Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Alataş, Mevlüt(11)
- Serdar, Osman(10)
- Yildirim, Ozal(10)
- Baloglu, Ulas Baran(9)
- Sayyed, M.I.(9)
- Gul, Muhammet(8)
- Mete, Suleyman(8)
- Talo, Muhammed(8)
- Batan, Nevzat(7)
- Paksoy, Mehmet Yavuz(7)
- Camas, Mustafa(6)
- Celik, Erkan(6)
- Ezer, Tülay(6)
- Kahraman, Gökhan(6)
- Serin, Faruk(6)
- Öğütcü, Murat(6)
- Aksu, Önder(5)
- Askin, A.(5)
- Benzer, Fulya(5)
- Camas, Anil Sazak(5)
- Ince, Muharrem(5)
- Kocabaş, Mehmet(5)
- Kwain, Samuel(5)
- Kyeremeh, Kwaku(5)
- Yildirim, Numan(5)
- Yucesan, Melih(5)
- Aslan, Serpil(4)
- Aydin, Handan(4)
- Aşkın, A.(4)
- Baskonus, H.M.(4)
- Deng, Hai(4)
- Erata, Hüseyin(4)
- Erişir, Mine(4)
- Jaspars, Marcel(4)
- Onat, Onur(4)
- Taşgın, Yahya(4)
- Tetevi, Gilbert Mawuli(4)
- Acharya, U Rajendra(3)
- Acharya, U. Rajendra(3)
- Ali, Atif Mossad(3)
Yayınlar
- MakaleAçık Erişim2019Scopus SJRQ2A deep learning model for automated sleep stages classification using PSG signals
Yildirim, Ozal; Baloglu, Ulas Baran; Acharya, U Rajendra
Sleep disorder is a symptom of many neurological diseases that may significantly affect the quality of daily life. Traditional methods are time-consuming and involve the manual scoring of polysomnogram (PSG) signals obtained in a laboratory environment. However, the automated monitoring of sleep stages can help detect neurological disorders accurately as well. In this study, a flexible deep learning model is proposed using raw PSG signals. A one-dimensional convolutional neural network (1D-CNN) is developed using electroencephalogram (EEG) and electrooculogram (EOG) signals for the classification of sleep stages. The performance of the system is evaluated using two public databases (sleep-edf and sleep-edfx). The developed model yielded the highest accuracies of 98.06%, 94.64%, 92.36%, 91.22%, and 91.00% for two to six sleep classes, respectively, using the sleep-edf database. Further, the proposed model obtained the highest accuracies of 97.62%, 94.34%, 92.33%, 90.98%, and 89.54%, respectively for the same two to six sleep classes using the sleep-edfx dataset. The developed deep learning model is ready for clinical usage, and can be tested with big PSG data.
AtıfScopus255OpenAlex265
İncele - MakaleAçık Erişim2019Scopus SJRQ2Automated Depression Detection Using Deep Representation and Sequence Learning with EEG Signals
Ay, Betul; Yildirim, Ozal; Talo, Muhammed; Baloglu, Ulas Baran; Aydin, Galip; Puthankattil, Subha D.; Acharya, U. Rajendra
Depression affects large number of people across the world today and it is considered as the global problem. It is a mood disorder which can be detected using electroencephalogram (EEG) signals. The manual detection of depression by analyzing the EEG signals requires lot of experience, tedious and time consuming. Hence, a fully automated depression diagnosis system developed using EEG signals will help the clinicians. Therefore, we propose a deep hybrid model developed using convolutional neural network (CNN) and long-short term memory (LSTM) architectures to detect depression using EEG signals. In the deep model, temporal properties of the signals are learned with CNN layers and the sequence learning process is provided through the LSTM layers. In this work, we have used EEG signals obtained from left and right hemispheres of the brain. Our work has provided 99.12% and 97.66% classification accuracies for the right and left hemisphere EEG signals respectively. Hence, we can conclude that the developed CNN-LSTM model is accurate and fast in detecting the depression using EEG signals. It can be employed in psychiatry wards of the hospitals to detect the depression using EEG signals accurately and thus aid the psychiatrists.
