Design of a recommendation system based on collaborative filtering and machine learning considering personal needs of the user

A modified neural detector and a method of principal components analysis were considered to improve a quality of network attacks detection.

The three-stage approach for missing data imputation in Big data interface is proposed in the paper. The first stage includes designing the Big data model in the task of missing data recovery, which enables to process the structured and semistructured data. The next stage is developing the method of missing data recovery based on functional dependencies and association rules. The estimating the algorithm complexity for missing data recovery is provided at the last stage. The proposed method of missing data recovery creates additional data values using a based domain and functional dependencies and adds these values in available training data. The performing the analysis of data different types is possible too. The correctness of the imputed values is verified on the classifier built on the original dataset. The proposed method performs in 12% better than the EM and RF methods for 30% missing data and enables the parallel execution in distributed databases. The acceleration for m=41 attributes is larger in 12.5 times for 20 servers (processors) comparing with the non-parallel modeю

The approach to compress network traffic parameters in systems for detecting attacks on telecommunication networks using the neural network and nonlinear principal components analysis is proposed. It is recommended to implement the deep neural network consists of a perceptron with lots of layers and that overcomes limitations of classic multilayer perceptron with the help of deep architecture. Experimental studies have confirmed that proposed approach provides a reduction of the data dimensionality and improves the accuracy of results during the analysis of network traffic parameters.

There is developed a combined method that is based on the integration of neural network detectors in an artificial immune system. This allowed them to adapt to new attacks with the help of cloning and mutation operations.

This research paper proposes an innovative approach to urban waste management using intelligent methods of classification, clustering, and forecasting. The application of this approach allows for more efficient waste management and contributes to the sustainable development of the urban environment. The aim of this research is to develop an intelligent method for urban waste management, which includes clustering of waste sources, accurate forecasting of waste volumes, and evaluation of forecast results. To achieve this goal, a real dataset with city characteristics and waste data was used. On account of the war in Ukraine, the authors faced the problem of obtaining open data on waste in Ukraine, so it was decided to use data from another city (Singapore). The results show the high efficiency of the developed method. Comparison of the obtained results with the results of the nearest similar works shows that the main feature of this study is the high accuracy of waste-volume forecasting using the XGBoost model, which reached a level of up to 98%.