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.

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.

The objective of this paper was to propose a classification method of innovative domains on the Internet. The proposed approach helped to estimate whether companies are innovative or not through analyzing their web pages. A Naïve Bayes classification committee was used as the classification system of the domains. The classifiers in the committee were based concurrently on Bernoulli and Multinomial feature distribution models, which were selected depending on the diversity of input data. Moreover, the information retrieval procedures were applied to find such documents in domains that most likely indicate innovativeness. The proposed methods have been verified experimentally. The results have shown that the diversified classification committee combined with the information retrieval approach in the preprocessing phase boosts the classification quality of domains that may represent innovative companies. This approach may be applied to other classification tasks.

Few days ago a SIMIS magazine publicated my article Crowdsourcing in rescue fire service – proposed application. In this article I describes the proposal to apply crowdsourcing in Polish rescue fire service. This article also describes basic principles for implementing an crowdsourcing information platform in rescue fire service as well as the scheme of its implementation. Of this paper also to I describes the genesis of this proposal related to the evaluation of research conducted by the author on text mining analysis and extraction of information in the design of information systems.

I finished implementation  a questionnaire for fire service on the previous day. This questionnaire was implemented for quantitative/qualitative research. The destination of this research is a creation a hybrid decision support system for Polish fire service.  It’s a complicate problem which require many of different  researches. This research integrate solution from logistic, transport, game theory,  artificial intelligence, linguistics, retrieval of text information  like a text mining and especially text reprezentation and his processing.  All project was described by process of design for trustworthy software (DFTS). The results of  investigations will be published after the completion questionnaire by respondenst.

Questionnaire was implemented using web technologies like a JAVA+JSF+Hibernate+MySQL.