Özetçe -Duygu analizi sosyal medya izleme çalışmaları için en kullanışlı yöntemlerden birisidir. Sosyal medya (Kişisel Blog, Twitter, Facebook) üzerinden elde edilen veri üzerinde duygu analizi uygulanarak, birşirketin müşteri servisinin, müşterilerden gelen olumlu ve olumsuz geri bildirimlere göre müşteri memnuniyetini saglaması ve maliyetleri düşürmesi saglanabilir. Ayrıca ekonomik, ticari ve kullanıcılara yönelik fikir madenciligi gibi çeşitli alanlarda kullanılarak anlamlı bilgiler elde edilebilir. Bu çalışmada, Türkçe Twitter mesajlardan oluşturulan veri seti metin sınıflandırma yöntemleri ile analiz edilerek olumlu veya olumsuz olup olmadıgı incelenmiştir. Deneysel sonuçlar SVM, Naive Bayes, Multinom Naive Bayes ve KNN algoritmalarıyla elde edilmiştir. Vector Space model ile temsil edilen öznitelikler, kelime torbası (Bag of Words, BoW) ve N-Gram model olmak üzere iki farklışekilde elde edilmiş ve bu durumun sınıflandırma sonuçlarına olan etkisi incelenmiştir.Anahtar Kelimeler-twitter, duygu analizi, duygu sınıflandırması, makine ögrenmesi, metin sınıflandırma.Abstract-Sentiment analysis is one of the most useful tools in social media monitoring. Implementing sentiment analysis on data gained from social media (Blogs, Twitter, and Facebook) can increase the customer satisfaction and decrease the costs for a company. Also sentiment analysis can be used in various domains, such as economic, commercial and opinion mining for the users to get meaningful information. In this study, Turkish Twitter feeds collected from Twitter API have been analyzed in terms of the sentiment context whether positive or negative using document classification methods. Experimental results have been conducted on machine learning algorithms such as SVM, Naive Bayes, Multinomial Naive Bayes and KNN. The features represented by vector space are extracted from two different models which are Bag of Words and N-Gram. The experimental results have been investigated on the effect of classification methods.

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Automatic flying target detection and tracking in video sequences acquired from a camera mounted on another Unmanned Aerial Vehicle (UAV) is a challenging task due to the presence of nonstationary cameras in the system, dynamic motion of the moving target, and high-cost computation for realtime applications. In this paper, our aim is to automatically detect and track moving UAV by another one while simultaneously flying in the air. In order to provide efficiently in real-time applications, we develop a vision-based low-cost hardware system integrated with an independent ground control station. We initially created a new public dataset called ATAUAV that includes different types of UAV images obtained from videos recording in our experiments and searches on Google Images for the training process. Deep learningbased YOLOv3-Tiny (You Only Look Once) is used for target detection with the highest accuracy and fastest results. Kernelized Correlation Filter (KCF) adapted with YOLO, which runs on low-cost hardware, is used for real-time detected target tracking. We compared the performance of the proposed approach with different tracking algorithms. Experimental results show that the proposed approach provides the highest accuracy rate as 82.7% and a mean fps speed as 29.6 on CPU. The dataset can be downloaded at http://cogvi.atauni.edu.tr/ResearchLab/PageDetail/Our-ATAUAVs-Dataset-86. INDEX TERMS Artificial neural networks, computer vision, kcf, object detection, object recognition, target tracking, unmanned aerial vehicles, yolo This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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Öz Foto-kapanlar genellikle ormanlık arazide sabit noktaya yerleştirilmiş ve doğal yaşamı izlemek için kullanılan görüntüleme cihazlarıdır. Foto-kapanlar kullanılarak canlıların doğal yaşamı üzerinde araştırma yapmak amacıyla milyonlarca görüntü kaydedilmektedir. Kaydedilmiş görüntüler üzerinde bilgisayar tabanlı yöntemler ile canlıların tespit edilmesi ve tanınması amacıyla otomatik yöntemler geliştirilmektedir. Ayrıca foto-kapan görüntülerinde arka plan karmaşıklığı, arka planın hareketli olması, ışık şiddeti değişimi ve nesnenin parçalı olması gibi problemler hareketli nesne tespitini zorlaştırmaktadır. Literatürde bu amaçla yapılan çalışmalarda hareketli nesnelere ait model görüntüler görüntü içerisinden el ile tespit edilerek sınıflandırma tabanlı yöntemlerde ön bilgi olarak kullanılmaktadır. Nesnelere ait model görüntülerin el ile tespit edilmesi ve kırpılması zor, zahmetli, zaman alan bir süreçtir ve yüksek iş yükü gerektirmektedir. Çalışmamızda bu iş yükünü azaltmak amacıyla doğal ortamdan elde edilmiş foto-kapan görüntülerinde nesnelere ait ön bilgi kullanılmadan hareketli nesneler otomatik tespit edilmiş ve hareketli nesnelerin görüntüdeki konumları belirlenmiştir. Önerilen yöntemde hareketli nesnelerin tespit edilmesi için görüntülere arka plan çıkarma ve çerçeve farkı yöntemleri uygulanmıştır. Arka plan modelinin oluşturulması için Değişen Gauss Ortalama ve Gaussların Karışımı, gürültülerin azaltılması ve nesnelerin belirginleştirilmesi amacıyla Gauss Bulanıklığı ve Medyan filtre, ön plan tespitindeki hataların giderilmesi için OTSU eşikleme kullanılmıştır. Foto-kapan veri setlerinde hareketli nesne tespit etme başarısı %83, nesne konumlandırma başarısı ise %80 olarak elde edilmiştir.

