Pub Date : 2020-12-28DOI: 10.35377/SAUCIS.04.01.787030
E. Erdem, Tolga Aydin
Pneumonia is a seasonal infectious lung tissue inflammatory disease. According to the World Health Organization (WHO), early diagnosis of the disease reduces the risk of its transmission and death. Various deep learning and machine learning algorithms were used for pneumonia detection. This study aims to analyze the lung images and diagnose pneumonia disease by employing deep learning approaches. We have suggested a novel deep learning framework for the detection of pneumonia in lung. A comparison was made between the proposed new deep learning model and pre-trained deep learning models. 88.62% accuracy rate has been obtained from the proposed deep learning structure. It was observed that by utilizing the new deep neural network developed, the accuracy results of VGG16 (88.78%) and VGG19 (88.30%), which are among the popular deep learning architectures, can be approximated. The test results show that our proposed model has a better recall value (97.43%) (VGG16 (93.33%) and VGG19 (96.92%)), and a better F1-Score (91.45%) (VGG16 (91.22%) and VGG19 (91.19%)).
{"title":"Detection of Pneumonia with a Novel CNN-based Approach","authors":"E. Erdem, Tolga Aydin","doi":"10.35377/SAUCIS.04.01.787030","DOIUrl":"https://doi.org/10.35377/SAUCIS.04.01.787030","url":null,"abstract":"Pneumonia is a seasonal infectious lung tissue inflammatory disease. According to the World Health Organization (WHO), early diagnosis of the disease reduces the risk of its transmission and death. Various deep learning and machine learning algorithms were used for pneumonia detection. This study aims to analyze the lung images and diagnose pneumonia disease by employing deep learning approaches. We have suggested a novel deep learning framework for the detection of pneumonia in lung. A comparison was made between the proposed new deep learning model and pre-trained deep learning models. 88.62% accuracy rate has been obtained from the proposed deep learning structure. It was observed that by utilizing the new deep neural network developed, the accuracy results of VGG16 (88.78%) and VGG19 (88.30%), which are among the popular deep learning architectures, can be approximated. The test results show that our proposed model has a better recall value (97.43%) (VGG16 (93.33%) and VGG19 (96.92%)), and a better F1-Score (91.45%) (VGG16 (91.22%) and VGG19 (91.19%)).","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124373755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-22DOI: 10.35377/SAUCIS...780465
A. Mhmood, A. Zengin
{"title":"Performance Evaluation of Manet Routing Protocols Using Network Simulator NS2","authors":"A. Mhmood, A. Zengin","doi":"10.35377/SAUCIS...780465","DOIUrl":"https://doi.org/10.35377/SAUCIS...780465","url":null,"abstract":"","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"42 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123859761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-18DOI: 10.35377/saucis.03.03.815556
Bihter Das
In recent years, different approaches and methods have been proposed to diagnose various diseases accurately. Since there are a variety of liver diseases, till late-stage liver disease and liver failure occur the symptoms tend to be specific for that illness. Therefore, early diagnosis can play a key role in preventing deaths from liver diseases. In this study, we compare the accuracy of different classification methods supported by the SAS software suite, such as Neural Network, Auto Neural, High Performance (HP) SVM, HP Forest, HP Tree (Decision Tree), and HP Neural for the diagnosis of liver diseases. In this study, the Indian Liver Patient Dataset (ILPD) provided by the University of California, Irvine (UCI) repository is used. Experimental results show that based on the metrics of our study, in the training phase while HP Forest achieves the highest accuracy rate, HP SVM and HP Tree do the lowest accuracy rates. However, in the validation phase, Neural Network achieves the highest accuracy rate and HP Forest does the lowest accuracy rate. Our experimental results may be useful for both researchers and practitioners working in related fields.
