural Net Using Gabor Layer And Dataaugmentation, Ali Erfani Sholeyan, Fereidoun Nowshiravan Rahatabad, Kamal Setaredan
Sleep Staging With Deep Structured Neural Net Using Gabor Layer And Dataaugmentation, Ali Erfani Sholeyan, Fereidoun Nowshiravan Rahatabad, Kamal Setaredan
Turkish Journal of Electrical Engineering and Computer Sciences
Slow wave sleep (SWS) and rapid eye movement (REM) are two of the most important sleep stages that are considered in many studies. Detection of these two sleep stages will help researchers in many applications to detect sleeprelated diseases and disorders and also in many fields of neuroscience studies such as cognitive impairment and memory consolidation. Since manual sleep staging is time-consuming, subjective, and expensive; designing an efficient automatic sleep scoring system will overcome some of these difficulties. Many studies have proposed automatic sleep staging systems with different methods. In recent years, deep learning methods show their potential in different …