11 CITAÇÕES

Do seu trabalho de investigação apresenta mais de 150 citações, sendo algumas indexadas no “ISI Web of Knowledge”. A maior parte corresponde a citações obtidas no “Scopus”, “Google Scholar”, e outros. O “Scopus” apresenta uma métrica, que em certa medida permite verificar o impacto das citações para um dado autor, sendo neste caso h-index = $6$.

A informação foi processada manualmente de forma a contabilizar as citações e filtrar auto-citações.

Listam-se as seguintes citações do seu trabalho no período que decorre entre 1993–2010 (à data de finalização deste documento)

2008

Citação do artigo:

• Ribeiro, B. and Vieira, A. and Neves, J. , “Supervised Isomap with Dissimilarity Measures in Embedding Learning”, in Proc. of the Ibero-American Conference on Pattern Recognition, Progress in Pattern Recognition, Image Analysis and Applications, Lecture Notes in Computer Science (LNCS), Springer Berlin / Heidelberg, pp. 389–396, Vol. 5197, September 2008.

Citado em:

1.

Feng-Yi Lin, Ching-Chiang Yeh, Meng-Yuan Lee, “The use of hybrid manifold learning and support vector machines in the prediction of business failure”, Knowledge-Based Systems, 2010

• B Ribeiro, A Vieira, J Duarte, C Silva, J C das Neves, Q Liu, and A H. Sung, “Learning Manifolds for Bankruptcy Analysis”, M. Köppen et al. (Eds.): ICONIP 2008, Part I, LNCS 5506, pp. 722–729, Springer-Verlag Berlin Heidelberg, 2009.

Citado em:

1.

Feng-Yi Lin, Ching-Chiang Yeh, Meng-Yuan Lee, “The use of hybrid manifold learning and support vector machines in the prediction of business failure”, Knowledge-Based Systems, 2010

Citação do artigo:

• Liu, Q. and Sung, A. and Ribeiro, B. , “Image Complexity and Feature Mining for Steganalysis of Least Significant Bit Matching Steganography”, Information Sciences, Vol. 178, # 1, pp. 21–36, Elsevier Science Inc. New York, NY, USA, January, 2008

Citado em:

1.

Xu, H., Wang, J., Kim, H.J., “Near-optimal solution to pair-wise LSB matching via an immune programming strategy”, Information Sciences, 180 (8), pp. 1201–1217, 2010

2.

Huang, B., Wu, J., Zhang, D., Li, N., “Tongue shape classification by geometric features”, Information Sciences, 180 (2), pp. 312–324, 2010.

3.

Zainab Famili , Karim Faez and Abbas Fadavi, “A New Steganography Based on X2 Technic”, Series Lecture Notes in Computer Science, Springer, Vol. 5856/2009, 1062–1069, Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 2009

4.

CC Lin, SC Chen, NL Hsueh, “Adaptive embedding techniques for VQ-compressed images”, Information Sciences, Vol. 179, Issues 1–2, pp. 140–149, Elsevier, January 2009

5.

F Yaghmaee, M Jamzad, “Estimating Watermarking Capacity in Gray scale Images based on Image”, EURASIP Advances on Signal Processing, 2009

6.

XU Man-kun, LI Tian-yun, PING Xi-jian, “Steganalysis of LSB Matching Based on Wavelet Estimation and Histogram Features”, Computer Engineering, Vol.35, No.19, 2009

7.

F.Yaghmaee, M. Jamzad, “Estimating data hiding capacity of gray scale images based image complexity”, The Fourth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Harbin, China, August, 2008

8.

Vasif Nabiyev, Mustafa Ulutas, Güzin Ulutas, “Estimation Complexity of Image Complexity for Steganalysis and Watermarking”, 2008

9.

Xu, X., Ping, X., Zhang, T., Wang, G., “Image restoration-based steganalysis directed to LSB matching steganography”, Journal of Computer-Aided Design and Computer Graphics, 21, (2), pp. 262–267, 2009.

10.

M Yoan, B Patrick, L Amaury, J Christian, S,, “Reliable Steganalysis Using a Minimum Set of Samples and Features”, EURASIP Journal on Information Security, 2009 (doi:10.1155/2009/901381)

11.

V Nabiyev, M Ulutas, G Ulutas, “Estimation of Image Complexity for Steganography and Watermarking”, science.az, 2009

12.

Mankun, X., Tianyun, L., Xijian, P., “Steganalysis of LSB matching based on histogram features in grayscale image”, Proc of 11th IEEE Int. Conf. on Communication Technology, ICCT, pp. 669–672, 2008

13.

Luo, X.-Y., Liu, F.-L., Wang, D.-S., “Image classification method of distinguishing LSB replacement from matching steganography”, Journal on Communication, 29 (SUPPL.), pp. 122–128, 2008

14.

Zhang, F., Pan, Z., Cao, K., Zheng, F., Wu, F., “The upper and lower bounds of the information-hiding capacity of digital images ”, Information Sciences, Elsevier, 178 (14), pp. 2950–2959, 2008

15.

Mankun, X., Tianyun, L., Xijian, P., “Steganalysis of LSB matching based on histogram features in grayscale image”, Proc of International Conference on Communication Technology, ICCT, art. no. 4716192, pp. 669-672, 2008.

2007
Citação do artigo:

• Ribeiro, B. and Marques, A. C. and Henriques, J. O. and Antunes, M. , “Choosing Real-Time Predictors For Ventricular Arrhythmias Detection”, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 21, # 8, pp. 1–15, World Scientific Publishing Co., December, 2007

Citado em:

1.

X Wang, M Ye, CJ Duanmu, “Classification of data from electronic nose using relevance vector machines”, Sensors & Actuators: B. Chemical, Elsevier Science Publishers, 2009

2.

Ali Farrokhi, Hadi Mazidi and Ali Barzegari, “Premature ventricular contraction and ventricular Tachycardia beat detection by using power and time estimation”, Recent Advances In Biology And Biomedicine, Proc of the 2nd WSEAS international conference on Computational chemistry, pp. 116–121, Puerto De La Cruz, Spain, 2008

Citação do artigo:

• Silva, C. and Ribeiro, B. and Sung, A. , “Boosting RVM Classifiers for Large Data Sets”, in Proc. of the International Conference on Adaptive and Natural Computing Algorithms, B. Beliczynski et al. (Eds.), Lecture Notes in Computer Science (LNCS), Part II, LNCS 4432, Springer-Verlag Berlin Heidelberg, pp. 228–237, ICANNGA 2007, Warsaw, Poland, April 2007

Citado em:

1.

Yanfang Ye , Lifei Chen , Dingding Wang , Tao Li , Qingshan Jiang and Min Zhao, “SBMDS: an interpretable string based malware detection system using SVM ensemble with bagging”, Journal in Computer Virology, Springer Paris, Vol. 5, Nr. 4, pp. 283–293, November, 2008

Citação do artigo:

• Silva, C. and Ribeiro, B. , “On Text-based Mining with Active Learning and Background Knowledge using SVM”, Journal of Soft Computing - A Fusion of Foundations, Methodologies and Applications, Vol. 11(6), pp. 519–530, Springer Verlag, January, 2007

Citado em:

1.