AtıfScopus279OpenAlex291İncele - MakaleAçık Erişim2019Scopus SJRQ1Al-doped ZnO as a multifunctional nanomaterial: Structural, morphological, optical and low-temperature gas sensing properties
Aydın, Handan; Yakuphanoglu, Fahrettin; Aydın, Cihat
In this study, zinc oxide thin films doped with Al at different atomic ratios (0.1%, 0.5%, 1%, 2%, 5%) were prepared using sol-gel spin coating method. Crystal structure, surface morphology, optical properties of the films and their response to ammonia gas were examined. XRD results showed that the ZnO thin films had a hexagonal polycrystalline structure. It was concluded from the calculation of the crystal size and lattice constant of the films that the produced samples were a nanomaterial. Scanning electron microscope (SEM) and atomic force microscope (AFM) images of the samples revealed that the morphology was composed of nanostructured particles and a cluster structure made of the combination of these particles. The optical band gap (Eg) of the prepared films were found within the range of 3.251 eV–3.342 eV by using the optical absorption method. Characteristics of susceptibility against ammonia gas were evaluated depending on the Al dopant. The obtained results showed that the gas response along with the structural, morphological, and optical properties of ZnO thin films changed with Al dopant. The produced thin film samples can be used as a sensor for the detection of ammonia gas in various industrial branches.
AtıfScopus66WoS59OpenAlex76İncele - MakaleYalnız künye2019Scopus SJRQ4ECG BEAT DETECTION and CLASSIFICATION SYSTEM USING WAVELET TRANSFORM and ONLINE SEQUENTIAL ELM
Yildirim, Ozal
Electrocardiogram (ECG) signals consist of data containing measurements of electrical activity in the heartbeats. These signals include relevant information used to detect abnormalities such as arrhythmia. In this study, a recognition system is proposed for detection and classification of heartbeats in ECG signals. Heartbeats in the ECG data were detected by using the wavelet transform (WT) method and these beats are segmented with determined periods. For obtaining distinctive features from the beats, multi-resolution WT is applied to these segmented signals, and wavelet coefficients are obtained from different frequency levels. Feature vectors are generated on these coefficients by using various statistical methods. The proposed recognition system is trained on feature vectors by using the Online Sequential Extreme Learning Machine (OSELM) classifier during the learning phase to automatically recognize the signals. Five different beat types were obtained from the MIT-BIH arrhythmia dataset. The multi-class dataset that includes five classes and the binary-class dataset that includes two classes were created among these beat types. Performance tests of the proposed wavelet-based-OSELM (W-OSELM) method were realized with these two datasets. The proposed recognition system provided 97.29% correct beat detection rate from raw ECG signals. The classification accuracy is 99.44% for the binary-class dataset and 98.51% for the multi-class dataset. Furthermore, the proposed classifier has shown very fast recognition performance on ECG signals.
AtıfScopus25WoS17OpenAlex26İncele - Kitap BölümüYalnız künye2019Masculine dreams: Henry V and the Jacobean politics of court performance
Öğütcü, Murat
AtıfScopus7OpenAlex3İncele - MakaleAçık Erişim2019Scopus SJRQ3On the exact and numerical solutions to the coupled Boussinesq equation arising in ocean engineering
Sulaiman, T.A.; Bulut, H.; Yokus, A.; Baskonus, H.M.
The studies of the dynamic behaviors of nonlinear models arising in ocean engineering play a significant role in our daily activities. In this study, we investigate the coupled Boussinesq equation which arises in the shallow water waves for two-layered fluid flow. The modified exp (- φ(ζ)) -expansion function method is utilized in reaching the solutions to this equation such as the topological kink-type soliton and singular soliton solutions. The interesting 2D and 3D graphics of the obtained analytical solutions in this study are presented. Via one of the reported analytical solutions, the finite forward difference method is used in obtaining the approximate numerical and exact solutions to this equation. The Fourier–Von Neumann analysis is used in checking the stability of the used numerical method with the studied model. The L 2 and L ∞ error norms are computed. We finally present a comprehensive conclusion to this study.
