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Biografia

Fernando Bação é Professor Catedrático na NOVA IMS, onde dirige o Mestrado em Gestão de Informação com especialização em Business Intelligence e co-dirige o Mestrado em Direito e Mercados Financeiros (uma parceria entre a NOVA IMS e a NOVA School of Law). É doutorado em Gestão de Informação, sendo as suas áreas de investigação principais o Machine Learning, a Ciência de Dados e a tecnologia Blockchain. É um dos investigadores incluídos no ranking dos 2% melhores cientistas do mundo, de acordo com a listagem da Universidade de Stanford. Presta serviços de consultoria a várias empresas, é convidado regularmente para conferências sobre temas como Data Science, Big Data e Analytics, e integra o júri dos Portugal Digital Awards.

Publicações Cientificas

Almeida, G., & Bação, F. (2025)

Counterfactual synthetic minority oversampling technique: Solving healthcare’s imbalanced learning challenge. Data Science and Management. https://doi.org/10.1016/j.dsm.2025.01.006

Crisóstomo, J., Bação, F., & Lobo, V. (2025)

Machine learning methods for detecting smart contracts vulnerabilities within Ethereum blockchain: A review. Expert Systems with Applications, 268, 1-16. Article 126353. https://doi.org/10.1016/j.eswa.2024.126353

Si, H-P., Zhao, W-R., Li, T-T., Li, F-T., Bação, F., Sun, C-X., & Li, Y-L. (2025)

BDMFuse: Multi-scale network fusion for infrared and visible images based on base and detail features. Journal of Infrared and Millimeter Waves (Hongwai Yu Haomibo Xuebao), 44(2), 275-284. https://doi.org/10.11972/j.issn.1001-9014.2025.02.015

Almeida, G., & Bação, F. (2024)

UMAP-SMOTENC: A Simple, Efficient, and Consistent Alternative for Privacy-Aware Synthetic Data Generation. Knowledge-Based Systems, 300, 1-14. Article 112174. https://doi.org/10.1016/j.knosys.2024.112174

Amaro, A., & Bação, F. (2024)

Topic Modeling: A Consistent Framework for Comparative Studies. Emerging Science Journal, 8(1), 125-139. https://doi.org/10.28991/ESJ-2024-08-01-09

Camacho, L., & Bação, F. (2024)

WSMOTER: a novel approach for imbalanced regression. Applied Intelligence. https://doi.org/10.1007/s10489-024-05608-6

Dong, P., Wang, M., Li, K., Qiao, H., Zhao, Y., Bação, F., Shi, L., Guo, W., & Si, H. (2024)

Research on the Identification of Wheat Fusarium Head Blight Based on Multispectral Remote Sensing from UAVs. Drones, 8(9), 1-18. Article 445. https://doi.org/10.3390/drones8090445

Mentzingen, H., António, N., Bação, F., & Cunha, M. (2024)

Textual similarity for legal precedents discovery: assessing the performance of machine learning techniques in an administrative court. International Journal of Information Management Data Insights, 4(2), 1-21. Article 100247. https://doi.org/10.1016/j.jjimei.2024.100247

Mutemi, A., & Bação, F. (2024)

Balancing Act: Tackling organized retail fraud on e-commerce platforms with imbalanced learning text models. International Journal of Information Management Data Insights, 4(2), 1-13. Article 100256. https://doi.org/10.1016/j.jjimei.2024.100256

Mutemi, A., & Bação, F. (2024)

E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review. Big Data Mining and Analytics, 7(2), 419-444. https://doi.org/10.26599/BDMA.2023.9020023

Ottersen, S. G., Pinheiro, F., & Bação, F. (2024)

Triplet extraction leveraging sentence transformers and dependency parsing. Array, 21(March), [100334]. https://doi.org/10.1016/j.array.2023.100334

Si, H., Li, W., Su, N., Li, T., Li, Y., Zhang, C., Bação, F., & Sun, C. (2024)

A cross-chain access control mechanism based on blockchain and the threshold Paillier cryptosystem. Computer Communications. https://doi.org/10.1016/j.comcom.2024.05.012

