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2021

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Articles 1321 - 1350 of 1378

Full-Text Articles in Computer Engineering

Joint Carrier Frequency And Phase Offset Estimation Algorithm For Cpm-Dsssbased Secure Point-To-Point Communication, Saima Shehzadi, Farzana Kulsoom, Muhammad Zeeshan, Qasim Umar Khan, Shahzad Amin Sheikh Jan 2021

Joint Carrier Frequency And Phase Offset Estimation Algorithm For Cpm-Dsssbased Secure Point-To-Point Communication, Saima Shehzadi, Farzana Kulsoom, Muhammad Zeeshan, Qasim Umar Khan, Shahzad Amin Sheikh

Turkish Journal of Electrical Engineering and Computer Sciences

A point-to-point (P2P) communication system based on the CPM-DSSS scheme ensures reliability, security, and antijamming capabilities. However, for reliable detection of data carrier synchronization of CPM-DSSS based system is one of the requirements. This paper presents a joint algorithm for carrier frequency offset (CFO) and carrier phase offset (CPO) estimation for CPM-DSSS based P2P system. The results indicate that the proposed CFO estimator is unbiased and can accurately estimate a wide range of offsets. Moreover, the proposed algorithm is compared with another research work. The results show that the proposed CFO and CPO estimation algorithm outperforms its counterpart with a …

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Csop+Rp: A Novel Constraints Satisfaction Model For Requirements Prioritizationin Large-Scale Software Systems, Soheil Afraz, Hassan Rashidi, Naser Mikaeilvand Jan 2021

Csop+Rp: A Novel Constraints Satisfaction Model For Requirements Prioritizationin Large-Scale Software Systems, Soheil Afraz, Hassan Rashidi, Naser Mikaeilvand

Turkish Journal of Electrical Engineering and Computer Sciences

One of the main factors in the failure of software projects is the lack of attention to their requirements prioritization. In this paper, we propose a decision-oriented methodology with a novel model for requirements prioritization (RP) in large-scale software systems. The model is formulated based on the constraint satisfaction optimization problems (CSOP) approach, which we call CSOP+RP. The main objective of the model is to maximize the quality of the software in total, subject to the constraints on the budgets and importance level that pre-determined by the administrator. To evaluate CSOP+RP, we applied it to the police command-and-control system (PCCS), …

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Scale-Invariant Histogram Of Oriented Gradients: Novel Approach For Pedestriandetection In Multiresolution Image Dataset, Sweta Panigrahi, Surya Narayana Raju Undi Jan 2021

Scale-Invariant Histogram Of Oriented Gradients: Novel Approach For Pedestriandetection In Multiresolution Image Dataset, Sweta Panigrahi, Surya Narayana Raju Undi

Turkish Journal of Electrical Engineering and Computer Sciences

This paper proposes a scale-invariant histogram of oriented gradients (SI-HOG) for pedestrian detection. Most of the algorithms for pedestrian detection use the HOG as the basic feature and combine other features with the HOG to form the feature set, which is usually applied with a support vector machine (SVM). Hence, the HOG feature is the most efficient and fundamental feature for pedestrian detection. However, the HOG feature produces feature vectors of different lengths for different image resolutions; thus, the feature vectors are incomparable for the SVM. The proposed method forms a scale-space pyramid wherein the histogram bin is calculated. Thus, …

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Improved Online Sequential Extreme Learning Machine: Os-Celm, Olcay Tosun, Recep Eryi̇ği̇t Jan 2021

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 …

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Deep Learning For Turkish Makam Music Composition, İsmai̇l Hakki Parlak, Yalçin Çebi̇, Ci̇han Işikhan, Derya Bi̇rant Jan 2021

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 …

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Evaluation Of Cable And Busbar System In Multiconductor Distribution Systems Interms Of Current And Magnetic Field Distributions, Yunus Berat Demi̇rol, Mehmet Aytaç Çinar, Bora Alboyaci Jan 2021

Evaluation Of Cable And Busbar System In Multiconductor Distribution Systems Interms Of Current And Magnetic Field Distributions, Yunus Berat Demi̇rol, Mehmet Aytaç Çinar, Bora Alboyaci