Tamara Polajnar , Simon Rogers and Mark Girolami, “Classification of Protein Interaction Sentences via Gaussian Processes”, Pattern Recognition in Bioinformatics, Lecture Notes in Computer Science, Springer Berlin / Heidelberg, Vol. 5780, pp. 282–292, 2009 (DOI10.1007/978-3-642-04031-3)

2.

D Tuia, F Ratle, F Pacifici, M. Kanevski and W. Emery,“Active Learning Methodsfor Remote Sensing Image Classification”, IEEE Transactions on Geoscience and Remote Sensing, 2008

3.

Jair Cervantes Canales,“Clasificación de grandes conjuntos de datos vía Máquinas de Vectores Soporte y aplicaciones en sistemas biológicos”, PhD Thesis, p.208, Centro de Investigacion Y de Estudios del Instituto Poliyécnico Nacional, Departamento de Computación, Mexico, 2009

Citação do artigo:

• Ribeiro, B. and Cardoso, A. J. , “Behavior Pattern Mining during the Evaluation Phase in an e-Learning Course”, in Proc. of the International Conference on Engineering Education (ICEE 2007), ICEE 2007 - International Conference on Engineering Education (CD-ROM), Coimbra, Portugal, September 2007.

Citado em:

1.

P Drazdilova, Gamila Obadi, K Slaninova, S Al-Dubaee, Jan Martinovic, Václav Snáşel “Computational Intelligence Methods for Data Analysis and Mining of eLearning Activities”, F.Xhafa et a. (Eds.): Computational Intelligence for Tech. Enhanced Learning, SCI 273, pp.195–224, Springer-Verlag Berlin Heidelberg, 2010

2006
Citação do artigo:

• Silva, C. and Ribeiro, B., “Two-level hierarchical hybrid SVM-RVM classification model”, in Proc. of the IEEE International Conference on Machine Learning Applications, pp. 89-94, IEEE ICMLA 2006, Orlando, USA, December 2006.

Citado em:

1.

Tashk, Ali Reza Bayesteh and Faez, Karim, “Boosted Bayesian Kernel Classifier Method for Face Detection”, ICNC 2007, Third International Conference on Natural Computation, pp. 533–537, 2007.

2.

Alireza Bayesteh, Abolghasem Sayadiyan, and Seyyed Majid Valiollahzadeh, “Face Detection Using Adaboosted RVM-based Component Classifier”, ISPA 2007, International Symposium on Image and Signal Processing and Analysis, pp. 351–355, 2007.

Citação do artigo:

• Silva, C. and Ribeiro, B. , “RVM Ensemble for Text Classification”, International Journal of Computational Intelligence Research, Vol. 3(1), pp. 31–35, January, 2007

Citado em:

1.

Bo Yu and Zong-ben Xu, “A comparative study for content-based dynamic spam classification using four machine learning algorithms”, Knowledge-Based Systems, Vol 21, Issue 4, pp. 355–362, May 2008

Citação do artigo:

• Liu, Q. and Sung, A. and Ribeiro, B. , “Image Complexity and Feature Extraction for Steganalysis of LSB Matching Steganography”, in Proc. of the 18th International Conference of Pattern Recognition (ICPR2006), pp. 267–270, Hong-Kong, China, August 2006

Citado em:

1.

Xu, H., Wang, J., Kim, H.J., “Near-optimal solution to pair-wise LSB matching via an immune programming strategy”, Information Sciences, 180 (8), pp. 1201-1217, 2010

2.

Chen Ming, Liu Fan-fan, Zhang Ru, Niu Xin-xin, Yang Yi-xian, “Steganalysis of LSB Matching in Gray Images Based on Regional Correlation Analysis, WRI World Congress on Computer Science and Information Engineering, USA, March 31-April 02,2009 (DOIBookmark:http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.577)

3.

Shen Ge, Yang Gao and Ruili Wang, “Least significant bit steganography detection with machine learning techniques”, International Conference on Knowledge Discovery and Data Mining, Proceedings of the 2007 international workshop on Domain Driven Data Mining, San Jose, California, pp. 24–32, 2007

Citação do artigo:

• Ribeiro, B. and Vieira, A. and Neves, J. , “Sparse Bayesian Classifiers: Bankruptcy-Predictors of Choice?”, Proc of International Joint Conference on Neural Networks, World Congress On Computational Intelligence, IEEE-IJCNN, pp. 3377–3381, Vancouver, Canada, July 2006

Citado em:

1.

A Verikas, Z Kalsyte, M Bacauskiene, A Gelzinis, “Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey”, Journal Soft Computing - A Fusion of Foundations, Methodologies and Applications, Springer Berlin / Heidelberg, September 16, 2009 (DOI 10.1007/s00500-009-0490-5)

Citação do artigo:

• Mukkamala, S. and Sung, A. and Ribeiro, B. and Vieira, A. , “Model Selection and Feature Ranking for Financial Distress Classification.”, in Proc. of the International Symposium on Neural Networks (ISNN 06), International Symposium on Neural Networks, May 2006

Citado em:

1.

HA Abdou, “Genetic programming for credit scoring: The case of Egyptian public sector banks”, Expert Systems With Applications, Elsevier Science Publishers, Vol. 36 , Issue 9, pp. 11402–11417, November, 2009

2.

Elish, M. O. 2009, “Improved estimation of software project effort using multiple additive regression trees”, Expert Systems with Applications, Vol 36, 7, pp. 10774–10778, Elsevier, 2009 (DOI= http://dx.doi.org/10.1016/j.eswa.2009.02.013)

Citação do artigo:

• Mukkamala, S., Sung, A., Ribeiro, B. and Vieira, A. , “Computational Intelligent Techniques for Financial Distress Detection”, Journal of Computational Intelligence Research (IJCIR), Vol. 2, # 1, pp. 60–65, Research India Publications, January 2006

Citado em:

1.

Davalos, Sergio, Leng, Fei, Feroz, Ehsan H. and Cao, Zhiyan , “Bankruptcy Classification of Firms Investigated by the US Securities and Exchange Commission: An Evolutionary Ensemble Computing Model Approach”, August 26, 2009). Available at SSRN: http://ssrn.com/abstract=1462565

2005
Citação do artigo:

• Ribeiro, B., “Support Vector Machines for Quality Monitoring in a Plastic Injection Molding Machine”, IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews, Vol. 35, # 3, pp. 401-410, IEEE, August 2005

Citado em:

1.

Gani, W., Taleb, H., Limam, M., “Support vector regression based residual control charts”, Journal of Applied Statistics, 37 (2), pp. 309—324, 2010

2.

Mariette Awad, Y Motai , J Näppi, and H Yoshida , “A Clinical Decision Support Framework for Incremental Polyps Classification in Virtual Colonoscopy”, Algorithms, 3(1), 1-20, 2010 (doi:10.3390/a3010001)

3.

Meng, X., Xie, Y., Dai, X., “Methodology of designing for time-varying performance of complex products”, Jixie Gongcheng Xuebao/Journal of Mechanical Engineering 46 (1), pp. 128–133, 2010

4.

Park, J.I., Baek, S.H., Jeong, M.K., Bae, S.J., “Dual features functional support vector machines for fault detection of rechargeable batteries”, IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 39 (4), pp. 480–485, 2009

5.

