BS Computer Science students from the Institute of Information and Computing Sciences presented research papers in the 3rd International Conference on Computer and Communication Systems, held in the Nagoya Institute of Technology, Japan last April 27-30, 2018.
The paper “NH093: Prediction and Visualization of the Disaster Risks in the Philippines using Discrete Wavelet Transform (DWT), Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN)” by Ms. Donata D. Acula (adviser), Gillian Lindsay V. Alquisola, Daniel Jose A. Coronel, Bryan Matthew F. Reolope, and Julia Nicole A. Roque won the “Best Paper Presentation” award under the category Neural Network and Cloud Computing.
According to the submitted abstract, “this study is all about predicting and visualizing the disaster risks in the Philippines brought by tropical cyclones. According to UNICEF Philippines (2017), the Philippines is highly exposed to natural hazards because it lies along the Pacific Typhoon Belt compounded by uncontrolled settlement in hazard-prone areas, high poverty rate, and failure to implement building codes and construction standards. The number of casualties resulting from these incidents can be reduced if the possible occurrence of risk can be foreseen which can help the community have awareness and form recommendations ahead. The researchers of this study
improved the results of Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) by using a de-noising model of Discrete Wavelet Transform (DWT), in predicting the disaster risk levels of the Philippine provinces in terms of casualties, damaged houses, and damaged properties. Based on the result of the study, the best model for casualty, damaged houses, and damaged properties is the DWT-ARIMA-ANN model.”
Meanwhile, two more research papers also advised by Asst. Prof. Acula were presented. The first was entitled “Prediction and Visualization of Electricity Consumption in the Philippines Using Artificial Neural Networks, Particle Swarm Optimization, and Autoregressive Integrated Moving Average.” This paper was written by Nicole Anne C. Atienza, Janica Arielle D.S. Angeles, Jave Renzo Augustine T. Jao, and Ernersto Lance T. Singzon, Jr.
The paper’s abstract read:
“Electricity is vital in the development of a country like the Philippines. The researchers of the study developed a long-term prediction model to predict and visualize the
regional electricity consumption in the Philippines through implementing Particle Swarm Optimization (PSO) instead of Backpropagation (BP) to train the Artificial Neural Networks (ANN) and implementing Autoregressive Integrated Moving Average (ARIMA) to forecast future predictors. The average prediction accuracy of BP-ANN is 85.95% while the average prediction accuracy of PSO-ANN is 92.40%. Moreover, the average accuracy of PSO-ANN and ARIMA is 96.05%. The results indicated that PSO-ANN is a better prediction model than BP-ANN and that ARIMA performed well in forecasting the future predictors. The study can be of great help to improve the electricity allocation in the Philippines.”
Finally, the paper entitled “Implementing Fact-Checking in Journalistic Articles Shared on Social Media in the Philippines Using Knowledge Graphs” was presented by Louise Aster C. Oblan, Tracy B. Pedroso, Katrine Jee V. RIosa, and Michelle Arianne R. Tolibas.
The paper’s abstract read:
In the technology age, articles with fraudulent content are rampant, especially articles shared on social media. Misinformation could just be an inaccuracy at its best, or it could lead to normalizing false information at worst. To aid the predicament, the researchers created a system that will “fact check” suspicious articles against those articles that have been deemed credible, reliable, and more accurate, in order to help fight deceiving content that may be detrimental to society. The journal regarding computational fact checking that was published by Ciampaglia, et. al. (2015) from the Indiana University in the USA entitled Computational Fact Checking from Knowledge Networks, was used as the basis and inspiration for this thesis. The researchers made use of the undirected graph (UG) together with a part-of-speech (POS) tagging algorithm to create a knowledge graph (KG) that would serve as the center of the system. Five different POS tagging algorithms were paired with the UG to assess which combination would yield the best results, these are Conditional Random Fields, Logistic Regression, a Hybrid of CRF and LR, Random Forests, and K-Nearest Neighbors. Random Forests and K-Nearest Neighbors were classification algorithms used in Ciampaglia’s study. It was concluded that among the 5 pairs of UG and POS Tagging algorithms, the Hybrid of CRF and LR used as a POS tagger, together with the UG, created the most efficient KG.
The conference proceedings will be published in IEEE and will be indexed in IE Compendia and Scopus.
