This manuscript presents the economic research results based on their input-output characteristics and functional description with inductive modeling methods and tools. There are a wide plethora of methods to be used for solving this type of problem, including various neural network models, linear and nonlinear regressions, reference vectors’ methods, fuzzy models, etc. The main disadvantage of these methods is that the obtained models cannot always interpret and obtain a model of optimal complexity. Unlike the mentioned methods and tools, the group method of data handling (GMDH) allows building models directly from a data sample without the attraction of additional a priori information. This algorithm admits finding internal dependencies in the data and determining optimal model complexity. There is a broad range of iterative GMDH algorithms that have been developed and studied. Oversampling algorithms are applicable for solving the structural identification problems for a limited number of arguments. Iteration algorithms are suitable for solving tasks with many arguments, but they do not guarantee proper structure development. Multi-row GMDH iteration algorithms are the most popular ones. However, they have several sufficient defects, such as informative argument loss or non-informative argument inclusion, as well as a polynomial degree of exponential growth. In this context, the applicability of the GMDH-based iterative and combined architectures for solving the model's interrelation problems between a volume of capital investments and GDP by activity types in the transport branch is considered. The determination coefficient is utilized for the estimation of the obtained models based on a complicated evaluation procedure. The Kolmogorov-Smirnov criterion estimates the model’s adequacy. The F-criterion Fisher assesses the significance of polynomial models. The demonstrated results proved that the combined iterative and combinatorial algorithms turned out to be the most effective solution for all evaluation criteria.

Using convolution neural network (CNN) for face recognition is being widely research with a promising significant in applications and it is interested by many authors. Moreover, the CNN model has brought successful applications in practice such as detection and identification face of people on Facebook users' photos application, they use DeepFace model. There are many articles which proposed CNN models for face recognition with using some modifications of popular models of large architectures such as VGG, ResNet, OpenFace or FaceNet. However, these models are large complexity for some applications in reality with limitations of computing resources. This paper proposes a design of CNN model with moderate complexity but still ensures the quality and efficiency of face recognition. We run experiments for evaluating the model on some popular datasets, the experiment shows effective results and indicates that the proposed model can be practically used.

Data transfer over the Internet comes with its range of challenges and associated prospects as a major milestone in the convergence of information and communication technology (ICT). Campus network implemented on IP-telephony defines a range of convergence technologies and applications that refers to a multi-service network that allows integration of data, audio, voice, and video solutions onto a converged infrastructure so that data can be transported via the use of open-source applications, protocols, hardware, and software. The study adopts the Federal College of Education Technical Asaba. It is observed that some issues in its implementation include packet loss, jitters, and latency. Jitters and packet loss can be curbed via an increased bandwidth allocation; while latency is minimized via constant upgrade in network infrastructure to increase speed. Overall, the proposed network seeks to provide its users with mobility, resilience, economy, flexibility, and productivity. Its results recommends that organizations wishing to harness its potentials should join forums and user-groups that will constantly update their knowledge in a bid to help them improve the efficiency and effectiveness of their infrastructure implementation..

Data mining is now commonly applied in the real estate market. Data mining's ability to extract relevant knowledge from raw data makes it very useful to predict house prices, key housing attributes, and many more. Research has stated that the fluctuations in house prices are often a concern for house owners and the real estate market. A survey of literature is carried out to analyze the relevant attributes and the most efficient models to forecast the house prices. The findings of this analysis verified the use of the Artificial Neural Network, Support Vector Regression and XGBoost as the most efficient models compared to others. Moreover, our findings also suggest that locational attributes and structural attributes are prominent factors in predicting house prices. This study will be of tremendous benefit, especially to housing developers and researchers, to ascertain the most significant attributes to determine house prices and to acknowledge the best machine learning model to be used to conduct a study in this field.

Course evaluation is a critical part of undergraduate curriculum in computer science. Most existing evaluation methods are based on questionnaire by analyzing the satisfaction rate of the respondents. However, there are many indicators such as attendance rate, activity level and average score that can reflect the overall effectiveness of the course. Limited research has taken all those indicators into account during course evaluation. This research chooses an innovative perspective that considers course evaluation as a multiple criteria decision-making problem. A hybrid model is proposed to measure the course effectiveness regarding various indicators. The indicators are first prioritized by a fuzzy Analytic Hierarchical Process (AHP) model which applies fuzzy numbers to deal with the uncertainty brought by subjective judgement. A hierarchical fuzzy inference system (FIS) is then designed to evaluate the course effectiveness, which reduces the number of the fuzzy IF-THEN rules and increases the efficiency compared to the traditional FIS. A numerical example is presented to demonstrate the application. The proposed model helps not only judge an individual course based on a comprehensive view but also rank multiple courses.