Araujo, F. C., Bação, F., & Yanaze, M. H. (2023)

Brand Valuation: Recognizing the brands as strategical assets in the balance sheet of the companies. Revista de Gestão e Secretariado - GeSec, 14(2), 1516-1537. https://doi.org/10.7769/gesec.v14i2.1629

Bação, F., & Mutemi, A. (2023)

The Discriminants of Long and Short Duration Failures in Fulfillment Sortation Equipment: A Machine Learning Approach. Journal of Engineering, 2023. https://doi.org/10.1155/2023/8557487

Fonseca, J., & Bacao, F. (2023)

Geometric SMOTE for imbalanced datasets with nominal and continuous features. Expert Systems with Applications, [121053]. https://doi.org/10.1016/j.eswa.2023.121053

Fonseca, J., & Bação, F. (2023)

Improving Active Learning Performance through the Use of Data Augmentation. International Journal of Intelligent Systems, 2023, 1-17. https://doi.org/10.1155/2023/7941878

Fonseca, J., & Bacao, F. (2023)

Tabular and latent space synthetic data generation: a literature review. Journal of Big Data, 10, 1-37. [115]. https://doi.org/10.1186/s40537-023-00792-7

Frank, F., & Bacao, F. (2023)

Advanced Genetic Programming vs. State-of-the-Art AutoML in Imbalanced Binary Classification. Emerging Science Journal, 7(4), 1349-1363. https://doi.org/10.28991/ESJ-2023-07-04-021

Mentzingen, H., António, N., & Bação, F. (2023)

Automation of legal precedents retrieval: Findings from a literature review. International Journal of Intelligent Systems, 2023, 1-22. [6660983]. https://doi.org/10.21203/rs.3.rs-2292464/v1, https://doi.org/10.21203/rs.3.rs-2292464/v2, https://doi.org/10.1155/2023/6660983

Mutemi, A., & Bacao, F. (2023)

A numeric-based machine learning design for detecting organized retail fraud in digital marketplaces. Scientific Reports, 13(1), 1-16. [12499]. https://doi.org/10.1038/s41598-023-38304-5

Si, H., Li, W., Wang, Q., Cao, H., Bação, F., & Sun, C. (2023)

A secure cross-domain interaction scheme for blockchain-based intelligent transportation systems. PeerJ Computer Science, (November 2023), 1-36. https://doi.org/10.7717/peerj-cs.1678, https://doi.org/10.7717/peerj-cs.1678/supp-1, https://doi.org/10.7717/peerj-cs.1678/supp-2

Si, H., Wan, L., Wang, Y., Song, J., Fernando, B., & Li, Y. (2023)

基于特征融合的玉米品种识别. Journal of the Chinese Cereals and Oils Association, 38(12), 191-196

Si, H., Wang, Y., Zhao, W., Wang, M., Song, J., Wan, L., Song, Z., Li, Y., Bação, F., & Sun, C. (2023)

Apple Surface Defect Detection Method Based on Weight Comparison Transfer Learning with MobileNetV3. Agriculture (Switzerland), 13(4), 1-26. [824]. https://doi.org/10.3390/agriculture13040824

Silva, D., & Bação, F. (2023)

MapIntel: A visual analytics platform for competitive intelligence. Expert Systems, [e13445]. https://doi.org/https://www.authorea.com/doi/full/10.22541/au.166785335.50477185, https://doi.org/10.1111/exsy.13445

Vaz, E., Damásio, B., Bação, F., Shaker, R. R., & Penfound, E. (2023)

Urban habitats and food insecurity: lessons learned throughout a pandemic. Habitat International, 135, 1-11. [102779]. https://doi.org/10.1016/j.habitatint.2023.102779

Zhang, J-J., Niu, Z., Ma, X-M., Wang, J., Xu, C-Y., Shi, L., Bação, F., & Si, H-P. (2023). 基于离散小波的土壤全氮高光谱特征提取与反演. 光谱学与光谱分析 [Hyperspectral Feature Extraction and Estimation of Soil Total Nitrogen Based on Discrete Wavelet Transform]. Spectroscopy and Spectral Analysis, 43(10), 3223-3229. https://doi.org/10.3964/j.issn.1000-0593(2023)

