Prof.dr. Horia F. Pop - Citations

Dumitrescu, D., Sarbu, C., Pop, H. A fuzzy divisive hierarchical-clustering algorithm for the optimal choice of sets of solvent systems. Analytical Lett. 27, 5 (1994), 1031-1054

  1. Ban, O. I., Ban, A. I., and Tuse, D. A. Importance-Performance Analysis by Fuzzy C-Means Algorithm. EXPERT SYSTEMS WITH APPLICATIONS 50 (MAY 15 2016), 9--16. Source: ISI Web of Knowledge.
  2. Brown, S. D., Sum, S. T., Despagne, F., and Lavine, B. K. Chemometrics. Analytical Chem. 68, 12 (1996), R21--R61. Source: ISI Web of Knowledge.

Boian, F. M., Pop, H. F., Iurian, S., Iurian, M., Vancea, A. The Unix environment at the Babeș-Bolyai University. In The Second International Conference on Open Systems ROSE'94, Technical Sessions (Bucharest, Romania, November 3-5, 1994), 145-149

  1. Rotaru, A. ROSE'94 Unix & Open Systems, istoria celei de-a doua conferinte organizare în România (ROSE'94 Unix & Open Systems, the history of the 2-nd conference organized in Romania). PC Report, 27 (1994), 8--9. Source: Google Scholar.

Pop, H. F. HPTEX - MS-DOS Turbo-Vision like text editor with a user-friendly LATEX interface, 1994-1995

  1. Horváth, S. Dacă este text stiintific, atunci este TEX (If it is scientifical text, then it is TEX). PC Report, 32 (1995), 50--51. Source: Google Scholar.

Pop, H. F., Dumitrescu, D., Sarbu, C. A study of roman pottery (terra sigillata) using hierarchical fuzzy clustering. Analytica Chimica Acta 310, 2 (1995), 269-279

  1. Ramanjaneyulu, P. S., Kundu, K., Sharma, M. K., and Nayak, S. K. Development of New Cs+ Ion-Selective Electrode with Alkyl-Bridged Calix[4]arene Crown-6 Compounds for the Determination of Cs+ in Nuclear Waste Streams. CHEMISTRYSELECT 2, 32 (NOV 13 2017), 10347--10353. Source: ISI Web of Knowledge.
  2. Chen, W. C., Cui, H., Zhang, L., and Liang, Y. Z. A new chemical clustering analysis method based on genetic evolution algorithm. Chem. J. Chinese Universities-chinese 18, 2 (1997), 196--201. Source: ISI Web of Knowledge.
  3. Simeonova, P., and Lovchinov, V. Classification of high-temperature superconducting YBCO thin films by fuzzy clustering. J. Optoelectronics Adv. Materials 7, 1 (2005), 419--422. Source: ISI Web of Knowledge.
  4. Barcelo, J. Computational intelligence in archaeology. 2008. Source: Scopus.

Dumitrescu, D., Pop, H. F. Degenerate and nondegenerate convex decomposition of finite fuzzy partitions .I. Fuzzy Sets Systems 73, 3 (1995), 365-376

  1. Bodjanova, S. T-Sharper Images and T-Level Cuts of Fuzzy Partitions. In DATA SCIENCE: INNOVATIVE DEVELOPMENTS IN DATA ANALYSIS AND CLUSTERING (2017), Palumbo, F and Montanari, A and Vichi, M, Ed., Studies in Classification Data Analysis and Knowledge Organization, SAS Inst; Springer; APT Servizi Regione Emilia Romagna; Ascom Bologna; Univ Bologna, pp. 61--72. 15th Conference of the International Federation of Classification Societies (IFCS), Bologna, ITALY, JUL 05-08, 2015. Source: ISI Web of Knowledge.
  2. Sellam, A. Artificial intelligence: Methodology, systems, applications. In Proceedings of the Seventh International Conference on Artificial Intelligence, Methodology, Systems, Applications (AIMSA'96), Sozopol, Bulgaria, September, 18-20, 1996 (1996), A. Ramsay, Ed., vol. 35, IOS Press, p. 126. Source: Google Scholar.
  3. Miranda, P., Torres, E., and Gil, P. Divergence measures and aggregation operations. International Journal of Uncertainty, Fuzziness and Knowledge Based Systems 8, 6 (2000), 677--690. Source: Google Scholar.
  4. Bodjanova, S. Linear intensification of probabilistic fuzzy partitions. Fuzzy Sets Systems 141, 2 (2004), 319--332. Source: ISI Web of Knowledge.
  5. Ferrigno, S., Gannoun, A., and Saracco, J. Inverse regression methods based on fuzzy partitions. International Journal of Pure and Applied Mathematics 43, 1 (2008), 43. Source: Google Scholar.
  6. Jayaram, B., and Mesiar, R. I-fuzzy equivalence relations and i-fuzzy partitions. Information Sciences 179, 9 (2009), 1278--1297. Source: ISI Web of Knowledge.