Turkish Journal of Electrical Engineering and Computer Sciences

The selection of power distribution components is of great importance in electrical facilities. Cable and busbar systems are widely used applications, such as electric vehicle charge stations, microgrids and energy storage systems, for power distribution in the distribution grid. In this study, the current distribution on the parallel conductors and magnetic field distributions around cable and busbar structures is evaluated for studied application where the power is distributed using a cable system between a converter transformer and a converter. All modeling and analyzes are conducted using ANSYS Electronics Suite software, by applying balanced and pure sinusoidal current excitation. Obtained results …

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A Model Of Service Differentiation Burst Assembling And Padding For Improvingtransmission Efficiency In Obs Networks, Van Hoa Le, Hong Quoc Nguyen, Thanh Chuong Dang, Viet Minh Nhat Vo Jan 2021

A Model Of Service Differentiation Burst Assembling And Padding For Improvingtransmission Efficiency In Obs Networks, Van Hoa Le, Hong Quoc Nguyen, Thanh Chuong Dang, Viet Minh Nhat Vo

Turkish Journal of Electrical Engineering and Computer Sciences

Service differentiation is an indispensable requirement for transmission in optical burst switching (OBS) networks, which can be based on offset-time, burst-length, or both, offset-time and burst-length. The offset time based approach sets a large offset time for high priority bursts and a small offset time for low priority bursts. Whereas, with burst length based approach, high priority bursts are short in size and low priority bursts are long in length. A combination of these two approaches promises to provide flexible service differentiation. The paper proposes a model of service differentiation burst assembling and padding, in which the assembly time threshold …

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Hc-Fft: Highly Configurable And Efficient Fft Implementation On Fpga, Paki̇ze Ergül, H. Fati̇h Uğurdağ, Doğancan Davutoğlu Jan 2021

Hc-Fft: Highly Configurable And Efficient Fft Implementation On Fpga, Paki̇ze Ergül, H. Fati̇h Uğurdağ, Doğancan Davutoğlu

Turkish Journal of Electrical Engineering and Computer Sciences

FFT is one of the basic building blocks in many applications such as sensors, radars, communications. For some applications, e.g., real-time spectral monitoring and analysis, FFT needs to be "run-time configurable" so that the system is real-time. When examining the previous work on configurable real-time (FPGA-based) FFT implementations, we see that the degree of configurability is less than what is desired. In this paper, a new FFT architecture is proposed, which has a high degree of run-time configurability and yet does not compromise area or throughput. The configurable parameters of this design are the number of FFT points (up to …

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Gene Expression Data Classification Using Genetic Algorithm-Basedfeature Selection, Öznur Si̇nem Sönmez, Mustafa Dağteki̇n, Tolga Ensari̇ Jan 2021

Gene Expression Data Classification Using Genetic Algorithm-Basedfeature Selection, Öznur Si̇nem Sönmez, Mustafa Dağteki̇n, Tolga Ensari̇

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, hybrid methods are proposed for feature selection and classification of gene expression datasets. In the proposed genetic algorithm/support vector machine (GA-SVM) and genetic algorithm/k nearest neighbor (GA-KNN) hybrid methods, genetic algorithm is improved using Pearson's correlation coefficient, Relief-F, or mutual information. Crossover and selection operations of the genetic algorithm are specialized. Eight different gene expression datasets are used for classification process. The classification performances of the proposed methods are compared with the traditional GA-KNN and GA-SVM wrapper methods and other studies in the literature. Classification results demonstrate that higher accuracy rates are obtained with the proposed methods …

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Clustered Mobile Data Collection In Wsns: An Energy-Delay Trade-Of, İzzet Fati̇h Şentürk Jan 2021

Clustered Mobile Data Collection In Wsns: An Energy-Delay Trade-Of, İzzet Fati̇h Şentürk

