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.

[1] O. Cosma, P. C. Pop, and L. Cosma, “A hybrid based genetic algorithm for solving the clustered generalized traveling salesman problem,” in Proc. Int. Conf. on Hybrid Artificial Intelligence Systems (HAIS), Springer, 2023, pp. [online]. DOI: 10.1007/978-3-031-40725-3_30. (Reported to 15.10.2023 – report 1) [details]
[2] P. C. Pop, O. Cosma, C. Sabo, and C. Pop Sitar, “A comprehensive survey on the generalized traveling salesman problem,” European Journal of Operational Research, vol. 308, pp. [online], Elsevier, 2023. DOI: 10.1016/j.ejor.2023.07.022. (Reported to 15.10.2023 – report 1) [details]
[3] C. Sabo, P. C. Pop, and A. Petrovan, “A comparison of different crossover operators in genetic algorithms for clustered shortest-path tree problem,” in Proc. 50th Int. Conf. on Computers and Industrial Engineering (CIE 50), 2024. (Reported to 15.01.2024 – report 2) [details]
[4] M. T. Sattari, A. Avram, H. Apaydin, and O. Matei, “Evaluation of feature selection methods in estimation of precipitation based on deep learning artificial neural networks,” Water Resources Management, vol. 37, Springer, 2023. DOI: 10.1007/s11269-023-03563-4. (Reported to 15.01.2024 – report 2) [details]
[5] O. Cosma and L. Cosma, “A Novel CNN Approach for Accurate Tomato Disease Classification,” in Proc. 30th ICE/IEEE ITMC Conf., IEEE, 2024. DOI: 10.1109/ICE/ITMC61926.2024.10794256. (Reported to 15.07.2024 – report 4)
[6] M. Gustavsson, O. Matei, L. Andreica, A. H. Lundkvist, and D. P. Thunqvist, “Design of a collaborative network for mapping digital skills for Industry 5.0,” in Proc. PRO-VE 2024 Conf., 2024. (Reported to 15.07.2024 – report 4)
[7] C. Sabo, B. Teglaș, P. C. Pop, and A. Petrovan, “Solving the clustered minimum routing tree problem using Prüfer-coding based hybrid genetic algorithms,” in Proc. 19th Int. Conf. on Hybrid Artificial Intelligence Systems (HAIS), 2024. (Reported to 15.07.2024 – report 4)
[8] E. M. Pasca, R. Erdei, D. Delinschi, and O. Matei, “Augmenting API security testing with automated LLM-driven test generation,” in Proc. 17th Int. Conf. on Computational Intelligence in Security for Information Systems (CISIS), 2024. (Reported to 15.07.2024 – report 4)
[9] D. Delinschi, R. Erdei, E. Pasca, and O. Matei, “Data Quality Assessment Methodology,” in Proc. 19th SOCO Int. Conf. on Soft Computing Models in Industrial and Environmental Applications, 2024. (Reported to 15.07.2024 – report 4)
[10] R. Erdei, E. Pasca, D. Delinschi, A. Avram, I. Chereja, and O. Matei, “Privacy assessment methodology for machine learning models and data sources,” in Proc. 19th SOCO Conf., 2024. (Reported to 15.07.2024 – report 4)
[11] R. Erdei, D. Delinschi, I. Băărăian, and O. Matei, “Aggregation strategy for federated machine learning algorithm,” in Proc. 19th SOCO Conf., 2024. (Reported to 15.07.2024 – report 4)
[12] O. Matei, L. Andreica, I. A. Danci, A. Avram, and B. Văduva, “Using Markov chains for determining the proximity contagion of smart specialization of localities,” in Proc. 19th SOCO Conf., 2024. (Reported to 15.07.2024 – report 4)
[13] C. Anton, A. Avram, O. Matei, L. Andreica, and B. Văduva, “Advancements in machine learning algorithms for precision crop yield prediction: A comprehensive review with focus on European Union,” in Proc. 19th SOCO Conf., 2024. (Reported to 15.07.2024 – report 4)
[14] O. Cosma and L. Cosma, “TPC Net: An efficient CNN architecture for tomato plant disease and pest classification,” in Proc. 19th SOCO Conf., Springer, 2024. DOI: 10.1007/978-3-031-75010-6_19. (Reported to 15.07.2024 – report 4)
[15] E. M. Pasca, R. Erdei, D. Delinschi, and O. Matei, “Enhancing API security testing against BOLA and authentication vulnerabilities through an LLM-enhanced framework,” in Proc. 19th SOCO Conf., 2024. (Reported to 15.07.2024 – report 4)