{"title":"A comparative study on the performance of classification algorithms for effective diagnosis of liver diseases","authors":"Bihter Das","doi":"10.35377/saucis.03.03.815556","DOIUrl":"https://doi.org/10.35377/saucis.03.03.815556","url":null,"abstract":"In recent years, different approaches and methods have been proposed to diagnose various diseases accurately. Since there are a variety of liver diseases, till late-stage liver disease and liver failure occur the symptoms tend to be specific for that illness. Therefore, early diagnosis can play a key role in preventing deaths from liver diseases. In this study, we compare the accuracy of different classification methods supported by the SAS software suite, such as Neural Network, Auto Neural, High Performance (HP) SVM, HP Forest, HP Tree (Decision Tree), and HP Neural for the diagnosis of liver diseases. In this study, the Indian Liver Patient Dataset (ILPD) provided by the University of California, Irvine (UCI) repository is used. Experimental results show that based on the metrics of our study, in the training phase while HP Forest achieves the highest accuracy rate, HP SVM and HP Tree do the lowest accuracy rates. However, in the validation phase, Neural Network achieves the highest accuracy rate and HP Forest does the lowest accuracy rate. Our experimental results may be useful for both researchers and practitioners working in related fields.","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126085744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-17DOI: 10.35377/saucis.03.03.805598
M. Akçay
While the investments in rail transportation systems continue without slowing down, various optimization issues come to the fore in order for the systems to work more efficiently. One of the most important of these issues is the optimization of the vehicle speed profile. Improvement in vehicle speed profile increases efficiency in operating traffic. Vehicle speed profile varies depending on the electrical-characteristic features of the vehicle, the distance between the stations and the line geometry. The vehicle's speed profile consists of several parts, such as acceleration, constant speed travel and braking zones. The constant speed in the constant velocity zone refers to the max operating speed, which is recommended for operation in the restricted area and remains within the limits. This part is critical in creating the speed profile of the vehicle. In this study, the estimation of the value of the constant speed time in the speed profile of the vehicles used in the city metro systems was made by using the Stochastic Gradient Descent method, which is one of the machine learning methods, and compared with various well-known methods. Coefficient of determination (R 2 ) values were calculated as 0.9955 and 0.9951, respectively, with random sampling hold out and cross validation methods.
{"title":"Estimation of constant speed time for railway vehicles by stochastic gradient descent algorithm","authors":"M. Akçay","doi":"10.35377/saucis.03.03.805598","DOIUrl":"https://doi.org/10.35377/saucis.03.03.805598","url":null,"abstract":"While the investments in rail transportation systems continue without slowing down, various optimization issues come to the fore in order for the systems to work more efficiently. One of the most important of these issues is the optimization of the vehicle speed profile. Improvement in vehicle speed profile increases efficiency in operating traffic. Vehicle speed profile varies depending on the electrical-characteristic features of the vehicle, the distance between the stations and the line geometry. The vehicle's speed profile consists of several parts, such as acceleration, constant speed travel and braking zones. The constant speed in the constant velocity zone refers to the max operating speed, which is recommended for operation in the restricted area and remains within the limits. This part is critical in creating the speed profile of the vehicle. In this study, the estimation of the value of the constant speed time in the speed profile of the vehicles used in the city metro systems was made by using the Stochastic Gradient Descent method, which is one of the machine learning methods, and compared with various well-known methods. Coefficient of determination (R 2 ) values were calculated as 0.9955 and 0.9951, respectively, with random sampling hold out and cross validation methods.","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125674584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-04DOI: 10.35377/saucis.vi.773465
M. F. Adak
Image processing techniques give highly successful results when used deep learning in classification studies. Applications benefit from this kind of work to make life easier. In this study, a mobile application is developed that takes photo of a plant and makes image processing on it to provide information about its name, the time to change the soil, the amount of sun light and nutrition it needs. The model is trained using the Convolutional Neural Networks, and dataset is successfully applied to the network. Currently, the application is capable to classify 43 different plants in mobile environment, and its classification capacity is planned to be expanded with new plant species as a future study. Up to 90% accuracy is reached in this study with the current version of the application.
{"title":"Identification of Plant Species by Deep Learning and Providing as A Mobile Application","authors":"M. F. Adak","doi":"10.35377/saucis.vi.773465","DOIUrl":"https://doi.org/10.35377/saucis.vi.773465","url":null,"abstract":"Image processing techniques give highly successful results when used deep learning in classification studies. Applications benefit from this kind of work to make life easier. In this study, a mobile application is developed that takes photo of a plant and makes image processing on it to provide information about its name, the time to change the soil, the amount of sun light and nutrition it needs. The model is trained using the Convolutional Neural Networks, and dataset is successfully applied to the network. Currently, the application is capable to classify 43 different plants in mobile environment, and its classification capacity is planned to be expanded with new plant species as a future study. Up to 90% accuracy is reached in this study with the current version of the application.","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116604899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-16DOI: 10.20944/preprints202010.0348.v1
Mehmet Bulut
With the year 2020, the world faced a new threat that affects all areas of life, negatively affects production in all areas, and paralyzes social life. The measures and restrictions taken by the country's governments to prevent the epidemic from spreading rapidly in the society with the effect of the Covid-19 virus, which first appeared in China and spread all over the world, brought a new lifestyle. Covid-19 has been much the impact on electricity use and electricity production in the period in Turkey as in other countries. There was a sharp decline in commercial and industrial electricity use. The coronavirus effect has also been reflected in the electricity demand and the consumption amount has undergone a great negative change. Due to the enactment of measures against the new type of coronavirus (COVID-19) epidemic and the partial or full-time curfews, electricity consumption was moved to homes, supermarkets, and hospitals in April 2020 from places where mass consumption is intense, such as industry, workplaces, and educational institutions. In this study, Covid-19 period, the first cases were examined electricity production and consumption in Turkey as of the date it is seen throughout, in comparison with electricity consumption data in the same month of the previous years corresponding to this period, the effects on electricity generation and consumption habits of this period were examined.