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At the beginning of 2020, the new coronavirus disease (Covid-19), a deadly viral illness, is declared as a public health emergency situation by WHO. Consequently, it is accepted as pandemic that affected millions of people worldwide. Italy is one of the most affected countries by Covid-19 disease among the world. In this article, our main goal is to investigate the effect of intensity of Covid-19 cases based on the population size and tourism factors in certain regions of Italy by visual data analysis. The regions of Lombardia, Veneto, Campania, Emilia-Romagna, Piemonte are the top five regions covering 58.50% of the total Covid-19 cases diagnosed in Italy. It has been shown by visual data analysis that population and tourism factors play an important role in the spread of Covid-19 cases in these five regions. In addition, a prediction model was created using Bi-LSTM and ARIMA algorithms to forecast the number of Covid-19 cases occurring in these five regions in order to take early action. We can conclude that these northern regions have been affected mostly by Covid-19 and the distribution of the resident population and tourist flow factors affected the number of Covid-19 cases in Italy. K E Y W O R D S coronavirus, Covid-19, forecasting method, visual data analysis 1 INTRODUCTION A new coronavirus disease that can cause fatal effects on humans, much like the Middle East respiratory syndrome (MERS) and severe acute respiratory syndrome (SARS), emerged in late 2019. The symptoms of the disease first became evident in China in December 2019, and the disease was diagnosed worldwide as Covid-19 at the beginning of the January 2020. 1 According to the Covid-19 timeline drawn up by the World Health Organization (WHO) Covid-19, patients in China who were eventually diagnosed as having Covid-19 on December 31, 2019 showed symptoms similar tothose of pneumonia. The disease was then reported to be a new coronavirus disease. The WHO established its Incident Management Support Team to address the spread and impacts of the new coronavirus disease on January 1, 2020. On January 13, 2020, it was reported that the first Covid-19 case outside of China occurred in Thailand on January 8, 2020. In addition, as per official statements, the spread of the coronavirus disease was attributed to one patient who traveled to Wuhan, China and returned to Thailand. 2 The WHO's status report on January 30, 2020, revealed a total of 7.818 cases of Covid-19 worldwide, 82 of which were located in 18 countries outside China. 3 According to the WHO's case report of January 31, 2020, Covid-19 had spread to an additional country, taking the total to 19 countries outside China. 4 The country that reported these new Covid-19 cases was Italy. With the addition of Italy, the number of European countries that had reported Covid-19 cases rose to four. Reports of Covid-19 in Italy started with two people, who mentioned the city of Wuhan as being part of their travel histories. Marta et al. explained that these two cases, which were recorded in Rome, includ...

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