J
Justin D. de Guia
Researcher at De La Salle University
Publications - 7
Citations - 84
Justin D. de Guia is an academic researcher from De La Salle University. The author has contributed to research in topics: Mean squared error & Ensemble learning. The author has an hindex of 2, co-authored 7 publications receiving 10 citations.
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Proceedings ArticleDOI
Aquaphotomics Determination of Total Organic Carbon and Hydrogen Biomarkers on Aquaponic Pond Water and Concentration Prediction Using Genetic Programming
Ronnie Concepcion,Sandy Lauguico,Jonnel Alejandrino,Justin D. de Guia,Elmer P. Dadios,Argel A. Bandala +5 more
TL;DR: In this paper, the authors employed device minimization by utilizing a combination of physical water sensors, namely temperature and electrical conductivity sensors, to predict total organic carbon (TOC) and hydrogen ion (H) concentrations in pond water.
Proceedings ArticleDOI
Using Stacked Long Short Term Memory with Principal Component Analysis for Short Term Prediction of Solar Irradiance based on Weather Patterns
Justin D. de Guia,Ronnie Concepcion,Hilario A. Calinao,Jonnel Alejandrino,Elmer P. Dadios,Edwin Sybingco +5 more
TL;DR: In this article, neural network models were defined to predict solar irradiance values based on weather patterns, including artificial neural network, convolutional neural network (CNN), bidirectional long short-term memory (LSTM) and stacked LSTM.
Proceedings ArticleDOI
Machine Vision-Based Prediction of Lettuce Phytomorphological Descriptors using Deep Learning Networks
Sandy Lauguico,Ronnie Concepcion,Rogelio Ruzcko Tobias,Jonnel Alejandrino,Justin D. de Guia,Marielet Guillermo,Edwin Sybingco,Elmer P. Dadios +7 more
TL;DR: In this article, a novel methodology on optimizing lettuce production in a smart aquaponics setup by predicting its phytomorphological features was discussed, where pre-trained deep learning-based networks were customized to train the machine vision-based feature extracted data.
Proceedings ArticleDOI
Performance Comparison of Classification Algorithms for Diagnosing Chronic Kidney Disease
TL;DR: Six machine learning algorithms were used for classification and its prediction performance was compared based on training time and F1 score, with and without hypertuning the parameters to assess chronic kidney disease.
Proceedings ArticleDOI
Application of Ensemble Learning with Mean Shift Clustering for Output Profile Classification and Anomaly Detection in Energy Production of Grid-Tied Photovoltaic System
Justin D. de Guia,Ronnie Concepcion,Hilario A. Calinao,Sandy Lauguico,Elmer P. Dadios,Ryan Rhay P. Vicerra +5 more
TL;DR: In this article, mean shift clustering was applied for pre-classification and anomaly detection of time-series data of electrical parameters from grid-tied inverter, and solar-irradiance.