基于离散小波的土壤全氮高光谱特征提取与反演. 光谱学与光谱分析 [Hyperspectral Feature Extraction and Estimation of Soil Total Nitrogen Based on Discrete Wavelet Transform]. Spectroscopy and Spectral Analysis, 43(10), 3223-3229. https://doi.org/10.3964/j.issn.1000-0593(2023)10-3223-07

Crisóstomo, J., Lobo, V., & Bação, F. (2023)

Detecting Fraudulent Wallets in Ethereum Blockchain Combining Supervised and Unsupervised Techniques: Using Autoencoders and XGboost. In J. M. Machado, P. Vieira, A. Abelha, L. Vigneri, J. Prieto, H. Peixoto, & D. Arroyo (Eds.), Blockchain and Applications, 5th  International Congress: BLOCKCHAIN 2023 (pp. 224-233). (Lecture Notes in Networks and Systems; Vol. 778). Springer, Cham. https://doi.org/10.1007/978-3-031-45155-3_23

Camacho, L., Douzas, G., & Bacao, F. (2022)

Geometric SMOTE for regression. Expert Systems with Applications, [116387]. [Advanced online publication on January 3th  , 2022]. https://doi.org/10.1016/j.eswa.2021.116387

Douzas, G., Lechleitner, M., & Bacao, F. (2022)

Improving the quality of predictive models in small data GSDOT: A new algorithm for generating synthetic data. PLoS ONE, 17(4), 1-15. [e0265626]. https://doi.org/10.1371/journal.pone.0265626

Paredes, A., Mendonça, J., Bação, F., & Damásio, B. (2022)

Does R&D tax credit impact firm behaviour? Micro evidence for Portugal. Research Evaluation, 31(2), 226–235. [rvac002]. https://doi.org/10.1093/reseval/rvac002

Lyra, M. S., Pinheiro, F. L., & Bacao, F. (2022)

Public Procurement Fraud Detection: A Review Using Network Analysis. In R. M. Benito, C. Cherifi, H. Cherifi, E. Moro, L. M. Rocha, & M. Sales-Pardo (Eds.), Complex Networks & Their Applications X: Proceedings of the Tenth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2021 (Vol. 1) (Vol. I, pp. 116-129). (Studies in Computational Intelligence; Vol. 1015). Springer, Cham. https://doi.org/10.1007/978-3-030-93409-5_11

Silva, D., & Bacao, F. (2022)

MapIntel: Enhancing Competitive Intelligence Acquisition Through Embeddings and Visual Analytics. In G. Marreiros, B. Martins, A. Paiva, A. Sardinha, & B. Ribeiro (Eds.), Progress in Artificial Intelligence: 21st  EPIA Conference on Artificial Intelligence, EPIA 2022, Lisbon, Portugal, August 31–September 2, 2022, Proceedings (pp. 599-610). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13566 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-16474-3_49

Silva, H., António, N., & Bacao, F. (2022)

A Rapid Semi-automated Literature Review on Legal Precedents Retrieval. In G. Marreiros, B. Martins, A. Paiva, B. Ribeiro, & A. Sardinha (Eds.), Progress in Artificial Intelligence: 21st  EPIA Conference on Artificial Intelligence, EPIA 2022, Lisbon, Portugal, August 31–September 2, 2022, Proceedings (pp. 53-65). (Lecture Notes in Artificial Intelligence; Vol. 13566). Springer, Cham. https://doi.org/10.1007/978-3-031-16474-3_5

Fonseca, J., & Bação, F. (2022)

Research Trends and Applications of Data Augmentation Algorithms. (pp. 1-23). Cornell University (ArXiv). https://doi.org/10.48550/arXiv.2207.08817

Albuquerque, V., Andrade, F., Ferreira, J. C., Dias, M. S., & Bacao, F. (2021)

