The Research Section of Neural Computing @ UPLB serves as a showcase of the Institute of Computer Science’s continuing commitment to advancing knowledge in artificial and computational intelligence. It highlights the publications, research presentations, and collaborative projects that explore how neural computing can be applied to solve complex, real-world problems.

Research in neural computing at UPLB is both theoretical and applied. It ranges from the study of algorithms and architectures that model learning and adaptation to the development of systems that address pressing challenges in agriculture, environment, engineering, and society. Projects often bridge disciplines—where neural models interact with data from plant science, environmental monitoring, or social systems—to yield insights that traditional computation alone cannot uncover.

This page will feature peer-reviewed journal articles, conference papers, poster presentations, and student-led research projects that represent the breadth and depth of work conducted under the Neural Computing program. Each entry reflects the university’s mission of combining scientific excellence with societal relevance, demonstrating how computational intelligence can contribute to national development.

The section also welcomes collaboration with researchers, practitioners, and institutions interested in extending neural approaches to new domains. By sharing our findings openly, we aim to foster a growing community of scholars and innovators committed to responsible and meaningful applications of neural computing.

1. ARNEJO ZO, JP Pabico & NP Bantayan. 2021. Automated detection of coconut palm encroachment in the Mt. Makiling Forest Reserve in Laguna, Philippines. In Bahk S, P Tran-Gia, JV der Spiegel & NX Quynh (eds.) Proceedings of the IEEE Eight International Conference on Communications and Electronics (IEEE ICCE 2020), pp. 686-691.

2. LAURON MLC & JP Pabico. 2016. Improved sampling techniques for learning an imbalanced data set. In Sioson AA, PL Fernandez & HN Adorna (eds.) Proceedings of the 16th Philippine Computing Science Congress (PCSC 2016), pp. 222-228.

3. MOJICA ERE, JP Pabico & JRL Micor. 2008. Primal and dual neural models for correlating [Co] and [Ni] with the absorbance profile of Co-Ni mixture. In Proceedings of the 4th Network of CALABARZON Educational Institutions, Inc. (NOCEI) Research Forum.

4. PABICO JP, AV De Grano & A. Zarsuela. 2008. Accuracy of neural models as classifiers in machine vision systems for automating the grading processes of tomatoes and eggs. In Proceedings of the 4th Network of CALABARZON Educational Institutions, Inc. (NOCEI) Research Forum.

5. PABICO JP, AV De Grano & A. Zarsuela. 2012. Neural network classifiers for natural food products. In Adorna HN, REO Roxas & AA Sioson (eds.) Proceedings of the 12th Philippine Computing Science Congress.

6. PABICO JP, JRL Micor & ERE Mojica. 2009. A neural prototype for a virtual chemical spectrophotometer. Philippine Computing Journal 4(2):39-42.

7. PABICO JP, ERE Mojica & JRL Micor. 2008. Design of a homogeneous ensemble for splice-site identification in human sequences. In Proceedings of the 10th International Conference on Molecular Systems Biology (ICMSB 2008), pp 60-62.

8. PABICO JP & AL Zarsuela. 2009. Improving the performance of a vision-based computerized egg grader. Proceedings of the 7th International Agricultural Engineering Conference and Exhibition.

9. RECARIO RC, JEI Encinas, JCM Barro, JDG Villate, AJ Jacildo, JS Baladad, AC Fajardo, JP Pabico, AC Manila-Fajardo & CR Cervancia. 2015. POLLIMAC II: A modular automated pollen image classifier. Journal of Nature Studies 14(1):22-35.

1. ARNEJO ZO, JP Pabico & NC Bantayan. 2019. An artificial neural network model for counting coconut palm in aerial images of Mt. Makiling Forest Reserve. 8th RESPhil-SCENe National Scientific Conference and General Assembly (RESPhil-SCENe 2019).

2. ARNEJO ZO & JP Pabico. 2019. Automated detection and counting of coconut palm in aerial images. ASTHRDP Graduate Scholar's Conference.

3. ARNEJO ZO, NC Bantayan & JP Pabico. 2018. Counting and classification of coconut tree crowns in forest aerial image using digital image processing and artificial neural networks. 53rd BIOTA Annual National Convention and Scientific Sessions (BIOTA 2018).

4. ARNEJO ZO, NC Bantayan & JP Pabico. 2017. Segmentation of coconut tree regions in forest aerial image of Mt. Makiling. 10th UPLB CAS Student-Faculty Research Conference (SFRC 2017).

5. BALADAD JS, EA Albacea, JP Pabico, JM Samaniego, CL Khan, AJ Jacildo, AC Fajardo & CR Cervancia. 2010. A neural network-based classifier for pollens. 10th AAA Conference and Api Expo.