Sarbu, C., Horowitz, O., Pop, H. F. A fuzzy cross-classification of the chemical elements, based on their physical, chemical, and structural features. J. Chem. Information Computer Sciences 36, 6 (1996), 1098-1108

  1. Holliday, J. D., Rodgers, S. L., Willett, P., Chen, M. Y., Mahfouf, M., Lawson, K., and Mullier, G. Clustering files of chemical structures using the fuzzy k-means clustering method. J. Chem. Information Computer Sciences 44, 3 (2004), 894--902. Source: ISI Web of Knowledge.

Pop, H. F., Sarbu, C., Horowitz, O., Dumitrescu, D. A fuzzy classification of the chemical elements. J. Chem. Information Computer Sciences 36, 3 (1996), 465-482

  1. ElDeredy, W. Pattern recognition approaches in biomedical and clinical magnetic resonance spectroscopy: A review. Nmr In Biomedicine 10, 3 (1997), 99--124. Source: ISI Web of Knowledge.
  2. Dickerson, J., Daaboul, Y., Jobe, T., and Helgason, C. Analysis of concomitant mechanisms in stroke pathogenesis usingfuzzy clustering techniques. In Fuzzy Information Processing Society, 1997. NAFIPS'97., 1997 Annual Meeting of the North American (1997), pp. 211--216. Source: Google Scholar.
  3. Malik, D. S., and Mordeson, J. N. Study of stroke pathogenesis using possibility theory and hierarchical fuzzy clustering techniques. World Multiconference On Systemics, Cybernetics Informatics, Vol 8, Proc. - Concepts Applications Systemics, Cybernetics Informatics (1999), 483--489. Source: ISI Web of Knowledge.
  4. Helgason, C. M., Jobe, T. H., Malik, D. S., and Mordeson, J. N. Analysis of stroke pathogenesis using hierarchical fuzzy clustering techniques. World Multiconference On Systemics, Cybernetics Informatics, Vol 8, Proc. - Concepts Applications Systemics, Cybernetics Informatics (1999), 477--482. Source: ISI Web of Knowledge.
  5. Brickmann, J., Keil, M., and Exner, T. Chapter# Fuzzy Logic Strategies for the Treatment of the Molecular Recognition Problem. In Chapter in Reviews of Modern Quantum Chemistry, K. Sen, Ed. World Scientific Pub Co Inc, 2001. https://www.tcd.uni-konstanz.de/download/parr.pdf Source: Google Scholar.
  6. Turner, J. Application of Artificial Neural Networks in Pharmacokinetics. PhD Thesis, University of Sydney (2006). https://hdl.handle.net/2123/488 Source: Google Scholar.
  7. Voga, G. P., and Belchior, J. C. An approach for interpreting thermogravimetric profiles using artificial intelligence. Thermochimica Acta 452, 2 (2007), 140--148. Source: ISI Web of Knowledge.
  8. Ban, O. I., Ban, A. I., and Tuse, D. A. Importance-performance Analysis by Fuzzy C-Means algorithm. Expert Systems With Applications 50 (2016), 9--16. Source: ISI Web of Knowledge.

