A Comparison of Data Mining Tools and Classification Algorithms: Content Producers on the Video Sharing Platform

International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME) -- APR 20-22, 2019 -- Antalya, TURKEYWith the development of internet technologies, the use of video sharing sites has increased. Video sharing sites allow users to watch videos of others. In addition, users can create an account to upload content and upload videos. These platforms stand out as the places where individuals are both producers and consumers. In this study, data about YouTube which is a video sharing site was used. The content of the content, which is also called as a channel on YouTube, was made by using a set of producers. The data set with 5000 samples on YouTube channels is taken from Kaggle. The data were classified using 4 different data mining tools such as Weka, RapidMiner, Knime and Orange using Naive Bayes and Random Forest algorithms. The parameters are requested from the user in order to obtain a more efficient result in the application of data mining algorithms and in the data preprocessing steps and in the data mining steps. Although these parameters are common in some data mining software, they are not included in all data mining software. Data mining software provides management of some parameters while other parameters cannot be managed. These changes affect the accuracy value in the study and affect the accuracy value in different ratios. Changing the values of the parameters revealed differences in the accuracy rates obtained. A data mining software model has been proposed by emphasizing to what extent the management of the parameters of the study and the extent of the management of the parameters should be connected to the data mining software developer.WOS:0006787710000422-s2.0-8508344979

Automated detection of lung nodules in computed tomography images: a review

Lung nodules refer to a range of lung abnormalities the detection of which can facilitate early treatment for lung patients. Lung nodules can be detected by radiologists through examining lung images. Automated detection systems that locate nodules of various sizes within lung images can assist radiologists in their decision making. This paper presents a study of the existing methods on automated lung nodule detection. It introduces a generic structure for lung nodule detection that can be used to represent and describe the existing methods. The structure consists of a number of components including: acquisition, pre-processing, lung segmentation, nodule detection, and false positives reduction. The paper describes the algorithms used to realise each component in different systems. It also provides a comparison of the performance of the existing approaches.S.L.A. Lee, A.Z. Kouzani and E.J. H