{"id":75,"date":"2013-06-21T09:39:38","date_gmt":"2013-06-21T09:39:38","guid":{"rendered":"http:\/\/www.cs.ubbcluj.ro\/~lauras\/test\/?page_id=75"},"modified":"2026-02-12T16:41:27","modified_gmt":"2026-02-12T16:41:27","slug":"publications","status":"publish","type":"page","link":"https:\/\/www.cs.ubbcluj.ro\/~lauras\/research-2\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"

Reviste<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
#<\/th>\nPublication<\/th>\n<\/tr>\n<\/thead>\n
1<\/td>\nDio\u015fan 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<\/td>\n<\/tr>\n
2<\/td>\nOltean M., Dio\u015fan L., An autonomous GP-based system for regression and classification problems, Applied Soft Computing, 2008, 9(1):49-60<\/td>\n<\/tr>\n
3<\/td>\nDio\u015fan L., Dumitrescu D., Evolutionary coalition formation in full connected and scale free networks, International Journal of Computers, Communications & Control (IJCCC), 2008, 3:259-265<\/td>\n<\/tr>\n
4<\/td>\nOltean M., Grosan C., Dio\u015fan L., Mihaila C., Genetic Programming with Linear Representation: a survey, International Journal on Artificial Intelligence Tools, 2009, 18(2):197-238<\/td>\n<\/tr>\n
5<\/td>\nDio\u015fan L., Oltean M., Evolutionary design of Evolutionary Algorithms, Genetic Programming and Evolvable Machines, 2009, 10(3):263-306<\/td>\n<\/tr>\n
6<\/td>\nDio\u015fan 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<\/td>\n<\/tr>\n
7<\/td>\nDio\u015fan L., Oltean M., Friction-based sorting, Natural Computing, 2011, 10(1):527-539<\/td>\n<\/tr>\n
8<\/td>\nDio\u015fan 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<\/td>\n<\/tr>\n
9<\/td>\nDio\u015fan L., Andreica A., Multi-objective breast cancer classification by using Multi-Expression Programming, Applied Intelligence, 2015, 43(3):499-511<\/td>\n<\/tr>\n
10<\/td>\nM\u0103rginean R., Andreica A., Dio\u0219an L., Balint Z., Butterfly Effect in Chaotic Image Segmentation, Entropy, 2020, 22(9):1028<\/td>\n<\/tr>\n
11<\/td>\nM\u0103rginean R., Andreica A., Dio\u0219an L., Balint Z., Feasibility of Automatic Seed Generation Applied to Cardiac MRI Image Analysis, Mathematics, 2020, 8(9):1511<\/td>\n<\/tr>\n
12<\/td>\nMursa B., Andreica A., Dio\u015fan L., Network motifs: A key variable in dynamic flow in Complex Networks, Knowledge-based Systems, 2021, 213:106648<\/td>\n<\/tr>\n
13<\/td>\nGalea R-R., Diosan L., Andreica A., Popa L., Manole S., B\u00e1lint Z., Region-of-Interest-Based Cardiac Image Segmentation with Deep Learning, Applied Sciences, 2021, 11(4):1965<\/td>\n<\/tr>\n
14<\/td>\nDumitru D., Dio\u0219an L., Andreica A., B\u00e1lint Z., A Transfer Learning Approach on the Optimization of Edge Detectors for Medical Images Using Particle Swarm Optimization, Entropy, 2021, 23(4):414<\/td>\n<\/tr>\n
15<\/td>\nMester A., Pop A., Mursa B.-E.-M., Grebl\u0103 H., Dio\u015fan L., Chira C., Network Analysis Based on Important Node Selection and Community Detection, Mathematics, 2021, 9(18):2294<\/td>\n<\/tr>\n
16<\/td>\nGuran A. M., Cojocar G. S., & Dio\u015fan L. S., The Next Generation of Edutainment Applications for Young Children, Mathematics, 2022, 10(4):645<\/td>\n<\/tr>\n
17<\/td>\nIancu S. et al., SERS liquid biopsy in breast cancer…, Spectrochimica Acta Part A, 2022, 273:120992<\/td>\n<\/tr>\n
