Reviste
| # | Publication |
|---|---|
| 1 | Dioşan L., Oltean M., Evolving the update strategy of the Particle Swarm Optimisation algorithms, International Journal on Artificial Intelligence Tools (IJAIT), 2007, 1(16):87-110 |
| 2 | Oltean M., Dioşan L., An autonomous GP-based system for regression and classification problems, Applied Soft Computing, 2008, 9(1):49-60 |
| 3 | Dioşan L., Dumitrescu D., Evolutionary coalition formation in full connected and scale free networks, International Journal of Computers, Communications & Control (IJCCC), 2008, 3:259-265 |
| 4 | Oltean M., Grosan C., Dioşan L., Mihaila C., Genetic Programming with Linear Representation: a survey, International Journal on Artificial Intelligence Tools, 2009, 18(2):197-238 |
| 5 | Dioşan L., Oltean M., Evolutionary design of Evolutionary Algorithms, Genetic Programming and Evolvable Machines, 2009, 10(3):263-306 |
| 6 | Dioşan L., Rogozan A., Pecuchet J.-P., Learning SVM with complex multiple kernels evolved by Genetic Programming, International Journal of Artificial Intelligence Tools, 2010, 19(5):647-677 |
| 7 | Dioşan L., Oltean M., Friction-based sorting, Natural Computing, 2011, 10(1):527-539 |
| 8 | Dioşan L., Rogozan A., Pecuchet J.-P., Improving classification performance of Support Vector Machine by genetically optimisation of kernel shape and hyper-parameters, Applied Intelligence, 2012, 36(2):280-294 |
| 9 | Dioşan L., Andreica A., Multi-objective breast cancer classification by using Multi-Expression Programming, Applied Intelligence, 2015, 43(3):499-511 |
| 10 | Mărginean R., Andreica A., Dioșan L., Balint Z., Butterfly Effect in Chaotic Image Segmentation, Entropy, 2020, 22(9):1028 |
| 11 | Mărginean R., Andreica A., Dioșan L., Balint Z., Feasibility of Automatic Seed Generation Applied to Cardiac MRI Image Analysis, Mathematics, 2020, 8(9):1511 |
| 12 | Mursa B., Andreica A., Dioşan L., Network motifs: A key variable in dynamic flow in Complex Networks, Knowledge-based Systems, 2021, 213:106648 |
| 13 | Galea R-R., Diosan L., Andreica A., Popa L., Manole S., Bálint Z., Region-of-Interest-Based Cardiac Image Segmentation with Deep Learning, Applied Sciences, 2021, 11(4):1965 |
| 14 | Dumitru D., Dioșan L., Andreica A., Bálint Z., A Transfer Learning Approach on the Optimization of Edge Detectors for Medical Images Using Particle Swarm Optimization, Entropy, 2021, 23(4):414 |
| 15 | Mester A., Pop A., Mursa B.-E.-M., Greblă H., Dioşan L., Chira C., Network Analysis Based on Important Node Selection and Community Detection, Mathematics, 2021, 9(18):2294 |
| 16 | Guran A. M., Cojocar G. S., & Dioşan L. S., The Next Generation of Edutainment Applications for Young Children, Mathematics, 2022, 10(4):645 |
| 17 | Iancu S. et al., SERS liquid biopsy in breast cancer…, Spectrochimica Acta Part A, 2022, 273:120992 |
| 18 | Dobrean D., Dioşan L., Mining the MVC software architecture on mobile apps, Soft Computing, 2022, 26:10493–10511 |
