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Niedługo rusza konferencja The 16th International Conference on Artificial Intelligence and Soft Computing ICAISC 2017, Zakopane, Poland, June 11-15, 2017. Na ww. konferencję został zgłoszony i zaakceptowany artykuł dotyczący klasyfikacji dokumentów wielojęzycznych. Podążając za abstraktem – The aim of this study was to propose a classification method for documents that include simultaneously text parts in various languages. For this purpose, we constructed a three-leveled classification system. On its first level, a data processing module prepares a suitable vector space model. Next, in the middle tier, a set of monolingual or multilingual classifiers assigns the probabilities of belonging each document or its parts to all possible categories. The models are trained by using Multinomial Naive Bayes and Long Short-Term Memory algorithms. Finally, in the last component, a multilingual decision module assigns a target class to each document. The module is built on a logistic regression classifier, which as the inputs receives probabilities produced by the classifiers. The system has been verified experimentally. According to the reported results, it can be assumed that the proposed system can deal with textual documents which content is composed of many languages at the same time. Therefore, the system can be useful in the automatic organizing of multilingual publications or other documents.

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Niedługo rusza konferencja The 16th International Conference on Artificial Intelligence and Soft Computing ICAISC 2017, Zakopane, Poland, June 11-15, 2017. Na ww. konferencję został zgłoszony i zaakceptowany artykuł dotyczący klasyfikacji logotypów. Podążając za abstraktem – The aim of this study was to describe a found method for detection of logotypes that indicate innovativeness of companies, where the images originate from their Internet domains. For this purpose, we elaborated a system that covers a supervised and heuristic approach to construct a reference dataset for each logotype category that is utilized by the logistic regression classifiers to recognize a logotype category. We proposed the approach that uses one-versus-the-rest learning strategy to learn the logistic regression classification models to recognize the classes of the innovative logotypes. Thanks to this we can detect whether a given company’s Internet domain contains an innovative logotype or not. More- over, we find a way to construct a simple and small dimension of feature space that is utilized by the image recognition process. The proposed feature space of logotype classification models is based on the weights of images similarity and the textual data of the images that are received from HTMLs ALT tags.

Z artykułem można zapoznać się w dziale Publikacje i jak zawsze życzę miłej lektury.