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METE, BÜŞRA RÜMEYSA

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METE

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BÜŞRA RÜMEYSA

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2018 - 20193
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    Digital Data Forgetting: A Machine Learning Approach
    (2018-10) Aşıroğlu, Batuhan; Yıldız, Eyyüp; Zencirli, Ahmet; Ensari, Tolga; Nalçakan, Yağız; GÜNAY, MELİKE; METE, BÜŞRA RÜMEYSA; 283334; 286270; 261185; 176400; 270484
    Digital transformation of the world goes very fast during last two decades. Today, data is power and very important. Firstly, magnetic tapes and then digital data storages have been used to collect all data. After this process, big data and its tool machine learning became very popular in both literature and industry. People use machine learning in order to obtain meaningful information from the big data. It brings valuable planning results. However, nowadays it is quite hard to collect and store all digital data to computers. This process is expensive and we will not have enough space to store data in the future. Therefore, we need and propose "Digital Data Forgetting" phrase with machine learning approach. With this digital / software solution, we will have more valuable data and will be able to erase the rest of the them. We called this operation "Big Cleaning". In this article, we use data set to get and extract more valuable data with principal component analysis (PCA), deep autoencoder and k-nearest neighbor machine learning methods in the experimental analysis section.
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    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 RÜMEYSA
    The 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.
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    Flower Classification with Deep CNN and Machine Learning Algorithms
    (2019-09-11) Ensari, Tolga; METE, BÜŞRA RÜMEYSA; 283334; 176400
    Development of the recognition of rare plant species will be advantageous in the fields such as the pharmaceutical industry, botany, agricultural, and trade activities. It was also very challenging that there is diversity of flower species and it is very hard to classify them when they can be very similar to each other indeed. Therefore, this subject has already become crucial. In this context, this paper presents a classification system for flower images by using Deep CNN and Data Augmentation. Recently, Deep CNN techniques have become the latest technol­ogy for such problems. However, the fact is that getting better performance for the flower classification is stuck due to the lack of labeled data. In the study, there are three primary contributions: First, we proposed a classification model to cultivate the perfor­mance of classifying of flower images by using Deep CNN for extracting the features and various machine learning algorithms for classifying purposes. Second, we demonstrated the use of image augmentation for achieving better performance results. Last, we compared the performances of the machine-learning classifiers such as SVM, Random Forest, KNN, and Multi-Layer Perceptron(MLP). In the study, we evaluated our classification system using two datasets: Oxford-17 Flowers, and Oxford-102 Flowers. We divided each dataset into the training and test sets by 0.8 and 0.2, respectively. As a result, we obtained the best accuracy for Oxford 102-FIowers Dataset as 98.5% using SVM Classifier. For Oxford 17-Flowers Dataset, we found the best accuracy as 99.8% with MLP Classifier. These results are better than others’ that classify the same datasets in the literature.