| 19 | Telecan T. et al., Textural Analysis & AI for Prostate Cancer Diagnosis — Review, Journal of Personalized Medicine, 2022, 12(6):983 |
| 20 | Dioșan L., Andreica A., Voiculescu I., Multi-objective evolutionary classifiers for breast cancer detection, PLoS ONE, 2022, 17(7):e0269950 |
| 21 | Muresanu S. et al., AI models in dentistry (CBCT): systematic review, Oral Radiology, 2022 |
| 22 | Coroamă D. et al., Fully automated bladder tumor segmentation using 3D U-Net, Frontiers in Oncology, 2023 |
| 23 | Coroamă L. et al., Light 3D U-Net for prostate lesions segmentation, Current Medical Imaging, 2023 |
| 24 | Gata A. et al., Machine learning predicts postoperative outcomes in chronic rhinosinusitis, Clinical Otolaryngology, 2023 |
| 25 | Boca B. et al., MRI-Based Radiomics in Bladder Cancer: Systematic Review, Diagnostics, 2023, 13(13):2300 |
| 26 | Mărginean A. et al., Teeth & Carious Lesions Segmentation in Panoramic X-Ray Images using CariSeg, Heliyon, 2024 |
| 27 | Munteanu B. et al., Value of original & generated ultrasound data for breast cancer detection, Information Systems Frontiers, 2024 |
| 28 | Manole A., Dioşan L., UOLO: Multitask U-Net–YOLO Hybrid for Railway Scene Understanding, IEEE T-IV, 2024 |
| 29 | Orzan F., Iancu S., Dioșan L., Bálint Z., AI & textural analysis for multiple sclerosis diagnosis — Review, Frontiers in Neuroscience, 2025 |
| 30 | Telecan T. et al., Automatic Characterization of Prostate Suspect Lesions Using ML, Diagnostics, 2025 |
| 31 | Nadăș M., Dioșan L., Tomescu A., Synthetic Data Generation Using LLMs, IEEE Access, 2025, 13:134615–134633 |
| 32 | Mureșanu S. et al., Tooth-level detection on panoramic radiographs using YOLOv11 & RT-DETR, MethodsX, 2025 |
Conferinte
| # | Publication |
|---|---|
| 1 | David, D., Dioşan, L., Dumitrescu, D., A New Nature-Inspired Computational Model – Ising Model with Rays, SYNASC 2005, IEEE, 2005, 315-320 |
| 2 | Dioşan, L., A multi-objective evolutionary approach to portfolio optimization, CIMCA 2005, IEEE, Vienna, 2005, 183-188 |
| 3 | Dioşan, L., Oltean M., Evolving the structure of Particle Swarm Optimization algorithms, EuroGP/EvoCOP 2006, LNCS, 2006, 25-36 |
| 4 | Dioşan, L., Oltean, M., Evolving crossover operators for function optimization, EuroGP/EvoCOP 2006, LNCS, 2006, 97-108 |
| 5 | Dioşan, L., Oltean M., Rogozan A., Pecuchet J.-P., Improving SVM Performance using Linear Combination of Kernels, ICANNGA 2007, LNCS 4432, 2007, 218-227 |
| 6 | Dioşan, L., Oltean, M., Evolving Evolutionary Algorithms using Evolutionary Algorithms, GECCO 2007, 2442–2449 |
| 7 | Dioşan, L., Oltean M., Rogozan A., Pecuchet J.-P., Genetically Designed Multiple-Kernels for Improving SVM Performance, GECCO 2007, 1873–1874 |
| 8 | Muntean O., Dioşan L., Oltean M., Best SubTree Genetic Programming, GECCO 2007, 1667–1673 |
| 9 | Dioşan L., Oltean M., Observing the swarm behaviour during evolutionary design, GECCO 2007, 2667–2674 |
| 10 | Dioşan L., Oltean M., PESA vs NSGA-II?, ISDA 2007, EMO Workshop, 869–874 |
| 11 | Dioşan L., Rogozan A., Pecuchet J.-P., Evolving Kernel Functions for SVMs, ICMLA 2007, 19–24 |
