JOURNAL ARTICLES

1. P. Walkikar, L. Shi, B. A. Tama, V. Janeja, Discovery of multi-domain spatiotemporal associations, GeoInformatica, 2023 [SCIE] [doi] (accepted and to appear)

2. M. Vania, B. A. Tama, H. Maulahela, S. Lim, Recent advances in applying machine learning and deep learning to detect upper gastrointestinal tract lesions, IEEE Access, Vol. 11, pp. 66544-66567, 2023  [SCIE]  [doi] (co-lead author)

3. B. A. Tama, M. Vania, S. Lee, S. Lim, Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals, Artificial Intelligence Review, Vol. 56, pp. 4667-4709, 2023 [SCIE] [doi]

4. M. H. L. Louk, B. A. Tama*, Dual-IDS: A bagging-based gradient boosting decision tree model for network anomaly intrusion detection system, Expert Systems with Applications, Vol. 213, Part B, pp. 119030, 2023 [SCIE] [doi] (*corresponding author)

5. B. A. Tama, S. Y. Lee, S. Lee,  A systematic mapping study and empirical comparison of data-driven intrusion detection techniques in industrial control networks, Archives of Computational Methods in Engineering, Vol. 29, No. 7, pp. 5353-5380, 2022 [SCIE] [doi] 

6. M. H. L. Louk, B. A. Tama*, PSO-driven feature selection and hybrid ensemble for network anomaly detection, Big Data and Cognitive Computing, Vol. 6, pp. 4, pp. 137, 2022 [doi] (*corresponding author)

7. S. Nurmaini, B. A. Tama, M. N. Rachmatullah, A. Darmawahyuni, A. I. Sapitri, Firdaus, B. Tutuko, An improved semantic segmentation with region proposal network for cardiac defect interpretation, Neural Computing and Applications, Vol. 34, pp. 13937–13950, 2022 [SCIE] [doi]

8. M. H. L. Louk, B. A. Tama*, Tree-based classifier ensembles for PE malware  analysis: A performance revisit, Algorithms, Vol. 15 No. 9, pp. 332, 2022 [doi] (*corresponding author) (highlight article.)

9. B. A. Tama, M. Comuzzi, Leveraging a heterogeneous ensemble learning for outcome-based predictive monitoring using business process event logs, Electronics, 11(16), pp. 2548, 2022 [SCIE] [doi]

10. M. H. L. Louk, B. A. Tama*, Revisiting gradient boosting-based approaches for learning imbalanced data: A case of anomaly detection on power grids, Big Data and Cognitive Computing, Vol. 6, No. 2, pp. 41, 2022 [doi] (*corresponding author)

11. B. A. Tama, M. Vania, I. Kim, S. Lim, An EfficientNet-based weighted ensemble model for industrial machine malfunction detection using acoustic signals, IEEE Access, Vol. 10, pp. 34625-34636, 2022 [SCIE] [doi]

12. M. H. L. Louk, B. A. Tama*, Exploring ensemble-based class imbalance learners for intrusion detection in industrial control networks, Big Data and Cognitive Computing, Vol. 5, No. 4, pp. 72, 2021 [doi] (*corresponding author) (editor's choice article)

13. B. A. Tama, S. Lee, Comments on "Stacking ensemble-based deep neural networks modeling for effective epileptic seizure detection", Expert Systems with Applications, Vol. 184, pp. 115488, December 2021 [SCIE] [doi] 

14. Y. Hwang, H. H. Lee, C. Park, B. A. Tama, J. S. Kim, D. Y. Cheung, W. C. Chung, Y. -S. Cho, K. -M. Lee, M. -G. Choi, S. Lee, B. -I. Lee, An improved classification and localization approach to small bowel capsule endoscopy using convolutional neural network, Digestive Endoscopy, Vol. 33, No. 4, May 2021, pp. 598-607  [SCIE] [doi]. (highlight article.) 

15. B. A. Tama, S. Lim, Ensemble learning for intrusion detection systems: A systematic mapping study and cross-benchmark evaluation, Computer Science Review, Vol. 39, 2021 [SCIE] [doi]. 

