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Sherwin B. Sapin
Laguna State Polytechnic University

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Fire incidents visualization and pattern recognition using machine learning algorithms Jonardo R. Asor; Jefferson L. Lerios; Sherwin B. Sapin; Jocelyn O. Padallan; Chester Alexis C. Buama
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i3.pp1427-1435

Abstract

A fire incident is a devastating event that can be avoided with enough knowledge on how and when it may occur. For the past years, fire incidents have become a big problem for the Philippines, since it affects the socio-economic growth of the country. Machine learning algorithm is a well-known technique to predict and analyze data. It can also be used to recognize pattern and develop models for artificial intelligence. Pattern recognition through machine learning algorithm is already established and have proven itself accurate in different fields such as education, crime, health and many others including fire incidents. This paper aims to develop a model for recognizing patterns of fire incidents in the province of Laguna, Philippines implementing a machine learning algorithm. With the foregoing project, it is found out that a recurrent neural network shows an astonishing result in terms of pattern recognition. Further, it is also found that Calamba City is the most vulnerable area in case of fire occurrence in the Province of Laguna.
Intelligent aquaculture system for pisciculture simulation using deep learning algorithm Sherwin B. Sapin; Bryan A. Alibudbud; Paulo B. Molleno; Maureen B. Veluz; Jonardo R. Asor
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp561-568

Abstract

The project aims to develop an intelligent system for simulating pisciculture in Taal Lake in the Philippines through geographical information system and deep learning algorithm. Records of 2018-2020 from the database of Bureau of fisheries and aquatic resources IV-A-protected area management board (BFAR IVA-PAMB) was collected for model development. Deep learning algorithm model was developed and integrated to the system for time series analysis and simulation. Different technologies including tensorflow.js were used to successfully developed the intelligent system. It is found on this paper that recurrent neural network (RNN) is a good deep learning algorithm for predicting pisciculture in Taal lake. Further, it is also shown in the initial visualization of the system that barangay Sampaloc in Taal has highest rate of fish production in Taal while Tilapia nilotica sp. is the major product of the latter.
Co-Authors Bryan A. Alibudbud Chester Alexis C. Buama Jefferson L. Lerios Jocelyn O. Padallan Jonardo R. Asor Maureen B. Veluz Paulo B. Molleno