Jakov Ivan S. Dumbrique

Details

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Sex: Male
Education:

  • Master of Science in Computer Science (ongoing), University of the Philippines-Diliman, present
  • Masters in Applied Mathematics Major in Mathematical Finance, Ateneo de Manila University, 2017
  • Bachelor of Science in Applied Mathematics with Specialization in Mathematical Finance, Minor in Economics, Ateneo de Manila University, 2016

Field of Specialization:
Computer Vision applied to Medical and Healthcare Data
Deep Learning & Machine Learning
Mathematical and Statistical Modelling


Researches:

Article title: Clinical evaluation of a deep learning-based model for pneumothorax detection and segmentation on chest x-ray images.
Authors: J. Dumbrique, R. Hernandez, J. Cruz, & P. Naval.
Publication title: EPOS Proceedings of the European Congress of Radiology. July 2022.

Abstract:
No available
Full text available upon request to the author

Article title: Underwater Gesture Recognition Using Classical Computer Vision and Deep Learning Techniques.
Authors: Mygel Andrei M. Martija, Jakov Ivan S. Dumbrique, and Prospero C. Naval, Jr
Publication title: EPOS Proceedings of the European Congress of Radiology, July 2022

Abstract:
Underwater Gesture Recognition is a challenging task since conditions which are normally not an issue in gesture recognition on land must be considered. Such issues include low visibility, low contrast, and unequal spectral propagation. In this work, we explore the underwater gesture recognition problem by taking on the recently released Cognitive Autonomous Diving Buddy Underwater Gestures dataset. The contributions of this paper are as follows: (1) Use traditional computer vision techniques along with classical machine learning to perform gesture recognition on the CADDY dataset; (2) Apply deep learning using a convolutional neural network to solve the same problem; (3) Perform confusion matrix analysis to determine the types of gestures that are relatively difficult to recognize and understand why; (4) Compare the performance of the methods above in terms of accuracy and inference speed. We achieve up to 97.06% accuracy with our CNN. To the best of our knowledge, our work is one of the earliest attempts, if not the first, to apply computer vision and machine learning techniques for gesture recognition on the said dataset. As such, we hope this work will serve as a benchmark for future work on the CADDY dataset.
Full text link https://tinyurl.com/bdeyksmj

Article title: Speech Emotion Recognition Using Support Vector Machines and Random Forests.
Authors: J. Dumbrique & L. Bautista.
Publication title: Mathematical Society of the Philippines Annual Convention. May 2019.

Abstract:
No abstract available
Full text available upon request to the author

Article title: Maternal Mortality Measurements Using National Surveys and Vital Statistics: Assessing the Quality and Content of Maternal Death Certificates.
Authors: J. De Guzman, M. Dayrit, P. Salting, A. Zosa, J. Dumbrique, & C. Dee.
Publication title: Proceedings of the 13th National Convention on Statistics. October 2016.

Abstract:
No abstract
Full text available upon request to the author

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