{"title":"Analysis of The Covid-19 Impact on Electricity Consumption and Production","authors":"Mehmet Bulut","doi":"10.20944/preprints202010.0348.v1","DOIUrl":"https://doi.org/10.20944/preprints202010.0348.v1","url":null,"abstract":"With the year 2020, the world faced a new threat that affects all areas of life, negatively affects production in all areas, and paralyzes social life. The measures and restrictions taken by the country's governments to prevent the epidemic from spreading rapidly in the society with the effect of the Covid-19 virus, which first appeared in China and spread all over the world, brought a new lifestyle. Covid-19 has been much the impact on electricity use and electricity production in the period in Turkey as in other countries. There was a sharp decline in commercial and industrial electricity use. The coronavirus effect has also been reflected in the electricity demand and the consumption amount has undergone a great negative change. Due to the enactment of measures against the new type of coronavirus (COVID-19) epidemic and the partial or full-time curfews, electricity consumption was moved to homes, supermarkets, and hospitals in April 2020 from places where mass consumption is intense, such as industry, workplaces, and educational institutions. In this study, Covid-19 period, the first cases were examined electricity production and consumption in Turkey as of the date it is seen throughout, in comparison with electricity consumption data in the same month of the previous years corresponding to this period, the effects on electricity generation and consumption habits of this period were examined.","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130703295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-09DOI: 10.35377/saucis.vi.755269
S. Uğuz
Tarim alaninda gerceklestirilen yapay zekâ temelli calismalar arasinda, derin ogrenmeye dayanan hastalik tespiti uygulamalarinin giderek yayginlastigi gorulmektedir. Bitki turleri arasindaki cesitlilik ve cogu bitki turunun belirli cografyalarda yetismesi bu alanda gerceklestirilen calismalarin sayisinin istenen duzeyde olmadigini gostermektedir. Dunyada sadece belirli bolgelerde yetisen zeytin bitkisine ait halkali leke hastaligi ozellikle Turkiye’de yaygin olarak gorulmektedir. Bu calismada halkali leke hastaligina ait semptomlarin populer derin ogrenme mimarilerinden olan Single Shot Detector ile tespitine donuk bir uygulama gerceklestirilmistir. Kontrollu kosullar altinda olusturulan veri seti, Single Shot Detector mimarisi uzerinde farkli IoU treshold degerleri ile egitilmistir. IoU=0.5 icin %96 duzeyinde Average Precision degeri elde edilmistir. Ayrica, gerek zeytin yetistiricileri gerekse de konu ile ilgili olan kisiler icin calismanin masaustu uygulamasi gelistirilmistir.
