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Abstract

The main issues in hunting management in Belarus are environmental protection, social-cultural, and economic problems. The total area of hunting grounds in the area is approx. 16.6 million hectares, including approx. 7.4 million hectares of forestlands, 8.2 million hectares of farmlands, and approx. 1 million hectares of wetlands. The territory of Belarus is characterized by lowland terrain features and a large number and area of stagnant and flowing waters. Protected areas (parks, reserves) account for 8.7% of the total area of the country. The hunting management is implemented in 250 legal entities. The main user of hunting grounds is the Belarusian Associa-tion of Hunters and Fishermen managing an area of ca. 10 million hectares. Hunting management is implemented based on national legislation of 2005. In 2015, the population of the moose was 32 thousand, deer – 15.2 thousand, roe deer – 74.6 thousand, beaver –58.3 thousand, capercaillie – 8.5 thousand, black grouse – 37.3 thousand. Over the last 10 years, the population of moose has doubled and the population of deer and roe deer has increased 2.5-fold and 1.5-fold, respectively. In relation to the habitat potential and breeding recommendations, the current populations of game species (moose, deer, and roe der) do not exceed 70% of the expected number. There are wild boars, but their numbers have been substantially reduced from 80 thousand to 2–3 thousand due to the epizootic threat (ASF). The hunting size is limited with reference to the number of individual species and the abundance dynamics. The level of exploitation of Cervidae is 10–13% of the total abundance, beavers – ca. 15%, and capercaillie and black grouse – 8–10%. Wolves are a hunting species and their population size over the last 10 years increased from 1000 to 1600 individuals, and the culling size increased from 700 to 1400.
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Authors and Affiliations

Yurij Shumski
Аnatolij Malazhavski
Vladymyr Yurgel
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Abstract

The paper presents new ensemble solutions, which can forecast the average level of particulate matters PM10 and PM2.5 with increased accuracy. The proposed network is composed of weak predictors integrated into a final expert system. The members of the ensemble are built based on deep multilayer perceptron and decision tree and use bagging and boosting principle in elaborating common decisions. The numerical experiments have been carried out for prediction of daily average pollution of PM10 and PM2.5 for the next day. The results of experiments have shown, that bagging and boosting ensembles employing these weak predictors improve greatly the quality of results. The mean absolute errors have been reduced by more than 30% in the case of PM10 and 20% in the case of PM2.5 in comparison to individually acting predictors.

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Authors and Affiliations

D. Triana
S. Osowski
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Abstract

The usage of wet methods for flue gas dedusting from coalfired boilers is associated with significant heat losses and water resources. Widespread emulsifiers of the first and second generation are satisfactory in terms of flue gas cleaning efficiency (up to 99.5%), but at the same time do not create conditions for deeper waste heat recovery, leading to lowering the temperature of gases. Therefore, in the paper, an innovative modernization, including installing an additional economizer in front of the scrubber (emulsifier) is proposed, as part of the flue gas passes through a parallel bag filter. At the outlet of the emulsifier and the bag filter, the gases are mixed in a suitable ratio, whereby the gas mixture entering the stack does not create conditions for condensation processes in the stack.
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Bibliography

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Authors and Affiliations

Iliya Krastev Iliev
1
Tomasz Kowalczyk
2
ORCID: ORCID
Hristo Kvanov Beloev
1
Angel Kostadinov Terziev
3
Krzysztof Jan Jesionek
4
Janusz Badur
2

  1. University of Ruse, Department of Thermotechnics, Hydraulics and Environmental Engineering, Studentska 8, 7017 Ruse, Bulgaria
  2. Energy Conversion Department, Institute of Fluid Flow Machinery, Polish Academy of Sciences, Fiszera 14, 80-251 Gdansk, Poland
  3. Technical University of Sofia, Department of Power Engineering and Power Machines, Kliment Ohridski 8, 1000 Sofia, Bulgaria
  4. Witelon Collegium State University, Faculty of Technical and Economic Science, Sejmowa 5C, 59-220 Legnica, Poland
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Abstract

Customer churn prediction is used to retain customers at the highest risk of churn by proactively engaging with them. Many machine learning-based data mining approaches have been previously used to predict client churn. Although, single model classifiers increase the scattering of prediction with a low model performance which degrades reliability of the model. Hence, Bag of learners based Classification is used in which learners with high performance are selected to estimate wrongly and correctly classified instances thereby increasing the robustness of model performance. Furthermore, loss of interpretability in the model during prediction leads to insufficient prediction accuracy. Hence, an Associative classifier with Apriori Algorithm is introduced as a booster that integrates classification and association rule mining to build a strong classification model in which frequent items are obtained using Apriori Algorithm. Also, accurate prediction is provided by testing wrongly classified instances from the bagging phase using generated rules in an associative classifier. The proposed models are then simulated in Python platform and the results achieved high accuracy, ROC score, precision, specificity, F-measure, and recall.
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Authors and Affiliations

Anitha M A
1
Sherly K K
2

  1. Faculty of Computer Science and Engineering, College of Engineering Cherthala, Alappuzha, Kerala, India
  2. Information Technology Department, Rajagiri School of Engineering & Technology Kochi-682039, Kerala, India

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