L Guo, J Chen, X Li, “Rolling Bearing Fault Classification Based on Envelope Spectrum and Support Vector”, Journal of Vibration and Control, 15 (9), pp. 1349–1363, 2009

6.

W Gani, HTM Limam , Statistical Process Control using Support Vector Machines: A Case Study, Journal of Vibration and Control, 2009

7.

Yu, X., Chu, F., Hao, R., “Fault diagnosis approach for rolling bearing based on support vector machine and soft morphological filters”, Journal of Mechanical Engineering, 45 (7), pp. 75–80, 2009

8.

Meng, X., Xie, Y., “System parameters identifying and performance predicting of ICEs combining multidisciplinary model with system responding data”, Proc of the 9th Biennial Conference on Engineering Systems Design and Analysis, Vol 2, pp. 729–734, 2009

9.

Yu, X.-T., Lu, W.-X., Chu, F.-L., “Rotating machinery fault diagnosis based on fuzzy proximal support vector machine optimized by particle swarm optimization”, Journal of Vibration and Shock, 28 (11), pp. 183–186+198, 2009

10.

Xavier Berjaga, Álvaro Pallarés, Joaquim Meléndez, and Francisco Ignacio Gamero, “Case-Based Diagnosis in the principal component space: Application to injection moulds”, 20th International Workshop on Principles of Diagnosis, June 14–17, Stockholm, Sweden, 2009.

11.

Yu, J., Xi, L., Zhou, X., “Intelligent monitoring and diagnosis of manufacturing processes using an integrated approach of KBANN and GA”, Computers in Industry, 59 (5), pp. 489–501, 2008

12.

Hong, X., Mitchell, R.J., Chen, S., Harris, C.J., Li, K., Irwin, G.W., “Model selection approaches for non-linear system identification: A review”, International Journal of Systems Science, 39 (10), pp. 925–946, 2008

13.

J. Wang, B. Jiang, Z Mao, “LSSVM Based Fault Diagnosis for Satellite Attitude Control Systems”, Control Engineering of China, Vol 15,N^o3, 2008

14.

Li, X., Hu, B., Du, R., “Predicting the parts weight in plastic injection molding using least squares support vector regression”, IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 38 (6), pp. 827-833, 2008

15.

Guo, L., Chen, J., Zhao, F.-G., Dong, G.-M., Wang, G.-W., “Application of SVM based geometric distance method in equipment performance degradation assessment”, Journal of Shanghai Jiaotong University, 42 (7), pp. 1077-1080, 2008

16.

X. Yu, F. Chu, R. Hao, “Fault Diagnosis Approach for Rolling Bearing Based on Support Vector Machine and Soft Morphological Filters”, Chinese Journal of Mechanical Engineering, 2008

17.

Berjaga, X., Melendez, J., and Pallares, A., “Statistical Monitoring of Injection Moulds”, In Proc. of the Conference on Artificial intelligence Research and Development, T. Alsinet, J. Puyol-Gruart, and C. Torras, (Eds.), Frontiers in Artificial Intelligence and Applications, vol. 184. IOS Press, Amsterdam, The Netherlands, pp. 236–243, 2008

18.

Sheng-Fa, Fu-Lei Chu, Support Vector Machines and Its Applications in Machine Fault Diagnosis, Journal of Vibration and Shock, Vol 26, No. 11, 2007

19.

Wang Ding-cheng, Jiang Bin, “Review of SVM-based Control and Online Training Algorithms”, Journal of System Simulation, Vol. 19, No.6, pp. 1177–1181, 2007

20.

Mats Nikus, Mikko Vermasvuori, Nikolai Vatanski, Sirkka-Liisa Jamsa-Jounela, “Support vector machines for detection of analyzer faults - A case study Applications of Large Scale Industrial Systems”, First IFAC Workshop on Applications of Large Scale Industrial Systems, Volume # 1, Part# 1, Elsevier, 2006

Citação do artigo:

• Liu, Q. and Sung, A. and Ribeiro, B. , “Statistical Correlations and Machine Learning for Steganalysis”, in Proc. of the 7th International Conference on Adaptive and Natural Computing Algorithms (ICANNGA05), pp. 437–440, International Conference on Adaptive and Natural Computing Algorithms, Coimbra, Portugal, March 2005

Citado em:

1.

Yuan, J., Qi, Y., ´´Audio steganalysis based on factor analysis and support vector machine”, Proceedings of SPIE, International Society for Optical Engineering, 7128, art. no. 71280S, 2008

Citação do artigo:

• Mukkamala, S. and Sung, A. and Ribeiro, B. , “Model Selection for Kernel Based Intrusion Detection Systems”, in Proc. of the 7th International Conference on Adaptive and Natural Computing Algorithms (ICANNGA05), pp. 458–461, International Conference on Adaptive and Natural Computing Algorithms, Coimbra, Portugal, March 2005

Citado em:

1.

Yang Li, Jun-Li Wang, Zhi-Hong Tian, Tian-Bo Lu and Chen Youn,, “Building lightweight intrusion detection system using wrapper-based feature selection mechanisms”, Computers & Security, Volume 28, Issue 6, Pages 466–475, September 2009

2.

DS Kim, SM Lee, JS Park, “Building Lightweight Intrusion Detection System Based on Random Forest”, Lecture Notes in Computer Science (LNCS), vol.3973, pp. 224–230, Springer Berlin / Heidelberg, 2006

3.

DS Kim, SM Lee, JS Park, “Toward Lightweight Intrusion Detection System Through Simultaneous Intrinsic Model Identification”, Frontiers of High Performance Computing and Networking - ISPA 2006 Workshops, Lecture Notes in Computer Science (LNCS), vol. 4331 pp. 981–989, Springer, 2006

4.

Srinivas Mukkamala, Dennis Xu and Andrew H. Sung, “Intrusion Detection Based on Behavior Mining and Machine Learning Techniques”, Lecture Notes in Computer Science (LNCS), Springer Berlin / Heidelberg, Vol.4031, pp. 619–628, 2006

Citação do artigo:

• Vieira A.S., J. C. Neves, B. Ribeiro , “A Method to Improve Generalization of Neural Networks: Application to the Problem of Bankruptcy Prediction”, Proc. of 7th International Conference on Adaptive and Natural Computing Algorithms (ICANNGA’05), Coimbra, Portugal, (Eds. B. Ribeiro, R. Albrech, A. Dobnikar, D. Pearson, N. Steele), Springer -Verlag, pp. 417–420, Wien, 2005.

Citado em:

1.

C. Estebanez and R. Aler, Generating Automatic Projections by Means of Genetic Programming, CER ALER, Optimization Techniques for Solving Complex Problems, Optimization, Edited by Enrique Alba et al., John Wiley & Sons, 2009

2.

C Estebanez, JM Valls, R Aler, GPPE: a method to generate ad-hoc feature extractors for prediction in financial domains, Applied Intelligence, Springer Netherlands, pp. 174–185, Vol. 29, Nr 2, October, 2008

3.