Turkish Journal of Electrical Engineering and Computer Sciences

Wireless sensor networks enable monitoring remote areas with limited human intervention. However, the network connectivity between sensor nodes and the base station (BS) may not be always possible due to the limited transmission range of the nodes. In such a case, one or more mobile data collectors (MDCs) can be employed to visit nodes for data collection. If multiple MDCs are available, it is desirable to minimize the energy cost of mobility while distributing the cost among the MDCs in a fair manner. Despite availability of various clustering algorithms, there is no single fits all clustering solution when different requirements …

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An Observer Based Temperature Estimation In Cooking Heterogeneous Mixtures:A Turkish Coffee Machine Application, Arda Dönerkayali, Türker Türker Jan 2021

An Observer Based Temperature Estimation In Cooking Heterogeneous Mixtures:A Turkish Coffee Machine Application, Arda Dönerkayali, Türker Türker

Turkish Journal of Electrical Engineering and Computer Sciences

A high-precision temperature information is required to follow the recipe in automatic cooking processes of heterogeneous liquids. Therefore, measurement equipment plays a crucial role in appliances developed for automatic cooking processes. However, it is difficult to obtain the temperature information in such appliances since the sensors cannot be located inside the heterogeneous liquid and the diffusion model is not precise in general. In this manner, a method is proposed to estimate the temperature of the heterogeneous mixture during the cooking process. This is achieved by the utilization of only one temperature sensor located at the outside wall of the cooking …

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A Hybrid Numerical Model For Long-Range Electromagnetic Wave Propagation, Gül Yesa Altun, Özlem Özgün Jan 2021

A Hybrid Numerical Model For Long-Range Electromagnetic Wave Propagation, Gül Yesa Altun, Özlem Özgün

Turkish Journal of Electrical Engineering and Computer Sciences

A hybrid numerical model is presented for solving long range electromagnetic wave propagation problems involving objects on or above the ground surface by hybridizing the two-way split-step parabolic equation (2W-SSPE) method with the method of moments (MoM). The advantages of the proposed model are twofold: (i) It reduces the staircasing error in irregular terrain modeling, which usually occurs when the standard SSPE method is used alone. This is achieved by employing the MoM to more accurately obtain the scattered fields from slanted/curved surfaces. (ii) It enables the SSPE method to handle the problems involving objects above the Earth's surface, which …

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Presentation Attack Detection For Face Recognition Using Remotephotoplethysmography And Cascaded Fusion, Mehmet Fati̇h Gündoğar, Çi̇ğdem Eroğlu Erdem Jan 2021

Presentation Attack Detection For Face Recognition Using Remotephotoplethysmography And Cascaded Fusion, Mehmet Fati̇h Gündoğar, Çi̇ğdem Eroğlu Erdem

Turkish Journal of Electrical Engineering and Computer Sciences

Spoofing (presentation) attacks are important threats for face recognition and authentication systems, which try to deceive them by presenting an image or video of a different subject, or by using a 3D mask. Remote (non-contact) photoplethysmography (rPPG) is useful for liveness detection using a facial video by estimating the heart-rate of the subject. In this paper, we first compare the presentation attack detection performance of three different rPPG-based heart rate estimation methods on four datasets (3DMAD, Replay-Attack, Replay-Mobile, and MSU-MFSD). We also present a cascaded fusion system, which utilizes a multistage ensemble of classifiers using rPPG, motion-based (including head-pose, eye-gaze …

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Comparison Of Metaheuristic Optimization Algorithms With A New Modifieddeb Feasibility Constraint Handling Technique, Murat Erhan Çi̇men, Zeynep Gari̇p, Ali̇ Fuat Boz Jan 2021

Comparison Of Metaheuristic Optimization Algorithms With A New Modifieddeb Feasibility Constraint Handling Technique, Murat Erhan Çi̇men, Zeynep Gari̇p, Ali̇ Fuat Boz

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, the modification of the Deb feasibility method is considered to solve the constrained optimization problems. In the developed modified Deb feasibility constraint method, the third rule in its procedure was revised in order to increase the performance of the Deb feasibility constraint handling method. The innovation in the method is based on generating a new individual by using both possible solutions that violate the constraints in the method used for solving the problem. In detail, discussions were given about the application and usefulness of six constrained handling techniques. Furthermore, genetic algorithm, particle swarm optimization, Harris hawks optimization, …

