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Customer Classification has important role in Customer Relationship Management (CRM) and has been applied in many industries, such as retail and manufacturing. However, there is no single model purposely created only for telecommunication wholesale segment, especially IP Transit. This research develops a model for customer classification with consideration of all aspects of customer - company relationship...
Surface electromyographic (EMG) signals have often been used in estimating upper and lower limb dynamics and kinematics for the purpose of controlling robotic devices such as robot prosthesis and finger exoskeletons. However, in estimating multiple and a high number of degrees-of-freedom (DOF) kinematics from EMG, output DOFs are usually estimated independently. In this study, we estimate finger joint...
Work on sentiment analysis has thus far been limited in the news article domain. This has mainly been caused by 1) news articles lacking a clearly defined target, 2) the difficulty in separating good and bad news from positive and negative sentiment, and 3) the seeming necessity of, and complexity in, relying on domain-specific interpretations and background knowledge. In this paper we propose, define,...
Posting online reviews and rating their satisfaction on purchased products has become an increasingly popular way to share the information for anonymous candidates who has interest in purchasing the product. In addition, people leave their interests and near-future purchasing plan on the web such as search history and search query volume. From this phenomenon, the prediction of sales performance is...
Rehabilitation robotic plays an important role in therapeutic exercises by combining robots with computer serious games into an attractive therapeutic platform. However, measuring the degree of engagement of the user is not a trivial task. The difficulty of applying question-based techniques, particularly for patients who have the speech capacity compromised due to cerebrovascular accidents, has inspired...
In order to provide a scientific basis for the resource allocation in the stage of checked baggage, improve the service efficiency of airport passenger terminal. According to the flight data of an international airport passenger terminal in 2012 May, this paper establish the BP artificial neural network and multiple regression prediction models respectively, in which the influencing factors are decided...
Sentiment analysis from large-scale networked data attracts increasing attention in recent years. Most previous works on sentiment prediction mainly focus on text or image data. However, voice is the most natural and direct way to express people's sentiments in real-time. With the rapid development of smart phone voice dialogue applications (e.g., Siri and Sogou Voice Assistant), the large-scale networked...
A methodology to retrieve soil moisture (SM) from multiinstrument remote sensing data is presented. The method uses a Neural Network (NN) to find the statistical relationship linking the input data to a reference SM dataset. The input data is composed of passive microwaves (L-band SMOS brightness temperatures), active microwaves (C-band ASCAT backscattering coefficients), and visible and infrared...
Internet has opened new interesting scenarios in the fields of commerce and marketing. In particular, the idea of e-commerce has enabled customers to perform their transactions in a faster and cheaper way than conventional markets, and it has allowed companies to increase their sales volume thanks to a world-wide visibility. However, one of the problems that can strongly affect the performance of...
This paper highlights input variable selection for neural network of the electrical system to predict the load demand in 168 hours ahead. Autocorrelation (ACF), partial autocorrelation (PACF) and cross correlation (CCF) analysis are used to identify the correlated input for the forecast model. The combination of time, time indicator, lagged load and respective weather variables are considered as forecast...
Artificial Neural Network (ANN) model has been developed to correlate age of severely osteoarthritic male and female specimens with key mechanical and structural characteristics of their trabecular bone. The complex interdependency between age, gender, compressive strength, porosity, morphology and level of interconnectivity was analysed in multi-dimensional space using a two-layer feedforward ANN...
Rapid growth of wind power generation in addition to its high penetration in electrical power systems has brought wind power prediction into play. Wind power is a complex signal for modeling and forecasting. In this paper, wind power prediction model based on neural network and imperialist competitive algorithm (ICA) is presented to forecast wind power generation of wind farm of Alberta. Finally,...
An LTE-Advanced Radio Access Network (RAN) would be able to activate an additional component carrier in a Licensed Spectrum Access (LSA) channel, allowing the mobile network operator (MNO) to boost capacity and data rates. But such a dynamic spectrum access strategy requires an effective method for estimating the impact of the LTA-Advanced RAN operation on the primary user and minimizing it. LTE-Advanced...
Feature selection (FS) of high dimensional electroencephalographic (EEG) data helps to identify and diagnose the brain conditions easily. Features can be selected with different ways where canonical correlation analysis (CCA) is one of them which are a statistical method. We employed neural network (NN) with CCA for salient features extraction of EEG data, called Neural Canonical Correlation Analysis...
Artificial Neural Networks (ANNs) are often viewed as black box. This limits the comprehensive understanding on how it deals with input neuron/data, as well as how it reached a particular decision. Input significance analysis (ISA) refers to the process of understanding these input neurons/data. And since this work is on classification problem, hence similarly, this process can also be called feature...
In this paper, we study neural network ensembles (NNE) classifier with regularized negative correlation learning (RNCL) and its application to pattern classification. In RNCL algorithm, the regularization parameter is used to control the trade off between mean square error and regularization, and to improve the ensemble's generalization ability. We propose an automatic RNCL algorithm based on gradient...
Predicting students' performance is very important if not crucial especially in engineering courses. This is to enable strategic intervention to be carried out before the students reach the higher semesters including the final semester before graduation. This paper presents a comparison study between Artificial Neural Network (ANN) and Linear Regression (LR) in predicting the academic performance...
Digital visual media is one of the most commonly used means of communication. But, with the use of low-cost editing tools, tampering and counterfeiting visual contents are increasing enormously. In almost all the Image forensic application areas, the device used for capturing the image is of utmost importance as the origin of the particular image can act as a key evidence to substantiate the legitimacy...
Accurate irradiance forecasting is one of the essential factor that helps facilitate the proliferation of grid-connected photovoltaic (GCPV) integration. In Malaysia, this topic has not been substantially explored. This paper attempts to investigate the use of neural network by using data obtained from meteorological condition measurement in Sepang, Malaysia to forecast hourly values of solar radiation...
In this study, optical character recognition (OCR) was carried out by using artificial neural network. Negative correlation learning (NCL) method was used to teach artificial neural network. Negative correlation learning, which trains artificial neural networks in groups instead of teaching them individually, is a new technique. By teaching different individual networks as only one network, different...
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