C Estebanez, JM Valls, R Aler, “Projecting Financial Data Using Genetic Programming in Classification and Regression Tasks”, Lecture Notes in Computer Science, Springer Berlin / Heidelberg, Vol. 3905, pp. 202–212, 2006

2004
Citação do artigo:

• Silva, C. and Ribeiro, B., “Margin-based Active Learning and Background Knowledge in Text Mining”, in Proc. of the Fourth International Conference on Hybrid Intelligent Systems, pp. 8-13, HIS 2004, Kitakyushu, Japan, December 2004.

Citado em:

1.

D Tuia, F Ratle, F Pacifici, MF Kanevski, WJ, “Active Learning Methods for Remote Sensing Image Classification, IEEE Transactions on Geoscience and Remote, 2009, vol. 47 (2), no 7, pp. 2218-2232, 2009

2.

Rongyan, L. Xin, J. Chunhui, W. Ning, and Z. Rongfang, B., “A new algorithm of Chinese text classification”, Journal of the Beijing Normal University, Natural Science Edition, Vol. 42, Part 5, pp. 501–505, ISSN 0476-0301, 2006.

Citação do artigo:

• Vieira A. S., B. Ribeiro, S. Mukkamala, J. C. Neves, and A. H. Sung, “On the Performance of Learning Machines for Bankruptcy Detection”, Proc. of Second IEEE International Conference on Computational Cybernetics, (Eds. Wilfried Elmenreich, Wolfgang Haidinger, and J. A. Tenreiro Machado), pp. 323–327, Vienna University of Technology, Austria, August, 2004.

Citado em:

1.

Espejo, P.G., Ventura, S., Herrera, F., “A survey on the application of genetic programming to classification”,IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 40 (2), art. no. 5340522, pp. 121-144, 2010

2.

W Yan, MD Sewell, CD Clack, “Learning to optimize profits beats predicting returns– comparing techniques for financial portfolio”, Proceedings of the 10th annual conference on Genetic and evolutionary computation, 1681–1688, Atlanta, USA, 2008

3.

Zhong Gao, Meng Cui and Lai-Man Po, “Enterprise Bankruptcy Prediction Using Noisy-tolerant Support Vector Machine”, International Seminar on Future Information Technology and Management Engineering, pp. 153–156, 2008

4.

Alfaro-Cid, E., Castillo, P.A., Esparcia, A., Sharman, K., Merelo, J.J., Prieto, A., Mora, A.M., Laredo, J.L.J., “Comparing multiobjective evolutionary ensembles for minimizing type I and II errors for bankruptcy prediction”, IEEE Congress on Evolutionary Computation CEC 2008, pp. 2902–2908, 2008

5.

Alfaro-Cid, E., Cuesta-Cañada, A., Sharman, K., Esparcia-Alcázar, A.I., “Strong typing, variable reduction and bloat control for solving the bankruptcy prediction problem using genetic programming, Studies in Computational Intelligence Vol 100, pp. 161–185, 2008

6.

E Alfaro-Cid, K Sharman, A Esparcia-Alcazar, “A Genetic Programming Approach for Bankruptcy Prediction Using a Highly Unbalanced Database”, In Proc of Applications of Evolutionary Computing, Lecture Notes in Computer Science, Springer Berlin / Heidelberg, Vol. 4448, pp. 169-178, 2007

2003
Citação do artigo:

• B. Ribeiro, “Kernelized based functions with Minkovsky’s norm for SVM Regression”, Proc. of INNS-IEEE International Joint Conference on Neural Networks (IJCNN 02), Vols 1–3, pp. 2198–2203, USA, May 12–17, 2002.

Citado em:

1.

Juang, C.-F., Hsieh, C.-D., “TS-fuzzy system-based support vector regression”, Fuzzy Sets and Systems, 160 (17), pp. 2486–2504, 2009

2.

Xiangyang, M., Taiyi, Z., A novel minimax probability machine, Information Technology Journal 8 (4), pp. 615–618, 2009

3.

Ke, L.-T., Lei, M.-Z., Xing, W. , “A control scheme for MIMO system based on SVM”, IEEE International Conference on Control and Automation, ICCA, pp. 2894–2898, 2007

4.

Yibo Zhang and Jia Ren, “A VSC Algorithm for Nonlinear System Based on SVM”, Bio-Inspired Computational Intelligence and Applications, Lecture Notes in Computer Science, Volume 4688, pp. 494–501, Springer Berlin / Heidelberg, 2007 (ISBN 978-3-540-74768-0)

5.

Y. Zhou, Z. Tai-yi, H. Liu, “Kernel-based machine learning method and the applications to multi-user detection: a survey”, Journal on Communications, Vol. 26, No.7, pp. 96–108, 2005

6.

Michael John Watts, “ Evolving Connectionist Systems Characterisation, Simplification, Formalisation, Explanation and Optimisation”, PhD Thesis, at the University of Otago, Dunedin, New Zealand, February 27, 2004

Citação do artigo:

• C. Silva, B. Ribeiro,“Labeled and Unlabeled Data in Text Categorization”, Proc. of 2004 IEEE International Joint Conference on Neural Networks (IJCNN’04), Budapest, Hungary, July, 2004.

Citado em:

1.

Pan-Jun Kim and Jae-Yun Lee, Utilizing Unlabeled Documents in Automatic Classification with Inter-document Similarities, Journal of the Korean society for information management, pp. 251–271, 2007

Citação do artigo:

• Silva, C. and Ribeiro, B., “The Importance of Stop Word Removal on Recall Values in Text Categorization”, in Proc. of the IEEE International Joint Conference on Neural Networks (IJCNN 2003), pp. 1661–1666, Vol. 3, Portland, USA, July 2003.

Citado em:

1.

Muhammad Usman Rashid, Balakrishna Garapati, “Prevention of Spyware by Runtime Classification of End User License Agreements”, MSc Thesis, Computer Science, Blekinge Institute of Technology, Sweden, 2009

2.

Bao, Y., Yang, G., Jin, W., “Evaluation of stop word list in Mongolian”, Journal of Information and Computational Science, 6 (3), pp. 1139–1145, 2009.

3.

Suge Wang and Wei Ying, “The influence of Stopist on the Chinese Text Sentiment Classification”, Information Technology, vol 27, N^o 2, 2008

4.

Noah, S.A., Ismail, F., “Automatic classifications of malay proverbs using Naïve Bayesian Algorithm”, Information Technology Journal, 7 (7), pp. 1016–1022, 2008

5.

Dina Adel Said, “Dimensionality Reduction Techniques for Enhancing Automatic Text Categorization”, MSc Thesis,Computer ENgineering, Faculty of Engineering, Cairo University, 2007

6.

Chen, Rui, Desai, Bipin C., and Zhou, Cong, “CINDI Robot: An Intelligent Web Crawler Based On Multi-level Inspection”, 11th International Database Engineering and Applications Symposium (IDEAS 2007), pp.93–101, 2007.

7.

Gu Yi-jun, Fan Xiao-zhong, Wang Jian-hua, Wang Tao,and Huang Wei-jin, “Automatic Selection of Chinese Stoplist”, Transactions of the Beijing Institute of Technology, Vol. 25, No. 4, pp. 337–340, 2005.

