S
Sandy Lauguico
Researcher at De La Salle University
Publications - 38
Citations - 419
Sandy Lauguico is an academic researcher from De La Salle University. The author has contributed to research in topics: Computer science & Fuzzy logic. The author has an hindex of 8, co-authored 36 publications receiving 155 citations.
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Proceedings ArticleDOI
Adaptive Neuro-Fuzzy Inference System on Aquaphotomics Development for Aquaponic Water Nutrient Assessments and Analyses
Sandy Lauguico,Renann G. Baldovino,Ronnie Concepcion,Jonnel Alejandrino,Rogelio Ruzcko Tobias,Elmer P. Dadios +5 more
TL;DR: In this paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based aquaphotomics approach is conducted on the study of water quality monitoring, assessment, and analysis in an aquaponics system are vital procedures in maintaining a productive and efficient ecosystem for cultivars being cultured.
Proceedings ArticleDOI
Estimation of Photosynthetic Growth Signature at the Canopy Scale Using New Genetic Algorithm-Modified Visible Band Triangular Greenness Index
Ronnie Concepcion,Sandy Lauguico,Rogelio Ruzcko Tobias,Elmer P. Dadios,Argel A. Bandala,Edwin Sybingco +5 more
TL;DR: In this paper, the authors employed GA to derive a visible band triangular greenness index (TGI) based on green waveband signal normalized TGI model called gvTGI.
Proceedings ArticleDOI
A Fuzzy Logic-Based Stock Market Trading Algorithm Using Bollinger Bands
Sandy Lauguico,Ronnie Concepcion,Jonnel Alejandrino,Dailyne Macasaet,Rogelio Ruzcko Tobias,Argel A. Bandala,Elmer P. Dadios +6 more
TL;DR: This study proposes an algorithm that undergoes a certain trading strategy using three fuzzy logic controllers that is implemented using NI LabVIEW and MATLAB, proving that the tests are yielding acceptable result.
Journal ArticleDOI
A Comparative Analysis of Machine Learning Algorithms Modeled from Machine Vision-Based Lettuce Growth Stage Classification in Smart Aquaponics
Sandy Lauguico,Ronnie Concepcion,Jonnel Alejandrino,Rogelio Ruzcko Tobias,Dailyne Macasaet,Elmer P. Dadios +5 more
TL;DR: A comparative analysis of three machine learning estimators showed that KNN having the tuned hyperparameters of n_neighbors=24, weights='distance', algorithm='auto', leaf_size = 10 was the most effective model for the given dataset, yielding a cross-validation mean accuracy of 87.06% and a classification accuracy of 91.67%.
Journal ArticleDOI
Lettuce growth stage identification based on phytomorphological variations using coupled color superpixels and multifold watershed transformation
Ronnie Concepcion,Jonnel Alejandrino,Sandy Lauguico,Rogelio Ruzcko Tobias,Edwin Sybingco,Elmer P. Dadios,Argel A. Bandala +6 more
TL;DR: In this paper, coupled color-based superpixels and multifold watershed transformation were used to segment a lettuce plant from complicated background taken from smart farm aquaponic system, and machine learning models used to classify lettuce plant growth as vegetative, head development and for harvest based on phytomorphological profile.