16. L. Nkenyereye, B. A. Tama, S. Lim, A stacking-based deep neural network approach for effective network anomaly detection, Computers, Materials & Continua, Vol. 66, No.2, pp. 2217-2227, 2021 [SCIE] [doi] 

17. B. A. Tama, S. Lim, A comparative performance evaluation of classification algorithms for clinical decision support system, Mathematics, Vol. 8, No. 10, pp. 1814, 2020 [SCIE] [doi]

18. B. A. Tama, S. W. Kim, G. Kim, D. Kim, S. Lee, Recent advances in the application of artificial intelligence in otorhinolaryngology-head and neck surgery, Clinical and Experimental Otorhinolaryngology, Vol.13, No. 4, pp. 326-339, 2020 [SCIE] [doi] 

19. B. A. Tama, M. Comuzzi, J. Ko, An empirical investigation of different classifiers, encoding and ensemble schemes for next event prediction using business process event logs, ACM Transactions on Intelligent Systems and Technology, Vol. 11, No. 6, pp. 1-34, 2020 [SCIE] [doi] 

20. K. H. Sun, H. Huh, B. A. Tama, S. Y. Lee, J. H. Jung, S. Lee, Vision-based fault diagnostics using explainable deep learning with class activation map, IEEE Access, Vol. 8, pp. 129169 - 129179 , 2020 [SCIE] [doi] 

21. S. W. Kim, Y. G. Lee, B. A. Tama, S. Lee, Reliability-enhanced camera lens module classification using semi-supervised regression method, Applied Sciences, Vol. 10, No. 11, 3832, 2020 [SCIE] [doi]

22. B. A. Tama, S. Im,  S. Lee, Improving an intelligent detection system for coronary heart disease using a two-tier classifier ensemble, Biomed Research International, Vol. 2020, pp. 1-10, 2020 [SCIE] [doi]

23. L. Nkenyereye, L. Nkenyereye, B. A. Tama, G. Alavalapati, J. Song,  Software-defined vehicular cloud networks: Architecture, applications and virtual machine migration, Sensors, Vol. 20, No. 4, pp. 1092, 2020 [SCIE] [doi]

24. B. A. Tama, L. Nkenyereye, S. M. R. Islam, K. -S. Kwak, An enhanced anomaly detection in Web traffic using a stack of classifier ensemble, IEEE Access, Vol. 8, No. 1, pp.  24120 - 24134 , 2020 [SCIE] [doi]

25. S. Y. Lee, B. A. Tama, C. Choi, J. Y.  Hwang, J. Bang, S. Lee,  Spatial and sequential deep learning approach for predicting temperature distribution in a steel-making continuous casting process, IEEE Access, Vol. 8, No. 1, pp.  21953 - 21965, 2020 [SCIE] [doi] 

26. L. Nkenyereye, B. A. Tama, M. K. Shahzad, Y. -H. Choi, Secure and blockchain based emergency driven message protocol for 5G enabled vehicular edge computing, Sensors, Vol. 20, No. 1,  pp. 154, 2020 [SCIE] [doi]

27. S. Y. Lee, B. A. Tama, S. J. Moon, S. Lee,  Steel surface defect diagnostic using deep convolutional neural network and class activation map, Applied Sciences, Vol. 9, No. 24, pp. 1-13, 2019 [SCIE] [doi] 

28. G. W. Song, B. A. Tama, J. Park, J. Y. Hwang, J. Bang, S.  J. Park, S. Lee, Temperature control optimization in a steel-making continuous casting process using multimodal deep learning approach, Steel Research International, Vol. 90, No. 12, pp. 1-10, 2019  [SCIE] [doi] 

29. B. A. Tama, M. Comuzzi, K. -H. Rhee, TSE-IDS: A two-stage classifier ensemble for intelligent anomaly-based intrusion detection system, IEEE Access, Vol. 7, pp. 94497 - 94507, 2019 [SCIE] [doi] 

30. W. Choi, H. Huh, B. A. Tama, G. Park, S. Lee, A neural network model for material degradation detection and diagnosis using microscopic images, IEEE Access, Vol. 7, pp.  92151 - 92160, 2019. [SCIE] [doi] 

