Scopus İndeksli Yayınlar / Scopus Indexed Publications
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Browsing Scopus İndeksli Yayınlar / Scopus Indexed Publications by Rights "Attribution-NonCommercial-NoDerivs 3.0 United States"
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Publication Metadata only 2D UAV path planning with radar threatening areas using simulated annealing algorithm for event detection(2018) Basbous, Bilal;Path Planning for Unmanned Aerial Vehicles (UAVs) can be used for many purposes. However, the problem becomes more and more complex when dealing with a large number of points to visit for detecting and catching different type of events and simple threat avoidance such as Radar Areas. In the literature different type of algorithms (especially evolutionary algorithms) are preferred. In this project, Simulated Annealing (SA) Algorithm is used for solving the path planning problem. Firstly, problem is converted to a part of Travelling Salesman Problem (TSP), and then the solutions are optimized with the 2-Opt approach and other simple algorithms. The code is implemented in MATLAB by using its visualization. Circular avoidance approach is developed and applied with the Simulated Annealing in order to escape from circular radar threats. Tests have been made to observe the results of SA algorithm and radar threats avoidance approaches, where the results show that after a period of time, SA algorithm gives acceptable solutions with the capacities of escaping from radar area threats. Where SA algorithm gives better solutions in less period of time when there are no radar threats. Experimental results depicted that the proposed model can result in an acceptable solution for UAVs in sufficient execution time. This model can be used as an alternative solution to the similar evolutionary algorithms.Publication Metadata only A comparative study of the yellow dent and purple flint maize kernel components by Raman spectroscopy and chemometrics(2019-05-15) Kabuk, Hayrunnisa Nur; Kaplan, Ekin Su; Halimoğlu, Gökhan; Fausto, Rui; ILDIZ, GÜLCE ÖĞRÜÇIn this investigation the potential of micro-Raman spectroscopy, coupled to a simple, standard chemometric method (principal component analysis, PCA), as a fast, cheap, field method to investigate maize kernel components (endosperm, germ and peel) is demonstrated. Particular emphasis was given to the determination of the relative protein and amylose/amylopectin contents in maize endosperm of yellow dent and purple flint corn species, the two major maize varieties produced in Turkey. It is shown that the studied yellow dent corn type has a comparatively larger content of protein (3.4%) and a higher amylopectin/amylose ratio in the endosperm than the studied purple flint variety (a 11% decrease of amylopectin was found in going from the studied yellow dent to the purple flint corn), while the germs of the two species differ mostly by the presence of a slightly larger amount of starch in the case of the yellow dent corn, the oil composition of both species being identical within the resolution of the used method of analysis. The spectra of the maize peels reveal essentially the presence of cellulose and lignin in similar amounts in the two types of corn. (C) 2019 Elsevier B.V. All rights reserved.Publication Metadata only A comparison of Gordon's functional health patterns model and standard nursing care in symptomatic heart failure patients: A randomized controlled trial(W.B. Saunders, 2020-06) Türen, Sevda; Enç, NurayBackground Heart failure (HF) is associated with poor quality of life and increased morbidity and mortality. Aim This study aimed to investigate effect of application of Gordon's functional health pattern (FHP) model in nursing care of symptomatic HF patients on quality of life, morbidity and mortality in the post-discharge 30-day. Methods This is a prospective randomized controlled study conducted in a single center. Experimental group received nursing care planned in accordance with Gordon's FHP model. 60 control and 60 experimental HF patients were included in the study. In the control group nursing care was given according to the standard protocol of the hospital whereas in the experimental group nursing care was given in accordance with Gordon's FHP model. Patients in both groups were followed up after discharge at 30th day. Results Mean Minnesota Living with Heart Failure Questionnaire score improved significantly in the experimental group compared to the control group at 30th day (40.2 ± 23.5 vs 62.3 ± 22.9 respectively, p = 0.001). Seven patients (11.7%) in the experimental group and 17 patients (28.3%) in the control group were readmitted in the post discharge 30-day (p = 0.02). Kaplan-Meier survival curve analysis revealed significant difference in 30-day event free survival rates between groups (log-rank p = 0.31). Conclusion Application of Gordon's FHP model in the nursing care of HF patients was associated with significantly improved quality of life, and reduced hospital readmission rates at 30th day. This was the only independent predictor of 30-day event free survival.Publication Metadata only A Holistic Approach For The Optimization of Offshore Wind Farm Layouts Considering