To study the environment of the Kipushi mining locality (LMK), the evolution of its landscape was observed using Landsat images from 2000 to 2020. The evolution of the landscape was generally modified by the unplanned expansion of human settlements, agricultural areas, associated with the increase in firewood collection, carbonization, and exploitation of quarry materials. The problem is that this area has never benefited from change detection studies and the LMK area is very heterogeneous. The objective of the study is to evaluate the performance of classification algorithms and apply change detection to highlight the degradation of the LMK. The first approach concerned the classifications based on the stacking of the analyzed Landsat image bands of 2000 and 2020. And the second method performed the classifications on neo-images derived from concatenations of the spectral indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Building Index (NDBI) and Normalized Difference Water Index (NDWI). In both cases, the study comparatively examined the performance of five variants of classification algorithms, namely, Maximum Likelihood (ML), Minimum Distance (MD), Neural Network (NN), Parallelepiped (Para) and Spectral Angle Mapper (SAM). The results of the controlled classifications on the stacking of Landsat image bands from 2000 and 2020 were less consistent than those obtained with the index concatenation approach. The Para and DM classification algorithms were less efficient. With their respective Kappa scores ranging from 0.27 (2000 image) to 0.43 (2020 image) for Para and from 0.64 (2000 image) to 0.84 (2020 image) for DM. The results of the SAM classifier were satisfactory for the Kappa score of 0.83 (2000) and 0.88 (2020). The ML and NN were more suitable for the study area. Their respective Kappa scores ranged between 0.91 (image 2000) and 0.99 (image 2020) for the LM algorithm and between 0.95 (image 2000) and 0.96 (image 2020) for the NN algorithm.

In this study, we utilized a convolutional neural network (CNN) trained on microscopic images encompassing the SARS-CoV-2 virus, the protozoan parasite “plasmodium falciparum” (causing of malaria in humans), the bacterium “vibrio cholerae” (which produces the cholera disease) and non-infected samples (healthy persons) to effectively classify and predict epidemics. The findings showed promising results in both classification and prediction tasks. We quantitatively compared the obtained results by using CNN with those attained employing the support vector machine. Notably, the accuracy in prediction reached 97.5% when using convolutional neural network algorithms.

Mapping land use and land cover (LULC) is essential for comprehending changes in the environment and promoting sustainable planning. To achieve accurate and effective LULC mapping, this work investigates the integration of Geographic Information Systems (GIS) with Machine Learning (ML) methodology. Different types of land covers in the Lucknow district were classified using the Random Forest (RF) algorithm and Landsat satellite images. Since the research area consists of a variety of landforms, there are issues with classification accuracy. These challenges are met by combining supplementary data into the GIS framework and adjusting algorithm parameters like selection of cloud free images and homogeneous training samples. The result demonstrates a net increase of 484.59 km² in built-up areas. A net decrement of 75.44 km² was observed in forest areas. A drastic net decrease of 674.52 km² was observed for wetlands. Most of the wastelands have been converted into urban areas and agricultural land based on their suitability with settlements or crops. The classifications achieved an overall accuracy near 90%. This strategy provides a reliable way to track changes in land cover, supporting resource management, urban planning, and environmental preservation. The results highlight how sophisticated computational methods can enhance the accuracy of LULC evaluations.

The cultivation of sugar beet (Beta vulgaris L.) for table or horticultural purposes is largely carried out in the conventional way which is characterized by intense mechanization causing soil degradation and high labor costs. New cultivation techniques are being employed in the production of vegetables aiming to ensure improvements in environmental and economic conditions, such as the no-till farming system. Thus, the objective of this work was to evaluate the vegetable classification and physicochemical characteristics of beets from different corn planting densities. The experiment was conducted in the period from October 2018 to June 2019 in the municipality of Nova Laranjeiras (PR). Corn was used as a cover plant and the vegetable used was beet cultivar Early Wonder Tall Top. The experimental design used was in interspersed blocks in unifactorial scheme (corn densities 40, 60, 80, 100 thousand plants/ha and control) with four blocks, with plots 3.60 m long and 1.20 m wide. The parameters evaluated 60 days after planting were: commercial classification (class, group, subgroup, category), length, diameter, mass, pulp firmness, soluble solids, titratable acidity, pH and ratio, phenolic compounds. Of which the variables that were not significant at 0.5 probability were length, category (defects), firmness, subgroup (flesh color), soluble solids and phenolic compounds. It is concluded that high densities of corn as mulch for SPDH of sugar beet crop negatively affect the grade and physicochemical characterization of the products.

Recognizing the discipline category of the abstract text is of great significance for automatic text recommendation and knowledge mining. Therefore, this study obtained the abstract text of social science and natural science in the Web of Science 2010-2020, and used the machine learning model SVM and deep learning model TextCNN and SCI-BERT models constructed a discipline classification model. It was found that the SCI-BERT model had the best performance. The precision, recall, and F1 were 86.54%, 86.89%, and 86.71%, respectively, and the F1 is 6.61% and 4.05% higher than SVM and TextCNN. The construction of this model can effectively identify the discipline categories of abstracts, and provide effective support for automatic indexing of subjects.

I summarize the current regulatory decisions aimed at combating the debt load of the population in Russia. Further, I show that the level of delinquency of the population on loans is growing despite the regulatory measures taken. In my opinion, the basis of regulatory policy should move from de facto pushing personal bankruptcies to preventing them. I put forward a hypothesis and statistically prove the expediency of quantitative restrictions on one borrower. It is necessary to introduce reports to the credit bureaus of some types of overdue debts, which are not actually reported now. It is also necessary to change the order of debt repayment established by law, allowing the principal and current interest to be paid first, which will prevent the expansion of the debt.