Citação do artigo:

• Silva, B.Ribeiro, “An Inductive Inference Approach to Large Scale Text Categorisation”, Proc. of International Conference on Artificial Neural networks and Genetic Algorithms (ICANNGA03), Roanne, França, 2003.

Citado em:

1.

Marcin JurCzak, “Complexity Control of SVM Network applied to Text Categorization”, Prezeglad Elektrotechiniczny (Electrotecnical Review Journal),V. 80, nr. 4, pp. 343-347,March, 2004.

Citação do artigo:

• Silva, C. and Ribeiro, B., “Navigating mobile robots with a modular neural architecture”, Journal of Neural Computing & Applications, Springer-Verlag London Ltd, pp. 200-211, December 2003.

Citado em:

1.

B. Szurgot, An Intelligent Sistem for a Mobile Robot Controller, MSc Thesis, Wroclaw University, 2007

2.

Sakamoto, Kouichi, and Zhao, Qiangfu, “A Study on Generating Good Environment Patterns for Evolving Robot Navigators”, IEEE SMC 2006, International Conference on Systems, Man and Cybernetics, Volume 4, pp. 3280–3285, 2006.

3.

Frants Lauritsen, “Universal sensors: Detection of the unknown contaminant”, Danks Kemi (in Danish), 86, nr.2, 2005.

4.

Costa, E., and Simões, A., “Inteligência Artificial - Fundamentos e Aplicações”, FCA Editors, pp. 499–505, 2004.

Citação do artigo:

• Noel Lopes, Bernardete Ribeiro, “An efficient Gradient-Based Learning Algorithm Applied to Neural Networks with Selective Actuation Neurons”, Neural, Parallel and Scientific Computations Journal, Vol. 11, pp. 253–272, September, 2003.

Citado em:

1.

Manuela Silva, Luiz Moutinho, Arnaldo Coelho, Alzira Marques, “Market orientation and performance: modelling a neural network”, European Journal of Marketing, Volume: 43 Issue: 3–4 Page: 421–437, 2009

2002
Citação do artigo:

• B. Ribeiro, “On Data Support Vector Clustering”, Proc. of IEEE 9th International Conference on Neural Information Processing (ICONIP 2002), VOLS 1-3, pp. 873–879, Nov 18-22, Singapore, 2002.

Citado em:

1.

SK Chalup, “Variations of the two-spiral task”, Connection Science, 2007 - informaworld.com

2.

Sung-Hae Jun and Kyung-Whan Oh, “A competitive co-evolving support vector clustering”, Lecture Notes in Computr Science, LNCS 4232, pp. 864–873, 2006

2001

Citação do artigo:

• Filipe Araújo, Bernardete Ribeiro and Luís Rodrigues, “A Neural Network for Shortest Path Computation”, IEEE Trans. on Neural Networks, Vol. 12, pp. 1067-1073, Sep. 2001.

Citado em:

1.

Zhang, Y., Jun, Y., Wei, G., Wu, L., “Find multi-objective paths in stochastic networks via chaotic immune PSO”, Expert Systems with Applications, 37 (3), pp. 1911-1919, 2010.

2.

Wen, Ue-Pyng, Lan, Kuen-Ming Shih, Hsu-Shih“A review of Hopfield neural networks for solving mathematical programming”, European Journal of Operational Research, Volume 198, Issue 3, 1 November 2009, Pages 675-687, 2009

3.

Ghatee, M., Mohades, A., “Motion planning in order to optimize the length and clearance applying a Hopfield neural network”, Expert Systems with Applications, 36 (3 PART 1), pp. 4688-4695, 2009

4.

Liu, W., Wang, L., “Solving the shortest path routing problem using noisy hopfield neural networks”, International Conference on Communications and Mobile Computing, CMC 2009 2, pp. 299–302, 2009

5.

Liu, S., Zhang, S., Zhou, J.,“ A modified neural network based approach for overlay multicast”, Chinese High Technology Letters, 19 (1), pp. 24–28, 2009

6.

Saeed, N.H., Abbod, M.F., Al-Raweshidy, H.S., “Intelligent MANET routing system”, International Conference on Advanced Information Networking and Applications, AINA, pp. 1260–1265, 2008

7.

A W Mohemmed, NC Sahoo, TK Geok, “Solving shortest path problem using particle swarm optimization”, Applied Soft Computing Journal, Elsevier Science Publishers, 2008

8.

Liu, S., Zhang, S., Zhou, J., Qiu, G., “‘ A two-layer recurrent Neural Network based approach for overlay multicast”, Journal of Electronics, 25 (2), pp. 209-217, 2008

9.

Dong JY, Zhang JY, Chen Z, “Autowave-competition neural network and its application to the single-source shortest-paths problem”,Acta Physica Sinica, 56 (9): 5013–5020, September 2007

10.

Mohemmed AW, Sahoo NC, “Efficient computation of shortest paths in networks using particle swarm optimization and noising metaheuristics”, Discrete Dynamics in Nature and Society, 2007

11.

Hou ZG, Gupta MM, Nikiforuk PN, et al.,“A recurrent neural network for hierarchical control of interconnected dynamic systems”, IEEE Transactions on Neural Networks, 18 (2): 466-481 MAR 2007

12.

Claudio M. Rocco and Enrico Zio, “Cellular Automata and Monte Carlo Simulation for Network Reliability and Availability Assessment”, Studies in Computational Intelligence, Pages 113–144, Springer Berlin / Heidelberg, Intelligence in Reliability Engineering, 2007 (ISBN 978-3-540-37371-1)

13.

Chen Liqing, Zhang Futai, Tao Zheng,“Research on Multiple Constraints -based QoS Multicast Routing Optimization Algorithms”, Computer and Digital ENgineering, Vol.34, No.12, pp. 11–14, 2006

14.

W Chengdong, Z Jungang, new Jing, X Ku, “Routing Algorithm of Wireless Trunk Network BAsed on Fuzzy Coupling in Intelligent Community”, Journal of Information and Control Engineering, Vol.22, n^o1, 2006

15.

Y. Chen, Q. Mai, Z. Lu, “Using Link Analysis Technique with a Modified Shortest-Path Algorithm to Fight Money Laundering”, Journal of Natural Sciences, Vol.11, No.5, pp.1352–1356, 2006

16.

Gao, Y., Dai, Y., Zhang, B., Yang, L., “Study on multistage decision-making problem with transiently chaotic neural network for dynamic selection of composite web services”, IEEE International Conference on Service Operations and Logistics, and Informatics, pp. 936–941, 2006

17.

Wang Xu, Y. Chen, “A New Method and Simulation for Path Planning Problem Based on Ant Colony Algorithm”, Computer Simulation, Vol.22, No.7,pp. 60–62, 2005.

18.

Wang Li, Shen Jin-yuan, “Applying Hopfield neural network to QoS routing in communication network”, Opto-Electronics Letters, Vol.1, No.3 pp. 217–220, 2005

19.

Lei Yang, Yu Dai, Bin Zhang and Yan Gao, “A Genetic Algorithm Optimized New Structured Neural Network for Multistage Decision-Making Problem, Parallel and Distributed Computing”, , Sixth International Conference on Applications and Technologies, pp. 925–929, 2005.

20.