Cable Layouts(2019-08) Alabaş Uslu, Çiğdem; ÜLKÜ, İLAYDA; 51700A wind farm, mainly, is composed of a set of turbines, one or more transmitters and a set of electrical cable connections between turbines and transmitters. Determination of turbine locations within the farm to maximize total power generation is called turbine location (TL) problem. Relative turbine positions affect the amount of overall energy because of wake effects. Determination of cable connections among turbines and transmitters to collect produced energy by turbines at transmitters is called cable layout (CL) problem. While TL problem is directly effective on the total energy production in the farm, CL problem indirectly affects the total energy due to the power losses. In the literature, TL and CL problems are solved sequentially where the layout found by solving of TL is used as an input of CL problem. To minimize wake effects in TL problem, distances between turbine pairs should be increased, however, as the distances are increased the cable cost increases in CL problem. A new mathematical model is developed to deal with simultaneously solving of TL and CL problems. A set of test instances are used to show the performance of the proposed model. The experiments show the practical use of the proposed holistic model.Publication Metadata only A novel fuzzy TOPSIS method using emerging interval-valued spherical fuzzy sets(2019-06-03) Kahraman, Cengiz; GÜNDOĞDU, FATMA KUTLUAll the extensions of ordinary fuzzy sets with three-dimensional membership functions such as intuitionistic fuzzy sets, second type intuitionistic fuzzy sets (or Pythagorean fuzzy sets) and neutrosophic sets aim at defining the judgments of decision makers/ experts with a more detailed description. As a new extension of intuitionistic fuzzy sets of second type, the emerging spherical fuzzy sets (SFS) have been proposed by Kutlu Gundogdu and Kahraman (2019b). In spherical fuzzy sets, the sum of membership, non-membership and hesitancy degrees must satisfy the condition0 <= mu(2) + v(2) + pi(2) <= 1 in which these parameters are assigned independently. SFS is an integration of Pythagorean fuzzy sets and neutrosophic sets. In this paper, novel interval-valued spherical fuzzy sets are introduced with their score and accuracy functions; arithmetic and aggregation operations such as interval-valued spherical fuzzy weighted arithmetic mean operator and interval-valued spherical fuzzy geometric mean operator. Later, interval-valued spherical fuzzy sets are employed in developing the extension of TOPSIS under fuzziness. Then, we use the proposed interval-valued spherical fuzzy TOPSIS method in solving a multiple criteria selection problem among 3D printers to verify the developed approach and to demonstrate its practicality and effectiveness. A comparative analysis with single-valued spherical TOPSIS is also performed.Publication Open Access A novel spherical fuzzy analytic hierarchy process and its renewable energy application(2020-03-01) Kutlu Gündoğdu Fatma; Kahraman, CengizThe extensions of ordinary fuzzy sets such as intuitionistic fuzzy sets, Pythagorean fuzzy sets, and neutrosophic sets, whose membership functions are based on three dimensions, aim at collecting experts’ judgments more informatively and explicitly. In the literature, generalized three-dimensional spherical fuzzy sets have been introduced by Kutlu Gündoğdu and Kahraman (J Intell Fuzzy Syst 36(1):337–352, 2019a), including their arithmetic operations, aggregation operators, and defuzzification operations. In this paper, our aim is to extend classical analytic hierarchy process (AHP) to spherical fuzzy AHP (SF-AHP) method and to show its applicability and validity through a renewable energy location selection example and a comparative analysis between neutrosophic AHP and SF-AHP.Publication Metadata only A novel VIKOR method using spherical fuzzy sets and its application to warehouse site selection(2019) Kahraman, Cengiz; GÜNDOĞDU, FATMA KUTLUThe extensions of ordinary fuzzy sets such as intuitionistic fuzzy sets (IFS), Pythagorean fuzzy sets (PFS), and neutrosophic sets (NS), whose membership functions are based on three dimensions, aim at collecting experts' judgments more informatively and explicitly. In the literature, generalized three-dimensional spherical fuzzy sets have been developed by Kutlu Gundogdu and Kahraman (2019), including their arithmetic operations, aggregation operators, and defuzzification operations. Spherical Fuzzy Sets (SFS) are a new extension of Intuitionistic, Pythagorean and Neutrosophic Fuzzy sets, a SFS is characterized by a membership degree, a nonmembership degree, and a hesitancy degree satisfying the condition that their squared sum is equal to or less than one. These sets provide a larger preference domain in 3D space for decision makers (DMs). In this paper, our aim is to extend classical VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to spherical fuzzy VIKOR (SF-VIKOR) method and to show its applicability and validity through an illustrative example and to present a comparative analysis between spherical fuzzy TOPSIS (SF-TOPSIS) and SF-VIKOR. We handle a warehouse location selection problem with four alternatives and four