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Progress in Pattern Recognition, Image Analysis and Applications, Springer, 2007, 397-406. [10] García V., Sánchez J., Mollineda R., On the k-NN performance in a challenging scenario of imbalance and overlapping, Pattern Analysis and Applications, 11, 3-4, 2008, 269-280. [11] García V., Sánchez J., Mollineda R., On the effectiveness of preprocessing methods when dealing with different levels of class imbalance, Knowledge-Based Systems, 23, 1, 2012, 13-21. [12] He H., Ma Y., Imbalanced Learning: Foundations, Algorithms and Applications, Wiley, 2013. [13] Van Hulse, J


This article discusses the impact of choosing a data preprocessing method for the calculated gas density necessary to determine the sorption capacity of a material. The sample of gas-bearing shale was subjected to a volumetric sorption test. The obtained data, pressure and temperature were preprocessed by three methods: moving average, polynomial regression and locally weighted scatterplot smoothing. The results include the excess and absolute sorption calculated from data that were filtered, and data without pre-treatment and Langmuir isotherms’ coefficients for every case.

area in the fabric image as the average value. In this research, the importance of fabric image preprocessing in the detection of individual ITP is presented. It is a new concept to identify each ITP in the image of fabric structures. The research is based on the individual ITP identification method used in morphometrical structural analysis in woven fabrics as shown in an article. The ITP parameters were described in three aspects: size, shape, and location in the fabric structure [ 13 ]. Digital image analysis of the fabric structure began in the 1980s. Many


Modeling tools and operators help the user / developer to identify the processing field on the top of the sequence and to send into the computing module only the data related to the requested result. The remaining data is not relevant and it will slow down the processing. The biggest challenge nowadays is to get high quality processing results with a reduced computing time and costs. To do so, we must review the processing sequence, by adding several modeling tools. The existing processing models do not take in consideration this aspect and focus on getting high calculation performances which will increase the computing time and costs. In this paper we provide a study of the main modeling tools for BigData and a new model based on pre-processing.


Dealing with data from the field of medicine is nowadays very current and difficult. On a global scale, a large amount of medical data is produced on an everyday basis. For the purpose of our research, we understand medical data as data about patients like results from laboratory analysis, results from screening examinations (CT, ECHO) and clinical parameters. This data is usually in a raw format, difficult to understand, non-standard and not suitable for further processing or analysis. This paper aims to describe the possible method of data preparation and preprocessing of such raw medical data into a form, where further analysis algorithms can be applied.


As data sets, in education too, change in size and structure, an appropriate design of the preprocessing stage of data mining for the implementation of data mining for educational purposes is becoming a hot research topic. The aim of the present research is to carry out interdisciplinary analysis of scientific literature on pre-processing in data mining and to design a pre-processing stage of data mining for educational purposes underpinning elaboration of a new research question. The present research employs both theoretical and empirical methods. Theoretical methods include analysis of scientific literature and theoretical modelling. The theoretical findings allow identifying sub-stages of the pre-processing stage for the implementation of data mining for educational purposes. The empirical study was carried out in 2018. The study was a case study The empirical results emphasize the main areas of analysis of teachers’ behaviour in an international project. The empirical study validates the model of the pre-processing stage of data mining for educational purposes. The practical application of the model allows drawing a conclusion that the model is valid. The novel contribution of this paper is the design of the sub-stages of the preprocessing stage for the implementation of data mining techniques for educational purposes.


To effectively produce clean heat energy from biomass, microwave (mw) pre-processing of its different types - pelletized wood (spruce), herbaceous biomass (reed canary grass) and their mixture (50:50) - was carried out at the 2.45 GHz frequency with different durations of biomass exposure to high-frequency oscillations. To estimate the mw pre-processing effect on the structure, composition and fuel characteristics of biomass, its thermogravimetric (TG), infrared spectroscopy (FTIR) measurements and elemental analysis were made. The pre-processing is shown to enhance the release of moisture and low-calorific volatiles and the partial destruction of biomass constituents (hemicelluloses, cellulose), promoting variations in the elemental composition and heating values of biomass. The field-enhanced variations of biomass characteristics and their influence on its gasification and combustion were studied using an integrated system of a biomass gasifier and a combustor with swirl-enhanced stabilization of the flame reaction zone. The results show that the mw pre-processing of biomass pellets provides a faster weight loss at the gasification, and, therefore, faster ignition and combustion of the activated pellets along with increased output of heat energy at their burnout


This paper presents a novel approach in non-destructive analysis of inkjet-printed documents. Our method is based on the combination of molecular spectroscopy in the Near Infrared Region (NIR) and a chemometric method - principal component analysis (PCA). The aim of this work was to prepare spectral data for the analysis of the interrelationships between 19 samples consisting of the same type of office paper on which black squares were full printed in black ink only. The spectra were obtained separately using the Ocean Optics System in two spectral regions, i.e., overtones: 1000-1600 nm and combination bands: 1600-2300 nm, with the paper base. Experimental results confirmed the high reliability of the proposed approach despite the sparse dataset.

References [1] A umann , Y., and L indell , Y. Security against covert adversaries: Efficient protocols for realistic adversaries. J. Cryptology 23 , 2 (2010), 281–343. [2] B aum , C., D amgård , I., and O rlandi , C. Publicly auditable secure multi-party computation. In Security and Cryptography for Networks - 9th International Conference, SCN 2014. Proceedings (2014), M. Abdalla and R. D. Prisco, Eds., vol. 8642 of LNCS , Springer, pp. 175–196. [3] B aum , C., D amgård , I., T oft , T., and Z akarias , R. Better preprocessing for secure multiparty

Real Time Motion Data Preprocessing

There is a lot of redundant data for image processing in an image, in motion picture as well. The more data for image processing we have, the more time is needed for preprocessing it. That is why we need to work with important data only. In order to identify or classify motion, data processing in real time is needed.