Lei Yang, Yu Dai, Bin Zhang and Yan Gao, “Dynamic selection of composite Web services based on a genetic algorithm optimized new structured neural network”, International Conference on Cyberworlds, 8 pp. 23–25 Nov., 2005. (ISBN: 0-7695-2378-1)

21.

J. Zhou, S. Zhang, “Routing algorithm based on two-layer recurrent neural network”, Journal of Electronics and Information Technology, Vol. 27, no. 12, pp. 1901–1904. Dec. 2005

22.

Shen, J.-Y., Wang, L., Chang, S.-J., Zhang, New method of multi-constrained routing in high-speed communication network Y.-X. ,Journal of Optoelectronics Laser, 16 (5), pp. 575–578, 2005.

23.

Dong Jiyang Chen Luzhuo, Algorithm for the Optimal Riding Scheme Problem in Public traffic, Department of Physics, Xiamen University, Xiamen Fujian, International Conference on Neural Networks and Brain, ICNN&B’05, Vol. 1, pp. 62–66, 13–15 Oct. 2005.

24.

Zhou Jingquan, Zhang S-yi, “Neural network based on independent variables calculate the shortest path”, Circuits and Systems, Vol 10, No. 4, 2005

25.

Xu JJ, Chen HC,“Fighting organized crimes: using shortest-path algorithms to identify associations in criminal networks”, Decision Support Systems, 38 (3):473–487, DEC 2004

26.

Zhang JY, Wang DF, Shi MH, et al.,“Output-threshold coupled neural network for solving the shortest path problems”, Science in China Series F-Information Sciences, 47 (1):20–33, FEB 2004

27.

Dianxun Shuai and Hongbin Zhao, “A new generalized cellular automata approach to optimization of fast packet switching”, Computer Networks Volume 45, Issue 4, pp. 399-419, 15 July 2004.

28.

Z Junying, W Defeng, SHI Meihong, WJ Yue, “Output-threshold coupled neural network for solving the shortest path problems”, Science in China Series F-Information Sciences, 2004

29.

An indexing structure and application model for vehicles moving on road networks. PhD Thesis. Xiangyu Ye, Florida International University, Miami, FL, USA, 2004.

30.

Junying Zhang, Meihong Shi, Defeng Wang, Zheng Bao, and Yue Wang, “Step-coupled neural network for solving the shortest path problems”, IEEE Transactions on Neural Networks, 2003.

31.

Robert W. Newcomb, University of Matyland, USA, 2003, (http://www.ee.umd.edu/-newcomb/-courses/-spring2003/-434/434$_$pprs$_$S03.pdf)

32.

Weng Kai, Journal of Computer Research and Development, pp 1181-1185, 2003

33.

Hu Shiyu and Xie Jianying, “Shortest Path Routing Algorithm Based on Chaotic Neural Network”, Journal of Systems Engineering and Electronics, Vol.14 No.4, pp.1–6, 2003

34.

Rocco CM, Moreno JA, “Network Reliability Assessment Using a Cellular Automata Approach”, Reliable Eng Systms Safety, Vol. 78, nn$\underline{o}$3, pp. 289-295, December, 2002

35.

Kuo-I- Hong, “The Shortest Path Problem: Solving by Neural Networks”, National Taiwan University of Science and Technology, in http:neuron.et.ntust.edu.tw, 2002.

36.

Chang, Fei-Shan, and Lee, Hahn-Ming, “A Neural Network for Shortest Path Computation by Hopfield Networks”, National Taiwan University of Science and Technology, 2002.

37.

Xu JJ, Chen HC, “Fighting organized crimes: using shortest-path algorithms to identify associations in criminal networks”, Decision Support Systems, 38, n$\underline{o}$3, pp. 473-487, December, 2004

38.

Zhang JY, Wang DF, Shi MH, et al., “Output-threshold coupled neural network for solving the shortest path problems”, Science in China Series F-Information Sciences, Vol. 47, n$\underline{o}$ 1, pp. 20-33, February, 2004

39.

Xu, DG.O , Network Analysis: Using Shortest Path Algorithms to Identify Criminal Associations, AI Laboratory, University of Arizona, USA, 2002.

40.

DG.O. Xu. Network Analysis: using shortest path algorithms to identify criminal associations. AI Laboratory, University of Arizona, USA, 2002.

41.

WANG Zeyan, GU Hongfang, A Primal-dual Neural Network for Shortest Path Problem, Computer Engineer, Vol.28, No.1, pp.58–61, 2002.

42.

Robert W. Newcomb, University of Matyland, USA, 2003, (URL: http://www.ee.umd.edu/newcomb/courses/spring2003/ 434/ 434 _pprs _S03.pdf)

43.

Jennifer Xu, Using Shortest Path Algorithms to Identify Criminal Associations, AI Laboratory, University of Arizona, USA, March, 2002. (URL: ai.bpa.arizona.edu/go/presentations/spring2002/ Jennifer_ShortestPath.ppt)

Citação do artigo:

• Dimirovski, G., A. Dourado, E. Ikonen, U. Kortela, J. Pico, B. Ribeiro, M. Stankovski and E. Tulunay, “Learning Control of Thermal Systems”. In: K. Åstr’́om, P. Albertos, M. Blanke, A. Isidori, W Schaufelberger and R. Sanz (Eds.) in Control of Complex Systems, Chap. 14., pp.317-338, Springer-Verlag, London, 2001.

Citado em:

1.

Georgi M. Dimirovski and Yuanwei Jing, “Towards Hybrid Soft Computing Approach to Control of Complex Systems”, Proc. of First International IEEE Symposium on “Intelligent Systems”, pp. 47-54, September, 2002.

Citação do artigo:

• Carvalho, P., A. Santos, A. Dourado, B. Ribeiro, “On the Estimation of the Spectral Data: A Genetic Algorithm Approach”, Proc of IEEE International Conference on Image Processing, Vol.1, pp: 866 –869, Greece, October, 7–10, 2001.

Citado em:

1.

M Ebner, “A Genetic Programming Approach to Deriving the Spectral Sensitivity of an Optical System”, Lecture Notes in Computer Science, LNCS 4971, pp. 61–72, 2008

2.

Marc Ebner, “Estimating the spectral sensitivity of a digital sensor using calibration targets”, Genetic And Evolutionary Computation Conference, Proc. of the 9th Annual Conf. on Genetic and Evolutionary Computation, pp. 642–649, 2007

Citação do artigo:

• Ribeiro, Bernardete, “Support Vector Machines and RBF Neural Networks For Fault Detection and Diagnosis”, Proc. of IEEE 8th International Conference on Neural Information Processing, (ICONIP01), Vol. 2, pp. 873–878, Shangai, China, November, 2001.

Citado em:

1.

SR Samantaray, PK Dash, “High impedance fault detection in distribution feeders using extended kalman filter”European Transactions on Electrical Power, interscience.wiley.com, 2009

2.

SR Samantaray, PK Dash,“Transmission line distance relaying using machine intelligence technique, Generation, Transmission & Distribution, IET, 2008 (ieeexplore.ieee.org)

3.

Poyhonen S, Arkkio A, Jover P, et al., “Coupling pairwise support vector machines for fault classification”, Control Engineering Practice, 13 (6): 759–769, June 2005

4.

