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.
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...
Aggregation Strategy for Federated Machine Learning Algorithm
Aggregation Strategy for Federated Machine Learning AlgorithmRudolf Erdei, Daniela Delinschi, Iulia Băărăian, Oliviu Matei Abstract. Federated learning allows multiple parties to collaboratively train a model without sharing their raw data, but the quality of the...
Evaluation of Feature Selection Methods in Estimation of Precipitation Based on Deep Learning Artificial Neural Networks
Precipitation is the most important element of the water cycle and an indispensable element of water resources management. This paper aims to model the monthly precipitation in 8 precipitation observation stations. The effects and role of different feature weights pre-processing methods (Weight by deviation, Weight by PCA, Weight by correlation, and Weight by Support Vector Machine) on artificial intelligence modeling were investigated.