Relevant test set using feature selection algorithm for early detection of dyslexia

The objective of feature selection is to find the most relevant features for classification. Thus, the dimensionality of the information will be reduced and may improve classification’s accuracy. This paper proposed a minimum set of relevant questions that can be used for early detection of dyslexia. In this research, we investigated and proposed a feature selection algorithm that is correlation based feature selection (CFS) and generate classification modelsbased on five different classifiers namely Bayes Net, Simple Logistic and Decision Table. This paper used dataset collected from a computer based screening test developed consists of 50 questions. The result shows that the new set of question suggested from the feature selection algorithm was significantly achieved 100% accuracy of classification and less time was taken for conducting screening test among students.Keywords: feature selection; dyslexic children; computer based screening test

Classification model and analysis on students’ performance

The purpose of this paper is to propose a classification model for classifying students’performance in SijilPelajaran Malaysia in order to help teachers plan suitable teachingactivities for their students based on the students’ performance. Five classifier algorithms have been used during the process which are Naïve Bayes, Random Tree, Multi Class Classifier, Conjunctive Rule and Nearest Neighbour. Data was collected from MaktabRendahSains MARA Kuala Berang, Terengganu, Malaysia starting May 2011 until December 2014. The students’ performance was evaluated based on the category of students according to their SPM Results. Parameters that contribute to students’ performance such as stream, state, gender and hometown are also investigated along with the examination data.This research shows that first semester results can be used to identify students’ performance.Keywords: educational data mining; classification model; feature selection

A classification framework for drug relapse prediction

This paper proposes a framework for relapse prediction using Artificial Neural Networkalgorithms among drug addicts at Pusat Rawatan Inabah. The data collected will be miningthrough Artificial Neural Network algorithms to generate patterns and useful knowledge andthen automatically classifying the relapse possibility. This research collaborates with PusatRawatan Inabah, which is one of the rehabilitation centers that provide a specific treatment torehabilitate the drug addicts from addiction. We expect that among the classification datamining algorithms, Artificial Intelligence Neural Network (ANN) is one of the bestalgorithms to predict relapse among drug addicts. This may help the rehabilitation center topredict relapse individually and the prediction result is hoped to prevent drug addicts fromrelapse.Keywords: classification; artificial neural network; drug addiction; Inabah rehabilitation

Churn classification model for local telecommunication company based on rough set theory

Customer care plays an important role in a company especially in managing churn for Telecommunication Company. Churn is perceived as the behaviour of a customer to leave or to terminate a service. This behaviour causes the loss of profit to companies because acquiring new customer requires higher investment compared to retaining existing ones. Thus, it is necessary to consider an efficient classification model to reduce the rate of churn. Hence, the purpose of this paper is to propose a new classification model based on the Rough Set Theory to classify customer churn. The results of the study show that the proposed Rough Set classification model outperforms the existing models and contributes to significant accuracy improvement.Keywords: customer churn; classification model; telecommunication industry; data mining;rough set

Fluvial processes in compound straight channels: a laboratory investigation

Floods are become frequent occurrence in every part of the world. The field of flood hydraulics has been keenly studied to enhance the understanding on its processes and impacts to the environment. The main impacts of frequent floods incidents are soil erosion phenomenon which leads to sedimentation problems in the drainage and river systems. It is extremely important to understand the sedimentation process and the flow behaviour patterns in the water course for post-flood events. Experimental investigations on the overbank flow in mobile bed straight channels have been undertaken. Significant changes on the bed morphology due to the changes in flow behaviour are studied. The findings on roughness coefficient, lateral distribution of stream-wise velocity, secondary currents, bed shear stress and bed formation are presented in this paper. Results show that the resistance coefficient increased with flow depth in the channel and the increments are about 32% and 42% for floodplain and main channel sections respectively

Selection of classification models from repository of model for water quality dataset