[16] A. Tatar, N. Fat, A. Petrovan, and O. Matei, “A new vision of social behavior on genetic algorithm performance,” in Proc. 19th SOCO Conf., 2024. (Reported to 15.07.2024 – report 4)
[17] C. Sabo, N. Balogh, P. C. Pop, and A. Petrovan, “A comparative study of different genetic algorithm approaches to the capacitated vehicle routing problem for collection of agricultural products,” in Proc. 19th SOCO Conf., 2024. (Reported to 15.07.2024 – report 4)
[18] O. Matei, L. Andreica, I. A. Danci, A. Avram, and T. Faragău, “Using machine learning for identifying the intrinsic economic specializations of localities,” in Proc. 19th SOCO Conf., 2024. (Reported to 15.07.2024 – report 4)
[19] B. Văduva, O. Matei, A. Avram, and L. Andreica, “Embedding GIS in crop field bonitation computation,” in Proc. 19th SOCO Conf., 2024. (Reported to 15.07.2024 – report 4)
[20] M. Măcelaru, P. Pop, and J. Barata, “A comparative study of machine learning models for plant disease identification,” in Proc. 19th SOCO Conf., 2024. (Reported to 15.07.2024 – report 4)
[21] I. Cionca, A. D. Costin, and T. Rusu, “Optimizing fertilization and crop management for triticale in the Lăpuș depression, Romania,” AgroLife Scientific Journal, vol. 14, no. 2, 2024. DOI: 10.17930/AGL202426. (Reported to 15.01.2025 – report 6)
[22] O. Matei, L. Andreica, and T. Faragău, “Using automation and artificial intelligence in the management of European social fund projects,” in Proc. ProjMAN Int. Conf. on Project Management, 2025. (Reported to 15.01.2025 – report 6)
[23] O. Matei, L. Andreica, and T. Faragău, “Benefits and limitations of digitalization in managing European Social funded projects,” in Proc. ProjMAN Int. Conf. on Project Management (Poster Paper), 2025. (Reported to 15.01.2025 – report 6)
[24] I. Chereja, R. Erdei, E. Pasca, D. Delinschi, A. Avram, and O. Matei, “A privacy assessment framework for data tiers in multilayered ecosystem architectures,” Mathematics (Basel), MDPI, 2025. (Reported to 15.04.2025 – report 7)
[25] E. M. Pasca, D. Delinschi, R. Erdei, and O. Matei, “LLM-driven, self-improving framework for security test automation: Leveraging Karate DSL for augmented API resilience,” IEEE Access, vol. 13, 2025. DOI: 10.1109/ACCESS.2025.3554960. (Reported to 15.04.2025 – report 7)
[26] O. S. Mintas et al., “Sustainability of the integrated waste management system: A case study of Bihor County, Romania,” Sustainability (Basel), MDPI, 2025. (Reported to 15.04.2025 – report 7)
[27] I. Chereja, R. Erdei, D. Delinschi, E. Pasca, A. Avram, and O. Matei, “Privacy-conducive data ecosystem architecture: By-design vulnerability assessment using privacy risk expansion factor and privacy exposure index,” Sensors (Basel), MDPI, 2025. DOI: 10.3390/s25113554. (Reported to 15.07.2025 – report 8)
[28] E. M. Pasca, D. Delinschi, R. Erdei, I. Băărăian, and O. Matei, “A vulnerable-by-design IoT sensor framework for cybersecurity in smart agriculture,” Agriculture (Basel), MDPI, 2025. (Reported to 15.07.2025 – report 8)
[29] D. Delinschi, R. Erdei, E. Pasca, I. Băărăian, and O. Matei, “Guide in designing an asynchronous performance-centric framework for heterogeneous microservices in time-critical cybersecurity applications: The BIECO use case,” Expert Systems, Wiley, 2025. DOI: 10.1111/exsy.70064. (Reported to 15.07.2025 – report 8)
[30] I. Băărăian, R. Erdei, R. Tamaian, D. Delinschi, E. Pasca, and O. Matei, “Trend-enabled recommender system with diversity enhancer for crop recommendation,” Agriculture (Basel), MDPI, 2025. DOI: 10.3390/agriculture15151614. (Reported to 15.10.2025 – report 9)
Guide in Designing an Asynchronous Performance-Centric Framework for Heterogeneous Microservices in Time-Critical Cybersecurity Applications. The BIECO Use Case
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.
Trend-Enabled Recommender System with Diversity Enhancer for Crop Recommendation
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.
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).