6. A.V. De Grano and J.P. Pabico. 2007. Automating the classification of tomato (Lycopersicon esculentum) maturity using image analysis and neural networks (Best Poster). 29th Annual Scientific Meeting of the National Academy of Science and Technology (NAST-ASM 2007).

7. A.V. De Grano and J.P. Pabico. 2007. A neural network-based computer color vision for grading tomatoes (Lycopersicon esculentum). 19th FCSSP Scientific Conference (FCSSP 2007).

8. CAYETANO CR & JP Pabico. 2004. A neural networks approach for determining credit card approvals. 4th Philippine Computing Science Congress (PCSC 2004).

9. ENCINAS JEI, AJ Jacildo, JS Baladad, JP Pabico, CR Cervancia & AJC Fajardo. 2011. POLLIMAC: Web-based user interface of an artificial neural network classifier for pollen images. 17th BEENET Conference and Techno-fora.

10. LASQUETY SOQ & JP Pabico. 2004. Heart disease diagnosis using artificial neural networks. 4th Philippine Computing Science Congress (PCSC 2004).

11. LAURON MLC, MG Carandang & JP Pabico. 2016. Automatic profiling of waitlisted students for future delinquency: Experiences, problems, and solutions. 9th UPLB CAS Student-Faculty Research Conference (SFRC 2016).

12. LAURON MLC & JP Pabico. 2016. Ensemble learning using selected algorithms for solutions of classification problems in data mining. 1st International DOST-SEI ASTHRDP-NSC Scholars' Conference.

13. LAURON MLC & JP Pabico. 2017. Profiling waitlisted incoming students for future delinquency with an ensemble of statistical machine learning algorithms. 18th National Student-Faculty Conference on the Statistical Sciences (SFCon-Stat 2017).

14. LUNA DA, DB Magcale-Macandog & JP Pabico. 2017. Classifying spatio-pixel data using pipelined binary neural networks. Philippine National LiDAR Conference (Phil-LiDAR 2017).

15. MICOR JRL, ERE Mojica & JP Pabico. 2008. Recognition of gene acceptor site via ensemble boosting. 10th Eurasia Conference on Chemical Sciences (EuAsC2S-10).

16. MOJICA ERE, JRL Micor, CC Deocaris & JP Pabico. 2005. Development and Validation of a Neural Network Model for Splice-Site Recognition in Human Genome Sequences. 27th Annual Scientific Meeting of the National Academy of Science and Technology (NAST-ASM 2005).

17. OBLENA GPV & JP Pabico. 2004. Diagnosis of hyperthyroidism using artificial neural networks. 4th Philippine Computing Science Congress (PCSC 2004).

18. PABICO JP. 2008. Molecular dynamics as a computational metaphor for search, optimization and machine learning (Invited Paper). First DOST-PCASTRD National Symposium on Science and Technology (NSST 2008).

19. PABICO JP. 2009. Improved automation of tomato maturity classification using an artificial eye and an artificial brain. 21st National Research Symposium.

20. PABICO JP. 2017. Improved classification of a multiclass problem using pipelined binary neural networks (Invited Paper). FAITH Faculty Research Colloquium.

21. PABICO JP & AV De Grano. 2007. Efficient grading of tomato maturity using a majority-vote committee of computer-based color classifiers (Best Paper). 7th ISSAAS-Philippines National Convention and Annual Meeting.

22. PABICO JP & CC Deocaris. 2003. A neural network model for splice-site recognition of human genome sequences. 30th Annual Convention of the Philippine Society for Biochemistry and Molecular Biology.

23. PABICO JP, AMV Gonzales, MJS Villanueva & AA Mendoza. 2015. Automatic identification of animal breeds and species using bioacoustics and artificial neural networks (Best Paper). 52nd Scientific Seminar and Annual Convention of the Philippines Society of Animal Science (PSAS 2015).

24. PABICO JP, ERE Mojica & JRL Micor. 2008. An improved exon-intron recognition via a committee of machines. 30th Annual Scientific Meeting of the National Academy of Science and Technology (NAST-ASM 2008).

25. PABICO JP, AR Salvacion & DB Magcale-Macandog. 2015. Data manipulation approaches in learning a model for predicting rare events: A case study of the Taal Lake fish kill predictive model from weather data. 8th National Research Workshop on Modeling, Simulation and Scientific Computing (MODEL 2015).

26. SIBUMA KH & JP Pabico. 2004. The use of artificial neural network for diagnosing breast cancer. 4th Philippine Computing Science Congress (PCSC 2004).

27. ZARSUELA AL & JP Pabico. 2007. A neural network model for classifying duck eggs using computer vision. 7th ISSAAS-Philippines National Convention and Annual Meeting.

28. ZARSUELA AL & JP Pabico. 2010. Improving the performance of a vision-based computerized egg grader. 32nd Annual Scientific Meeting of the National Academy of Science and Technology (NAST-ASM 2010).