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Machine learning approach for flood risks prediction Nazim Razali; Shuhaida Ismail; Aida Mustapha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

Flood is one of main natural disaster that happens all around the globe caused law of nature. It has caused vast destruction of huge amount of properties, livestock and even loss of life. Therefore, the needs to develop an accurate and efficient flood risk prediction as an early warning system is highly essential. This study aims to develop a predictive modelling follow Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology by using Bayesian network (BN) and other Machine Learning (ML) techniques such as Decision Tree (DT), k-Nearest Neighbours (kNN) and Support Vector Machine (SVM) for flood risks prediction in Kuala Krai, Kelantan, Malaysia. The data is sourced from 5-year period between 2012 until 2016 consisting 1,827 observations. The performance of each models were compared in terms of accuracy, precision, recall and f-measure. The results showed that DT with SMOTE method performed the best compared to others by achieving 99.92% accuracy. Also, SMOTE method is found highly effective in dealing with imbalance dataset. Thus, it is hoped that the finding of this research may assist the non-government or government organization to take preventive action on flood phenomenon that commonly occurs in Malaysia due to the wet climate.

A regression approach for prediction of Youtube views Lau Tian Rui; Zehan Afizah Afif; R. D. Rohmat Saedudin; Aida Mustapha; Nazim Razali
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

YouTube has grown to be the number one video streaming platform on Internet and home to millions of content creator around the globe. Predicting the potential amount of YouTube views has proven to be extremely important for helping content creator to understand what type of videos the audience prefers to watch. In this paper, we will be introducing two types of regression models for predicting the total number of views a YouTube video can get based on the statistic that are available to our disposal. The dataset we will be using are released by YouTube to the public. The accuracy of both models are then compared by evaluating the mean absolute error and relative absolute error taken from the result of our experiment. The results showed that Ordinary Least Square method is more capable as compared to the Online Gradient Descent Method in providing a more accurate output because the algorithm allows us to find a gradient that is close as possible to the dependent variables despite having an only above average prediction.

A regression approach for prediction of Youtube views Lau Tian Rui; Zehan Afizah Afif; R. D. Rohmat Saedudin; Aida Mustapha; Nazim Razali
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

YouTube has grown to be the number one video streaming platform on Internet and home to millions of content creator around the globe. Predicting the potential amount of YouTube views has proven to be extremely important for helping content creator to understand what type of videos the audience prefers to watch. In this paper, we will be introducing two types of regression models for predicting the total number of views a YouTube video can get based on the statistic that are available to our disposal. The dataset we will be using are released by YouTube to the public. The accuracy of both models are then compared by evaluating the mean absolute error and relative absolute error taken from the result of our experiment. The results showed that Ordinary Least Square method is more capable as compared to the Online Gradient Descent Method in providing a more accurate output because the algorithm allows us to find a gradient that is close as possible to the dependent variables despite having an only above average prediction.

Pattern Analysis of Goals Scored in Malaysia Super League 2015 Nazim Razali; Aida Mustapha; Filipe Manuel Clemente; Md Fauzi Ahmad; Mohamad Aizi Salamat
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 2: August 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v11.i2.pp718-724

This paper is set to study the pattern of goals scored across 132 matches in 2015 Malaysian Super League (MSL). Despite of well-known researches in European football, no information in Malaysian football teams has been done consistently. To identify the patterns, this research focuses on goals scored based on timing that includes scoring frequency, the impact of first goal achieved by team towards the final match outcome, as well as the impact of having home advantage for matches during MSL 2015. The findings of this paper will provide useful information on MSL besides helping coaches with fresh insight for creating effective training and tactical plan as the time progressed during match. At the same time, players will also be more prepared for a consistent performance especially when training for matches as the visiting teams.

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