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Opinion Dynamics Of Stubborn Agents Under The Presence Of A Troll Asdifferential Game, Aykut Yildiz, Ari̇f Bülent Özgüler Jan 2021

Opinion Dynamics Of Stubborn Agents Under The Presence Of A Troll Asdifferential Game, Aykut Yildiz, Ari̇f Bülent Özgüler

Turkish Journal of Electrical Engineering and Computer Sciences

The question of whether opinions of stubborn agents result in Nash equilibrium under the presence of troll is investigated in this study. The opinion dynamics is modelled as a differential game played by n agents during a finite time horizon. Two types of agents, ordinary agents and troll, are considered in this game. Troll is treated as a malicious stubborn content maker who disagrees with every other agent. On the other hand, ordinary agents maintain cooperative communication with other ordinary agents and they disagree with the troll. Under this scenario, explicit expressions of opinion trajectories are obtained by applying Pontryagin?s …

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Brain Tumor Detection From Mri Images With Using Proposed Deep Learningmodel: The Partial Correlation-Based Channel Selection, Atinç Yilmaz Jan 2021

Brain Tumor Detection From Mri Images With Using Proposed Deep Learningmodel: The Partial Correlation-Based Channel Selection, Atinç Yilmaz

Turkish Journal of Electrical Engineering and Computer Sciences

A brain tumor is an abnormal growth of a mass or cell in the brain. Early diagnosis of the tumor significantly increases the chances of successful treatment. Artificial intelligence-based systems can detect the tumor in early stages. In this way, it could be possible to detect a tumor and resolve this problem that may endanger human life early. In the study, the partial correlation-based channel selection formula was presented that allowed the selection of the most prominent feature that differs from the other studies in the literature. Additionally, the multi-channel convolution structure was proposed for the feature network phase of …

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Benchmarking Of Deep Learning Algorithms For Skin Cancer Detection Based On Ahybrid Framework Of Entropy And Vikor Techniques, Baidaa Al-Bander, Qahtan M. Yas, Hussain Mahdi, Rwayda Kh. S. Al-Hamd Jan 2021

Benchmarking Of Deep Learning Algorithms For Skin Cancer Detection Based On Ahybrid Framework Of Entropy And Vikor Techniques, Baidaa Al-Bander, Qahtan M. Yas, Hussain Mahdi, Rwayda Kh. S. Al-Hamd

Turkish Journal of Electrical Engineering and Computer Sciences

Skin cancer is one of the most common cancers worldwide caused by excessive development of skin cells. Considering the rapid growth of the use of deep learning algorithms for skin cancer detection, selecting the optimal algorithm has become crucial to determining the efficiency of computer-aided diagnosis (CAD) systems developed for the healthcare sector. However, a sufficient number of criteria and parameters must be considered when selecting an ideal deep learning algorithm. A generally accepted method for benchmarking deep learning models for skin cancer classification is unavailable in the current literature. This paper presents a multi-criteria decision-making framework for evaluating and …

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A Deep Transfer Learning Based Model For Automatic Detection Of Covid-19from Chest X-Rays, Prateek Chhikara, Prakhar Gupta, Prabhjot Singh, Tarunpreet Bhatia Jan 2021

A Deep Transfer Learning Based Model For Automatic Detection Of Covid-19from Chest X-Rays, Prateek Chhikara, Prakhar Gupta, Prabhjot Singh, Tarunpreet Bhatia

Turkish Journal of Electrical Engineering and Computer Sciences

Deep learning in medical imaging has revolutionized the way we interpret medical data, as high computational devices' capabilities are far more than their creators. With the pandemic causing havoc for the second straight year, the findings in our paper will allow researchers worldwide to use and create state-of-the-art models to detect affected persons before it reaches the R number. The paper proposes an automated diagnostic tool using the deep learning models on chest x-rays as an input to reach a point where we surpass this pandemic (COVID-19 disease). A deep transfer learning-based model for automatic detection of COVID-19 from chest …

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Attention-Based End-To-End Cnn Framework For Content-Based X-Ray Imageretrieval, Şaban Öztürk, Adi Alhudhaif, Kemal Polat Jan 2021

