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.
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...
Using Markov chains for determining the proximity contagion of smart specialization of localities
Using Markov chains for determining the proximity contagion of smart specialization of localitiesOliviu Matei, Laura Andreica, Ioan Alin Danci, Anca Avram, Bogdan Văduva Abstract. Smart specialization strategies depend on understanding how economic specialization...
Advancements in Machine Learning Algorithms for Precision Crop Yield Prediction: A Comprehensive Review with focus on European Union
Advancements in Machine Learning Algorithms for Precision Crop Yield Prediction: A Comprehensive Review with focus on European UnionCarmen Anton, Anca Avram, Oliviu Matei, Laura Andreica, Bogdan Văduva Abstract. Accurate crop yield prediction is a key enabler of...
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.