In this paper a novel statistical approach for comparing meta-heuristic stochastic optimization algorithms according to the distribution of the solutions in the search space is introduced, known as extended Deep Statistical Comparison. The approach is an extension of the recently proposed Deep Statistical Comparison approach used for comparing meta-heuristic stochastic optimization algorithms according to the solutions values. Its main contribution is that the algorithms are compared not only according to obtained solutions values, but also according to the distribution of the obtained solutions in the search space. The information it provides can additionally help to identify exploitation and exploration powers of the compared algorithms. This is important when dealing with a multimodal search space, where there are a lot of local optima with similar values. The benchmark results show that our proposed approach gives promising results and can be used for a statistical comparison of meta-heuristic stochastic optimization algorithms according to solutions values and their distribution in the search space.
COBISS.SI-ID: 32238631
Using the AI methodology, the changes in the food consumption patterns before and during the COVID-19 pandemic are obvious. The highest positive difference in the food consumption can be found in foods such as “Pulses/ plants producing pulses”, “Pancake/Tortilla/Outcake”, and “Soup/pottage”, which increase by 300%, 280%, and 100%, respectively. Conversely, the largest decrease in consumption can be find for food such as “Order Perciformes”, “Corn/cereals/grain”, and “Wine-making”, with a reduction of 50%, 40%, and 30%, respectively. This kind of analysis is valuable in times of crisis and emergencies, which is a very good example of the scientific support that regulators require in order to take quick and appropriate response.
COBISS.SI-ID: 26928643
The availability of commercial wearable bio-sensors provides an opportunity for developing smart phone applications for real-time diagnosis that can be used to improve the health of the user. We propose a multi-level information fusion approach for learning a predictive model for blood pressure (BP) using electrocardiogram (ECG) sensor data. The approach fuses the information on five different levels: i) data collection, where data from multiple ECG sensors is collected; ii) feature extraction, where features are extracted from the collected data by different preprocessing methods; iii) information fusion, fusing the evaluation information from different classifiers; iv) information fusion using the information from multi-target regression models for each BP class; and v) information fusion using the information from multi-target regression models from all configurations as a single model. This is used for predicting the blood pressure values (systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP)). Evaluating the methodology by using a separate test set indicates that the multi-level information fusion provides promising results, which are acceptable and comparable to the state-of-the-art results obtained for blood pressure prediction.
COBISS.SI-ID: 33002535
Forecasting the residential natural gas demand for large groups of buildings is extremely important for efficient logistics in the energy sector. In this paper different forecast models for residential natural gas demand of an urban area were implemented and compared. The models forecast gas demand with hourly resolution up to 60 h into the future. The model forecasts are based on past temperatures, forecasted temperatures and time variables, which include markers for holidays and other occasional events. The models were trained and tested on gas-consumption data gathered in the city of Ljubljana, Slovenia. Machine-learning models were considered, such as linear regression, kernel machine and artificial neural network. Additionally, empirical models were developed based on data analysis. Two most accurate models were found to be recurrent neural network and linear regression model. In realistic setting such trained models can be used in conjunction with a weather-forecasting service to generate forecasts for future gas demand.
COBISS.SI-ID: 31841575
Democratic principles, from the freedom of speech, to fair business practices, rely on Net neutrality, i.e. equal access to communication infrastructure and services. While a number of national and international regulations stipulate Net neutrality, the actual enforcement is challenging as regulators have to collect and analyze a large amount of network measurements, and pinpoint cases of neutrality violations. Through a large-scale distributed crowdsourced measurements campaign, the Agency for Communication Networks and Services of the Republic of Slovenia (AKOS) has acquired a massive dataset of Internet performance measurements in Slovenia. In this work we analyze about one million multi-dimensional data records gathered by the AKOS Test Net measurement system and identify the practices, such as port blocking, that might violate Net neutrality principles. We then chart the limitations of the employed measurement approach and propose a holistic multi-stakeholder approach ensuring high quality measurement data upon which reliable Net neutrality violation inferences should be based.
COBISS.SI-ID: 18356739