Bike-sharing mobility patterns: a data-driven analysis for the city of Lisbon. EAI Endorsed Transactions on Smart Cities, 1-20. [169580]. [Advanced online publication on 4 May 2021]. https://doi.org/10.4108/eai.4-5-2021.169580

Albuquerque, V., Sales Dias, M., & Bacao, F. (2021)

Machine Learning Approaches to Bike-Sharing Systems: A Systematic Literature Review. ISPRS International Journal of Geo-Information, 10(2), [62]. https://doi.org/10.3390/ijgi10020062

Douzas, G., Rauch, R., & Bacao, F. (2021)

G-SOMO: An oversampling approach based on self-organized maps and geometric SMOTE. Expert Systems with Applications, 183, 1-11. [115230]. https://doi.org/10.1016/j.eswa.2021.115230

Fonseca, J., Douzas, G., & Bacao, F. (2021)

Improving imbalanced land cover classification with k-means smote: Detecting and oversampling distinctive minority spectral signatures. Information (Switzerland), 12(7), 1-20. [266]. https://doi.org/10.3390/info12070266

Fonseca, J., Douzas, G., & Bacao, F. (2021)

Increasing the effectiveness of active learning: Introducing artificial data generation in active learning for land use/land cover classification. Remote Sensing, 13(13), 1-20. [2619]. https://doi.org/10.3390/rs13132619

Lyra, M. D. S., Curado, A., Damásio, B., Bação, F., & Pinheiro, F. L. (2021)

Characterization of the Firm-Firm Public Procurement Co-Bidding Network from the State of Ceará (Brazil) Municipalities. Applied Network Science, 6, 1-10. [77]. https://doi.org/10.1007/s41109-021-00418-y

Vaz, E., Bação, F., Damásio, B., Haynes, M., & Penfound, E. (2021)

Machine learning for analysis of wealth in cities: A spatial-empirical examination of wealth in Toronto. Habitat International, 108, 1-9. [102319]. https://doi.org/10.1016/j.habitatint.2021.102319

Vaz, E., Cusimano, M. D., Bação, F., Damásio, B., & Penfound, E. (2021)

Open data and injuries in urban areas: A spatial analytical framework of Toronto using machine learning and spatial regressions. PLoS ONE, 16(March), 1-17. [e0248285]. https://doi.org/10.1371/journal.pone.0248285

Vaz, E., Damásio, B., Bação, F., Kotha, M., Penfound, E., & Rai, S. K. (2021)

Mumbai's business landscape: A spatial analytical approach to urbanisation. Heliyon, 7(7), [e07522]. https://doi.org/10.1016/j.heliyon.2021.e07522

Zhao, Y., & Bacao, F. (2021)

How does gender moderate customer intention of shopping via live-streaming apps during the COVID-19 pandemic lockdown period? International Journal of Environmental Research and Public Health, 18(24), 1-24. [13004]. https://doi.org/10.3390/ijerph182413004

Zhao, Y., & Bacao, F. (2021)

How does the pandemic facilitate mobile payment? : An investigation on users’ perspective under the COVID-19 pandemic. International Journal of Environmental Research and Public Health, 18(3), 1-22. [1016]. https://doi.org/10.3390/ijerph18031016

Bação, F., Santos, M. Y., & Behnisch, M. (2020)

Spatial Data Science. [Editorial]. ISPRS International Journal of Geo-Information, 9(7), 1-5. [428]. https://doi.org/10.3390/ijgi9070428

Bação, F., Santos, M. Y., & Behnisch, M. (Eds.) (2020)

Special Issue "Spatial Data Science". ISPRS International Journal of Geo-Information, 9(7). Link: https://www.mdpi.com/journal/ijgi/special_issues/Spatial_Data_Science

Zhao, Y., & Bacao, F. (2020)

What factors determining customer continuingly using food delivery apps during 2019 novel coronavirus pandemic period? International Journal of Hospitality Management, 91, 1-12. [102683]. https://doi.org/10.1016/j.ijhm.2020.102683

Zhao, Y., & Bacao, F. (2020)