Pop, H. F., Sarbu, C. A new fuzzy regression algorithm. Analytical Chem. 68, 5 (1996), 771-778

  1. Jensen, O. N., Mortensen, P., Vorm, O., and Mann, M. Automation of matrix-assisted laser desorption/ionization mass spectrometry using fuzzy logic feedback control. Analytical Chem. 69, 9 (1997), 1706--1714. Source: ISI Web of Knowledge.
  2. Saila, S. B., and Ferson, S. Fuzzy regression in fisheries science: Some methods and applications. Fishery Stock Assessment Models 15 (1998), 339--354. Source: ISI Web of Knowledge.
  3. Rajko, R. Calibration of chemical measurements. On quality of analytical information. Magyar Kemiai Folyoirat 107, 2 (2001), 45--59. Source: ISI Web of Knowledge.
  4. Munteanu, A. Optimizarea Constructivă a Retelei Electrice de Distributie din Incinta Consumatorului (Constructive optimisation of electrical distribution network from the consumer). PhD thesis, Faculty of Building Engineering, Techninal University of Cluj-Napoca, Cluj-Napoca, Romania, 2003. Source: Google Scholar.
  5. Ortiz, M. C., Sarabia, L. A., and Herrero, A. Robust regression techniques - A useful alternative for the detection of outlier data in chemical analysis. Talanta 70, 3 (2006), 499--512. Source: ISI Web of Knowledge.
  6. Ortiz, M. C., Sarabia, L. A., Garcia, I., Gimenez, D., and Melendez, E. Capability of detection and three-way data. Analytica Chimica Acta 559, 1 (2006), 124--136. Source: ISI Web of Knowledge.
  7. Astel, K., and Astel, A. BIOINDYKACJA + CHEMOMETRIA = ? Statsoft Polska (2007), 45--57. https://www.statsoft.com.pl/czytelnia/8_2007/Astel06.pdf. Source: Google Scholar.
  8. Anderson, A., Floyd, B., and Hrachowitz, M. Russell Creek Annual Report 2006/07. Russell Creek (2007). Source: Google Scholar.
  9. Dalkilic, T., Tank, F., and Kula, K. Neural networks approach for determining total claim amounts in insurance. Insurance: Mathematics and Economics 45, 2 (2009), 236--241. Source: Scopus.
  10. Bashiria, M., and Moslemia, A. A robust moving average iterative weighting method to analyze the effect of outliers on the response surface design. International Journal of Industrial Engineering Computation 2, 4 (2011), 851--862. Source: Google Scholar.
  11. D'Urso, P., Massari, R., and Santoro, A. Robust fuzzy regression analysis. Information Sciences 181, 19 (2011), 4154--4174. Source: ISI Web of Knowledge.
  12. Tsakovski, S., Simeonova, P., and Simeonov, V. Sediment Pollution Assessment by Chemometric Methods. Ecological Chemistry and Engineering S-Chemia I Inzynieria Ekologiczna S 18, 2 (2011), 141--170. Source: ISI Web of Knowledge.
  13. Palage, M., Tiperciuc, B., Oniga, S., Araniciu, C., Benedec, D., and Oniga, O. The Evaluation of the Lipophilic Properties of Some Thiazolyl-Oxadiazolines With Antiinflammatory Activity. Farmacia 59, 3 (2011), 347--357. Source: ISI Web of Knowledge.
  14. Han, S.-y., Liang, C., Qiao, J.-q., Lian, H.-z., Ge, X., and Chen, H.-y. A novel evaluation method for extrapolated retention factor in determination of n-octanol/water partition coefficient of halogenated organic pollutants by reversed-phase high performance liquid chromatography. Analytica Chimica Acta 713 (2012), 130--135. Source: ISI Web of Knowledge.
  15. Gharibnezhad, F., Mujica, L., Rodellar, J., and Fritzen, C.-P. Damage detection using robust fuzzy principal component analysis. vol. 1, pp. 609--616. Source: Scopus.
  16. Kodogiannis, V., Kontogianni, E., and Lygouras, J. Neural network based identification of meat spoilage using fourier-transform infrared spectra. Journal of Food Engineering 142 (2014), 118--131. Source: Scopus.
  17. Kodogiannis, V. S., Kontogianni, E., and Lygouras, J. N. Neural network based identification of meat spoilage using Fourier-transform infrared spectra. Journal of Food Engineering 142 (2014), 118--131. Source: ISI Web of Knowledge.
  18. Erbay Dalkiliç, T., and Sanli Kula, K. Parameter estimation by fuzzy adaptive networks and comparison with robust regression methods. Gazi University Journal of Science 28, 1 (2015), 103--113. Source: ISI Web of Knowledge.