Sanna Pöyhönen, “Supprt Vector Machine based Classification in Condition Monitoring of Induction Motors”, Helsinki University of Technology Control Engineering Laboratory, Tech Report 141, Espoo, Finland, 2004

2000
Citação do artigo:

• Silva, C. and M Crisostomo and Ribeiro, B., “MONODA: A Neural Modular Architecture for Obstacle Avoidance Without Knowledge of the Environment”, in Proc. of the IEEE International Joint Conference on Neural Networks, IJCNN 2000, pp. 6634–6639, Como, Italy, July 2000.

Citado em:

1.

Radoslaw Sobolewski, “MachineLearning for Automated Robot Navigation in Rough Terrain”, MSc Thesis, Computer Science Department, University of Oxford, 2009

2.

Zou, A.-M., Hou, Z.-G., Fu, S.-Y., and Tan, M., “Neural networks for mobile robot navigation: A survey”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3972 LNCS, pp. 1218-1226, 2006.

Citação do artigo:

• Dimirovski, G.M., Dourado, A., Gough, N.E., Ribeiro, B., Stankovski, M.J., Ting, I.H., Tulunay, E., “On learning Control in Industrial Furnaces and Boilers”, Proc. of IEEE International Symposium on Intelligent Control (ISIC) (P. Groumpos, N Koussulas & M Polycarpu, eds), pp. 67–72, Patras, Greece, July 17–19, 2000.

Citado em:

1.

Georgi M. Dimirovski and Yuanwei Jing, Kolmogorov Networks and Process Characteristic Input-Output Modes Decomposition, Proc. of First International IEEE Symposium on “Intelligent Systems”,pp. 59-66, September, 2002.

2.

N Lyupco, M Stankovski, T Gugulovska, “Fuzzy Control Systems for Thermal Processes: Synthesis, Design and Implementation, Journal of Electrical & Electronics Engineering, Vol 3, N^o 1, pp. 759–773, 2003

3.

MD Georgi, J Yuanwei, “Comparative Analysis of Kolmogorov ANN and Process Characteristic Input-Output Modes”, Journal of Electrical & Electronics Engineering, Vol 3, N^o 1, pp. 745–758, 2003

Citação do artigo:

• Filipe Araújo, Bernardete Ribeiro and Luís Rodrigues. “A Neural Network for Shortest Path Computation”. Technical Report DI/FCUL TR-00-2. Department of Computer Science, University of Lisbon, April, 2000.

Citado em:

1.

A new generalized cellular automata approach to optimization of fast packet switching, Computer Networks: The International Journal of Computer and Telecommunications Networking, Elsevier, Vol. 45, Issue 4, July, 2004, Pp. 399–419

2.

Xiangyu Ye, “An Indexing Structure and Application Model for Vehicles Moving on Road Networks”, PhD Thesis, Computer Science Department, Florida University, USA, 2004

1999 - 1993
Citação do artigo:

• B. Ribeiro, “Fault Detection in a Thermoplastic Molding Injection Process Using Neural Networks”, Proc. of INNS-IEEE International Joint Conference on Neural Networks (IJCNN’99), Vol.5, pp. 3352–3355, Washington, USA, July, 1999.

Citado em:

1.

MS Huang, TY Lin,, “Simulation of a regression-model and PCA based searching method developed for setting the robust”, International Journal of Heat and Mass Transfer, Elsevier, 2008

2.

CY Yang, “Improvement on Holding Pressure of All-Electric Injection Molding Machine”, PhD Thesis, National Taiwan University of Science and Technology, China, 2004, (thesis.lib.ncu.edu.tw)

Citação do artigo:

• B. Ribeiro, A. Dourado and E. Costa, “Integrated Process Supervision and Control by Neural Networks in an Industrial Lime Kiln”, Proc. of IEEE World Conference on Neural Networks (WCNN96), pp. 104–109, San Diego, CA, September, 1996.

Citado em:

1.

H Ye, R Nicolai, L Reh, “A Bayesian-Gaussian neural network and its applications in process engineering´´, Chemical Engineering and Processing, Vol 37, Issue 5, September 1998, pp. 439–449, Elsevier, 1998.

Citação do artigo:

• B. Ribeiro and A. Dourado, “Lime Kiln Process Identification and Control: A Neural Network Approach, Proc. of International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA’93), Innsbruck, Austria, (Eds. R. F. Albrecht, et al.), pp. 117-124, Springer-Verlag, Wien, 1993.

Citado em:

1.

A. B. Bulsari, “Application of Neural Networks in Process Dynamics”, in “Neural Networks for Chemical Engineers”, (Ed:Bulsari), Elsevier Publishers, North-Holland, pp. 231-283, 1995.

Citação do artigo:

• A. Kovačec and B Ribeiro, “Kolmogorov’s Theorem: From Algebraic Equations and Nomography to Neural Networks”, Proc. of International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA’93), Innsbruck, Austria, (Eds. R. F. Albrecht, et al.), pp. 40-47, Springer-Verlag, Wien, 1993.

Citado em:

1.

Rietman EA, “A neural network model of a contact plasma etch process for VLSI production”, IEEE Transactions on Semicondutor Manufacturing, Vol. 9, n.1, pp. 95–100, February, 1996.

Citação do artigo:

• B. Ribeiro and A. Dourado, “Lime Kiln Simulation and Control by Neural Networks” in Neural Networks for Chemical Engineers, Chapter 7, pp.163-191, (Ed:Bulsari), Elsevier Publishers, North-Holland, 1995.

Citado em:

1.

Faur-Brasquet C, Le Cloirec P, “Modelling of the flow behavior of activated carbon cloths using a neural network approach, Chemical Engineering and Processing, Vol. 42, Nr. 8-9, pp. 645–652, AUG-SEP 2003

2.

Vydianathapuram R. Radhakrishnan, “Soft Sensors for the Cement Industry - Neural Network Models forKiln and Grinding Mill”, Intelligent Systems, pp. 29–35, Platform, Dec 2003

3.

VR Radhakrishnan, Soft sensors for the Cement Industry - Neural Network Models for kiln, Oilfield Gas Treatment, 29, Vol. 3, Nr. 2, July-December, 2003

4.

Gugulovska, MG Spasovski, Z Milkovski, P, “ Intelligent Control of a Tunnel Kiln for Clay-Bricks Production”, Pergamon, 2001

5.

M Stankovski, T Kolemishevska-Gugulovska, G Boshkovski, G Dimirovski, “Advanced Industrial Control using Fuzzy Logical of Tunnel Bricks Production”, IFAC Proceedings, 2005

6.

M. Järvensivu, Intelligent Control of the Lime Kiln Process With Respect to Environmental Requirements, PhD Thesis, Helsinki University of Technology, Laboratory of Process Control and Automation, Espo, Finland, 2003.

7.

E Juuso, M Jarvensivu, O Ahava, “Intelligent supervisory control of an industrial rotary kiln”, Industrial Applications of Soft Computing: Paper, Mineral and Metal Processing Industries, (ed. K Leiviska), Studies of Fuzziness and Soft Computing, 2001.

8.