31. B. A. Tama, M. Comuzzi, An empirical comparison of classification techniques for next event prediction using business process event logs, Expert Systems with Applications, Vol. 129, 2019 [SCIE] [doi] 

32. B. A. Tama, K. -H. Rhee, An in-depth experimental study of anomaly detection using gradient boosted machine, Neural Computing and Applications, Vol. 31, No. 4, 2019 [SCIE] [doi]

33. B. A. Tama, K. -H. Rhee, Tree-based classifier ensembles for early detection method of diabetes: An exploratory study, Artificial Intelligence Review, Vol. 51, No. 3, 2019 [SCIE] [doi]

34. B. A. Tama, K. -H. Rhee, A comparative study of phishing websites classification based on classifier ensembles, Journal of Multimedia Information System, Vol. 21 No. 5, 2018 [KCI] [doi]

35. B. A. Tama, K. -H. Rhee, In-depth Analysis of Neural Network Ensembles for Early Detection Method of Diabetes Disease, International Journal of Medical Engineering and Informatics, Vol.10 No.4, pp.327-341, 2018 [doi]

36. B. A. Tama, K. -H. Rhee, A Comparative Study of Classifier Ensembles for Detecting Inactive Learner in University, International Journal of Data Analysis Techniques and Strategies, Vol.10 No.4, pp. 351-368, 2018 [doi] 

37. B. A. Tama, K. -H. Rhee, HFSTE: Hybrid Feature Selections and Tree-based Classifiers Ensemble for Intrusion Detection Systems, IEICE Transactions on Information and Systems, Vol. 100 No. 8, 2017 [SCIE] [doi]

38. B. A. Tama, K. -H. Rhee, An Extensive Empirical Evaluation of Classifier Ensembles for Intrusion Detection Task, Computer Systems Science & Engineering, Vol. 32 No. 2, 2017 [SCIE][doi] 

39. B. A. Tama, K. -H. Rhee, Attack classification analysis of IoT network via deep learning approach, Research Briefs on Information & Communication Technology Evolution, Vol. 3, 2017. [doi] 

40. B. A. Tama, K. -H. Rhee, A Detailed Analysis of Classifier Ensembles for Intrusion Detection System in Wireless Network, Journal of Information Processing System, Vol. 13 No. 5, 2017  [doi] 

41. B. A. Tama, K. -H. Rhee, Performance Evaluation of Intrusion Detection System Using Classifier Ensembles, International Journal of Internet Protocol Technology, Vol. 10 No. 1, 2017 [doi] 

42. B. A. Tama, K. -H. Rhee, Data Mining Techniques in DoS/DDoS Attack Detection: A Literature Review, Information (Japan), Vol.18 No.8, August 2015  (link). 

43. L. Nkenyereye, B. A. Tama, , Y. Park, K. -H. Rhee, A Fine-Grained Privacy Preserving Protocol over Attribute Based Access Control for VANETs, Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, Vol.6 No.2, June 2015  [doi] 

44. B. A. Tama, Learning to Prevent Inactive Students of Indonesia Open University, Journal of Information Processing Systems, Vol.11 No. 2, June 2015 [doi]

45. B. A. Tama, Data Mining for Predicting Customer Satisfaction in Fast-food Restaurant, Journal of Theoretical and Applied Information Technology, Vol.75 No.1, May 2015  [doi]

46. B. A. Tama, Rodiyatul F. S., Hermansyah, An Early Detection Method of Type-2 Diabetes-Mellitus in Public Hospital, TELKOMNIKA, Vol.9 No.2, August 2011 [doi]

CONFERENCE PROCEEDINGS | BOOK CHAPTERS | PRESENTATIONS

1. B. A. Tama, S. Purushotam, V. Janeja, A Pilot Study on the Challenges in Ice Layer Annotations, 13th International Conference on Climate Informatics,  London, UK, 2024 (accepted)

2. B. A. Tama, V. Janeja, S. Purushotam, Assessing  Annotation Accuracy in Ice Sheet Using Quantitative Metrics, IGARSS 2024,  Athens, Greece, 2024 (accepted)