A comprehensive model integrating UTAUT and ECM with espoused cultural values for investigating users' continuance intention of using mobile payment. In Proceedings of the 2020 3rd  International Conference on Big Data Technologies, ICBDT 2020 (pp. 155-161). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3422713.3422754

Zhao, Y., & Bacao, F. (2020)

Theoretical Development: Extending the Flow Theory with Variables from the UTAUT2 Model. In 2020 IEEE 6th  International Conference on Computer and Communications (ICCC): December 11-14, 2020, Chengdu, China (pp. 2427-2431). Association for Computing Machinery. https://doi.org/10.1109/ICCC51575.2020.9345049

Aparicio, M., Oliveira, T., Bação, F., & Painho, M. (2019)

Gamification: a key determinant of massive open online course (MOOC) success. Information and Management, 56(1), 39-54. [advanced online publication on 20 june 2018]. DOI: 10.1016/j.im.2018.06.003

Douzas, G., & Bacao, F. (2019)

Geometric SMOTE a geometrically enhanced drop-in replacement for SMOTE. Information Sciences, 501, 118-135. https://doi.org/10.1016/j.ins.2019.06.007

Douzas, G., Bacao, F., Fonseca, J., & Khudinyan, M. (2019)

Imbalanced learning in land cover classification: Improving minority classes' prediction accuracy using the geometric SMOTE algorithm. Remote Sensing, 11(24), [3040]. https://doi.org/10.3390/rs11243040

Bação, F. J. F. L., Henriques, R., & Antunes, J. (2017)

Contribution Towards Smart Cities: Exploring Block Level Census Data for the Characterization of Change in Lisbon. In M. Behnisch, & G. Meinel (Eds.), Trends in Spatial Analysis and Modelling: Decision-Support and Planning Strategies (pp. 59-73). [Chapter 4] (Geotechnologies and the Environment; Vol. 19). Springer. https://doi.org/10.1007/978-3-319-52522-8_4

Cruz-Jesus, F., Oliveira, T., & Bação, F. (2018)

The global digital divide: evidence and drivers. Journal Of Global Information Management, 26(2), 1-26. DOI: 10.4018/JGIM.2018040101

Douzas, D.; Bação, F. & Last, F. (2018)

Improving Imbalanced Learn-ing Through a Heuristic Oversampling Method Based on K-Means and SMOTE. Information Sciences, 465, 1-20. doi: 10.1016/j.ins.2018.06.056

Georgios Douzas & Fernando Bacao (2018)

Effective data generation for imbalanced learning using Conditional Generative Adversarial Networks. Expert Systems with Applications 91, 464-471 (Advanced online publication on 13 September 2017). doi: https://doi.org/10.1016/j.eswa.2017.09.030

Aparício, M., Bacao, F. & Oliveira, T. (2017)

Grit in the path to e-learning success. Computers In Human Behavior, 66, 388-399. DOI: 10.1016/j.chb.2016.10.009

Cruz-Jesus, F., Oliveira, T., Bação, F., & Irani, Z. (2017)

Assessing the pattern between economic and digital development of countries. 19(4), 835–854. DOI: 10.1007/s10796-016-9634-1

Georgios Douzas & Fernando Bacao (2017)

Self-Organizing Map Oversampling (SOMO) for imbalanced data set learning. Expert Systems with Applications, 82 (1), 40-52. https://doi.org/10.1016/j.eswa.2017.03.073

Joel Silva, Fernando Bacao, Maguette Dieng, Giles M. Foody & Mario Caetano (2017)

Improving specific class mapping from remotely sensed data by cost-sensitive learning. International Journal of Remote Sensing, 38(11), 3294-3316. http://dx.doi.org/10.1080/01431161.2017.1292073

Silva, J., Bacao, F., & Caetano, M. (2017)

Specific land cover class mapping by semi-supervised weighted support vector machines. Remote Sensing, 9(2), [181]. DOI: 10.3390/rs9020181

Aparicio, M., Bacao, F., & Oliveira, T. (2016)

An e-Learning Theoretical Framework. Educational Technology & Society, 19(1), 292-307.

