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Volume 23 (2022): Edition 3 (June 2022)

Volume 23 (2022): Edition 2 (April 2022)

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Volume 21 (2020): Edition 3 (June 2020)

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Volume 20 (2019): Edition 4 (December 2019)

Volume 20 (2019): Edition 3 (June 2019)

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Volume 19 (2018): Edition 4 (December 2018)

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Volume 18 (2017): Edition 4 (December 2017)

Volume 18 (2017): Edition 3 (September 2017)

Volume 18 (2017): Edition 2 (June 2017)

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Volume 17 (2016): Edition 4 (December 2016)

Volume 17 (2016): Edition 3 (September 2016)

Volume 17 (2016): Edition 2 (June 2016)

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Volume 16 (2015): Edition 4 (December 2015)

Volume 16 (2015): Edition 3 (September 2015)

Volume 16 (2015): Edition 2 (June 2015)

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Volume 15 (2014): Edition 4 (December 2014)

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Volume 14 (2013): Edition 4 (December 2013)

Volume 14 (2013): Edition 3 (September 2013)

Volume 14 (2013): Edition 2 (June 2013)

Volume 14 (2013): Edition 1 (March 2013)

Volume 13 (2012): Edition 4 (December 2012)

Volume 13 (2012): Edition 3 (September 2012)

Volume 13 (2012): Edition 2 (June 2012)

Volume 13 (2012): Edition 1 (March 2012)

Détails du magazine
Format
Magazine
eISSN
1407-6179
Première publication
20 Mar 2000
Période de publication
4 fois par an
Langues
Anglais

Chercher

Volume 23 (2022): Edition 2 (April 2022)

Détails du magazine
Format
Magazine
eISSN
1407-6179
Première publication
20 Mar 2000
Période de publication
4 fois par an
Langues
Anglais

Chercher

6 Articles
Accès libre

Feature Selection Method for Ml/Dl Classification of Network Attacks in Digital Forensics

Publié en ligne: 30 Apr 2022
Pages: 131 - 141

Résumé

Abstract

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.

Mots clés

  • IDS
  • anomaly detection
  • machine learning
  • feature selection
  • AHP
  • ANN
Accès libre

Research of an Influence of a Traffic Flow Movement Intensity Change on the Possibility of Nonstop Passage of the Traffic Lights Objects

Publié en ligne: 30 Apr 2022
Pages: 142 - 150

Résumé

Abstract

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.

Mots clés

  • traffic lights
  • data exchange
  • vehicle
  • traffic flow
  • Covid-19
  • nonstop pas-sage
  • movement intensity
Accès libre

Predicting Australia’s Domestic Airline Passenger Demand using an Anfis Approach

Publié en ligne: 30 Apr 2022
Pages: 151 - 159

Résumé

Abstract

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.

Mots clés

  • adaptive neuro-fuzzy inference system (ANFIS)
  • Australia
  • forecasting methods
  • domestic airlines
  • passenger forecasting
Accès libre

Social Distance Evaluation in Transportation Systems and Other Public Spaces using Deep Learning

Publié en ligne: 30 Apr 2022
Pages: 160 - 167

Résumé

Abstract

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.

Mots clés

  • COVID-19 pandemic
  • Social distance evaluation
  • Deep Learning
  • YOLOv3 model
Accès libre

Mapping Undermined Role of Information and Communication Technologies in Floods

Publié en ligne: 30 Apr 2022
Pages: 168 - 179

Résumé

Abstract

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.

Mots clés

  • Multinomial Logistic Regression
  • Intelligent transport system technologies
  • Emerging Technologies
  • Transport-Disaster scenarios
Accès libre

A Public Value-Based, Multilevel Evaluation Framework to Examine Public Bike-Sharing Systems. Implications for Cities’ Sustainable Transport Policies

Publié en ligne: 30 Apr 2022
Pages: 180 - 194

Résumé

Abstract

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.

Mots clés

  • public bike-sharing system
  • multilevel evaluation framework
  • public economy
  • public value
  • public services
6 Articles
Accès libre

Feature Selection Method for Ml/Dl Classification of Network Attacks in Digital Forensics

Publié en ligne: 30 Apr 2022
Pages: 131 - 141

Résumé

Abstract

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.

Mots clés

  • IDS
  • anomaly detection
  • machine learning
  • feature selection
  • AHP
  • ANN
Accès libre

Research of an Influence of a Traffic Flow Movement Intensity Change on the Possibility of Nonstop Passage of the Traffic Lights Objects

Publié en ligne: 30 Apr 2022
Pages: 142 - 150

Résumé

Abstract

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.

Mots clés

  • traffic lights
  • data exchange
  • vehicle
  • traffic flow
  • Covid-19
  • nonstop pas-sage
  • movement intensity
Accès libre

Predicting Australia’s Domestic Airline Passenger Demand using an Anfis Approach

Publié en ligne: 30 Apr 2022
Pages: 151 - 159

Résumé

Abstract

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.

Mots clés

  • adaptive neuro-fuzzy inference system (ANFIS)
  • Australia
  • forecasting methods
  • domestic airlines
  • passenger forecasting
Accès libre

Social Distance Evaluation in Transportation Systems and Other Public Spaces using Deep Learning

Publié en ligne: 30 Apr 2022
Pages: 160 - 167

Résumé

Abstract

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.

Mots clés

  • COVID-19 pandemic
  • Social distance evaluation
  • Deep Learning
  • YOLOv3 model
Accès libre

Mapping Undermined Role of Information and Communication Technologies in Floods

Publié en ligne: 30 Apr 2022
Pages: 168 - 179

Résumé

Abstract

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.

Mots clés

  • Multinomial Logistic Regression
  • Intelligent transport system technologies
  • Emerging Technologies
  • Transport-Disaster scenarios
Accès libre

A Public Value-Based, Multilevel Evaluation Framework to Examine Public Bike-Sharing Systems. Implications for Cities’ Sustainable Transport Policies

Publié en ligne: 30 Apr 2022
Pages: 180 - 194

Résumé

Abstract

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.

Mots clés

  • public bike-sharing system
  • multilevel evaluation framework
  • public economy
  • public value
  • public services

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