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CPS1060 Taik Guan T. et al.
growth by about 1.38% [1]. According to the International Telecommunication
Union’s (ITU) 2012 report, a 10% increase in broadband penetration will
contribute to a 0.7% increase in Malaysia’s GDP [2]. Many researches have
addressed the different magnitudes of the positive impacts of BS, depending
on the current economic situations of those geographic areas. However, only
the developing and developed economies are normally assumed to be
commercially viable for private investments. The underdeveloped economies
require legislative support for broadband development because quick profits
are unattainable in these areas. Collectively, the various research results
provide confidence to the privately-owned telco to provide broadband
services in urban and selected suburban but not rural areas. Furthermore,
there are no empirical models made available for the telco to predict the
economic potential of certain geographic areas so that they can prioritize their
network investments in promising rural areas. As a result, the telco continues
using the ROI model to strategize their network investment plans and to
deploy their BS in urban or suburban areas only.
This paper aims to provide a framework of machine learning technique
(MLT) which can help the telco and government agencies to predict if a
selected geographic area has the socioeconomic potential for broadband
investments. The proposed machine learning technique is an SVM which is a
classifier that predicts the socioeconomic potential in correspondence to the
local features or characteristics of a geographic area. In this framework, a
curve-fitting technique will be applied to formulate the empirical model by
using prototyping data from the World Bank databank. The empirical model
is then used as a fitness function for a GA to generate large virtual data to
train, validate and test the SVM. Real-life field data for geographic areas in
Malaysia is then provided to the SVM to predict which areas have the
socioeconomic potential for broadband investment. The curve-fitting
technique is a statistical model to formulate the empirical model by
establishing the interdependency of the geographical features with the
socioeconomic status of geographic areas. The curve-fitting examines the
relationship between given sets of independent and dependent variable
features. The fitted curves obtained can be used in data visualization, and
finding relationships between two or more variable features [3].
The data on local features of rural areas are lacking or difficult to obtain.
Hence data on local features of countries from the World Bank database is
applied in this research. The availability of data from the World Bank’s
database is limited by the number of countries worldwide. The few hundred
data sets in the World Bank’s database provide a small sample available to
train and optimize the accuracy of the SVM. The limited sample size will affect
the accuracy of machine learning. Large virtual samples are essential to
address the issue of insufficient raw data, which will help overcome the
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