Pop, H. F., Sarbu, C. The fuzzy hierarchical cross-clustering algorithm. Improvements and comparative study. J. Chem. Information Computer Sciences 37, 3 (1997), 510-516

  1. Holliday, J. D., Rodgers, S. L., Willett, P., Chen, M. Y., Mahfouf, M., Lawson, K., and Mullier, G. Clustering files of chemical structures using the fuzzy k-means clustering method. J. Chem. Information Computer Sciences 44, 3 (2004), 894--902. Source: ISI Web of Knowledge.
  2. Salim, N., Shamsuddin, S., and Alwee, R. Development of compound clustering techniques using hybrid soft-computing algorithms. Project Report. Faculty of Computer Science and Information System, Skudai, Johor (2008). Source: Google Scholar.
  3. Reghunadhan, R., and Arulmozhi, V. Fuzzy logic for chemoinformatics - a review. Journal of Theoretical and Applied Information Technology 47, 1 (2013), 86--92. Source: ResearchGate.
  4. Xu, Z. Hesitant Fuzzy Sets Theory. In Studies in Fuzziness and Soft Computing, vol. 314. 2014, pp. 1--466. Source: ISI Web of Knowledge.
  5. Na, C., Ze-shui, X., and Mei-mei, X. Hierarchical hesitant fuzzy K-means clustering algorithm. Applied Mathematics - A Journal of Chinese Universities Series B 29, 1 (2014), 1--17. Source: ISI Web of Knowledge.

Dumitrescu, D., Pop, H. F. Degenerate and non-degenerate convex decomposition of finite fuzzy partitions (ii). Fuzzy Sets Systems 96, 1 (1998), 111-118

  1. Bodjanova, S. T-Sharper Images and T-Level Cuts of Fuzzy Partitions. In DATA SCIENCE: INNOVATIVE DEVELOPMENTS IN DATA ANALYSIS AND CLUSTERING (2017), Palumbo, F and Montanari, A and Vichi, M, Ed., Studies in Classification Data Analysis and Knowledge Organization, SAS Inst; Springer; APT Servizi Regione Emilia Romagna; Ascom Bologna; Univ Bologna, pp. 61--72. 15th Conference of the International Federation of Classification Societies (IFCS), Bologna, ITALY, JUL 05-08, 2015. Source: ISI Web of Knowledge.
  2. Mu, Y. M., Yu, J., Huang, H. K., and Lin, Y. F. On the solution of the fuzzy c-means model. 2003 Int. Conference On Machine Learning Cybernetics, Vols 1-5, Proc. (2003), 2711--2714. Source: ISI Web of Knowledge.
  3. Bodjanova, S. Linear intensification of probabilistic fuzzy partitions. Fuzzy Sets Systems 141, 2 (2004), 319--332. Source: ISI Web of Knowledge.
  4. Ferrigno, S., Gannoun, A., and Saracco, J. Inverse regression methods based on fuzzy partitions. International Journal of Pure and Applied Mathematics 43, 1 (2008), 43. Source: Google Scholar.

Blaga, P. A., and Pop, H. F. LATEX 2e, Technical Publishing Co., Bucharest (1999)

  1. Morosanu, C. Elemente de bază ale sistemului TEX si plainTEX (Basic elements of the TEX and plainTEX system). Alexandru Ioan Cuza University Press, Iasi, Romania, 2001. Source: Google Scholar.

Foth, K. A., Menzel, W., Pop, H. F., Schroder, I. An experiment in incremental parsing using weighted constraints. In The 18-th International Conference on Computational Linguistics (COLING2000) (Saarbrucken, Germany, July, 31 - August, 4 2000), 1026-1030

  1. Holan, T., Kubon, V., Platek, M., and Oliva, K. A Theoretical Basis of an Architecture of a Shell of a Reasonably Robust Syntactic Analyser. Lecture Notes in Computer Science 2807 (2003), 58--65. Published by Springer. Source: Google Scholar.
  2. Harper, M., and Wang, W. Constraint Dependency Grammars: SuperARVs, Language Modeling, and Parsing. In Supertagging Using Complex Lexical Descriptions in Natural Language Processing, S. Bangalore and A. K. Joshi, Eds. MIT Press, Cambridge, London, 2009, ch. 10, pp. 207--239. https://www.umiacs.umd.edu/~mharper/papers/harper-cdg.pdf Source: Google Scholar.