AtıfScopus83OpenAlex108İncele - MakaleAçık Erişim2019Scopus SJRQ2l-Arginine Improves Sperm Motility in Threatened Coruh Trout Salmo coruhensis (Teleostei: Salmonidae)
Kocabaş, Mehmet; Kutluyer, Filiz; Erişir, Mine; Benzer, Fulya
l-arginine is a versatile amino acid and precursor of nitric oxide that provides protection for preventing free radical damage. Herein, experiments were designed to clarify the effect of l-arginine supplementation on the sperm motility of a threatened trout, Coruh trout Salmo coruhensis. Activation solution was supplemented with levels of 0 mM (Control), 2 mM, 4 mM, and 8 mM l-arginine, and motility and survival of sperm cells were assessed. The selected concentrations were determined according to results of our preliminary experiments. The sperm motility rates at l-arginine concentrations of 0, 2, 4, and 8 mM were 73.33 ± 5.77%, 76.67 ± 5.75%, 80.01 ± 0.01%, and 90.00 ± 0.05%, respectively; notility durations were 21.67 ± 2.08, 32.33 ± 3.79, 32.67 ± 1.15, and 37.00 ± 6.38 s, respectively. Significant effect of l-arginine supplementation was determined on the percentage and duration of motile spermatozoa (p < 0.05). The maximum increment was evoked at the greatest concentration (8 mM). Here, we showed that l-arginine supplement can improve sperm motility of S. coruhensis.
AtıfScopus1WoS1OpenAlex2İncele - MakaleAçık Erişim2019Scopus SJRQ3What is the predictive power of visual mathematics literacy perception and its sub-dimensions for geometry success?; [Görsel Matematik Okuryazarlığı Alt Boyutlarının Geometri Başarısını Yordama Gücü Nedir?]
Ilhan, Aziz; Tutak, Tayfun; Celik, Halil Coskun
Purpose: In this study, it was aimed to examine the relationship between the visual mathematics literacy perceptions and its sub-dimension for geometry success levels of prospective teachers. It was also aimed to examine to what extent visual mathematics literacy perception and its sub-dimensions predicted geometry success. Research Methods: This study was designed in accordance with a qualitative, scanning model. The research was carried out with 232 (97 males and 135 females) prospective teachers who studied in Mathematics Education Program at Firat University, Education Faculty, and were selected by simple random sampling method. “Visual Mathematics Literacy Perception Scale” and “Geometry Success Test” were employed in this study as data collection tools. Correlation analysis and multiple regression analysis were used for analyzing the data. Findings: After the data were analyzed, it was determined that there was a positive relationship between the visual mathematics literacy perception and geometry success of prospective teachers. It was also determined that visual mathematics literacy perception is a meaningful predictor of geometry success. Implications for research and practice: Providing trainings on these concepts in the direction of the results, to investigate the concept of perception on other learning areas of mathematics and conducting experimental studies on the perception of visual mathematics literacy are suggested.
AtıfScopus5WoS2OpenAlex7TR Dizin2
İncele - MakaleYalnız künye2019Uptake of some elements with aquatic plants exposed to the effluent of wastewater treatment plant
Tatar, S.; Öbek, E.; Arslan Topal, E.I.; Topal, M.
In this study, in the removal of macro (P, S, Na, K, Ca, Mg) and micro (Sb, Ba, Co, Cu, Fe, Pb, Mn, Hg, Mo, Se, Ag, Zn) elements in the effluent of Elazig Wastewater Treatment Plant, the efficiency of Lemna minor L. and Lemna gibba L. has been studied comparatively. For this aim, fronds of these plants have been adapted to the effluent of the treatment plant that feeds pilot scale reactors. The concentrations of elements in fronds harvested during the working period were analyzed by ICP/MS. The analytical results show that Lemna minor L. has a high accumulation potential for P, Sb, Ba, Co, Fe, Pb, Mn, Hg, Ag and Zn while Lemna gibba L. has a high accumulation potential for S, Na, Ca, Mg, Cu, Mo and Se. The results show that Lemna minor L. and Lemna gibba L. can be used as phytoremediators of wastewater.
