• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1999.10a
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• pp.293-296
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• 1999

In conventional automatic container-terminal, the gantry cranes are operated manually or semi-automatically. But UGC is an unmanned-operated gantry crane and the positioning information for UGC is supplied only by position-measuring devices. In order to enhance the operation efficiency of UGC, it is required that the position-measuring devices have long maintenance period and are not sensitive to the weather and environment condition. And in order to be used practically in container terminal, the cost of position-measuring devices is not higher than currently used measuring devices. In the study, it is discussed the requirements for position-measuring devices in UGC, And it is studied on the measuring devices suitable to UGC. From this study, it is expected that the combination of a rotary encoder and a ferrous metal detector is useful for position-measuring devices in UGC.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.294-302
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• 2022

Detection of fake news is a complex and a challenging task. Generation of fake news is very hard to stop, only steps to control its circulation may help in minimizing its impacts. Humans tend to believe in misleading false information. Researcher started with social media sites to categorize in terms of real or fake news. False information misleads any individual or an organization that may cause of big failure and any financial loss. Automatic system for detection of false information circulating on social media is an emerging area of research. It is gaining attention of both industry and academia since US presidential elections 2016. Fake news has negative and severe effects on individuals and organizations elongating its hostile effects on the society. Prediction of fake news in timely manner is important. This research focuses on detection of fake news spreaders. In this context, overall, 6 models are developed during this research, trained and tested with dataset of PAN 2020. Four approaches N-gram based; user statistics-based models are trained with different values of hyper parameters. Extensive grid search with cross validation is applied in each machine learning model. In N-gram based models, out of numerous machine learning models this research focused on better results yielding algorithms, assessed by deep reading of state-of-the-art related work in the field. For better accuracy, author aimed at developing models using Random Forest, Logistic Regression, SVM, and XGBoost. All four machine learning algorithms were trained with cross validated grid search hyper parameters. Advantages of this research over previous work is user statistics-based model and then ensemble learning model. Which were designed in a way to help classifying Twitter users as fake news spreader or not with highest reliability. User statistical model used 17 features, on the basis of which it categorized a Twitter user as malicious. New dataset based on predictions of machine learning models was constructed. And then Three techniques of simple mean, logistic regression and random forest in combination with ensemble model is applied. Logistic regression combined in ensemble model gave best training and testing results, achieving an accuracy of 72%.