18<\/td>\nDobrean D., Dio\u015fan L., Mining the MVC software architecture on mobile apps, Soft Computing, 2022, 26:10493\u201310511<\/td>\n<\/tr>\n
19<\/td>\nTelecan T. et al., Textural Analysis & AI for Prostate Cancer Diagnosis \u2014 Review, Journal of Personalized Medicine, 2022, 12(6):983<\/td>\n<\/tr>\n
20<\/td>\nDio\u0219an L., Andreica A., Voiculescu I., Multi-objective evolutionary classifiers for breast cancer detection, PLoS ONE, 2022, 17(7):e0269950<\/td>\n<\/tr>\n
21<\/td>\nMuresanu S. et al., AI models in dentistry (CBCT): systematic review, Oral Radiology, 2022<\/td>\n<\/tr>\n
22<\/td>\nCoroam\u0103 D. et al., Fully automated bladder tumor segmentation using 3D U-Net, Frontiers in Oncology, 2023<\/td>\n<\/tr>\n
23<\/td>\nCoroam\u0103 L. et al., Light 3D U-Net for prostate lesions segmentation, Current Medical Imaging, 2023<\/td>\n<\/tr>\n
24<\/td>\nGata A. et al., Machine learning predicts postoperative outcomes in chronic rhinosinusitis, Clinical Otolaryngology, 2023<\/td>\n<\/tr>\n
25<\/td>\nBoca B. et al., MRI-Based Radiomics in Bladder Cancer: Systematic Review, Diagnostics, 2023, 13(13):2300<\/td>\n<\/tr>\n
26<\/td>\nM\u0103rginean A. et al., Teeth & Carious Lesions Segmentation in Panoramic X-Ray Images using CariSeg, Heliyon, 2024<\/td>\n<\/tr>\n
27<\/td>\nMunteanu B. et al., Value of original & generated ultrasound data for breast cancer detection, Information Systems Frontiers, 2024<\/td>\n<\/tr>\n
28<\/td>\nManole A., Dio\u015fan L., UOLO: Multitask U-Net\u2013YOLO Hybrid for Railway Scene Understanding, IEEE T-IV, 2024<\/td>\n<\/tr>\n
29<\/td>\nOrzan F., Iancu S., Dio\u0219an L., B\u00e1lint Z., AI & textural analysis for multiple sclerosis diagnosis \u2014 Review, Frontiers in Neuroscience, 2025<\/td>\n<\/tr>\n
30<\/td>\nTelecan T. et al., Automatic Characterization of Prostate Suspect Lesions Using ML, Diagnostics, 2025<\/td>\n<\/tr>\n
31<\/td>\nNad\u0103\u0219 M., Dio\u0219an L., Tomescu A., Synthetic Data Generation Using LLMs, IEEE Access, 2025, 13:134615\u2013134633<\/td>\n<\/tr>\n
32<\/td>\nMure\u0219anu S. et al., Tooth-level detection on panoramic radiographs using YOLOv11 & RT-DETR, MethodsX, 2025<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Conferinte<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
#<\/th>\nPublication<\/th>\n<\/tr>\n<\/thead>\n
1<\/td>\nDavid, D., Dio\u015fan, L., Dumitrescu, D., A New Nature-Inspired Computational Model – Ising Model with Rays, SYNASC 2005, IEEE, 2005, 315-320<\/td>\n<\/tr>\n
2<\/td>\nDio\u015fan, L., A multi-objective evolutionary approach to portfolio optimization, CIMCA 2005, IEEE, Vienna, 2005, 183-188<\/td>\n<\/tr>\n
3<\/td>\nDio\u015fan, L., Oltean M., Evolving the structure of Particle Swarm Optimization algorithms, EuroGP\/EvoCOP 2006, LNCS, 2006, 25-36<\/td>\n<\/tr>\n
4<\/td>\nDio\u015fan, L., Oltean, M., Evolving crossover operators for function optimization, EuroGP\/EvoCOP 2006, LNCS, 2006, 97-108<\/td>\n<\/tr>\n
5<\/td>\nDio\u015fan, L., Oltean M., Rogozan A., Pecuchet J.-P., Improving SVM Performance using Linear Combination of Kernels, ICANNGA 2007, LNCS 4432, 2007, 218-227<\/td>\n<\/tr>\n
6<\/td>\nDio\u015fan, L., Oltean, M., Evolving Evolutionary Algorithms using Evolutionary Algorithms, GECCO 2007, 2442\u20132449<\/td>\n<\/tr>\n