| 12 | Dioşan L., Dumitrescu D., Hybrid GA based on Potts system, SYNASC 2007, 453–456 |
| 13 | Dioşan L., Rogozan A., Pecuchet J.-P., Optimising multiple kernels for SVM, EuroGP/EvoCOP 2008, LNCS, 230–241 |
| 14 | Dioşan L., Rogozan A., Pecuchet J.-P., Automatic Alignment of Medical & General Terminologies, ESANN 2008, 487–492 |
| 15 | Oltean M., Dioșan L., Adaptive GP for evolving digital circuits, KES 2008, 376–383 |
| 16 | Rus A., Rogozan A., Dioșan L., Benshrair A., Pedestrian recognition (multi-modality), SYNASC 2014, 258–263 |
| 17 | Rus A., Rogozan A., Dioșan L., Benshrair A., Pedestrian recognition with dynamic modality selection, ITSC 2015, 1862–1867 |
| 18 | Andreica A., Dioșan L., Șandor A., Cellular Automata Neighborhoods for Image Segmentation, CIMA–ECAI 2016, 1–8 |
| 19 | Andreica A., Dioșan L., Șandor A., Neighborhoods in CA for Segmentation, ICCP 2016, 249–255 |
| 20 | Mocan R., Dioșan L., Multiclass clustering classification for traffic scenes, ICCP 2016, 257–261 |
| 21 | Dioșan L., Andreica A., Voiculescu I., Parameterized CA in Image Segmentation, SYNASC 2016, 199–205 |
| 22 | Rus A., Rogozan A., Dioșan L., Benshrair A., Dynamic modality fusion for pedestrians, ICCP 2015, 393–400 |
| 23 | Dioșan L., Andreica A., Voiculescu I., Boros I., CA in Image Processing, EvoApplications 2017, LNCS 10199, 282–296 |
| 24 | Sándor A., Dioșan L., Andreica A., Hybrid topology in GrowCut, ECAL 2017, 19–20 |
| 25 | Serban C., Vescan A., Dioșan L., Chisalita-Cretu C., Test Case Prioritization via Requirements Dependencies, ICCP 2017, 181–188 |
| 26 | Marinescu A., Balint Z., Dioșan L., Andreica A., Autonomous Image Segmentation (GrowCut), ESANN 2018, 67–72 |
| 27 | Enescu A., Andreica A., Dioșan L., Two-stage Edge Detection with CA, SYNASC 2018, 417–424 |
| 28 | Marinescu A., Balint Z., Dioșan L., Andreica A., 3D autonomous GrowCut, SYNASC 2018, 401–408 |
| 29 | Nechita S., Dioșan L., 4-phase meta-heuristic for scheduling, SYNASC 2018, 394–400 |
| 30 | Mursa B., Andreica A., Dioșan L., Parallel acceleration of motif detection, SYNASC 2018, 191–198 |
| 31 | Mursa B., Andreica A., Dioșan L., Motifs & articulation points, ECIS 2019 |
| 32 | Dobrean D., Dioșan L., MVC in iOS development, SEKE 2019, 547–552 |
| 33 | Dobrean D., Dioșan L., Analysis of MVC architectures, ICSOFT 2019, 178–185 |
| 34 | Mursa B., Andreica A., Dioșan L., Mining motif discovery, HAIS 2019, 73–84 |
| 35 | Mursa B., Andreica A., Dioșan L., Motifs frequency vs topology, KES 2019, 333–341 |
| 36 | Mărginean R., Andreica A., Diosan L., Balint Z., Competitive GrowCut, SYNASC 2019, 313–319 |
| 37 | Dumitru D., Andreica A., Diosan L., Balint Z., PSO for CA edge detection, SYNASC 2019, 320–325 |
| 38 | Enescu A., Andreica A., Dioșan L., CA for grey images, SYNASC 2019, 325–332 |
| 39 | Tolciu T., Toma S., Matei C., Diosan L., Feature extraction for FER, ICCP 2019, 251–257 |
| 40 | Enescu A., Andreica A., Dioșan L., CA for binary edges, ICCP 2019, 351–358 |