criteria in order to demonstrate the performance of the proposed SF-VIKOR method.Publication Open Access A pipeline for adaptive filtering and transformation of noisy left-arm ECG to its surrogate chest signal(MDPI AG, 2020-05) Tanneeru, Akhilesh; Lee, Bongmook; Misra, Veena; Mohaddes, F.; Zhou, Y.; Lobaton, E.; AKBULUT, FATMA PATLARThe performance of a low-power single-lead armband in generating electrocardiogram (ECG) signals from the chest and left arm was validated against a BIOPAC MP160 benchtop system in real-time. The filtering performance of three adaptive filtering algorithms, namely least mean squares (LMS), recursive least squares (RLS), and extended kernel RLS (EKRLS) in removing white (W), power line interference (PLI), electrode movement (EM), muscle artifact (MA), and baseline wandering (BLW) noises from the chest and left-arm ECG was evaluated with respect to the mean squared error (MSE). Filter parameters of the used algorithms were adjusted to ensure optimal filtering performance. LMS was found to be the most effective adaptive filtering algorithm in removing all noises with minimum MSE. However, for removing PLI with a maximal signal-to-noise ratio (SNR), RLS showed lower MSE values than LMS when the step size was set to 1 × 10−5. We proposed a transformation framework to convert the denoised left-arm and chest ECG signals to their low-MSE and high-SNR surrogate chest signals. With wide applications in wearable technologies, the proposed pipeline was found to be capable of establishing a baseline for comparing left-arm signals with original chest signals, getting one step closer to making use of the left-arm ECG in clinical cardiac evaluations.Publication Metadata only A Wearable Device for Virtual Cyber Therapy of Phantom Limb Pain(2018-09) Tarakçı, Ela; Aydın, Muhammed; Zaim, Abdul Halim; AKBULUT, AKHAN; AŞCI, GÜVEN; 285689; 116056; 101760; 176402; 8693Phantom limb pain (PLP) is the condition most often occurs in people who have had a limb amputated and it is may affect their life severely. When the brain sends movement signals to the phantom limb, it returns and causes a pain. Many medical approaches aim to treat the PLP, however the mirror therapy still considered as the base therapy method. The aim of this research is to develop a wearable device that measures the EMG signals from PLP patients to classify movements on the amputated limb. These signals can be used in virtual reality and augmented reality environments to realize the movements in order to reduce pain. A data set was generated with measurements taken from 8 different subjects and the classification accuracy achieved as 90% with Neural Networks method that can be used in cyber therapies.This type of therapy provides strong visuals which make the patient feel he/she really have the limb. The patient will have great therapy session time with comparison to the other classical therapy methods that can be used in home environments.Publication Metadata only An analytical approach for analysing the impact of risks on production planning: Case of Öztiryakiler(2020) Telatar, E.; Bekeç, T.; Başaran, A.; Balıkçı, N.; Bilgin, B.; İlbay, E.; AKTİN, AYŞE TÜLİNAccurate and applicable production plans are a must for manufacturing companies. Although companies tend to prepare ideal production plans, some exogenous factors can affect their validity. These risks, which occur unexpectedly, will have a negative influence on the plan. This study aims to determine the exogenous factors affecting the success of production planning of square and rectangular food containers manufactured by Öztiryakiler, and analyse their impacts on the plans. The risk factors are evaluated using Failure Mode and Effects Analysis, and their risk priority numbers are calculated. A mixed-integer linear programming model with the objective of total cost minimisation is developed to obtain the production plan of containers. Initially, an ideal data set is used as input; hence, this model’s output displays a risk-free plan. Similarly, for each of the risk factor scenarios, mathematical models are solved with risk-related data. GAMS software and CPLEX solver is utilised in the solution of all models. Finally, for each of the selected high risk alternative, the expected total costs are calculated. This is achieved by multiplying the normalized risk priority number obtained from the Failure Mode and Effects Analysis with the corresponding optimal total cost of the risky plan. This analysis highlights the most critical risks, and comparison with the risk-free plan helps in proposing system improvements. © 2020, Springer Nature Switzerland AG.Publication Metadata only An entropy-based design evaluation model for architectural competitions through multiple factors(MDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND, 2019-11) Şener, Sinan Mert; GÜZELCİ, ORKAN ZEYNELGenerally, the evaluations in architectural competitions are based on quality where many criteria are involved. Additionally, many other inter-related criteria, identified by the members of the jury, emerge during jury evaluation. Hence, a great number of criteria play a role, with varying degrees of importance, in the evaluation process. The order of importance and