AtıfScopus10OpenAlex6İncele - MakaleYalnız künye2019Scopus SJRQ2Evaluation of health risks from exposure to arsenic and heavy metals through consumption of ten fish species
Varol, Memet; Kaya, Gülderen Kurt; Sünbül, Muhammet Raşit
In this study, the levels of 11 heavy metal(loid)s (HMs) were determined in ten different fish species (wild, farmed, freshwater, and marine) frequently consumed in Turkey and compared with the maximum permissible limits (MPLs) established by international standards. Also, human health risks for the consumers were assessed. Among 11 HMs, only Al and Cr levels did not show significant differences among fish species. The highest concentrations of As were found in red mullet, which is a demersal fish. The average concentrations of Cd, Cr, Cu, and Pb in all fish species were below the MPLs, whereas those of Zn in European anchovy and Mediterranean horse mackerel, and that of inorganic As in red mullet exceeded the MPLs. The highest and lowest toxic metal contents (Al, As, Cd, and Pb) were recorded in red mullet and farmed rainbow trout, respectively. The estimated daily intakes of HMs in each fish species were very lower than their corresponding tolerable daily intakes, suggesting the daily intake of HMs through fish consumption would not pose health risks for the consumers. The target hazard quotient for individual HMs and hazard index for combined HMs in fish species were below 1, which indicated that non-carcinogenic health effects are not expected. The carcinogenic risk results revealed that there was a carcinogenic risk from exposure to inorganic As via the consumption of red mullet. Among all investigated fish species, rainbow trout and common carp were safer in terms of both non-carcinogenic and carcinogenic risks from exposure to HMs.
AtıfScopus72WoS63OpenAlex82İncele - MakaleYalnız künye2019Scopus SJRQ1Pythagorean fuzzy VIKOR-based approach for safety risk assessment in mine industry
Gul, Muhammet; Ak, M. Fatih; Guneri, Ali Fuat
Introduction: Underground mining is considered one of the most hazardous industries and is often associated with serious work-related fatalities; this paper addresses job-related hazards and associated risks. Method: A risk assessment approach is proposed (Pythagorean fuzzy environment) and a case study is carried out in an underground copper and zinc mine. Results: Results of the study demonstrate that hazards can be categorized into different risk levels via compromised solutions of the fuzzy approach. Conclusion: The study provides a theoretical contribution by suggesting a Pythagorean fuzzy numbers-based VlseKriterijumska Optimizacija I Kompromisno Resenje (PFVIKOR) approach. Moreover, it contributes to improving overall safety levels of underground mining by considering and advising on the potential hazards of risk management. Practical applications: The proposed approach will improve the existing safety risk assessment mechanism in underground copper and zinc mining.
AtıfScopus184WoS156OpenAlex180İncele - MakaleAçık Erişim2019Scopus SJRQ3Optimization of medical waste routing problem: The case of TRB1 region in Turkey
Mete, Suleyman; Serin, Faruk
A fundamental problem concerning medical waste disposal is the evaluation of the real and potential risks arising from waste with the focus on the risk of infection. Therefore, the optimization of medical waste routing from collection to disposal center can minimize the risk of infection. The routing of medical waste considers significant to determine potential routes and select the route with minimum distance. The management of the medical waste is important decision for environmental sustainability and includes the collection, transportation and disposal of these materials. In this paper, a geographic information system (GIS) solution approach is proposed to determine the best location of disposal center. Proposed approach is applied to medical waste transportation between 167 health institutions (collection centers) and predetermined 5 disposal centers through TRB1 region in Turkey, which consist of Malatya, Elazığ, Bingöl and Tunceli provinces. The results of case study are examined and suggestions for future research are provided.
AtıfScopus7OpenAlex7İncele - MakaleYayıncıda açık erişim2019Scopus SJRQ3An integrated best-worst and interval type-2 fuzzy TOPSIS methodology for green supplier selection
Yucesan, Melih; Mete, Suleyman; Serin, Faruk; Celik, Erkan; Gul, Muhammet
Supplier selection is one of the most important multi-criteria decision-making (MCDM) problems for decision-makers in the competitive market. Today's organizations are seeking new ways to reduce the negative effects they have on the environment and to achieve a greener system. Currently, the concept of green supplier selection has gained great importance for its ability to incorporate environmental or green criteria into classical supplier selection practices. Therefore, in this study, a multi-phase MCDM model based on the best-worst method (BWM) and the interval type-2 fuzzy technique for order preference by similarity to ideal solution (IT2F TOPSIS) is proposed. A case study in a plastic injection molding facility in Turkey was carried out to show the applicability of the proposed integrated methodology. The paper offers insights into decision-making, methodology, and managerial implications. Results of the case study are examined and suggestions for future research are provided.
AtıfScopus93WoS85OpenAlex98İncele