Attention-Based End-To-End Cnn Framework For Content-Based X-Ray Imageretrieval, Şaban Öztürk, Adi Alhudhaif, Kemal Polat

Turkish Journal of Electrical Engineering and Computer Sciences

The widespread use of medical imaging devices allows deep analysis of diseases. However, the task of examining medical images increases the burden of specialist doctors. Computer-assisted systems provide an effective management tool that enables these images to be analyzed automatically. Although these tools are used for various purposes, today, they are moving towards retrieval systems to access increasing data quickly. In hospitals, the need for content-based image retrieval systems is seriously evident in order to store all images effectively and access them quickly when necessary. In this study, an attention-based end-to-end convolutional neural network (CNN)framework that can provide effective access …

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Classification Of P300 Based Brain Computer Interface Systems Using Longshort-Term Memory (Lstm) Neural Networks With Feature Fusion, Ali̇ Osman Selvi̇, Abdullah Feri̇koğlu, Derya Güzel Jan 2021

Classification Of P300 Based Brain Computer Interface Systems Using Longshort-Term Memory (Lstm) Neural Networks With Feature Fusion, Ali̇ Osman Selvi̇, Abdullah Feri̇koğlu, Derya Güzel

Turkish Journal of Electrical Engineering and Computer Sciences

Enabling to obtain brain activation signs, electroencephalography is currently used in many applications as a medical diagnostic method. Brain-computer interface (BCI) applications are developed to facilitate the lives of individuals who have not lost their brain functions yet have lost their motor and communication abilities. In this study, a BCI system is proposed to make classification using Bi-directional long short term memory (Bi-LSTM) neural networks. In the designed system, spectral entropy method including instantaneous frequency change of signal is used as feature fusion. In the study, electroencephalography (EEG) data of 10 participants are collected with Emotiv EPOC+ device using 2x2 …

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Mri Based Genomic Analysis Of Glioma Using Three Pathway Deep Convolutionalneural Network For Idh Classification, Sonal Gore, Jayant Jagtap Jan 2021

Mri Based Genomic Analysis Of Glioma Using Three Pathway Deep Convolutionalneural Network For Idh Classification, Sonal Gore, Jayant Jagtap

Turkish Journal of Electrical Engineering and Computer Sciences

As per 2016 updates by World Health Organization (WHO) on cancer disease, gliomas are categorized and further treated based on genomic mutations. The imaging modalities support a complimentary but immediate noninvasive diagnosis of cancer based on genetic mutations. Our aim is to train a deep convolutional neural network for isocitrate dehydrogenase (IDH) genotyping of glioma by auto-extracting the most discriminative features from magnetic resonance imaging (MRI) volumes. MR imaging data of total 217 patients were obtained from The Cancer Imaging Archives (TCIA) of high and low-grade gliomas. A 3-pathway convolutional neural network was trained for IDH classification. The multipath neural …

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Employing Deep Learning Architectures For Image-Based Automatic Cataractdiagnosis, Emrullah Acar, Ömer Türk, Ömer Faruk Ertuğrul, Erdoğan Aldemi̇r Jan 2021

Employing Deep Learning Architectures For Image-Based Automatic Cataractdiagnosis, Emrullah Acar, Ömer Türk, Ömer Faruk Ertuğrul, Erdoğan Aldemi̇r

Turkish Journal of Electrical Engineering and Computer Sciences

Various eye diseases affect the quality of human life severely and ultimately may result in complete vision loss. Ocular diseases manifest themselves through mostly visual indicators in the early or mature stages of the disease by showing abnormalities in optics disc, fovea, or other descriptive anatomical structures of the eye. Cataract is among the most harmful diseases that affects millions of people and the leading cause of public vision impairment. It shows major visual symptoms that can be employed for early detection before the hypermature stage. Automatic diagnosis systems intend to assist ophthalmological experts by mitigating the burden of manual …

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Evolution Of Histopathological Breast Cancer Images Classification Using Stochasticdilated Residual Ghost Model, Ramgopal Kashyap Jan 2021

