Publications & Posters
Explore the comprehensive repository of journal papers and publications dedicated to the COSA project, showcasing groundbreaking research and innovative developments in this pioneering field.
- Mohammad Taghi Sattari, Anca Avram, Halit Apaydin, Oliviu Matei, Evaluation of Feature Selection Methods in Estimation of Precipitation Based on Deep Learning Artificial Neural Networks, 2023
- Cosmin Sabo, Petrică Claudiu Pop, Adrian Petrovan, A Comparison of different crossover operators in genetic algorithms for clusters shortest-path tree problem, 2023
- Petrică C. Pop, Ovidiu Cosma, Cosmin Sabo, Corina Pop Sitar, A comprehensive survey on the generalized traveling salesman
problem, 2023 - Ovidiu Cosma, Petrică C. Pop, Laura Cosma, A hybrid-based genetic algorithm for solving the clustered generalized traveling salesman problem, 2023
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.
A Comparison of different crossover operators in genetic algorithms for clusters shortest-path tree problem
The clustered shortest-path tree (CluSPT) problem is an extension of the classical shortest path problem, given a graph with the nodes partitioned into several mutually exclusive and collectively exhaustive clusters looks for a shortest-path spanning tree from a predefined source node to all the other nodes of the graph, with the property that every cluster should generate a connected subgraph.
A comprehensive survey on the generalized traveling salesman problem
The generalized traveling salesman problem (GTSP) is an extension of the classical traveling salesman
problem (TSP), and it is among the most researched combinatorial optimization problems due to its theoretical properties, complexity aspects, and real-life applications in various areas: location-routing problems, material flow design problem, distribution of medical supplies, urban waste collection management, airport selection and routing the courier airplanes, image retrieval and ranking, digital garment manufacturing, etc.
A hybrid based genetic algorithm for solving the clustered generalized traveling salesman problem
We study the clustered generalized traveling salesman problem (CGTSP), which is an extension of the generalized traveling salesman problem (GTSP), which in turn generalizes the well-known traveling salesman problem (TSP).