Volumen 23 (2022): Edición 2 (April 2022)

The research is related to machine learning and deep learning (ML/DL) methods for clustering and classification that are compatible with anomaly detection (network attacks detection) in digital forensics. Research is conducted in the field of selecting subsets of features of a dataset useful for constructing a good predictor (classifier). In this study, a new feature selection method for a classifier based on the Analytical Hierarchy Process (AHP) method is presented and tested. The proposed step-by-step algorithm for the iterative selection of these features makes it possible to obtain the minimum required list of features that are associated with attack events and can be used to detect them. For the classification, Artificial Neural Network (ANN) method is used. The accuracy of attack detection by the proposed method has been verified in numerical experiments.

There were examined the problems of passage of the regulated parts of a road. There were investigated the changes of a traffic movement intensity in Lutsk (Ukraine) during the spread of Covid-19 pandemic. The graphic dependences of the drivers’ actions estimation while passing the traffic lights objects on a chosen movement route at the beginning of quarantine measures, during the least movement intensity and at the increasing of movement intensity, were obtained. A method of increasing of a possibility of the traffic lights objects nonstop passage was offered.

The forecasting of future airline passenger demand is critical task for airline management. The objective of the present study was to develop an adaptive neuro-fuzzy inference system (ANFIS) for predicting Australia’s domestic airline passenger demand. The ANFIS model was trained, tested, and validated in the study. Sugeno fuzzy rules were used in the ANFIS structure and Gaussian membership function, and linear membership functions were also developed. The hybrid learning algorithm and the subtractive clustering partition method were used to generate the optimum ANFIS models. The results found that the mean absolute percentage error (MAPE) for the overall data set of the ANFIS model was 3.25% demonstrating that the ANFIS model has high predictive capabilities. The ANFIS model could be used in other domestic air travel markets.

This research put forward an efficacious real-time deep learning-based technique to automate the process of monitoring the social distancing in transportation systems (e.g., bus stops, railway stations, airport terminals, etc.) and other public spaces with the purpose to mitigate the impact of coronavirus pandemic. The proposed technique makes use of the YOLOv3 model to segregate humans from the background of each image of a surveillance video and the linear Kalman filter for tracking the humans’ motion even in case in which another object or person overlaps the trajectory of the person under analysis. The performance of the model in human detection is extremely high as demonstrated by the accuracy of the model that reaches values higher than 95%. The detection algorithm can be applied for alerting people to keep a safe distance from each other when they are in crowded places or in groups.

This paper reports the undermined potential of broad range of (Information and communication technologies) ICTs that remained effective yet unnoticed in different flood-phases to exchange traffic, travel, and evacuation related information. The objective was to identify convenient ICTs that people found operational in life cycle of a flood. For the purpose, ICTs were tested in relation to 18 different variables based on personal capabilities, demographic, and vehicle-based information etc.

Samples of 105 and 102 subjects were recruited from flood-prone communities of developing and developed case-studies respectively, through random sampling and analyzed through Multinomial Logistic Regression. Those categories of independent variables that showed p-value ≥ 0.05 were considered to model the results. The main findings showed that in developed countries TV, mobile phone subscriptions and international news channels were prominent source of information whilst in developing countries multiple messengers, Facebook and contributory websites were impactful for information dissemination. The results are useful for academia, engineers, and policy makers and for future work same variables can be tested for different disaster affected communities.

This article proposes a multilevel bike-sharing assessment framework based on the concept of public value. This approach makes it possible to combine customer satisfaction with the transport service system with determinants of demand for bicycle services in the form of value. The framework aims to evaluate the parameters of public bike systems (PBS) that determine user value, and that co-create user value, system value, and social and ecological value, to identify the characteristics of the bicycle that need improvement in order to meet users’ needs and optimize quality. The framework uses empirical verification through satisfaction surveys of PBS users in Lodz, Poland. The results of the study were subjected to factor analysis, which revealed four groups of factors that satisfy public bike users: (1) impact on the health, environment, mobility and traffic in the city, (2) reliability, and comfort, (3) intramodality, (4) price and technical availability.