Some Aspects Regarding Internet Advertising

The aim of this paper is to examine internet advertising, to understand its strengths and weaknesses, to compare the content and potential of traditional media with Web sites and to describe the specific types of internet advertising. The paper also tries to present some facts and figures regarding internet advertising

Automatic Visual Speech Recognition

Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc

An exploratory and comparative assessment of the tourist circulation at the level of some countries of the South-Eastern European union in the current pandemic context

The coronavirus epidemic (COVID19) has affected the global economy and the services sector. Quarantine measures related to travel restrictions have led to an unprecedented decline in the tourism industry with repercussions on tourism service providers, transport companies and state budgets. Travel is necessary for tourism, therefore, any factor that prevents travel can have a profound impact on the tourism industry. In the current pandemic context, the forecast in the field of tourist travel has played an important role in supporting the revival of this sector. In this study, econometric and interpretive methods were combined to predict the demand. In this study we approached a prediction model that is based on the seasonal stationary and adjustment of observed and FFT data. Experimental results show that the proposed prediction model has demonstrated a good medium-term forecast and can be used successfully in short and medium periods of time. For a certification of the exploratory evaluation of tourism forecasts there were comparatively analyzed the results obtained for three countries in south-eastern Central Europe, countries with similar natural and anthropic tourist resources (Bulgaria, Croatia and Romania)

Landsat Time Series Analysis for Modelling Temporal Probability for Landslide Occurrences in Curvature Subcarpathians, Romania. GI_Forum 2013 – Creating the GISociety|

The assessment of temporal probability of landslides occurrence requires understanding the factors that control the stability of slopes. These factors are classified in predisposing (geomorphology, geology, etc.) and triggering factors (precipitation or earthquakes). Some of these factors remain constant over time (like geology), some are in a constant change (like land-use and land-cover) and some are rapidly changing their state (like rainfall intensity). If geology can be mapped in the field and rainfall can be measured with rain gauges, the changes in land-cover related to phenological phases are more difficult to measure and map. For the latter, the use of satellite images has been proven the most reliable solution. The present study uses Landsat archive for modelling the changes of rainfall interception over time as the result of phenological changes in land-cover. Over 300 Landsat scenes from 1973 until 2011 were used to calculate leaf area index (LAI). LAI has an important contribution in the rainfall interception model with direct impact on the landslides hydrological model. The spatial and temporal probability of landslides occurrence is calculated using Bayesian Dynamic Network (BDN). The factors for BDN are mapped or derived with deterministic analyses. The model is running in monthly time steps and the results are validated with recorded landslides, triggered in different time periods. For each occurrence of a landslide the local and temporal conditions (including the modelled and the observed values) are stored and statistically analysed

Landsat Time Series Analysis for Modelling Temporal Probability for Landslide Occurrences in Curvature Subcarpathians, Romania. GI_Forum 2013 – Creating the GISociety|

The assessment of temporal probability of landslides occurrence requires understanding the factors that control the stability of slopes. These factors are classified in predisposing (geomorphology, geology, etc.) and triggering factors (precipitation or earthquakes). Some of these factors remain constant over time (like geology), some are in a constant change (like land-use and land-cover) and some are rapidly changing their state (like rainfall intensity). If geology can be mapped in the field and rainfall can be measured with rain gauges, the changes in land-cover related to phenological phases are more difficult to measure and map. For the latter, the use of satellite images has been proven the most reliable solution. The present study uses Landsat archive for modelling the changes of rainfall interception over time as the result of phenological changes in land-cover. Over 300 Landsat scenes from 1973 until 2011 were used to calculate leaf area index (LAI). LAI has an important contribution in the rainfall interception model with direct impact on the landslides hydrological model. The spatial and temporal probability of landslides occurrence is calculated using Bayesian Dynamic Network (BDN). The factors for BDN are mapped or derived with deterministic analyses. The model is running in monthly time steps and the results are validated with recorded landslides, triggered in different time periods. For each occurrence of a landslide the local and temporal conditions (including the modelled and the observed values) are stored and statistically analysed