weights of criteria (factors) in the evaluation phases are not fixed and differ according to the approaches of the jury members. The objective of this study is to investigate whether subjective means of evaluation can be associated with an objective and computable evaluation model. Entropy, an objective method used to measure disorder in buildings, offers significant potential in enhancing the comprehensibility of subjective tendencies in jury evaluation of architectural competitions. Previous studies have identified an inverted U relationship between entropy and subjective responses based on single and multiple factors. The Entropy-Based Design Evaluation Model (EBDEM), a method, analyzes the level of objectivity in jury evaluation and questions the predictability of evaluations through examining the relationship between the entropy values of projects and success outcomes. The Weighted Overall Entropy (WOE) was obtained by multiplying multiple factor entropy values with different weight coefficients with the purpose of ranking each project on an inverted U graph similar to jury results. The relationship between WOE values calculated and the ranking of the projects in the competitions were investigated. The findings within this study indicate that there are no relationships between single factor entropy values and ranking of the projects. Additionally, it was found that WOE values calculated for single-competition compared to multiple-competitions were more similar to jury evaluation results.Publication Metadata only An Integrated Pythagorean Fuzzy AHP & TOPSIS Method for the Selection of the Most Appropriate Clean Energy Technology(2019-07) Karaşan, Ali; Kahraman, Cengiz; GÜNDOĞDU, FATMA KUTLU; 273471; 227871; 9178Clean energy technologies which include renewable energy, electric vehicles, nuclear power and biofuels focus on ways to boost demand and de ployment by societies. Governments in many countries aim to increase the clean energy technologies and oner incentives to increase tendency on that sec tor. One of the reasons of these incentives is to prevent the global warming. For example. 30% of global electricity can be produced from wind and solar power in the long tenn, without adding to the total cost of reaching a low-carbon fu ture. In this study, we use an integrated Pythagorean fuzzy MCDM method consist of AHP and TOPSIS for the selection of the most appropriate clean en ergy7 technology for the Marmara Region.Publication Metadata only An Unsupervised Data Mining Approach for Clustering Customers of Abrasive Manufacturer(2019-07) AKBURAK, DİLEK; 275470Customer segmentation is the process of dividing customers into groups based on common similar characteristics such as value, location, demography etc. Companies can communicate with each group effectively and appropriately by considering these common properties. Data mining algorithms are the most utilized techniques which lead direct marketers to develop their marketing strategies tailored to particular segments and/or individuals. Clustering is one of the unsupervised data mining methods used for grouping set of objects such a way that objects in the same group have maximum similarity while between group similarities are low. K-means clustering is a commonly used non-hierarchical clustering method for performing non-parametrical learning tasks. This study aims to identify customer types according to their profitability, value and risk in order to take appropriate action for each group via clustering. In this study, data items are grouped according to coded customer profile with respect to the consumers’ total expenditures. Customers are segmented as VIP, Platinum, Gold, and Bronze into 4 groups according to their values within 2 years.Publication Open Access Analysis of Facial Emotion Expression in Eating Occasions Using Deep Learning(Springer, 2023) ELİF, YILDIRIM; AKBULUT, FATMA PATLAR; Çatal, ÇağatayEating is experienced as an emotional social activity in any culture. There are factors that influence the emotions felt during food consumption. The emotion felt while eating has a significant impact on our lives and affects different health conditions such as obesity. In addition, investigating the emotion during food consumption is considered a multidisciplinary problem ranging from neuroscience to anatomy. In this study, we focus on evaluating the emotional experience of different participants during eating activities and aim to analyze them automatically using deep learning models. We propose a facial expression-based prediction model to eliminate user bias in questionnaire-based assessment systems and to minimize false entries to the system. We measured the neural, behavioral, and physical manifestations of emotions with a mobile app and recognize emotional experiences from facial expressions. In this research, we used three different situations to test whether there could be any factor other than the food that could affect a person’s mood. We asked users to watch videos, listen to music or do nothing while eating. This way we found out that not only food but also external factors play a role in emotional change. We employed three Convolutional Neural Network (CNN) architectures, fine-tuned VGG16, and