Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Keyword
-
- Deep learning (26)
- Simulation (14)
- Machine learning (12)
- COVID-19 (10)
- Genetic algorithm (9)
-
- Convolutional neural network (7)
- Reinforcement learning (7)
- Support vector machine (7)
- Transfer learning (7)
- Classification (6)
- Computer generated forces (6)
- Sliding mode control (6)
- Deep neural network (5)
- Modeling and simulation (5)
- System dynamics (5)
- Artificial intelligence (4)
- Augmented reality (4)
- Bayesian optimization (4)
- Body of knowledge (4)
- Convolutional neural networks (4)
- Electric vehicle (4)
- Energy consumption (4)
- Fault diagnosis (4)
- Feature extraction (4)
- Modeling (4)
- Optimization (4)
- Particle swarm optimization (4)
- Path planning (4)
- Simulated annealing (4)
- Simulation platform (4)
Articles 511 - 526 of 526
Full-Text Articles in Computer Engineering
Csop+Rp: A Novel Constraints Satisfaction Model For Requirements Prioritizationin Large-Scale Software Systems, Soheil Afraz, Hassan Rashidi, Naser Mikaeilvand
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), …
Robust And Efficient Ebg-Backed Wearable Antenna For Ism Applications, Ayesha Saeed, Asma Ejaz, Humayun Shahid, Yasar Amin, Hannu Tenhunen
Robust And Efficient Ebg-Backed Wearable Antenna For Ism Applications, Ayesha Saeed, Asma Ejaz, Humayun Shahid, Yasar Amin, Hannu Tenhunen
Turkish Journal of Electrical Engineering and Computer Sciences
A structurally compact, semiflexible wearable antenna composed of a distinctively miniaturized electromagnetic band gap (EBG) structure is presented in this work. Designed for body-centric applications in the 5.8 GHz band, the design draws heavily from a novel planar geometry realized on Rogers RT/duroid 5880 laminate with a compact physical footprint spanning lateral dimensions of $0.6$$\lambda$$_0$$\times$$0.06$$\lambda$$_0$. Incorporating a 2$\times$2 EBG structure at the rear of the proposed design ensures sufficient isolation between the body and the antenna, doing away with the performance degradation associated with high permittivity of the tissue layer. The peculiar antenna geometry allows for reduced backward radiation and …
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
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 …
Hc-Fft: Highly Configurable And Efficient Fft Implementation On Fpga, Paki̇ze Ergül, H. Fati̇h Uğurdağ, Doğancan Davutoğlu
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 …
An Observer Based Temperature Estimation In Cooking Heterogeneous Mixtures:A Turkish Coffee Machine Application, Arda Dönerkayali, Türker Türker
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 …
Comparison Of Metaheuristic Optimization Algorithms With A New Modifieddeb Feasibility Constraint Handling Technique, Murat Erhan Çi̇men, Zeynep Gari̇p, Ali̇ Fuat Boz
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, …
A Hybrid Convolutional Neural Network Approach For Feature Selection Anddisease Classification, Prajna Paramita Debata, Puspanjali Mohapatra
A Hybrid Convolutional Neural Network Approach For Feature Selection Anddisease Classification, Prajna Paramita Debata, Puspanjali Mohapatra
Turkish Journal of Electrical Engineering and Computer Sciences
: Many researchers have analyzed the high dimensional gene expression data for disease classification using several conventional and machine learning-based approaches, but still there exists some issues which make this task nontrivial. Due to the growing complexities of the unstructured data, the researchers focus on the deep learning approach, which is the latest form of machine learning algorithm. In the presented work, a kernel-based Fisher score (KFS) approach is implemented to extract the notable genes, and an improvised chaotic Jaya (CJaya) algorithm optimized convolutional neural network (CJaya-CNN) model is applied to classify high dimensional gene expression or microarray data. This …
Brain Tumor Detection From Mri Images With Using Proposed Deep Learningmodel: The Partial Correlation-Based Channel Selection, Atinç Yilmaz
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 …
A Deep Transfer Learning Based Model For Automatic Detection Of Covid-19from Chest X-Rays, Prateek Chhikara, Prakhar Gupta, Prabhjot Singh, Tarunpreet Bhatia
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 …
Attention-Based End-To-End Cnn Framework For Content-Based X-Ray Imageretrieval, Şaban Öztürk, Adi Alhudhaif, Kemal Polat
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 …
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
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 …
Leukocyte Classification Based On Feature Selection Using Extra Trees Classifier: Atransfer Learning Approach, Diana Baby, Sujitha Juliet Devaraj, Jude Hemanth, Anishin Raj M M
Leukocyte Classification Based On Feature Selection Using Extra Trees Classifier: Atransfer Learning Approach, Diana Baby, Sujitha Juliet Devaraj, Jude Hemanth, Anishin Raj M M
Turkish Journal of Electrical Engineering and Computer Sciences
The criticality of investigating the white blood cell (WBC) count cannot be underestimated, as white blood cells are an important component of the body's defence system. From helping to diagnose hidden infections to insinuating the presence of comorbidities like immunodeficiency, an accurate white blood cell count can contribute significantly to shape a physician?s assessment. The manual process performed by the pathologists for the classification of WBCs is a time consuming and tedious task, which is further disadvantaged by a lack of accuracy. This study concentrates on the automatic detection and classification of WBC without data augmentation into four subtypes such …
Employing Deep Learning Architectures For Image-Based Automatic Cataractdiagnosis, Emrullah Acar, Ömer Türk, Ömer Faruk Ertuğrul, Erdoğan Aldemi̇r
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 …
Evolution Of Histopathological Breast Cancer Images Classification Using Stochasticdilated Residual Ghost Model, Ramgopal Kashyap
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 …
Deep Hyperparameter Transfer Learning For Diabetic Retinopathy Classification, Mahesh Patil, Satyadhyan Chickerur, Yeshwanth Kumar V S, Vijayalakshmi Bakale, Shantala Giraddi, Vivekanand Roodagi, Yashaswini Kulkarni
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 …
Attention Augmented Residual Network For Tomato Disease Detection Andclassification, Getinet Yilma Abawatew, Seid Belay, Kumie Gedamu, Maregu Assefa, Melese Ayalew, Ariyo Oluwasanmi, Zhiguang Qin
Attention Augmented Residual Network For Tomato Disease Detection Andclassification, Getinet Yilma Abawatew, Seid Belay, Kumie Gedamu, Maregu Assefa, Melese Ayalew, Ariyo Oluwasanmi, Zhiguang Qin
Turkish Journal of Electrical Engineering and Computer Sciences
Deep learning techniques help agronomists efficiently identify, analyze, and monitor tomato health. CNN (convolutional neural network) locality constraint and existing small train sample adversely influenced disease recognition performance. To alleviate these challenges, we proposed a discriminative feature learning attention augmented residual (AAR) network. The AAR network contains a stacked pre-activated residual block that learns deep coarse level features with locality context, whereas the attention block captures salient feature sets while maintaining the global relationship in data points, attention features augment the learning of the residual block. We used conditional variational generative adversarial network (CVGAN) image reconstruction network and augmentation techniques …