Evolution Of Histopathological Breast Cancer Images Classification Using Stochasticdilated Residual Ghost Model, Ramgopal Kashyap

Turkish Journal of Electrical Engineering and Computer Sciences

Breast cancer detection is a complex problem to solve, and it is a topic that is still being studied. Deep learning-based models aid medical science by helping to classify benign and malignant cancers and saving lives. Breast cancer histopathological image classification (BreakHis) and breast cancer histopathological annotation and diagnosis (BreCaHAD) datasets are used in the proposed model. The study led to the resolution of four essential issues: 1) Addresses the color divergence issue caused by strain normalization during image generation 2) Data augmentation uses several factors like as flip, rotation, shift, resize, and gamma value in order to overcome overfitting …

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Medical Image Fusion With Convolutional Neural Network In Multiscaletransform Domain, Asan Abas, Hasan Erdi̇nç Koçer, Nurdan Baykan Jan 2021

Medical Image Fusion With Convolutional Neural Network In Multiscaletransform Domain, Asan Abas, Hasan Erdi̇nç Koçer, Nurdan Baykan

Turkish Journal of Electrical Engineering and Computer Sciences

Multimodal medical image fusion approaches have been commonly used to diagnose diseases and involve merging multiple images of different modes to achieve superior image quality and to reduce uncertainty and redundancy in order to increase the clinical applicability. In this paper, we proposed a new medical image fusion algorithm based on a convolutional neural network (CNN) to obtain a weight map for multiscale transform (curvelet/ non-subsampled shearlet transform) domains that enhance the textual and edge property. The aim of the method is achieving the best visualization and highest details in a single fused image without losing spectral and anatomical details. …

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New Normal: Cooperative Paradigm For Covid-19 Timely Detection Andcontainment Using Internet Of Things And Deep Learning, Farooque Hassan Kumbhar, Ali Hassan Syed, Soo Young Shin Jan 2021

New Normal: Cooperative Paradigm For Covid-19 Timely Detection Andcontainment Using Internet Of Things And Deep Learning, Farooque Hassan Kumbhar, Ali Hassan Syed, Soo Young Shin

Turkish Journal of Electrical Engineering and Computer Sciences

The spread of the novel coronavirus (COVID-19) has caused trillions of dollars of damages to the governments and health authorities by affecting the global economies. It is essential to identify, track and trace COVID-19 spread at its earliest detection. Timely action can not only reduce further spread but also help in providing an efficient medical response. Existing schemes rely on volunteer participation, and/or mobile traceability, which leads to delays in containing the spread. There is a need for an autonomous, connected, and centralized paradigm that can identify, trace and inform connected personals. We propose a novel connected Internet of Things …

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A Transfer Learning-Based Deep Learning Approach For Automated Covid-19diagnosis With Audio Data, Devri̇m Akgün, Abdullah Talha Kabakuş, Zehra Karapinar Şentürk, Arafat Şentürk, Enver Küçükkülahli Jan 2021

A Transfer Learning-Based Deep Learning Approach For Automated Covid-19diagnosis With Audio Data, Devri̇m Akgün, Abdullah Talha Kabakuş, Zehra Karapinar Şentürk, Arafat Şentürk, Enver Küçükkülahli

Turkish Journal of Electrical Engineering and Computer Sciences

The COVID-19 pandemic has caused millions of deaths and changed daily life globally. Countries have declared a half or full lockdown to prevent the spread of COVID-19. According to medical doctors, as many people as possible should be tested to identify their status, and corresponding actions then should be taken for COVID-19 positive cases. Despite the clear necessity of these medical tests, many countries are still struggling to acquire them. This fact clearly indicates the necessity of a large-scale, cheap, fast, and accurate alternative prescreening tool that can be used for the diagnosis of COVID-19 while waiting for the medical …

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Deep Hyperparameter Transfer Learning For Diabetic Retinopathy Classification, Mahesh Patil, Satyadhyan Chickerur, Yeshwanth Kumar V S, Vijayalakshmi Bakale, Shantala Giraddi, Vivekanand Roodagi, Yashaswini Kulkarni Jan 2021