Deepface to recognize emotional responses during eating. The experimental results demonstrated that the fine-tuned VGG16 provides remarkable results with an overall accuracy of 77.68% for recognizing the four emotions. This system is an alternative to today’s survey-based restaurant and food evaluation systems.Publication Metadata only Analysis of linear lung models based on state-space models(Elsevier Ireland Ltd., 2020-01) Saatçi, Ertuğrul; Akan, Aydın; SAATÇI, ESRABackground and Objectives: Linear parametric respiratory system models have been used in the model-based analysis of the respiratory system. Although there are studies exploring the physiological correctness and fitting accuracy of the models, they are not analysed in terms of interaction between parameters and dynamics of the model. In this study we propose to use state-space modelling to yield the time-varying nature of the system incorporated by the parameters. Methods: We tested controllability, observability and stability characteristics of the equation of motion, 2-comp. parallel, 2-comp. series, viscoelastic, 6-element and mead models while using the parameters given in the literature. In the sensitivity analysis we proposed to use dual Desensitized Linear Kalman Filter (DKF) and Extended Kalman Filter (EKF) method. In this method, state error covariance revealed the parameter sensitivities for each model. Results: Results showed that all models, except 2-comp. parallel and mead models, are both controllable and observable models. On the other hand all models, except mead model, are stable models. Regarding to the sensitivity analysis, dual DKF - EKF method estimated states of the models successfully with a low estimation error. Sensitivity analysis results showed that airway parameters have higher effects on the state estimation than the other parameters have. Conclusion: We proved that state-space evaluation of the previously proposed parametric models of the respiratory system led us to quantitative and qualitative assessments of the respiratory models. Moreover parameter values found in the literature have different effects on the models. (C) 2019 Elsevier B.V. All rights reserved.Publication Metadata only Analysis of the use of computational intelligence techniques for air-conditioning systems: A systematic mapping study(SAGE PUBLICATIONS LTD, 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND, 2019) Çakır, Mustafa; AKBULUT, AKHAN; ÖNEN, YUSUF HATAYIn our systematic mapping study, we examined 289 published works to determine which intelligent computing methods (e.g. Artificial Neural Networks, Machine Learning, and Fuzzy Logic) used by air-conditioning systems can provide energy savings and improve thermal comfort. Our goal was to identify which methods have been used most in research on the topic, which methods of data collection have been employed, and which areas of research have been empirical in nature. We observed the rules for literature reviews in identifying published works on databases (e.g. the Institute of Electrical and Electronics Engineers database, the Association for Computing Machinery Digital Library, SpringerLink, ScienceDirect, and Wiley Online Library) and classified identified works by topic. After excluding works according to the predefined criteria, we reviewed selected works according to the research parameters motivating our study. Results reveal that energy savings is the most frequently examined topic and that intelligent computing methods can be used to provide better indoor environments for occupants, with energy savings of up to 50%. The most common intelligent method used has been artificial neural networks, while sensors have been the tools most used to collect data, followed by searches of databases of experiments, simulations, and surveys accessed to validate the accuracy of findings.Publication Open Access An Architectural Query of Anthropocene Era: Planned Obsolescence(Kare Publishing, 2023) AYDIN, HANIM GÜL; BİRER, EMELAfter Modernity, the human has become the subject, and the world redefined by the human has turned into a painting. However, the efforts of human subjectivity to reveal the world in the Anthropocene Era, with negative practices such as the “planned obsolescence theory,” which is the research subject, even prepares for the end of its existence. According to the research hypothesis evaluated through the theory’s effect on architectural problems, “secularization should take place against planned obsolete architecture.” The research aims to show that positive feedback can be provided in society and ecology by reversing architectural consumption. It is to open up for discussion that architecture, which is left in the tension of life and death but revived by the urbanites and nature despite the negativity of decay, can be sustained by becoming secularized. How planned obsolete architectures become secularized is revealed through visual documents and tables and discourse and descriptive analysis methods through architectures of different scales and geographies, which can be reactivated in human-nature activity while in crisis of decay. At the micro and macro scale of architecture, Hawthorne Plaza Shopping Center, Banker Han (Banker