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Articles 1831 - 1860 of 10319
Full-Text Articles in Physical Sciences and Mathematics
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 …
On The Closed-Form Evaluation Of The Po Integral Using The Radon Transforminterpretation For Linear Triangles, Aslihan Aktepe, Hüseyi̇n Arda Ülkü
On The Closed-Form Evaluation Of The Po Integral Using The Radon Transforminterpretation For Linear Triangles, Aslihan Aktepe, Hüseyi̇n Arda Ülkü
Turkish Journal of Electrical Engineering and Computer Sciences
This letter presents the complete mathematical formulation for the closed-form evaluation of the time domain physical optics (PO) integral on linear triangular patches using Radon transform (RT) interpretation. The incident field is assumed to be an impulsively excited plane wave and scattered fields are observed at far-zone. The PO integral is evaluated in closed-form as the intersection of the triangle and the plane formed by the incident and observation directions. In addition, a formula is suggested for the special case, which occurs if there is no intersection of the plane and all scatterer. Accuracy of the closed-form expressions is demonstrated …
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 …
A New Model For Minimizing The Electric Vehicle Battery Capacity In Electrictravelling Salesman Problem With Time Windows, Kazim Erdoğdu, Korhan Karabulut
A New Model For Minimizing The Electric Vehicle Battery Capacity In Electrictravelling Salesman Problem With Time Windows, Kazim Erdoğdu, Korhan Karabulut
Turkish Journal of Electrical Engineering and Computer Sciences
The growing pollution in the environment and the negative shift in the global climate compel authorities to take action to protect the environment and human health. Transportation is one of the major contributors to this environmental decay. The harmful gases released to the air by the vehicles using petroleum fuel increase each day. One of the solutions is to make a gradual transition to electric vehicles. A major part of manufacturing an electric vehicle is to produce an efficient electric motor and battery for it. Reducing the manufacturing and operating costs of these components will result in reducing the overall …
Multiagent Q-Learning Based Uav Trajectory Planning For Effective Situationalawareness, Erdal Akin, Kubi̇lay Demi̇r, Hali̇l Yetgi̇n
Multiagent Q-Learning Based Uav Trajectory Planning For Effective Situationalawareness, Erdal Akin, Kubi̇lay Demi̇r, Hali̇l Yetgi̇n
Turkish Journal of Electrical Engineering and Computer Sciences
In the event of a natural disaster, arrival time of the search and rescue (SAR) teams to the affected areas is of vital importance to save the life of the victims. In particular, when an earthquake occurs in a geographically large area, reconnaissance of the debris within a short-time is critical for conducting successful SAR missions. An effective and quick situational awareness in postdisaster scenarios can be provided via the help of unmanned aerial vehicles (UAVs). However, off-the-shelf UAVs suffer from the limited communication range as well as the limited airborne duration due to battery constraints. If telecommunication infrastructure is …
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 …
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 …
Design Development And Performance Analysis Of Distributed Least Square Twinsupport Vector Machine For Binary Classification, Bakshi Rohit Prasad, Sonali Agarwal
Design Development And Performance Analysis Of Distributed Least Square Twinsupport Vector Machine For Binary Classification, Bakshi Rohit Prasad, Sonali Agarwal
Turkish Journal of Electrical Engineering and Computer Sciences
Machine learning (ML) on Big Data has gone beyond the capacity of traditional machines and technologies. ML for large scale datasets is the current focus of researchers. Most of the ML algorithms primarily suffer from memory constraints, complex computation, and scalability issues.The least square twin support vector machine (LSTSVM) technique is an extended version of support vector machine (SVM). It is much faster as compared to SVM and is widely used for classification tasks. However, when applied to large scale datasets having millions or billions of samples and/or large number of classes, it causes computational and storage bottlenecks. This paper …