7<\/td>\nDio\u015fan, L., Oltean M., Rogozan A., Pecuchet J.-P., Genetically Designed Multiple-Kernels for Improving SVM Performance, GECCO 2007, 1873\u20131874<\/td>\n<\/tr>\n
8<\/td>\nMuntean O., Dio\u015fan L., Oltean M., Best SubTree Genetic Programming, GECCO 2007, 1667\u20131673<\/td>\n<\/tr>\n
9<\/td>\nDio\u015fan L., Oltean M., Observing the swarm behaviour during evolutionary design, GECCO 2007, 2667\u20132674<\/td>\n<\/tr>\n
10<\/td>\nDio\u015fan L., Oltean M., PESA vs NSGA-II?, ISDA 2007, EMO Workshop, 869\u2013874<\/td>\n<\/tr>\n
11<\/td>\nDio\u015fan L., Rogozan A., Pecuchet J.-P., Evolving Kernel Functions for SVMs, ICMLA 2007, 19\u201324<\/td>\n<\/tr>\n
12<\/td>\nDio\u015fan L., Dumitrescu D., Hybrid GA based on Potts system, SYNASC 2007, 453\u2013456<\/td>\n<\/tr>\n
13<\/td>\nDio\u015fan L., Rogozan A., Pecuchet J.-P., Optimising multiple kernels for SVM, EuroGP\/EvoCOP 2008, LNCS, 230\u2013241<\/td>\n<\/tr>\n
14<\/td>\nDio\u015fan L., Rogozan A., Pecuchet J.-P., Automatic Alignment of Medical & General Terminologies, ESANN 2008, 487\u2013492<\/td>\n<\/tr>\n
15<\/td>\nOltean M., Dio\u0219an L., Adaptive GP for evolving digital circuits, KES 2008, 376\u2013383<\/td>\n<\/tr>\n
16<\/td>\nRus A., Rogozan A., Dio\u0219an L., Benshrair A., Pedestrian recognition (multi-modality), SYNASC 2014, 258\u2013263<\/td>\n<\/tr>\n
17<\/td>\nRus A., Rogozan A., Dio\u0219an L., Benshrair A., Pedestrian recognition with dynamic modality selection, ITSC 2015, 1862\u20131867<\/td>\n<\/tr>\n
18<\/td>\nAndreica A., Dio\u0219an L., \u0218andor A., Cellular Automata Neighborhoods for Image Segmentation, CIMA\u2013ECAI 2016, 1\u20138<\/td>\n<\/tr>\n
19<\/td>\nAndreica A., Dio\u0219an L., \u0218andor A., Neighborhoods in CA for Segmentation, ICCP 2016, 249\u2013255<\/td>\n<\/tr>\n
20<\/td>\nMocan R., Dio\u0219an L., Multiclass clustering classification for traffic scenes, ICCP 2016, 257\u2013261<\/td>\n<\/tr>\n
21<\/td>\nDio\u0219an L., Andreica A., Voiculescu I., Parameterized CA in Image Segmentation, SYNASC 2016, 199\u2013205<\/td>\n<\/tr>\n
22<\/td>\nRus A., Rogozan A., Dio\u0219an L., Benshrair A., Dynamic modality fusion for pedestrians, ICCP 2015, 393\u2013400<\/td>\n<\/tr>\n
23<\/td>\nDio\u0219an L., Andreica A., Voiculescu I., Boros I., CA in Image Processing, EvoApplications 2017, LNCS 10199, 282\u2013296<\/td>\n<\/tr>\n
24<\/td>\nS\u00e1ndor A., Dio\u0219an L., Andreica A., Hybrid topology in GrowCut, ECAL 2017, 19\u201320<\/td>\n<\/tr>\n
25<\/td>\nSerban C., Vescan A., Dio\u0219an L., Chisalita-Cretu C., Test Case Prioritization via Requirements Dependencies, ICCP 2017, 181\u2013188<\/td>\n<\/tr>\n
26<\/td>\nMarinescu A., Balint Z., Dio\u0219an L., Andreica A., Autonomous Image Segmentation (GrowCut), ESANN 2018, 67\u201372<\/td>\n<\/tr>\n
27<\/td>\nEnescu A., Andreica A., Dio\u0219an L., Two-stage Edge Detection with CA, SYNASC 2018, 417\u2013424<\/td>\n<\/tr>\n
28<\/td>\nMarinescu A., Balint Z., Dio\u0219an L., Andreica A., 3D autonomous GrowCut, SYNASC 2018, 401\u2013408<\/td>\n<\/tr>\n
29<\/td>\nNechita S., Dio\u0219an L., 4-phase meta-heuristic for scheduling, SYNASC 2018, 394\u2013400<\/td>\n<\/tr>\n