Sarbu, C., Pop, H. F. Fuzzy clustering analysis of the first 10 MEIC chemicals. Chemosphere 40, 5 (2000), 513-520

  1. Xue, M., Zhou, L., Kojima, N., dos Muchangos, L. S., Machimura, T., and Tokai, A. Application of fuzzy c-means clustering to PRTR chemicals uncovering their release and toxicity characteristics. SCIENCE OF THE TOTAL ENVIRONMENT 622 (MAY 1 2018), 861--868. Source: ISI Web of Knowledge.
  2. Pedrycz, W. Knowledge-Based Clustering. From Data to Information Granules. John Wiley & Sons, Hoboken, New Jersey, 2005. Source: Google Scholar.
  3. Onkal-Engin, G., Demir, I., and Engin, S. N. e-nose response classification of sewage odors by neural networks and fuzzy clustering. Adv. In Natural Computation, Pt 2, Proc. 3611 (2005), 648--651. Source: ISI Web of Knowledge.
  4. Ziembik, Z., Dolhanczuk-Srodka, A., Komosa, A., Orzel, J., and Waclawek, M. Assessment of Cs-137 and Pu-239,Pu-240 Distribution in Forest Soils of the Opole anomaly. Water Air Soil Pollution 206, 1-4 (2010), 307--320. Source: ISI Web of Knowledge.
  5. Pan, T. H., Huang, B., Xing, J. Z., Zhang, W. P., Gabos, S., and Chen, J. Recognition of chemical compounds in contaminated water using time-dependent multiple dose cellular responses. ANALYTICA CHIMICA ACTA 724 (APR 29 2012), 30--39. Source: ISI Web of Knowledge.
  6. Panteleimonov, A. V., and Kholin, Y. V. Algorithm of object identification in qualitative chemical analysis based on fuzzy similarity criteria. JOURNAL OF ANALYTICAL CHEMISTRY 68, 11 (2013), 942--948. Source: ISI Web of Knowledge.

Cundari, T. R., Deng, J., Pop, H. F., Sarbu, C. Structural analysis of transition metal beta-X substituent interactions. Toward the use of soft computing methods for catalyst modeling. J. Chem. Information Computer Sciences 40, 4 (2000), 1052-1061

  1. Medford, A. J., Kunz, M. R., Ewing, S. M., Borders, T., and Fushimi, R. Extracting Knowledge from Data through Catalysis Informatics. ACS CATALYSIS 8, 8 (AUG 2018), 7403--7429. Source: ISI Web of Knowledge.
  2. Landrum, G. A., and Genin, H. Application of machine-learning methods to solid-state chemistry: ferromagnetism in transition metal alloys. J. Solid State Chem. 176, 2 (2003), 587--593. Source: ISI Web of Knowledge.
  3. Wolter, T., and Maier, W. Combinatorial Search for Low-Temperature Combustion Catalysts. In Materials Research Society Symposium Proceedings (2004), vol. 804, Warrendale, Pa.; Materials Research Society; 1999, pp. 283--294. Source: Google Scholar.
  4. Landrum, G. A., Penzotti, J., and Putta, S. Machine-learning models for combinatorial catalyst discovery. Combinatorial Artificial Intelligence Methods In Materials Science Ii 804 (2004), 301--306. Source: ISI Web of Knowledge.
  5. Melville, J. L., Lovelock, K. R. J., Wilson, C., Allbutt, B., Burke, E. K., Lygo, B., and Hirst, J. D. Exploring phase-transfer catalysis with molecular dynamics and 3D/4D quantitative structure-selectivity relationships. J. Chem. Information Modeling 45, 4 (2005), 971--981. Source: ISI Web of Knowledge.
  6. Landrum, G. A., Penzotti, J. E., and Putta, S. Machine-learning models for combinatorial catalyst discovery. Measurement Science & Technology 16, 1 (2005), 270--277. Source: ISI Web of Knowledge.
  7. Burello, E., and Rothenberg, G. In silico design in homogeneous catalysis using descriptor modelling. Int. J. Mol. Sciences 7, 9 (2006), 375--404. Source: ISI Web of Knowledge.
  8. Leon, F., Curteanu, S., Lisa, C., and Hurduc, N. Machine learning methods used to predict the liquid-crystalline behavior of some copolyethers. Mol. Crystals Liquid Crystals 469 (2007), 1--22. Source: ISI Web of Knowledge.
  9. Drummond, M. L., and Sumpter, B. G. Use of drug discovery tools in rational organometallic catalyst design. Inorg. Chem. 46, 21 (2007), 8613--8624. Source: ISI Web of Knowledge.
  10. Rothenberg, G. Data mining in catalysis: Separating knowledge from garbage. Catalysis Today 137, 1 (2008), 2--10. Source: ISI Web of Knowledge.
  11. Schunk, S. A., Boehmer, N., Futter, C., Kuschel, A., Prasetyo, E., and Roussiere, T. High throughput technology: approaches of research in homogeneous and heterogeneous catalysis. SPR-Catalysis 25 (2013), 172--215. Source: ISI Web of Knowledge.

