Francis Jesmar P. Montalbo

About

Google Scholar https://scholar.google.com/citations?user=PV8dJDkAAAAJ&hl=en&authuser=4&oi=ao
EMAIL: [email protected]; [email protected]
Webpage: https://francismontalbo.github.io

Dr. Francis Jesmar P. Montalbo (F. J. P. Montalbo) is a Filipino researcher, author, and Associate Professor specializing in computer vision, deep learning, and data science. He obtained his Doctorate in Information Technology from the Technological Institute of the Philippines-Manila and has since been actively contributing to the field of Artificial Intelligence (AI), specifically Deep Learning (DL), through publications in reputable international journals and presentations at recognized conferences. Currently, Dr. Montalbo holds the position of Associate Professor at Batangas State University, The National Engineering University Philippines, where he is dedicated to helping other researchers in the field. In addition to his academic responsibilities, he also serves as a peer-reviewer for respected journals and conferences such as Elsevier, Springer, ACM, Taylor and Francis, PlosOne, Wiley, IEEE, Frontiers Media, and Hindawi. Furthermore, Dr. Montalbo contributes as an associate editor for a notable local university-based journal in the Philippines.

Employment

Employment history is unavailable.

Education

Education history is unavailable.

Projects & Funding

Projects & funding information is unavailable.

Publications (24)

• MHADFormer: A cost-efficient multiscale hybrid transformer mixer model for automating Alzheimer’s disease diagnosis from MRI scans
  Applied Soft Computing 2026 DOI: 10.1016/j.asoc.2026.114624
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• On the optimized fused filament fabrication of polylactic acid using multiresponse central composite design and desirability function algorithm
  Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 2026 DOI: 10.1177/09544089241247454
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• DySARNet: a lightweight self-attention deep learning model for diagnosing dysarthria from speech recordings
  Multimedia Tools and Applications 2024 DOI: 10.1007/s11042-024-20053-w
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• Performance Analysis of Lightweight Vision Transformers and Deep Convolutional Neural Networks in Detecting Brain Tumors in MRI Scans: An Empirical Approach
  2023 DOI: 10.1145/3634875.3634878
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• Machine-based mosquito taxonomy with a lightweight network-fused efficient dual ConvNet with residual learning and Knowledge Distillation
  Applied Soft Computing 2023 DOI: 10.1016/j.asoc.2022.109913
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• Automating mosquito taxonomy by compressing and enhancing a feature fused EfficientNet with knowledge distillation and a novel residual skip block
  MethodsX 2023 DOI: 10.1016/j.mex.2023.102072
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• An overview of remote monitoring methods in biodiversity conservation
  Rout George Kerry, Francis Jesmar P. Montalbo, Rajeswari Das +10 more
  Environmental Science and Pollution Research 2022 DOI: 10.1007/s11356-022-23242-y
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• Various transmission codes for the control of bit error rate in both optical wired and wireless communication channels
  Ahmed Nabih Zaki Rashed, S. K. Hasane Ahammad, Malek G. Daher +6 more
  Journal of Optical Communications 2022 DOI: 10.1515/joc-2022-0044
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• Non return to zero line coding with suppressed carrier in FSO transceiver systems under light rain conditions
  Ahmed Nabih Zaki Rashed, Malek G. Daher, S. K. Hasane Ahammad +5 more
  Journal of Optical Communications 2022 DOI: 10.1515/joc-2022-0039
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• Diagnosing gastrointestinal diseases from endoscopy images through a multi-fused CNN with auxiliary layers, alpha dropouts, and a fusion residual block
  Biomedical Signal Processing and Control 2022 DOI: 10.1016/j.bspc.2022.103683
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• Truncating fined-tuned vision-based models to lightweight deployable diagnostic tools for SARS-CoV-2 infected chest X-rays and CT-scans
  Multimedia Tools and Applications 2022 DOI: 10.1007/s11042-022-12484-0
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• Automated diagnosis of diverse coffee leaf images through a stage-wise aggregated triple deep convolutional neural network
  Machine Vision and Applications 2022 DOI: 10.1007/s00138-022-01277-y
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• Fusing compressed deep ConvNets with a self-normalizing residual block and alpha dropout for a cost-efficient classification and diagnosis of gastrointestinal tract diseases
  MethodsX 2022 DOI: 10.1016/j.mex.2022.101925
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• Diagnosing Covid-19 chest x-rays with a lightweight truncated DenseNet with partial layer freezing and feature fusion
  Biomedical Signal Processing and Control 2021 DOI: 10.1016/j.bspc.2021.102583
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• Empirical analysis of a fine-tuned deep convolutional model in classifying and detecting Malaria parasites from blood smears
  KSII Transactions on Internet and Information Systems 2021 DOI: 10.3837/TIIS.2021.01.009
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• Truncating a densely connected convolutional neural network with partial layer freezing and feature fusion for diagnosing COVID-19 from chest X-rays
  MethodsX 2021 DOI: 10.1016/j.mex.2021.101408
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• A computer-aided diagnosis of brain tumors using a fine-tuned yolo-based model with transfer learning
  KSII Transactions on Internet and Information Systems 2020 DOI: 10.3837/tiis.2020.12.011
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• An IoT Smart Lighting System for University Classrooms
  Proceedings - 2020 International Symposium on Educational Technology, ISET 2020 2020 DOI: 10.1109/ISET49818.2020.00011
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• An Optimized Classification Model for Coffea Liberica Disease using Deep Convolutional Neural Networks
  Proceedings - 2020 16th IEEE International Colloquium on Signal Processing and its Applications, CSPA 2020 2020 DOI: 10.1109/CSPA48992.2020.9068683
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• Classifying barako coffee leaf diseases using deep convolutional models
  International Journal of Advances in Intelligent Informatics 2020 DOI: 10.26555/ijain.v6i2.495
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