Aparicio, M., Bacao, F., & Oliveira, T. (2016)

Cultural impacts on e-learning systems' success. Internet and Higher Education, 31, 58-70. doi: 10.1016/j.iheduc.2016.06.003

Cruz-Jesus, F., Vicente, María R., Bacao, F., & Oliveira, T. (2016)

The education-related digital divide: An analysis for the EU-28. Computers in Human Behavior, 56, 72-82. doi: http://dx.doi.org/10.1016/j.chb.2015.11.027

Bacao, F., Santos, M. Y., & Painho, M. (2015)

Preface AGILE 2015: Geographic Information Science as an Enabler of Smarter Cities and Communities: Springer.

Bacao, F., Santos, M. Y., & Painho, M. (Eds.). (2015)

AGILE 2015: Geographic Information Science as an Enabler of Smarter Cities and Communities. Heidelberg: Springer.

Costa, H., Carrao, H., Bacao, F., & Caetano, M. (2014)

Combining per-pixel and object-based classifications for mapping land cover over large areas. International Journal of Remote Sensing, 35(2), 738-753. doi: 10.1080/01431161.2013.873151.

Cruz-Jesus, F., Oliveira, T., & Bacao, F. (2014)

Exploring the Pattern between Education Attendance and Digital Development of Countries. Procedia Technology, 16(0), 452-458. doi: http://dx.doi.org/10.1016/j.protcy.2014.10.112

Aparicio, M., Bacao, F., & Oliveira, T. (2014)

MOOC's business models: turning black swans into gray swans. Paper presented at the Proceedings of the International Conference on Information Systems and Design of Communication, Lisbon, Portugal.

Aparicio, M., Bacao, F., & Oliveira, T. (2014)

Trends in the e-Learning Ecosystem: A Bibliometric Study. Paper presented at the Twentieth Americas Conference on Information Systems, Savannah.

Pinto, A., Lobo, V., Bação, F., & Bacelar-Nicolau, H. (2013)

Self-perception of Health Status and Socio-Economic Differences in the Use of Health Services. In J. Lita da Silva, F. Caeiro, I. Natário & C. A. Braumann (Eds.), Advances in Regression, Survival Analysis, Extreme Values, Markov Processes and Other Statistical Applications (pp. 355-362): Springer Berlin Heidelberg.

Pinto, A., Rodrigues, T., Mendes, J., Bação, F., & Lobo, V. (2013)

Medication and Polymedication in Portugal. In P. E. Oliveira, M. da Graça Temido, C. Henriques & M. Vichi (Eds.), Recent Developments in Modeling and Applications in Statistics (pp. 59-68): Springer Berlin Heidelberg.

Aparicio, M., & Bacao, F. (2013)

e-learning concept trends. Paper presented at the 2013 International Conference on Information Systems and Design of Communication, Lisboa, Portugal.

Cruz-Jesus, F., Oliveira, T., & Bacao, F. (2012)

Digital divide across the European Union. Information & Management, 49(6), 278-291. doi: 10.1016/j.im.2012.09.003

Henriques, R., Bacao, F., & Lobo, V. (2012)

Exploratory geospatial data analysis using the GeoSOM suite. Computers Environment and Urban Systems, 36(3), 218-232. doi: 10.1016/j.compenvurbsys.2011.11.003

Henriques, R., Lobo, V., & Bação, F. (2012)

Spatial Clustering Using Hierarchical SOM. In M. Johnsson (Ed.), Applications of Self-Organizing Maps: InTech.

Bacao, F. (2011)

Clinical Data Mining for Physician Decision Making and Investigating Health Outcomes: Methods for Prediction and Analysis. Online Information Review, 35(4), 685-686. doi: 10.1108/14684521111162025

Cruz-Jesus, F., Oliveira, T., & Bacao, F. (2011)

Exploratory Factor Analysis for the Digital Divide: Evidence for the European Union - 27 In M. M. Cruz-Cunha, J. Varajão, P. Powell & R. Martinho (Eds.), ENTERprise Information Systems (Vol. 219, pp. 44-53): Springer Berlin Heidelberg.