Pop, H. F. Principal Components Analysis based on a fuzzy sets approach. Studia Universitatis Babeș-Bolyai, Informatica 46, 2 (2001), 45-52

  1. Xian, S., Qiu, D., and Zhang, S. A fuzzy principal component analysis approach to hierarchical evaluation model for balanced supply chain scorecard grading. Journal of Optimization Theory and Applications 159, 2 (2013), 518--535. Source: ResearchGate (ISI journal).

Sarbu, C., Pop, H. F. Fuzzy robust estimation of central location. Talanta 54, 1 (2001), 125-130

  1. Munteanu, A. Optimizarea Constructivă a Retelei Electrice de Distributie din Incinta Consumatorului (Constructive optimisation of electrical distribution network from the consumer). PhD thesis, Faculty of Building Engineering, Techninal University of Cluj-Napoca, Cluj-Napoca, Romania, 2003. Source: Google Scholar.
  2. Li, C. H., Li, Y. B., and Zhang, Z. J. Study on model construction of IV lateral motion pattern-space for its. 2005 IEEE International Conference On Vehicular Electronics And Safety Proceedings (2005), 217--221. Source: ISI Web of Knowledge.
  3. Daszykowski, M., Kaczmarek, K., Heyden, Y. V., and Walczak, B. Robust statistics in data analysis -- a review: Basic concepts. Chemometrics and Intelligent Laboratory Systems 85, 2 (2007), 203--219. Source: ResearchGate (ISI journal).
  4. Bin, C., Bin, L., and Zhisong, P. Robust Location Estimation with Possibilistic Clustering. In 2009 ISECS International Colloquium on Computing, Communication, Control, and Management, vol III (2009), Luo, Q and Yi, J and Bin, C, Ed., IEEE Technol Management Council; Intelligent Informat Technol Applicat Res Assoc; IEEE SMC TC; Yangzhou Univ; Wuhan Inst Technol; Guangdong Univ Business Studies, pp. 312--315. 2nd ISECS International Colloquium on Computing, Communication, Control and Management (CCCM 2009), Sanya, PEOPLES R CHINA, AUG 08-09, 2009. Source: ISI Web of Knowledge.
  5. Panteleimonov, A. V., and Kholin, Y. V. Algorithm of object identification in qualitative chemical analysis based on fuzzy similarity criteria. Journal of Analytical Chemistry 68, 11 (2013), 942--948. Source: ISI Web of Knowledge.