An Mih-Enhanced Fully Distributed Mobility Management (Mf-Dmm) Solutionfor Real And Non-Real Time Cvbr Traffic Classes In Mobile Internet, Sankaranarayanan Parasuraman, Gayathri Rajaraman, Tamijetchelvy Ramachandiran
An Mih-Enhanced Fully Distributed Mobility Management (Mf-Dmm) Solutionfor Real And Non-Real Time Cvbr Traffic Classes In Mobile Internet, Sankaranarayanan Parasuraman, Gayathri Rajaraman, Tamijetchelvy Ramachandiran
Turkish Journal of Electrical Engineering and Computer Sciences
The integration of wireless access networks has progressed rapidly in recent years. Within the mobile communication environment, the operator provides multiple interface options for the mobile node (MN) to switch its connection to any access network during mobility to achieve the quality of service (QoS) for various traffic classes. The conventional centralised mobility management (CMM) scheme lacks reliability, dynamic anchoring and a single point of failure. This stimulates the distributed mobility management (DMM) scheme to handle mobility at the access network rather in a centralised manner. Therefore, in this paper, an IEEE 802.21 media independent handover (MIH) enhanced fully DMM …
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), …
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 …
Clustered Mobile Data Collection In Wsns: An Energy-Delay Trade-Of, İzzet Fati̇h Şentürk
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 …
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 …
Presentation Attack Detection For Face Recognition Using Remotephotoplethysmography And Cascaded Fusion, Mehmet Fati̇h Gündoğar, Çi̇ğdem Eroğlu Erdem
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 …
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 …
Malignant Skin Melanoma Detection Using Image Augmentation By Oversamplingin Nonlinear Lower-Dimensional Embedding Manifold, Olusola Oluwakemi Abayomi-Alli, Robertas Damasevicius, Sanjay Misra, Rytis Maskeliunas, Adebayo Abayomi-Alli
Malignant Skin Melanoma Detection Using Image Augmentation By Oversamplingin Nonlinear Lower-Dimensional Embedding Manifold, Olusola Oluwakemi Abayomi-Alli, Robertas Damasevicius, Sanjay Misra, Rytis Maskeliunas, Adebayo Abayomi-Alli
Turkish Journal of Electrical Engineering and Computer Sciences
The continuous rise in skin cancer cases, especially in malignant melanoma, has resulted in a high mortality rate of the affected patients due to late detection. Some challenges affecting the success of skin cancer detection include small datasets or data scarcity problem, noisy data, imbalanced data, inconsistency in image sizes and resolutions, unavailability of data, reliability of labeled data (ground truth), and imbalance of skin cancer datasets. This study presents a novel data augmentation technique based on covariant Synthetic Minority Oversampling Technique (SMOTE) to address the data scarcity and class imbalance problem. We propose an improved data augmentation model for …
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
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 …
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 …
Deep Learning-Based Covid-19 Detection System Using Pulmonary Ct Scans, Rajit Nair, Adi Alhudhaif, Deepika Koundal, Rumi Iqbal Doewes, Preeti Sharma
Deep Learning-Based Covid-19 Detection System Using Pulmonary Ct Scans, Rajit Nair, Adi Alhudhaif, Deepika Koundal, Rumi Iqbal Doewes, Preeti Sharma
Turkish Journal of Electrical Engineering and Computer Sciences
One of the most significant pandemics has been raised in the form of Coronavirus disease 2019 (COVID19). Many researchers have faced various types of challenges for finding the accurate model, which can automatically detect the COVID-19 using computed pulmonary tomography (CT) scans of the chest. This paper has also focused on the same area, and a fully automatic model has been developed, which can predict the COVID-19 using the chest CT scans. The performance of the proposed method has been evaluated by classifying the CT scans of community-acquired pneumonia (CAP) and other non-pneumonia. The proposed deep learning model is based …