{"title":"Automatic Olive Peacock Spot Disease Recognition System Development by Using Single Shot Detector","authors":"S. Uğuz","doi":"10.35377/saucis.vi.755269","DOIUrl":"https://doi.org/10.35377/saucis.vi.755269","url":null,"abstract":"Tarim alaninda gerceklestirilen yapay zekâ temelli calismalar arasinda, derin ogrenmeye dayanan hastalik tespiti uygulamalarinin giderek yayginlastigi gorulmektedir. Bitki turleri arasindaki cesitlilik ve cogu bitki turunun belirli cografyalarda yetismesi bu alanda gerceklestirilen calismalarin sayisinin istenen duzeyde olmadigini gostermektedir. Dunyada sadece belirli bolgelerde yetisen zeytin bitkisine ait halkali leke hastaligi ozellikle Turkiye’de yaygin olarak gorulmektedir. Bu calismada halkali leke hastaligina ait semptomlarin populer derin ogrenme mimarilerinden olan Single Shot Detector ile tespitine donuk bir uygulama gerceklestirilmistir. Kontrollu kosullar altinda olusturulan veri seti, Single Shot Detector mimarisi uzerinde farkli IoU treshold degerleri ile egitilmistir. IoU=0.5 icin %96 duzeyinde Average Precision degeri elde edilmistir. Ayrica, gerek zeytin yetistiricileri gerekse de konu ile ilgili olan kisiler icin calismanin masaustu uygulamasi gelistirilmistir.","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125749142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-29DOI: 10.35377/saucis.02.02.598963
C. Çeken, Dini Abdurahman
Dunya ekonomisinde gorulen carpici buyume, lojistik hizmetin cevik, esnek ve duyarli olmasini isteyen tedarik zinciri endustrisinide hizlandirmaktadir. Internet teknolojileri, musteri ve lojistik saglayici arasindaki bilgi aktarimi konusunda oldukca basarilidirlar. Bununla birlikte, lojistik hizmetindeki mal akisi ile bilgi akisi arasindaki mevcut bosluk, sicakliga duyarli urunler hakkinda gercek zamanli bilgi edinme konusunda bir sorun teskil etmektedir ve bu da lojistik yonetimini karar vericiler acisindan daha zorlu hale getirmektedir. Nesnelerin Interneti teknolojisi, soguk zincir endustrisinde ortam goruntuleme, yonetim ve karar alma surecleri acisindan umut verici bir cozum gibi gorunmektedir. Bu calisma, soguk zincirin gercek zamanli ortam sicakligini yoneterek, izleyerek ve sicaga duyarli urunlerin raf omrunu tahmin ederek tum aktorlerin karar destegini gelistirmeye yardimci olan Nesnelerin Interneti tabanli bir soguk zincir lojistigi onermektedir. Bu on calismada, ortam parametrelerinin gercek zamanli verileri IEEE 802.15.4 tabanli kablosuz algilayici aglari kullanilarak toplanmis ve bir ag gecidi uzerinden uzak sunucuya aktarilarak urunlerin raf omrunun karar destek sistemi tarafindan tahmin edilebilmesi saglanmistir. Gelistirilen uygulama cerisinde, soguk zincirde bulunan bozulabilir urunlerin tanimlanmasiyla amaciyla radyo frekansli tanimlama (RFID) sistemi de modellenmistir.
{"title":"Simulation Modeling of An IoT Based Cold Chain Logistics Management System","authors":"C. Çeken, Dini Abdurahman","doi":"10.35377/saucis.02.02.598963","DOIUrl":"https://doi.org/10.35377/saucis.02.02.598963","url":null,"abstract":"Dunya ekonomisinde gorulen carpici buyume, lojistik hizmetin cevik, esnek ve duyarli olmasini isteyen tedarik zinciri endustrisinide hizlandirmaktadir. Internet teknolojileri, musteri ve lojistik saglayici arasindaki bilgi aktarimi konusunda oldukca basarilidirlar. Bununla birlikte, lojistik hizmetindeki mal akisi ile bilgi akisi arasindaki mevcut bosluk, sicakliga duyarli urunler hakkinda gercek zamanli bilgi edinme konusunda bir sorun teskil etmektedir ve bu da lojistik yonetimini karar vericiler acisindan daha zorlu hale getirmektedir. Nesnelerin Interneti teknolojisi, soguk zincir endustrisinde ortam goruntuleme, yonetim ve karar alma surecleri acisindan umut verici bir cozum gibi gorunmektedir. Bu calisma, soguk zincirin gercek zamanli ortam sicakligini yoneterek, izleyerek ve sicaga duyarli urunlerin raf omrunu tahmin ederek tum aktorlerin karar destegini gelistirmeye yardimci olan Nesnelerin Interneti tabanli bir soguk zincir lojistigi onermektedir. Bu on calismada, ortam parametrelerinin gercek zamanli verileri IEEE 802.15.4 tabanli kablosuz algilayici aglari kullanilarak toplanmis ve bir ag gecidi uzerinden uzak sunucuya aktarilarak urunlerin raf omrunun karar destek sistemi tarafindan tahmin edilebilmesi saglanmistir. Gelistirilen uygulama cerisinde, soguk zincirde bulunan bozulabilir urunlerin tanimlanmasiyla amaciyla radyo frekansli tanimlama (RFID) sistemi de modellenmistir.","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116709776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-29DOI: 10.35377/saucis.02.02.593888