30<\/td>\nMursa B., Andreica A., Dio\u0219an L., Parallel acceleration of motif detection, SYNASC 2018, 191\u2013198<\/td>\n<\/tr>\n
31<\/td>\nMursa B., Andreica A., Dio\u0219an L., Motifs & articulation points, ECIS 2019<\/td>\n<\/tr>\n
32<\/td>\nDobrean D., Dio\u0219an L., MVC in iOS development, SEKE 2019, 547\u2013552<\/td>\n<\/tr>\n
33<\/td>\nDobrean D., Dio\u0219an L., Analysis of MVC architectures, ICSOFT 2019, 178\u2013185<\/td>\n<\/tr>\n
34<\/td>\nMursa B., Andreica A., Dio\u0219an L., Mining motif discovery, HAIS 2019, 73\u201384<\/td>\n<\/tr>\n
35<\/td>\nMursa B., Andreica A., Dio\u0219an L., Motifs frequency vs topology, KES 2019, 333\u2013341<\/td>\n<\/tr>\n
36<\/td>\nM\u0103rginean R., Andreica A., Diosan L., Balint Z., Competitive GrowCut, SYNASC 2019, 313\u2013319<\/td>\n<\/tr>\n
37<\/td>\nDumitru D., Andreica A., Diosan L., Balint Z., PSO for CA edge detection, SYNASC 2019, 320\u2013325<\/td>\n<\/tr>\n
38<\/td>\nEnescu A., Andreica A., Dio\u0219an L., CA for grey images, SYNASC 2019, 325\u2013332<\/td>\n<\/tr>\n
39<\/td>\nTolciu T., Toma S., Matei C., Diosan L., Feature extraction for FER, ICCP 2019, 251\u2013257<\/td>\n<\/tr>\n
40<\/td>\nEnescu A., Andreica A., Dio\u0219an L., CA for binary edges, ICCP 2019, 351\u2013358<\/td>\n<\/tr>\n
41<\/td>\nEnescu A., Andreica A., Dio\u0219an L., CA for edge detection, GECCO 2019 Companion, 316\u2013317<\/td>\n<\/tr>\n
42<\/td>\nDio\u0219an L., Motogna S., AI meets Software Engineering education, EASEAI 2019, 35\u201338<\/td>\n<\/tr>\n
45<\/td>\nLimboi S., Dio\u0219an L., Hybrid features for Twitter sentiment, ICAISC 2020, 210\u2013219<\/td>\n<\/tr>\n
46<\/td>\nDumitru D., Andreica A., Balint Z., Diosan L., Robust CA rules for edges, KES 2020, 713\u2013722<\/td>\n<\/tr>\n
47<\/td>\nEnescu A., Andreica A., Dio\u0219an L., Dumitru D., Unsupervised CA edge detector, KES 2020, 470\u2013479<\/td>\n<\/tr>\n
48<\/td>\nGuran A., Cojocar G., Dio\u0219an L., Preschooler satisfaction via emotion recognition, KES 2020, 632\u2013641<\/td>\n<\/tr>\n
49<\/td>\nDumitru D., Andreica A., Dio\u0219an L., Balint Z., Evolutionary curriculum learning for CA, GECCO 2020, 63\u201364<\/td>\n<\/tr>\n
50<\/td>\nDobrean D., Dio\u0219an L., MVC detection via clustering, ICSOFT 2020, 196\u2013203<\/td>\n<\/tr>\n
51<\/td>\nGuran A., Cojocar G., Dio\u0219an L., Smart edutainment for preschoolers, EASEAI 2020<\/td>\n<\/tr>\n
52<\/td>\nDobrean D., Dio\u0219an L., Hybrid MVC analysis, ENASE 2021, 36\u201346<\/td>\n<\/tr>\n
53<\/td>\nMoroz-Dubenco C., Diosan L., Andreica A., Better GrowCut for mammography, KES 2021, 308\u2013317<\/td>\n<\/tr>\n
54<\/td>\nCern\u0103u L., Dio\u0219an L., \u0218erban C., Hybrid complexity metric, ICSOFT 2022, 433\u2013440<\/td>\n<\/tr>\n
55<\/td>\nCern\u0103u L., Dio\u0219an L., \u0218erban C., Pedagogical AI\/software quality integration, EASEAI 2022<\/td>\n<\/tr>\n
56<\/td>\nLimboi S., Dio\u0219an L., Unsupervised Twitter sentiment for US elections, INISTA 2022, 1\u20136<\/td>\n<\/tr>\n
57<\/td>\nLimboi S., Dio\u0219an L., Twitter-Lex system, KDIR 2022, 180\u2013187<\/td>\n<\/tr>\n
58<\/td>\nAlexandrescu A., Manole A., Diosan L., Railway switch classification using DNNs, VISAPP 2023, 769\u2013776<\/td>\n<\/tr>\n
59<\/td>\nMoroz-Dubenco C., Diosan L., Andreica A., Generalized GrowCut for mammography, HAIS 2023, 709\u2013720<\/td>\n<\/tr>\n