M. Järvensivu, K. Saari, S.L. Jainsa-Jounela, “Intelligent Control System of an Industrial Lime Kiln Process”, Control Engineering Practice, vol.9, Nr. 6, pp. 586–606, 2001.

9.

Dimirovski GM, Dourado A, Gough NE, et al., “ On learning control in industrial furnaces and boilers”, IEEE International Symposium on Intelligent Control/8th IEEE Mediterranean Conference on Control and Automation, JUL 17-19, 2000.

10.

M. Järvensivu, K. Saari, S.L. Jainsa-Jounela, “Intelligent Control System of an Industrial Lime Kiln Process”, Proc. of 14th IFAC World Congress, Jul 05–09, Beijing, CHINA, 1999.

11.

Järvensivu M, Juuso E, Ahava O, Intelligent supervisory-level control of industrial processes, PAP PUU-PAP TIM, Vol. 82, n$\underline{o}$ 6, pp. 386–391, 2000

12.

Hussain, M. A . “Review of the Applications of Neural Networks in Chemical Process Control- Simulation and Online Implementation”, Artificial Intelligence in Engineering, Vol. 13, pp. 55-68, 1999.

13.

K. SatheeshKumar, S. Savithri and T. R. Ramamohan, “Chaotic dynamics and rheology of suspensions of periodically forced slender rods in simple shear flow”, Jpn J. Appl. Phys., Vol. 35, pp. 5901–5908, 1996.

Citação do artigo:

• B. Ribeiro, A. Dourado and E. Costa, “Lime Kiln Fault Diagnosis by Neural Networks”, Proc. of International Conference on Neural Networks and Genetic Algorithms (ICANNGA95), Alès, France, (Eds. D: W: Pearson et al.), pp. 112-116, Springer-Verlag, Wien, 1995.

Citado em:

1.

Jia-Zhou He Zhi-Hua Zhou Xu-Ri Yin Shi-Fu Chen, Using Neural Networks for Fault Diagnosis, Proc. of the IEEE-INNS-ENNS International Joint Conference on Neural Networks(IJCNN2000), Vol.5, pp.217-220, Italia, 2000.

Citação do artigo:

• B. Ribeiro and A. Dourado, “Industrial Lime Kiln Computer Dynamic Simulation for Improving Efficiency”, in Energy Efficiency in Process Technology,(Ed. P.A. Pilavachi), Elsevier Science Publishers, pp. 779–788, London, 1993. (ISBN 1-85861-019-2).

1.

Mika Järvensivu, Intelligent Control of the Lime Kiln Process With Respect to Environmental Requirements, PhD Thesis, Helsinki University of Technology, Laboratory of Process Control and Automation, Espo, Finland, 2003.

Citação do artigo:

• B. Ribeiro, “Prediction of Lime Availability of an Industrial Kiln by Neural Networks”, Proc. of IEEE World Congress on Computational Intelligence (WCCI98), pp. 1987-1991, Vol. 3, 1998.

Citado em:

1.

Mika Järvensivu, Intelligent Control of the Lime Kiln Process With Respect to Environmental Requirements, phD Thesis, Helsinki University of Technology, Laboratory of Process Control and Automation, Espo, Finland, 2003.

Citação do artigo:

• Industrial visual inspection of lime granules by neural networks, Carvalho P, Costa N, Ribeiro B, Dourado A., Computers & Industrial Engineering, Vol. 35, nn$\underline{o}$ 3-4, pp. 539-542, December, 1998.

Citado em:

1.

Wong BK, Lai VS, Lam J , “A bibliography of neural network business applications research: 1994-1998”, Computer Operation Research, Vol. 27, n. 11-12, pp. 1045-1076, September, 2000

Citação do artigo:

• B. Ribeiro, A. Dourado e E. Costa, “Lime Kiln Fault Detection and Diagnosis by Neural Networks”, Proc. of International Conference on Neural Networks and Genetic Algorithms (ICANNGA’95), Alès, França, (Eds. D. W. Pearson, et al.), pp. 112-116, Springer-Verlag, Wien, 1995.

Citado em:

1.

Pedro Larranaga and Jose A Lozano, “Estimation of Distribution of Algorithms: A New Tool for Evolutionary Computation”, Kluwer, 2002

2.

Jia-Zhou He, Zhi-Hua Zhou,Xu-Ri Yin,Shi-Fu Chen,Using Neural Networks for Fault Diagnosis, IEEE-INNS-ENNS International Joint Conference on Neural Networks, (IJCNN), Volume 5,pp. 217–220, Como, Italy, July 24–July 27, 2000

3.

D. Enrique Alba Torres D. Ignacio Reyes Moreno, “Entrenamiento de Redes Neuronales utilizando Algoritmos Evolutivos y Combinaciones con Búsqueda Local”, Universidad de Málaga, Spain, 2002

Citação do artigo:

• Noel Lopes, Bernardete Ribeiro, “Part Quality in an Injection Moulding Process Using Neural Networks”, Proc. of World Manufacturing Congress (WMC), ISM, 1999)

1.

D. Torkar, S. Novak and F. Novak , “Apparent viscosity prediction of alumina-paraffin suspensions using artificial neural networks”, Journal of Materials Processing Technology, Volume 203, Issues 1-3, pp 208–215, , 18 July 2008

2.

Nouha Baccour Sellami, Heni Kaaniche, Mohamed Chtourou, and Maher Ben Jemaa, “Recurrent neural network based time series prediction: Particular design problems”, International Conference on Smart Systems and Devices (SSD 2007), Hammamet , Tunisia, March 19-22 2007

3.

Nouha Baccour Sellami, “Conception d’ume Nouvelle Stratégie de Routage Dynamique pour les Réseaux Mobiles Ad Hoc”, MSc Thesis, Génie Industrielle Informatique, École de Ingénieurs de Sfax, Tunisie, 2006

4.

Distrim,“TECNOMOLDE - Plataforma Tecnológica de Projecto, Engenharia de Moldes para Injecção de Termoplásticos”, Universidade de Aveiro, Fevereiro, 2002.

Citação do artigo:

• B. Ribeiro and A. Dourado, “Lime Kiln Process Identification and Control: A Neural Network Approach”, Proc. of International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA’93), Innsbrük, Austria, (Eds. R. F. Albrecht, et al.), pp. 117–124, Springer-Verlag, Wien 1993.

1.

E Juuso, M Jarvensivu, O Ahava, “Intelligent supervisory control of an industrial rotary kiln”, Industrial Applications of Soft Computing: Paper, Mineral and Metal Processing Industries (ed. K Leiviska), Studies of Fuzziness and Soft Computing, 2001.

2.

Rajesh K, Ray AK, “Artificial neural network for solving paper industry problems: A review”, Journal Scientific & Industrial Research, Vol. 65, Nr. 7, pp. 565–573, JUL 2006

Citação de material Pedagógico:

• B Ribeiro, “Princípios de Redes Neuronais”, DEI-FCTUC, 1997

1.

M. Holand and M Paula e Silva, “Reconhecimento de Padrões ultilizando Redes de Hopfield”, Projecto Final, Universidade de Rio de Janeiro, Junho 2003.

2.

ISEG, Instituto Superior de Economia e Gestão, Universidade Técnica de Lisboa, Lisboa, 1998.