Deep Hyperparameter Transfer Learning For Diabetic Retinopathy Classification, Mahesh Patil, Satyadhyan Chickerur, Yeshwanth Kumar V S, Vijayalakshmi Bakale, Shantala Giraddi, Vivekanand Roodagi, Yashaswini Kulkarni

Turkish Journal of Electrical Engineering and Computer Sciences

The detection of diabetic retinopathy (DR) in millions of diabetic patients across the globe is a challenging problem. Diagnosis of retinopathy is a lengthy and tedious process, requiring a medical professional to assess the individual fundus images of a patient's retina. This process can be automated by applying deep learning (DL) technology given a huge dataset. The problems associated with DL are the unavailability of a large dataset and their higher training time. The DL model's best performance is achieved using set of optimal hyperparameters (OHPs) obtained by performing costly iterations of hyperparameter optimization (HPO). These problems can be addressed …

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Detection Of Amyotrophic Lateral Sclerosis Disease By Variational Modedecomposition And Convolution Neural Network Methods From Event-Relatedpotential Signals, Fatma Lati̇foğlu, Firat Orhan Bulucu, Rami̇s İleri̇ Jan 2021

Detection Of Amyotrophic Lateral Sclerosis Disease By Variational Modedecomposition And Convolution Neural Network Methods From Event-Relatedpotential Signals, Fatma Lati̇foğlu, Firat Orhan Bulucu, Rami̇s İleri̇

Turkish Journal of Electrical Engineering and Computer Sciences

Amyotrophic lateral sclerosis (ALS), also known as motor neuron disease, is a neurological disease that occurs as a result of damage to the nerves in the brain and restriction of muscle movements. Electroencephalography (EEG) is the most common method used in brain imaging to study neurological disorders. Diagnosis of neurological disorders such as ALS, Parkinson's, attention deficit hyperactivity disorder is important in biomedical studies. In recent years, deep learning (DL) models have been started to be applied in the literature for the diagnosis of these diseases. In this study, event-related potentials (ERPs) were obtained from EEG signals obtained as a …

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Improved Cell Segmentation Using Deep Learning In Label-Free Optical Microscopyimages, Aydin Ayanzadeh, Özden Yalçin Özuysal, Devri̇m Pesen Okvur, Sevgi̇ Önal, Behçet Uğur Töreyi̇n, Devri̇m Ünay Jan 2021

Improved Cell Segmentation Using Deep Learning In Label-Free Optical Microscopyimages, Aydin Ayanzadeh, Özden Yalçin Özuysal, Devri̇m Pesen Okvur, Sevgi̇ Önal, Behçet Uğur Töreyi̇n, Devri̇m Ünay

Turkish Journal of Electrical Engineering and Computer Sciences

The recently popular deep neural networks (DNNs) have a significant effect on the improvement of segmentation accuracy from various perspectives, including robustness and completeness in comparison to conventional methods. We determined that the naive U-Net has some lacks in specific perspectives and there is high potential for further enhancements on the model. Therefore, we employed some modifications in different folds of the U-Net to overcome this problem. Based on the probable opportunity for improvement, we develop a novel architecture by using an alternative feature extractor in the encoder of U-Net and replacing the plain blocks with residual blocks in the …

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Learning Adl Daily Routines With Spatiotemporal Neural Networks, Shan Gao, Ah-Hwee Tan, Rossi Setchi Jan 2021

Learning Adl Daily Routines With Spatiotemporal Neural Networks, Shan Gao, Ah-Hwee Tan, Rossi Setchi

Research Collection School Of Computing and Information Systems

The activities of daily living (ADLs) refer to the activities performed by individuals on a daily basis and are the indicators of a person’s habits, lifestyle, and wellbeing. Learning an individual’s ADL daily routines has significant value in the healthcare domain. Specifically, ADL recognition and inter-ADL pattern learning problems have been studied extensively in the past couple of decades. However, discovering the patterns performed in a day and clustering them into ADL daily routines has been a relatively unexplored research area. In this paper, a self-organizing neural network model, called the Spatiotemporal ADL Adaptive Resonance Theory (STADLART), is proposed for …

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