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)
Mapping the Future of Soil: AI & GIS for Dynamic Land Evaluation | COSA Project Research Spotlight (6/6)
Everything in agriculture starts with the land. But how do we accurately measure the true potential of our soil in a constantly changing climate? For our sixth and final Q1 publication highlight from the COSA project, our research team merged spatial geography with...
Nature-Inspired Algorithms: Solving the Farm’s Toughest Routing Puzzles | COSA Project Research Spotlight (5/6)
🧬 Nature-Inspired Algorithms: Solving the Farm's Toughest Routing Puzzles | COSA Project Research Spotlight (5/6) As smart agriculture scales up, so does the complexity of connecting everything efficiently. How do we design the absolute best, most cost-effective...
Defending the Digital Farm: Proactive Cybersecurity for IoT Sensors | COSA Project Research Spotlight (4/6)
As farms become increasingly connected, the sensors monitoring our crops, soil, and machinery become prime targets for cyberattacks. How do we build better defenses for our food systems? By learning exactly how these digital systems fail. For our fourth Q1 publication...
Securing Complex IoT Ecosystems: A Framework for Data Privacy | COSA Project Research Spotlight (3/6)
🔐 Securing Complex IoT Ecosystems: A Framework for Data Privacy | COSA Project Research Spotlight (3/6) As smart agriculture evolves into an interconnected "Internet of Fields," massive amounts of data flow through multiple layers—from ground-level sensors to cloud...
Predicting Precipitation with Deep Learning: AI for Smarter Water Management | COSA Project Research Spotlight (2/6)
Water is agriculture’s most precious resource. In an era of unpredictable climate shifts, knowing exactly when and how much it will rain is the difference between a thriving crop and a failed harvest. For our second Q1 publication highlight from the COSA project, we...
Optimizing Complex Logistics in Smart Agriculture | COSA Project Research Spotlight (1/6)
To build the intelligent farms of the future, we need to solve the complex mathematical puzzles that run behind the scenes. As part of the COSA project, our research team has tackled one of the most critical challenges in logistics and optimization: the Generalized...