60<\/td>\nMoroz-Dubenco C., Diosan L., Andreica A., Unsupervised GrowCut for mammography, ICVS 2023, 102\u2013111<\/td>\n<\/tr>\n
61<\/td>\nLimboi S., Dio\u0219an L., Lexicon feature for Twitter sentiment, ICCP 2022, 95\u2013102<\/td>\n<\/tr>\n
62<\/td>\nGuran A., Cojocar G., Dio\u0219an L., Smart Edutainment proposal, ITS 2021, 439\u2013443<\/td>\n<\/tr>\n
63<\/td>\nOlar A., Dio\u0219an L., PyResolveMetrics, CSEDU 2024<\/td>\n<\/tr>\n
64<\/td>\nTodericiu I., Pop M., \u0218erban C., Dio\u0219an L., Quiz-ifying Education, CSEDU 2024<\/td>\n<\/tr>\n
65<\/td>\nIacob B., Dio\u0219an L., CNNs + texture for mammography, KES 2024<\/td>\n<\/tr>\n
66<\/td>\nAlexandrescu A., Dio\u0219an L., Active learning for railway segmentation, KES 2024<\/td>\n<\/tr>\n
67<\/td>\nZirbo S., Hoszu B., Dio\u0219an L., Coroiu A., Croitoru A., Weather & health prediction, KES 2024<\/td>\n<\/tr>\n
68<\/td>\nIacob B., Dio\u0219an L., Mammographic texture explanation, ICAART 2025<\/td>\n<\/tr>\n
69<\/td>\nTodericiu I., Dio\u0219an L., \u0218erban C., Alexa vs Copilot, ICAART 2025<\/td>\n<\/tr>\n
70<\/td>\nTodericiu I., \u0218erban C., Dio\u0219an L., Accessibility through smart speakers, KES 2021, 883\u2013892<\/td>\n<\/tr>\n
71<\/td>\nCern\u0103u L., Dio\u0219an L., \u0218erban C., Software Metrics adoption challenges, ENASE 2025<\/td>\n<\/tr>\n
72<\/td>\nAlexandrescu A.-R., Petec R., Manole A., Dio\u0219an L., ContRail: ControlNet for railway image synthesis, KES 2025<\/td>\n<\/tr>\n
73<\/td>\nManole A., Dio\u0219an L., Hierarchical Siamese networks for vehicles, InnoComp 2025<\/td>\n<\/tr>\n
74<\/td>\nNad\u0103\u0219 M., Dio\u0219an L., Evaluating LLMs for Romanian diacritics, InnoComp 2025<\/td>\n<\/tr>\n
75<\/td>\nUrsa A., Dio\u0219an L., AugRoSent: sentiment augmentation for Romanian, InnoComp 2025<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n","protected":false},"excerpt":{"rendered":"

Reviste # Publication 1 Dio\u015fan 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., Dio\u015fan L., An autonomous GP-based system for regression and classification problems, Applied Soft Computing, 2008, 9(1):49-60 3 Dio\u015fan L., Dumitrescu D., Evolutionary coalition formation in […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":18,"menu_order":2,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/www.cs.ubbcluj.ro\/~lauras\/wp-json\/wp\/v2\/pages\/75"}],"collection":[{"href":"https:\/\/www.cs.ubbcluj.ro\/~lauras\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.cs.ubbcluj.ro\/~lauras\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.cs.ubbcluj.ro\/~lauras\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.cs.ubbcluj.ro\/~lauras\/wp-json\/wp\/v2\/comments?post=75"}],"version-history":[{"count":41,"href":"https:\/\/www.cs.ubbcluj.ro\/~lauras\/wp-json\/wp\/v2\/pages\/75\/revisions"}],"predecessor-version":[{"id":1779,"href":"https:\/\/www.cs.ubbcluj.ro\/~lauras\/wp-json\/wp\/v2\/pages\/75\/revisions\/1779"}],"up":[{"embeddable":true,"href":"https:\/\/www.cs.ubbcluj.ro\/~lauras\/wp-json\/wp\/v2\/pages\/18"}],"wp:attachment":[{"href":"https:\/\/www.cs.ubbcluj.ro\/~lauras\/wp-json\/wp\/v2\/media?parent=75"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}