This paper proposes a new technique, Model Selection Technique (MST) for selection andranking of models from the repository of models by combining three performance measures(Acc, TPR and TNR). This technique provides weightage to each performance measure to findthe most suitable model from the repository of models. A number of classification modelshave been generated to classify water quality using the most significant features andclassifiers such as J48, JRip and BayesNet. To validate this technique proposed, the waterquality dataset of Kinta River was used in this research. The results demonstrate that theFunction classifier is the optimal model with the most outstanding accuracy of 97.02%, TPR =0.96 and TNR = 0.98. In conclusion, MST is able to find the most relevant model from therepository of models by using weights in classifying the water quality dataset.Keywords: selection of models; water quality; classification model; models repository

Preparation, characterization and performances of photocatalytic TiO2-Ag2O/PESf membrane for methylene blue removal

This study proposed an effective method of methylene blue (MB) removal using a membrane with photocatalytic properties. The photocatalytic membrane, made of polyethersulfone (PESf) was incorporated with titanium dioxide (TiO2) and silver oxide (Ag2O) as the photocatalyst during the phase inversion process. TiO2 was synthesized using sol-gel method before being modified by Ag2O via wet pre-deposition method. The PESf/TiO2/Ag2O (PTA) membrane was characterized using scanning electron microscope coupled with elementary dispersion X-ray (SEM-EDX), X-ray diffraction analysis (XRD), attenuated Fourier transform infrared (ATR-FTIR), and ultraviolet visible near infrared (UV-vis NIR). The PTA membrane with 0.2 wt.% loading of TiO2/Ag2O shows uniform distribution of the photocatalyst materials and exhibits the highest degradation of MB at 85%. The TiO2/Ag2O presence was confirmed by the crystallinity analysis using XRD. UV-Vis NIR revealed that the band gap of TiO2 reduced from 3.2 to 2.1 eV when modified with Ag2O. This proved that membrane separation assisted with photocatalytic degradation is able to perform high degradation of MB dyes and has potential to be applied in industrial application

Flood flow characteristics and bed load transport in non-vegetated compound straight channels

Floods are the most common natural disasters in Malaysia and have damaged structures, infrastructures, crops and even causes fatalities. It may also lead to erosion and sedimentation in rivers and this will result to complex river behaviour. A hydraulic laboratory experimental study was carried out. Also, flood flow and sediment transport in straight compound channels involving flow resistance, distribution of depth-averaged velocity, stream-wise vorticity patterns, channel bed morphology and bed load transport rate in non-vegetated compound straight mobile bed channels were investigated. The finding showed that the Darcy Weisbach friction factor f increased by 40% and 54% for floodplain and main channel, respectively when relative flood flow depth increase from 0.30 to 0.50. The small bed load transport rates of 0.09 g/s and 0.03 g/s for shallow and deep overbank flows, respectively were measured due to effect of very gentle or mild channel bed slope which was fixed at a gradient of 0.1%

Preparation, characterization and performance evaluation of supported zeolite on porous glass hollow fiber for desalination application

A-type zeolite membranes were synthesized on porous glass hollow fibers that prepared using the in-situ hydrothermal process. The porous glass hollow fibers were prepared using the phase inversion and sintering technique with the addition of yttria stabilized zirconia (YSZ) to improve their porosity. The glass hollow fibers were characterized using the scanning electron microscope (SEM), Fourier transform infrared (FTIR), mechanical properties and water permeability. The porosities of pure glass hollow fiber were improved by the addition of YSZ particles, which lead to an increase in the pure water permeability. The water permeability shows that the glass hollow fiber prepared form spinning suspension E, which has 30 wt% zeolite particles and 20 wt% YSZ particles, has the highest permeability of 155.65 L m−2 hr−1 bar−1 compared to the previous work, which was only 4.0 L m−2 hr−1 bar−1. This glass hollow fiber was later used as the support for the incorporation of zeolite membrane for the desalination application. The performance of membranes is separating sodium chloride (NaCl) salt solution were tested using two different setups, namely pressure driven reverse osmosis (RO) and sweeping liquid assisted reverse osmosis (SLRO). The solute flux for 5,000 and 10,000 ppm NaCl salt solutions were 24.45 and 17.86 L m−2 hr−1, respectively. Both operations enabled the solute rejection up to 98%