İ. Öztel, Cemil Öz
Trafikteki arac sayisi ve sehirlerdeki nufus yogunlugu refah seviyesindeki yukselis ile birlikte artmaktadir. Bu durumunun yaninda insanlar trafik ile ilgili yeterince bilgi sahibi degildir. Karayollari Genel Mudurlugu’ne gore Turkiye’deki trafik kazalarinin 2017 yilindaki sayisi 1.202.716’dir. Dunya Saglik Orgutu’nun verilerine gore dunya capindaki trafik kazalarindaki olum sayilari her yil yaklasik olarak 1.35 milyondur. Bu bilgiler goz onunde tutuldugunda trafik egitiminin onemi on plana cikmaktadir. Bu amacla, bu calisma kapsaminda teorik ve pratik olmak uzere bir trafik egitim sistemi gelistirilmistir. Sistemin teorik ayaginda trafik, motor ve ilkyardim bilgisi olmak uzere uc konu baslikli testler yer almaktir. Teorik asama her kullanici icin 10 adet test icermektedir. Sistemin diger bir parcasi ise sistemin donanimsal ayagi ve simulasyon yazilimindan olusmaktadir. Sistemin genel olarak kullaniminda, bir kullanici ilk olarak uc konu basliginda girdigi test sinavinda %70 oraninda basarili olmalidir. Bu sarti sagladiktan sonra kullanici simulator egitimine gecebilir. Simulator direksiyon, pedal sistemi, vites kolu, surucu koltugu, simulasyon ekrani ve sanal ortam yazilimlarindan olusmaktadir. Bu sistem sayesinde acemi suruculer gercek hayatin risklerinden uzak olarak tecrube kazanmalari mumkun olacaktir.
{"title":"Traffic Education for Inexperienced Drivers with Virtual Driving Simulator","authors":"İ. Öztel, Cemil Öz","doi":"10.35377/saucis.02.02.593888","DOIUrl":"https://doi.org/10.35377/saucis.02.02.593888","url":null,"abstract":"Trafikteki arac sayisi ve sehirlerdeki nufus yogunlugu refah seviyesindeki yukselis ile birlikte artmaktadir. Bu durumunun yaninda insanlar trafik ile ilgili yeterince bilgi sahibi degildir. Karayollari Genel Mudurlugu’ne gore Turkiye’deki trafik kazalarinin 2017 yilindaki sayisi 1.202.716’dir. Dunya Saglik Orgutu’nun verilerine gore dunya capindaki trafik kazalarindaki olum sayilari her yil yaklasik olarak 1.35 milyondur. Bu bilgiler goz onunde tutuldugunda trafik egitiminin onemi on plana cikmaktadir. Bu amacla, bu calisma kapsaminda teorik ve pratik olmak uzere bir trafik egitim sistemi gelistirilmistir. Sistemin teorik ayaginda trafik, motor ve ilkyardim bilgisi olmak uzere uc konu baslikli testler yer almaktir. Teorik asama her kullanici icin 10 adet test icermektedir. Sistemin diger bir parcasi ise sistemin donanimsal ayagi ve simulasyon yazilimindan olusmaktadir. Sistemin genel olarak kullaniminda, bir kullanici ilk olarak uc konu basliginda girdigi test sinavinda %70 oraninda basarili olmalidir. Bu sarti sagladiktan sonra kullanici simulator egitimine gecebilir. Simulator direksiyon, pedal sistemi, vites kolu, surucu koltugu, simulasyon ekrani ve sanal ortam yazilimlarindan olusmaktadir. Bu sistem sayesinde acemi suruculer gercek hayatin risklerinden uzak olarak tecrube kazanmalari mumkun olacaktir.","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115732708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-29DOI: 10.35377/saucis.02.02.596400
Gözde Yolcu Öztel, Serap Kazan
Online alisveris son zamanlarda populer olmasina ragmen bu alisveris turunde urun deneyememe gibi bazi dezavantajlar mevcuttur. Son zamanlardaki teknolojik gelismeler ile birlikte sanal aynalar araciligi ile insanlar satin almak istedikleri urunleri deneyebilmektedir. Bu yaklasimdan esinlenilerek, kullanicilarin el cantalarini sanal olarak deneyebildikleri ve farkli pozlarini bir ekran uzerinde gorebildikleri bir sistem gelistirilmistir. Ayrica, kullanicilar ses komutlari ile cantalarin farkli renklerini de deneyebilmektedir. Bu calismada, kullanici hareketlerinin ve el iskelet bagantilarinin takibi icin Kinect Sensor kullanilmistir. Ses komutlarini tanimak icin korelasyon yonteminden faydalanilmistir.
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