Kastelli), Doel Village, and Houtouwan Village were selected as purposeful examples.Publication Metadata only Atiprimod induce apoptosis in pituitary adenoma: Endoplasmic reticulum stress and autophagy pathways(WILEY, 111 RIVER ST, HOBOKEN 07030-5774, NJ USA, 2019-12) Çoker Gürkan , Ajda; Keçeoğlu, Gizem; Palavan Unsal, Narcin; ARISAN, ELİF DAMLA; ŞAHİN, BURCU AYHAN; YERLİKAYA, PINAR OBAKANPituitary adenoma is the most common tumor with a high recurrence rate due to a hormone-dependent JAK/signal transducer and activator of transcriptions (STAT) signaling. Atiprimod, a novel compound belonging to the azaspirane class of cationic amphiphilic drugs, has antiproliferative, anticarcinogenic effects in multiple myeloma, breast, and hepatocellular carcinoma by blocking STAT3 activation. Therapeutic agents' efficiency depends on endoplasmic reticulum (ER) stress-autophagy regulation during drug-mediated apoptotic cell death decision. However, the molecular machinery of dose-dependent atiprimod treatment regarding ER stress-autophagy has not been investigated yet. Thus, our aim is to investigate the ER stress-autophagy axis in atiprimod-mediated apoptotic cell death in GH-secreting rat cell line (GH3) pituitary adenoma cells. Dose-dependent atiprimod treatment decreased GH3 cell viability, inhibited cell growth, and colony formation. Upregulation of Atg5, Atg12, Beclin-1 expressions, cleavage of LC-3II and formation of autophagy vacuoles were determined only after 1 mu M atiprimod exposure. In addition, atiprimod-triggered ER stress was evaluated by BiP, C/EBP-homologous protein (CHOP), p-PERK upregulation, and Ca+2 release after 1 mu M atiprimod exposure. Concomitantly, increasing concentration of atiprimod induced caspase-dependent apoptotic cell death via modulating Bcl(2) family members. Moreover, by N-acetyl cycteinc pretreatment, atiprimod triggered reactive oxygen species generation and prevented apoptotic induction. Concomitantly, dose-dependent atiprimod treatment decreased both GH and STAT3 expression in GH3 cells. In addition, overexpression of STAT3 increased atiprimod-mediated cell viability loss and apoptotic cell death through suppressing autophagy and ER stress key molecules expression profile. In conclusion, a low dose of atiprimod exposure triggers autophagy and mild-ER stress as a survival mechanism, but increased atiprimod dose induced caspase-dependent apoptotic cell death by targeting STAT3 in GH3 pituitary adenoma cells.Publication Metadata only Automatic HTML code generation from mock-up images using machine learning techniques(2019) Asiroğlu, Batuhan; Yıldız, Eyyüp; Nalçakan, Yağız; Sezen, Alper; Dağtekin, Mustafa; Ensari, Tolga; METE, BÜŞRA RÜMEYSAThe design cycle for a web site starts with creating mock-ups for individual web pages either by hand or using graphic design and specialized mock-up creation tools. The mock-up is then converted into structured HTML or similar markup code by software engineers. This process is usually repeated many more times until the desired template is created. In this study, our aim is to automate the code generation process from hand-drawn mock-ups. Hand drawn mock-ups are processed using computer vision techniques and subsequently some deep learning methods are used to implement the proposed system. Our system achieves 96% method accuracy and 73% validation accuracy.Publication Metadata only Autonomous vehicle control for Lane and vehicle tracking by using deep learning via vision(2018) Olgun, Masum Celil; Baytar, Zakir; Akpolat, Kadir Metin; ŞAHİNGÖZ, ÖZGÜR KORAYCamera-based lane detection and vehicle tracking algorithms are one of the keystones for many autonomous systems. The navigational process of those systems is mainly focused on the output of detection algorithms. However, detection algorithms for lane detection need more pre-processing time and computational effort. They are also affected by environmental conditions and must regularly be improved. In this paper machine learning techniques and computer vision algorithms are utilized for the tasks of the lane and vehicle tracking of an autonomous vehicle control scenario. With the nature of used learning algorithm, the proposed system can handle complex image problems. The vehicle, on which we implement our algorithms, can manage to carry out the following tasks autonomously; tracking the lanes, following another vehicle, and stopping in necessary conditions. For that, one of the primary purposes is image-based lane tracking methodology by using learning algorithms. Data augmentation is applied to create diversity for the dataset. Application in this methodology has been discussed. For lane tracking Convolutional Neural Network architecture which is based on NVIDIA's PilotNet is preferred. For detecting objects and vehicles, the system is trained on the faster region-based convolutional neural network (Faster R-CNN) to identify traffic light and stop sign are by Haar Cascade Classifier. All these learning models are trained on NVIDIA GTX 1070 Graphics Processing Unit (GPU) to reduce training time. Experimental results showed that the proposed system gives a favorable result to autonomously control vehicles for lane and vehicle tracking purposes by vision.