| 41 | Enescu A., Andreica A., Dioșan L., CA for edge detection, GECCO 2019 Companion, 316–317 |
| 42 | Dioșan L., Motogna S., AI meets Software Engineering education, EASEAI 2019, 35–38 |
| 45 | Limboi S., Dioșan L., Hybrid features for Twitter sentiment, ICAISC 2020, 210–219 |
| 46 | Dumitru D., Andreica A., Balint Z., Diosan L., Robust CA rules for edges, KES 2020, 713–722 |
| 47 | Enescu A., Andreica A., Dioșan L., Dumitru D., Unsupervised CA edge detector, KES 2020, 470–479 |
| 48 | Guran A., Cojocar G., Dioșan L., Preschooler satisfaction via emotion recognition, KES 2020, 632–641 |
| 49 | Dumitru D., Andreica A., Dioșan L., Balint Z., Evolutionary curriculum learning for CA, GECCO 2020, 63–64 |
| 50 | Dobrean D., Dioșan L., MVC detection via clustering, ICSOFT 2020, 196–203 |
| 51 | Guran A., Cojocar G., Dioșan L., Smart edutainment for preschoolers, EASEAI 2020 |
| 52 | Dobrean D., Dioșan L., Hybrid MVC analysis, ENASE 2021, 36–46 |
| 53 | Moroz-Dubenco C., Diosan L., Andreica A., Better GrowCut for mammography, KES 2021, 308–317 |
| 54 | Cernău L., Dioșan L., Șerban C., Hybrid complexity metric, ICSOFT 2022, 433–440 |
| 55 | Cernău L., Dioșan L., Șerban C., Pedagogical AI/software quality integration, EASEAI 2022 |
| 56 | Limboi S., Dioșan L., Unsupervised Twitter sentiment for US elections, INISTA 2022, 1–6 |
| 57 | Limboi S., Dioșan L., Twitter-Lex system, KDIR 2022, 180–187 |
| 58 | Alexandrescu A., Manole A., Diosan L., Railway switch classification using DNNs, VISAPP 2023, 769–776 |
| 59 | Moroz-Dubenco C., Diosan L., Andreica A., Generalized GrowCut for mammography, HAIS 2023, 709–720 |
| 60 | Moroz-Dubenco C., Diosan L., Andreica A., Unsupervised GrowCut for mammography, ICVS 2023, 102–111 |
| 61 | Limboi S., Dioșan L., Lexicon feature for Twitter sentiment, ICCP 2022, 95–102 |
| 62 | Guran A., Cojocar G., Dioșan L., Smart Edutainment proposal, ITS 2021, 439–443 |
| 63 | Olar A., Dioșan L., PyResolveMetrics, CSEDU 2024 |
| 64 | Todericiu I., Pop M., Șerban C., Dioșan L., Quiz-ifying Education, CSEDU 2024 |
| 65 | Iacob B., Dioșan L., CNNs + texture for mammography, KES 2024 |
| 66 | Alexandrescu A., Dioșan L., Active learning for railway segmentation, KES 2024 |
| 67 | Zirbo S., Hoszu B., Dioșan L., Coroiu A., Croitoru A., Weather & health prediction, KES 2024 |
| 68 | Iacob B., Dioșan L., Mammographic texture explanation, ICAART 2025 |
| 69 | Todericiu I., Dioșan L., Șerban C., Alexa vs Copilot, ICAART 2025 |
| 70 | Todericiu I., Șerban C., Dioșan L., Accessibility through smart speakers, KES 2021, 883–892 |
| 71 | Cernău L., Dioșan L., Șerban C., Software Metrics adoption challenges, ENASE 2025 |
| 72 | Alexandrescu A.-R., Petec R., Manole A., Dioșan L., ContRail: ControlNet for railway image synthesis, KES 2025 |
| 73 | Manole A., Dioșan L., Hierarchical Siamese networks for vehicles, InnoComp 2025 |
| 74 | Nadăș M., Dioșan L., Evaluating LLMs for Romanian diacritics, InnoComp 2025 |
| 75 | Ursa A., Dioșan L., AugRoSent: sentiment augmentation for Romanian, InnoComp 2025 |