Use cases
Use cases of the COSA Project.
Use cases of COSA
The COSA framework is validated through three complementary use cases that cover land evaluation, aerial monitoring and an interactive demonstrator.
Use Case 1 — Crop Yield Prediction & Land Bonitation
Combining soil studies, GIS and machine learning to evaluate land potential and forecast agricultural yields.
Use Case 2 — Aerial Crop Monitoring & Precision Treatment
Using drones and aerial imagery to detect crop conditions and apply precise, targeted treatments.
Use Case 3 — Interactive Demonstrator
Open the COSA Use Case 3 platform in a dedicated environment.
Optimizing fertilization and crop management for triticale in the Lăpuș depression, Romania
Optimizing fertilization and crop management for triticale in the Lăpuș depression, RomaniaI. Cionca, A. D. Costin, T. Rusu Abstract. Triticale is an important cereal crop in mountainous and hilly areas of Romania, where soil and climatic conditions can limit the...
Design of a collaborative network for mapping digital skills for Industry 5.0
Design of a collaborative network for mapping digital skills for Industry 5.0Maria Gustavsson, Oliviu Matei, Laura Andreica, Agneta Halvarsson Lundkvist, Daniel Persson Thunqvist Abstract. The transition to Industry 5.0 brings new demands for the workforce, where...
Solving the clustered minimum routing tree problem using Prüfer-coding based hybrid genetic algorithms
Solving the clustered minimum routing tree problem using Prüfer-coding based hybrid genetic algorithmsCosmin Sabo, Bogdan Teglaș, Petrică C. Pop, Adrian Petrovan Abstract. The clustered minimum routing tree problem (CluMRTP) extends the classical minimum routing tree...
Augmenting API Security Testing with Automated LLM-Driven Test Generation
Augmenting API Security Testing with Automated LLM-Driven Test GenerationEmil Marian Pasca, Rudolf Erdei, Daniela Delinschi, Oliviu Matei Abstract. API security testing is an essential step in modern software development, but manually crafting comprehensive test...
Data Quality Assessment Methodology
Data Quality Assessment MethodologyDaniela Delinschi, Rudolf Erdei, Emil Pasca, Oliviu Matei Abstract. High-quality data is a precondition for reliable machine learning, analytics and decision support. This paper introduces a methodology for systematic data quality...
Privacy Assessment Methodology for Machine Learning Models and Data Sources
Privacy Assessment Methodology for Machine Learning Models and Data SourcesRudolf Erdei, Emil Pasca, Daniela Delinschi, Anca Avram, Ionela Chereja, Oliviu Matei Abstract. The widespread use of machine learning amplifies privacy risks both at the level of training data...