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 …
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 …
Mri Based Genomic Analysis Of Glioma Using Three Pathway Deep Convolutionalneural Network For Idh Classification, Sonal Gore, Jayant Jagtap
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 …
Medical Image Fusion With Convolutional Neural Network In Multiscaletransform Domain, Asan Abas, Hasan Erdi̇nç Koçer, Nurdan Baykan
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. …
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
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 …
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
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 …
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 …
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
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 …
Eocene-Oligocene Succession At Kıyıköy (Midye) On The Black Sea Coast In Thrace, Aral I. Okay, Michael D. Simmons, Ercan Özcan, Stephen Starkie, Michael D. Bidgood, Andrew R.C. Kylander-Clark
Eocene-Oligocene Succession At Kıyıköy (Midye) On The Black Sea Coast In Thrace, Aral I. Okay, Michael D. Simmons, Ercan Özcan, Stephen Starkie, Michael D. Bidgood, Andrew R.C. Kylander-Clark
Turkish Journal of Earth Sciences
A belt of Upper Eocene-Lower Oligocene marine sedimentary rocks extends from Kıyıköy on the Black Sea coast to Pınarhisar in the Thrace Basin, suggesting a marine connection between the Black Sea and the Thrace Basin during this period. The Cenozoic succession of this marine corridor was studied in the vicinity of Kıyıköy along two measured stratigraphic sections. The sequence lies unconformably over metamorphic basement rocks and consists of ~75 m of bioclastic limestone and sandstone of the Soğucak Formation, overlain by ~40 m of limestone, marl, mudstone, sandstone, and acidic tuff, which are assigned to the newly defined Servez Formation. …
Geochemistry And Mineralogy Of The Shallow Subsurface Red Sea Coastal Sediments, Rabigh, Saudi Arabia: Provenance And Paleoenvironmental Implications, Rabea Haredy, Ibrahim Mohamed Ghandour
Geochemistry And Mineralogy Of The Shallow Subsurface Red Sea Coastal Sediments, Rabigh, Saudi Arabia: Provenance And Paleoenvironmental Implications, Rabea Haredy, Ibrahim Mohamed Ghandour
Turkish Journal of Earth Sciences
Mineralogical and geochemical characteristics of the shallow subsurface sediments retrieved from a short sediment core (2.05 m long) collected from the tidal flat south of Al-Kharrar Lagoon, Rabigh area, Saudi Arabia, are presented to determine the impact of temporal change of depositional environment and distinguish the principal control(s) on their chemical composition. The sediments are dominantly siliciclastic, consisting of two vertically stacked sedimentary facies: lagoonal (LG) gray silt-rich mud to argillaceous very fine sand at the base and intertidal flat (TF) brown mud and argillaceous very fine-grained sands at the top. The sediments of the two facies are similar in …
Petrology Of The Late Triassic Mafic Volcanic Rocks From The Antalya Complex, Southernturkey: Evidence For Mantle Source Characteristics During The Neotethyan Rifting, Utku Bağci, Tamer Rizaoğlu, Güzi̇de Önal, Osman Parlak
Petrology Of The Late Triassic Mafic Volcanic Rocks From The Antalya Complex, Southernturkey: Evidence For Mantle Source Characteristics During The Neotethyan Rifting, Utku Bağci, Tamer Rizaoğlu, Güzi̇de Önal, Osman Parlak
Turkish Journal of Earth Sciences
The Antalya Complex in southern Turkey comprises a number of autochthonous and allochthonous units that originated from the Southern Neotethys. Late Triassic volcanic rocks are widespread in the Antalya Complex and are important for the onset of the rifting stage of the southern Neotethys. The studied Late Triassic volcanic rocks within the Antalya Complex are exposed in the southern part of Saklıkent (Antalya) region. They are represented by pillow, massive, and columnar-jointed lava flows with volcaniclastic breccias and pelagic limestone intercalations. Spilitic basalts exhibit intersertal, microlithic porphyritic, and ophitic textures and are represented by plagioclase, pyroxene, and olivine. Secondary phases …