Abstract In this paper, we consider the task of learning interpretable process-based models of dynamic systems. While most case studies have focused on the descriptive aspect of such models, we focus on the predictive aspect. We use multi-year data, considering it as a single consecutive dataset or as several one-year datasets. Additionally, we also investigate the effect of interpolation of sparse... In this paper, we consider the task of learning interpretable process-based models of dynamic systems. While most case studies have focused on the descriptive aspect of such models, we focus on the predictive aspect. We use multi-year data, considering it as a single consecutive dataset or as several one-year datasets. Additionally, we also investigate the effect of interpolation of sparse data on the learning process. We evaluate and then compare the considered approaches on the task of predictive modeling of phytoplankton dynamics in Lake Zürich. Read More Read Less

Abstract The goal of our work is to help people to automatically identify the species and worker/queen type of bumblebee based on their recorded buzzing. Many recent studies of insect and bird classification based on their sound have been published, but there is no thorough study that deals with the complex nature of buzzing sound characteristic of bumblebees. In this paper,... The goal of our work is to help people to automatically identify the species and worker/queen type of bumblebee based on their recorded buzzing. Many recent studies of insect and bird classification based on their sound have been published, but there is no thorough study that deals with the complex nature of buzzing sound characteristic of bumblebees. In this paper, a database of recorded buzzings of eleven species were preprocessed and segmented into a series of sound samples. Then we applied J48, MLP and SVM supervised classification algorithms on some predetermined sets of feature vectors. For five species the recognition rate was above 80% and for other six species it was above 60%. At the end we consider how to further improve the results. Read More Read Less

Abstract Nowadays there is no facility to check the heaviness of the vehicle in tollgate. This system will be implemented on tollgate area to detect the load carried by the vehicle using weight sensor. The main objective of this system is to check whether the vehicle is overloaded or not. The load of the vehicle will be showed on the display screen and... Nowadays there is no facility to check the heaviness of the vehicle in tollgate. This system will be implemented on tollgate area to detect the load carried by the vehicle using weight sensor. The main objective of this system is to check whether the vehicle is overloaded or not. The load of the vehicle will be showed on the display screen and the weight will be checked against the database which consists the ideal weight of each type of the vehicles as per the government law. If the vehicle capacity is overloaded then it creates an alarm. This system is considered as a protected system because it prevents accident by preventing the vehicle to carry extra load and also it increases the life time of the road. We hope that if it comes in practice, it will play a major role in government sector. Read More Read Less