Bacao, F. (2010)

Integrating Geographic Information Systems into Library Services: A Guide for Academic Libraries. Library Hi Tech, 28(4), 719-721. doi: 10.1108/07378831011096349

Bacao, F. (2010)

Website Visibility: The Theory and Practice and Improving Rankings. Online Information Review, 34(5), 817-819. doi: 10.1108/14684521011084663

Moreira, F., Catry, F. X., Rego, F., & Bacao, F. (2010)

Size-dependent pattern of wildfire ignitions in Portugal: when do ignitions turn into big fires? Landscape Ecology, 25(9), 1405-1417. doi: 10.1007/s10980-010-9491-0

Catry, F. X., Rego, F. C., Bacao, F., & Moreira, F. (2009)

Modeling and mapping wildfire ignition risk in Portugal. International Journal of Wildland Fire, 18(8), 921-931. doi: 10.1071/wf07123

Henriques, R., Bacao, F., & Lobo, V. (2009)

Carto-SOM: cartogram creation using self-organizing maps. International Journal of Geographical Information Science, 23(4), 483-511.

Koomen, E., Rietveld, P., & Bacao, F. (2009)

The third dimension in urban geography: the urban-volume approach. Environment and Planning B-Planing & Design, 36(6), 1008-1025.

Henriques, R., Bação, F., & Lobo, V. (2009)

Cartograms, Self-Organizing Maps, and Magnification Control. Paper presented at the Advances in Self-Organizing Maps 7th  International Workshop, WSOM 2009, St. Augustine, USA.

Henriques, R., Bacao, F., & Lobo, V. (2009)

GeoSOM Suite: A Tool for Spatial Clustering. In O. Gervasi, D. Taniar, B. Murgante, A. Lagana, Y. Mun & M. L. Gavrilova (Eds.), Computational Science and Its Applications - Iccsa 2009, Pt I (Vol. 5592, pp. 453-466). Berlin: Springer-Verlag Berlin.

Henriques, R., Bacao, F., & Lobo, V. (2009)

UAV path planning based on event density detection. In S. Dragicevic, D. Roman & V. Tanasescu (Eds.), International Conference on Advanced Geographic Information Systems and Web Services: Geows 2009, Proceedings (pp. 112-116). Los Alamitos: IEEE Computer Soc.

Costa, H., Carrão, H., Bação, F., & Caetano, M. (2009)

Land cover classification in Portugal with multitemporal AWiFS images: a comparative study, Remote Sensing for a Changing Europe. Paper presented at the Proceedings of the 28th  Symposium of the European Association of Remote Sensing Laboratories, Istanbul, Turkey.

Neto, M. C., Lopes, C., Maia, J., & Bação, F. (2009)

Business Intelligence in the Vineyard. Paper presented at the World Conference on Agricultural Information and IT, Tokyo, Japan.

Pinto, A., Lobo, V., Bação, F., & Bacelar-Nicolau, H. (2009)

A auto-percepção do estado de saúde e as diferenças sócio-económicas na utilização dos serviços de saúde, ao nível da NUTS II. Paper presented at the XVII Congresso da Sociedade Portuguesa de Estatística, Sesimbra, Portugal.

Bacao, F. (2008)

Data Mining and Knowledge Discovery Technologies. Online Information Review, 32(6), 866-867. doi: 10.1108/14684520810923980

Bação, F., Lobo, V., & Painho, M. (2008)

Applications of Different Self-Organizing Map Variants to Geographical Information Science Problems Self-Organising Maps, Applications in Geographical Information Science. West Sussex: John Wiley & Sons.

Catry, F. X., Rego, F. C., Moreira, F., & Bacao, F. (2008)

Characterizing and modelling the spatial patterns of wildfire ignitions in Portugal: fire initiation and resulting burned area. Paper presented at the Modelling, Monitoring and Management of Forest Fires, Southampton.

Fincke, T., Lobo, V., & Bação, F. (2008)

Visualizing self-organizing maps with GIS. Paper presented at the GI Days 2008, Munster.