A5029487118- Recommender Systems and Techniques
- Text and Document Classification Technologies
- Technology and Data Analysis
- Explainable Artificial Intelligence (XAI)
- Online Learning and Analytics
- Video Surveillance and Tracking Methods
- Rough Sets and Fuzzy Logic
- Species Distribution and Climate Change
- Remote Sensing in Agriculture
- Scientific Computing and Data Management
- Climate change impacts on agriculture
- Impact of Light on Environment and Health
- Cognitive Computing and Networks
- Semantic Web and Ontologies
- Topic Modeling
- Human Mobility and Location-Based Analysis
Technical University of Munich
2023-2024
University of Ss. Cyril and Methodius in Trnava
2017
Saints Cyril and Methodius University of Skopje
2017
Accurate and up-to-date mapping of the human population is fundamental for a wide range disciplines, from effective governance establishing policies to disaster management crisis dilution. The traditional method gathering data through census costly time-consuming. Recently, with availability large amounts Earth observation sets, deep learning methods have been explored estimation; however, they are either limited by availability, inter-regional evaluations, or transparency. In this paper, we...
Automated crop-type classification using Sentinel-2 satellite time series is essential to support agriculture monitoring. Recently, deep learning models based on transformer encoders became a promising approach for classification. Using explainable machine reveal the inner workings of these an important step towards improving stakeholders' trust and efficient In this paper, we introduce novel explainability framework that aims shed light crop disambiguation patterns learned by...
Climate variability and extremes are known to represent major causes for crop yield anomalies. They can lead the reduction of productivity, which results in disruptions food availability nutritional quality, as well rising prices. Extreme climates will become even more severe global warming proceeds, challenging achievement security. These extreme events, especially droughts heat waves, already evident food-production regions like United States. Crops cultivated this country such corn...
Traditional recommendation systems rely on past usage data in order to generate new recommendations. Those approaches fail sensible recommendations for users and items into the system due missing information about their interactions. In this paper, we propose a solution successfully addressing item-cold start problem which uses model-based approach recent advances deep learning. particular, use latent factor model recommendation, predict factors from item's descriptions using convolutional...