Schroder, I., Pop, H.F., Menzel, W., Foth, K.A., Learning grammar weights using genetic algorithms, Proceedings Recent Advances in Natural Language Processing, RANLP (2001), 235-239

  1. Wang, J., Quan, Q., Yu, J., and Pang, H. Using hybrid parsing models as predictors for a symbolic parser. In Second International Symposium on Intelligent Information Technology Application (2008), IEEE, pp. 895--899. Source: Google Scholar.
  2. Øvrelid, L. Argument Differentiation. Soft constraints and data-driven models. rapport nr.: Data linguistica 20 (2008). Source: Google Scholar.
  3. Figueroa, A., and Neumann, G. Genetic algorithms for data-driven web question answering. Evolutionary computation 16, 1 (2008), 89--125. Source: Google Scholar.
  4. Kar, P. Why we respect our Teachers. A Note on Language Learnabilty and Active Learning. https://home.iitk.ac.in/~purushot/ll-nerd.pdf (2009). Source: Google Scholar.
  5. Din, R., and Samsudin, A. Intelligent steganalytic system: application on natural language environment. WSEAS Transactions on Systems and Control 4, 8 (2009), 379--388. Source: Google Scholar.
  6. McCrae, P. A Computational Model for the Influence of Cross-Modal Context upon Syntactic Parsing. PhD thesis, Universitaet Hamburg, 2010. Source: Google Scholar.

Pop, H. F., Pop, T. L., and Sarbu, C. Assessment of heart disease using fuzzy classification techniques. The Scientific World Journal 1 (2001), 369-390

  1. Jianhua, X., Luyi, S., Yu, Z., Li, G., Honggang, F., Haikun, M., and Hongbin, W. The fuzzy model for diagnosis of animal disease. Computer and Computing Technologies in Agriculture III (2010), 364--368. Source: Google Scholar.
  2. Mohammadpour, R. A., Abedi, S. M., Bagheri, S., and Ghaemian, A. Fuzzy rule-based classification system for assessing coronary artery disease. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE (2015). Source: ISI Web of Knowledge.

Cundari, T. R., Sarbu, C., Pop, H. F. Robust fuzzy principal component analysis (FPCA). A comparative study concerning interaction of carbon-hydrogen bonds with molybdenum-oxo bonds. J. Chem. Information Computer Sciences 42, 6 (2002), 1363-1369