3. A. Jebeli, B. A. Tama, S. Purushotam, V. Janeja, Tracing Englacial Layers in Radargram via Semi-supervised Method: A Preliminary Result, AAAI Fall Symposium Series -  AI and Climate: The Role of AI in a Climate-Smart Sustainable Future, VA, USA, 2023 [doi]

4. A. Jebeli, B. A. Tama, V. Janeja, N. Holschuh, C. Jensen, M. Morlighem, J. A. MacGregor, M. Fahnestock,  TSSA: Two-step semi-supervised annotation for englacial radargrams on the Greenland ice sheet, IGARSS 2023, Pasadena, CA, USA, 2023 [doi]

5. N. Tack, B. A. Tama, A. Jebeli, V. Janeja, D. Engel, R. Williams, Metrics for the quality and consistency of ice layer annotations, IGARSS 2023, Pasadena, CA, USA, 2023 [doi]

6. M. H. Suwito, B. A. Tama, B. Santoso, S. Dutta, H. Tan, Y. Ueshige, K. Sakurai, A systematic study of bulletin board and its application, ACM AsiaCCS 2022, Nagasaki, Japan, ACM, 2022 [doi]

7. B. A. Tama, L. Manovich, C. A. Piarso, M. Cha, How ideas spread? Analyzing growth and diffusion of topics in event-based social network, IC2S2, ETH Zurich, July 2021 (extended abstract) [link].

8. R. Nadia, B. A. Tama, J. Song, Seamless biometric IoT authentication with machine learning techniques, ICTC 2020, Jeju Island, Republic of Korea, IEEE, 2020 [doi].

9. B. A. Tama, S. Y. Lee, S. Lee, An overview of deep learning techniques for fault detection using vibration signal, Internoise-2020, Seoul, Republic of Korea, August 2020 [link].

10. B. A. Tama, K. H. Sun, H. Huh, S. Lee, CNN-based machine fault diagnosis method using vibration videos, The 5th International Conference on the Interface between Statistics and Engineering, Seoul, Republic of Korea, 26-28 June 2019.

11. B. A. Tama, K. -H. Rhee, An integration of PSO-based feature selection and random forest for anomaly detection in IoT network, MATEC Web of Conferences, Vol. 159, 2018 [doi].

12. B. A. Tama, B. J. Kweka, Y. Park, K. -H. Rhee, A critical review of blockchain and its current applications, ICECOS 2017, IEEE, 2017 [doi].

13. A. S. Patil, B. A. Tama, Y. Park, K. -H. Rhee, A framework for blockchain-based secure smart green house farming, Advances in Computer Science and Ubiquitous Computing, LNEE, Springer, 2017 [doi].

14. B. A. Tama, K. -H. Rhee, A novel anomaly detection method in wireless network using multi-level classifier ensembles, Advances in Multimedia and Ubiquitous Engineering, LNEE, Springer, 2017 [doi].

15. R. Primartha, B. A. Tama*, Anomaly detection using random forest: a performance revisited, ICoDSE 2017, IEEE, 2017 [doi]. 

16. K. D. Tania, B. A. Tama*, Implementation of regular expression (regex) on knowledge management system, ICoDSE 2017, IEEE, 2017 [doi].

17. B. A. Tama, A. S. Patil, K. -H. Rhee, An improved model of anomaly detection using two-level classifier ensemble, AsiaJCIS 2017, IEEE, 2017 [doi].

18. B. A. Tama, K. -H. Rhee, Performance analysis of multiple classifier system in DoS attack detection, Information Security Applications, LNCS, 2016 [doi].

19. B. A. Tama, K. -H. Rhee, Classifier ensemble design rotation forest to enhance attack detection of IDS in wireless network, AsiaJCIS 2016, IEEE, 2016 [doi].

20. B. A. Tama, K. -H. Rhee, A combination of PSO-based feature selection and tree-based classifier ensemble for intrusion detection system, Advances in Computer Science and Ubiquitous Computing, LNEE, 2015 [doi].

21. M. A. Firdaus, R. Nadia, B. A. Tama, Detecting major disease in public hospital using ensemble techniques, ISTMET 2014, IEEE, 2014 [doi].