Forecasting electricity consumption using SARIMA method in IBM SPSS software

Forecasting is a prediction of future values based on historical data. It can be conducted using various methods such as statistical methods or machine learning techniques. Electricity is a necessity of modern life. Hence, accurate forecasting of electricity demand is important. Overestimation will cause a waste of energy but underestimation leads to higher operation costs. Univesity Tun Hussein Onn Malaysia (UTHM) is a developing Malaysian technical university, therefore there is a need to forecast UTHM electricity consumption for future decisions on generating electric power, load switching, and infrastructure development. The monthly UTHM electricity consumption data exhibits seasonality-periodic fluctuations. Thus, the seasonal Autoregressive Integrated Moving Average (SARIMA) method was applied in IBM SPSS software to predict UTHM electricity consumption for 2019 via Box-Jenkins method and Expert Modeler. There were a total of 120 observations taken from January year 2009 to December year 2018 to build the models. The best model from both methods is SARIMA(0, 1, 1)(0, 1, 1)12. It was found that the result through the Box-Jenkins method is approximately the same with the result generated through Expert Modeler in SPSS with MAPE of 8.4%

The method of lines solution of the Forced Korteweg-De Vries-Burgers equation (FKdVB)

In this paper, the application of the method of lines (MOL) to the Forced Korteweg-de Vries-Burgers equation with variable coefficient (FKdVB) is presented. The MOL is a powerful technique for solving partial differential equations by typically using finite-difference approximations for the spatial derivatives and ordinary differential equations (ODEs) for the time derivative. The MOL approach of the FKdVB equation led to a system of ODEs. Solution of the system of ODEs was obtained by applying the Fourth-OrderRunge-Kutta (RK4) method. In order to show the accuracy of the presented method, the numerical solutions obtained were compared with its progressive wave solution in terms of maximum absolute error at certain times. It was found that the maximum absolute errors are in theorder of 10-6

Method of lines and pseudospectral solutions of the forced korteweg-de vries equation with variable coefficients arises in elastic tube

In this paper, we solved the forced Korteweg-de vries (FKdV) with variable coef- ficient arises in nonlinear wave propagation in an elastic tube with a symmetrical stenosis filled with an inviscid fluid by two numerical methods, namely method of lines and pseudospectral method. We then compared both numerical solutions with its progressive wave solution. Both methods solve FKdV equation with maximum absolute errors of 10−4

Solving initial-Value Problem of the First-Order Differential Equation by Euler’s Method using Casio fx 570EX Classwiz Scientific Calculator

Solving numerical methods manually without using a scientific calculator or using scientific calculator traditionally without input the iterative formula into the calculator can be quite tedious and boring due to its repetitive calculations. There is a series of studies discussing the implementation of numerical methods using Excel spread sheet as well as Casio scientific calculators, such as Casio fx-570 MS scientific calculator, Casio fx-570 ES scientific calculator and Casio fx-570 ES plus scientific calculator. Even though Excel spreadsheet made the implementation of numerical methods easier to be understood by the learner of numerical methods, but it is not port classrooms (teaching and learning purposes) and examination hall (evaluation purpose). All the three mentioned models of scientific calculators allow the input of iterative formula but users still need to rein put the new inputs then press CALC bu iterative solutions. The new model of Casio fx FX classwiz scientific calculator offers a spreadsheet ability of 45 rows and 5 columns which made the implementation of the numerical methods much easier if compared to previous models. Hence, in this paper, we solved the initial-value problem (IVP) of the first order ordinary differential equations by the Euler’s method using Casio fx-570 FX class calculator for the classroom and examination purposes

Simulation of Internal Undular Bores in Rapidly Varying Topography

This paper intends to look at the influence of rapidly varying regions on the propagation of internal undular bores in a two-layer fluid flow. Internal undular bores have been observed in the ocean around the world. The appropriate mathematical model to describe the evolution of internal undular bores in a stratified fluid is the variable-coefficient extended Korteweg-de Vries equation. The governing equation is solved numerically using the method of lines to simulate the propagation of internal undular bores. Our numerical results show that the effects of rapidly varying topography lead to adiabatic and nonadiabatic deformation of the internal undular bores, including the generation of solitary wavetrain, generation of nonlinear wavetrain and generation of rarefaction wave. Besides, multi-phase behaviour is also observed during the evolution

The method of lines solution of the Forced Korteweg-de Vries-Burgers equation

In this paper, the application of the method of lines (MOL) to the Forced Korteweg-de Vries-Burgers equation with variable coefficient (FKdVB) is presented. The MOL is a powerful technique for solving partial differential equations by typically using finite-difference approximations for the spatial derivatives and ordinary differ- ential equations (ODEs) for the time derivative. The MOL approach of the FKdVB equation leads to a system of ODEs. The solution of the system of ODEs is obtained by applying the Fourth-Order Runge-Kutta (RK4) method. The numerical solution obtained is then compared with its progressive wave solution in order to show the accuracy of the MOL method

Phishing Email Detection Technique by using Hybrid Features

Phishing emails is growing at an alarming rate in this few years. It has caused tremendous financial losses to internet users. Phishing techniques getting more advance everyday and this has created great challenge to the existing anti-phishing techniques. Hence, in this paper, we proposed to detect phishing emails through hybrids features. The hybrid features consist of content-based, URL-based, and behaviorbased features. Based on a set of 500 phishing emails and 500 legitimate emails, the proposed method achieved overall accuracy of 97.25% and error rate of 2.75%. This promising result verify the effectiveness of the proposed hybrid features in detecting phishing email

Phishing detection via identification of website identity

In this paper, we propose an anti-phishing method to protect Internet users from the phishing attacks. The scope of our study is on the Internet phishing, particularly focusing on the detection of phishing website. In order to do that, our proposed method will render a screenshot of the webpage and segment the region of interest, which consists of the website logo. Next, we will utilize Google image database to identify the website identity based on the segmented website logo. During the identification process, we employ the content-based image retrieval mechanism provided in Google Image Search engine to locate the most similar logo from Google image database. The returned results will reveal the real identity of the website. With the real identity, we can differentiate a phishing website from the legitimate website by assessing the domain name of the query website. The conducted experiments show promising results and our findings prove that we can effectively detect a phishing website when we manage to determine the real identity of a websit

Modelling COVID-19 Hotspot Using Bipartite Network Approach

COVID-19 causes a jarring impact on the livelihoods of people in Malaysia and globally. To prevent an outbreak in the community, identifying the likely sources of infection (hotspots) of COVID-19 is important. The goal of this study is to formulate a bipartite network model of COVID-19 transmissions by incorporating patient mobility data to address the assumption on population homogeneity made in the conventional models and focus on indirect transmission. Two types of nodes – human and location – are the main concern in the research scenario. 21 location nodes and 31 human nodes are identified from a patient’s pre-processed mobility data. The parameters used in this study for location node and human node quantifications are the ventilation rate of a location and the environmental properties of the location that affect the stability of the virus such as temperature and relative humidity. The summation rule is applied to quantify all nodes in the network and the link weight between the human node and the location node. The ranking of location and human nodes in this network is computed using a web search algorithm. This model is considered verified as the error obtained from the comparison made between the benchmark model and the COVID-19 bipartite network model is small. As a result, the higher ranking of the location is denoted as a hotspot in this study, and for a human node attached to this node will be ranked higher in the human node ranking. Consequently, the hotspot has a higher risk of transmission compared to other locations. These findings are proposed to provide a framework for public health authorities to identify the sources of infection and high-risk groups of people in the COVID-19 cases to control the transmission at the initial stage