  1. Couso, I., Borgelt, C., Huellermeier, E., and Kruse, R. Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning. IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 14, 1 (FEB 2019), 31--44. Source: ISI Web of Knowledge.
  2. Hadri, A., Chougdali, K., and Touahni, R. Identifying intrusions in computer networks using Robust Fuzzy PCA. In 2017 IEEE/ACS 14TH INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS AND APPLICATIONS (AICCSA) (2017), International Conference on Computer Systems and Applications, IEEE; ACS; IEEE Comp Soc; Arab Comp Soc; Univ Arizona; Univ Centrale; Wevioo Grp; ATIA Tunisia; Qatar Comp Res Inst; TUNISIE INNOVAT, pp. 1261--1268. 14th IEEE/ACS International Conference on Computer Systems and Applications (AICCSA), Hammamet, TUNISIA, OCT 30-NOV 03, 2017, Source: ISI Web of Knowledge.
  3. Serra, S. R. Q., Graca, M. A. S., Doledec, S., and Feio, M. J. Chironomidae traits and life history strategies as indicators of anthropogenic disturbance. ENVIRONMENTAL MONITORING AND ASSESSMENT 189, 7 (JUL 2017). Source: ISI Web of Knowledge.
  4. Abadpour, A. Rederivation of the fuzzy-possibilistic clustering objective function through Bayesian inference. FUZZY SETS AND SYSTEMS 305 (DEC 15 2016), 29--53. Source: ISI Web of Knowledge.
  5. Landrum, G. A., Penzotti, J., and Putta, S. Machine-learning models for combinatorial catalyst discovery. Combinatorial Artificial Intelligence Methods In Materials Science Ii 804 (2004), 301--306. Source: ISI Web of Knowledge.
  6. Abadpour, A., and Kasaei, S. A new FPCA-based fast segmentation method for color images. Proceedings Of The Fourth IEEE International Symposium On Signal Processing And Information Technology (2004), 72--75. Source: ISI Web of Knowledge.
  7. Landrum, G. A., Penzotti, J. E., and Putta, S. Machine-learning models for combinatorial catalyst discovery. Measurement Science & Technology 16, 1 (2005), 270--277. Source: ISI Web of Knowledge.
  8. Peters, M. B., and Merz, K. M. Semiempirical comparative binding energy analysis (SE-COMBINE) of a series of trypsin inhibitors. J. Chem. Theory Computation 2, 2 (2006), 383--399. Source: ISI Web of Knowledge.
  9. Liu, H. C., Jiang, W., Tangirala, A., and Shah, S. An adaptive regression adjusted monitoring and fault isolation scheme. J. Chemometrics 20, 6-7 (2006), 280--293. Source: ISI Web of Knowledge.
  10. Fey, N., Tsipis, A. C., Harris, S. E., Harvey, J. N., Orpen, A. G., and Mansson, R. A. Development of a ligand knowledge base, Part 1: Computational descriptors for phosphorus donor ligands. Chemistry-a European J. 12, 1 (2006), 291--302. Source: ISI Web of Knowledge.
  11. Burello, E., and Rothenberg, G. In silico design in homogeneous catalysis using descriptor modelling. Int. J. Mol. Sciences 7, 9 (2006), 375--404. Source: ISI Web of Knowledge.
  12. Wang, L., Geng, Z. X., Lu, X. Q., Liu, H. D., Wang, R., and Chen, J. Predictive studies on interaction between DNA and target molecules. Chem. J. Chinese Universities-chinese 28, 1 (2007), 34--39. Source: ISI Web of Knowledge.
  13. Leon, F., Curteanu, S., Lisa, C., and Hurduc, N. Machine learning methods used to predict the liquid-crystalline behavior of some copolyethers. Mol. Crystals Liquid Crystals 469 (2007), 1--22. Source: ISI Web of Knowledge.
  14. Aoki, S., Toyozumi, K., and Tsuji, H. Visualizing method for data envelopment analysis. In IEEE International Conference on Systems, Man and Cybernetics, 2007. ISIC (2007), pp. 474--479. Source: Google Scholar.
  15. Yang, C., Lu, L. J., Lin, H. P., Guan, R. C., Shi, X. C., and Liang, Y. C. A Fuzzy-Statistics-Based Principal Component Analysis (FS-PCA) Method for Multispectral Image Enhancement and display. IEEE Transactions On Geoscience Remote Sensing 46, 11 (2008), 3937--3947. Source: ISI Web of Knowledge.
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Pop, H. F., and Frentiu, M. Detecting mistakes in students measurement projects. In Symposium Colocviul Academic Clujean de Informatica (Babeș-Bolyai University, Cluj-Napoca, Romania, June 1-2 2005), pp. 105-110

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Boian, F., Boian, R., Vancea, A., and Pop, H. F. Distance learning and supporting tools at Babeș-Bolyai university. In The Informatics Education Europe Conference (IEE II) (Thessaloniki, Greece, November 29-30 2007), pp. 332-340

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Vescan, A., Grosan, C., Pop, H. F. Evolutionary algorithms for the component selection problem. In The 2nd International Workshop Evolutionary Techniques in Data Processing (2008), vol. 1529-4188, IEEE Computer Society Press, 509-513

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Vescan, A., and Pop, H. F. Automatic configuration for the component selection problem. In CSTST '08: Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology (Cergy-Pontoise, France, October 27-31 2008), ACM, New York, NY, USA, pp. 479-483

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Vescan, A., and Pop, H. F. Constraint optimization-based component selection problem. Studia Universitatis Babeș-Bolyai, Informatica 53, 2 (2008), 3-14

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Serban, C., Vescan, A., Pop, H. F. A new component selection algorithm based on metrics and fuzzy clustering analysis. In Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems. Lecture Notes in Artificial Intelligence, LNCS 5572/2009 (Salamanca, Spain, June 10-12 2009), 621-628

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Serban, C., Vescan, A., Pop, H. F. A conceptual framework for component-based system metrics definition. In 9-th RoEduNet IEEE International Conference (Lucian Blaga University, Sibiu, Romania, June 24-26 2010), 73-78

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Gaceanu, R. D., and Pop, H. F. An incremental approach to the set covering problem. Studia Universitatis Babeș-Bolyai, Informatica 57, 2 (2012), 61-72

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© Prof.dr. Horia F. Pop