Support Vector Machine with Theta-Beta Band Power Features Generated from Writing of Dyslexic Children

The classification of dyslexia using EEGrequires the detection of subtle differences between groups of children in an environment that are known to be noisy and full of artifacts. It is thus necessary for the feature extraction to improve the classification. The normal and poor dyslexic are found to activate similar areas on the left hemisphere during reading and writing. With only a single feature vector of beta activation, it is difficult to distinguish the difference between the two groups. Our work here aims to examine the classification performance of normal, poor and capable dyslexic with theta-beta band power ratio as an alternative feature vector. EEG signals were recorded from 33 subjects (11 normal, 11 poor and 11 capable dyslexics) during tasks of reading and writing words and non-words. 8 electrode locations (C3, C4, FC5, FC6, P3, P4, T7, T8) on the learning pathway and hypothesized compensatory pathway in capable dyslexic were applied. Theta and beta band power features were extracted using Daubechies, Symlets and Coiflets mother wavelet function with different orders. These are then served as inputs to linear and RBF kernel SVM classifier, where performance is measured by Area Under Curve(AUC) of Receiver Operating Characteristic (ROC) graph. Result shows the highest average AUC is 0.8668 for linear SVM with features extracted from Symlets of order 2, while 0.9838 for RBF kernel SVM with features extracted from Daubechies of order 6. From boxplot, the normal subjects are found to have a lower theta-beta ratio of 2.5:1, as compared to that of poor and capable dyslexic, ranging between 3 to 5, for all the electrodes

Machine learning and deep learning performance in classifying dyslexic children’s electroencephalogram during writing

Dyslexia is a form of learning disability that causes a child to have difficulties in writing alphabets, reading words, and doing mathematics. Early identification of dyslexia is important to provide early intervention to improve learning disabilities. This study was carried out to differentiate EEG signals of poor dyslexic, capable dyslexic, and normal children during writing using machine learning and deep learning. three machine learning algorithms were studied: k-nearest neighbors (KNN), support vector machine (SVM), and extreme learning machine (ELM) with input features from coefficients of beta and theta band power extracted using discrete wavelet transform (DWT). As for the deep learning (DL) algorithm, long short-term memory (LSTM) architecture was employed. The kernel parameters of the classifiers were optimized to achieve high classification accuracy. Results showed that db8 achieved the greatest classification accuracy for all classifiers. Support vector machine with radial basis function kernel yields the highest accuracy which is 88% than other classifiers. The support vector machine with radial basis function kernel with db8 could be employed in determining the dyslexic children’s levels objectively during writing

SIGNAL PROCESSING FOR RAMAN SPECTRA FOR DISEASE DETECTION

Raman Spectroscopy enables in-depth study into the molecular structure of solid, liquid and gasses from its scattering spectrum. As such, the spectrum could offer a biochemical fingerprint to identify unknown molecules. Surface Enhanced Raman Spectroscopy (SERS) amplifies the weak Raman signal by 10+3 to 10+7 times, revolutionary making the method appealing to the research community. SERS has been proven useful for disease detection from a medium such as a cell, serum, urine, plasma, saliva, tears. The spectra displayed are noisy and complicated by the presence of other molecules, besides the targeted one. Moreover, the difference between the infected and controlled samples is far too minute for detection by the naked human eyes. Hence, signal processing techniques are found crucial to single out fingerprint of the target molecule from biological spectra. Our work here examines signal processing techniques attempted on SERS spectra for disease detection, such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Artificial Neural Network (ANN), Support Vector Machine (SVM) and Logistic Regression Analysis (LRA). It is found that PCA-LDA is the most popular (45%), ensued by PCA-ANN (33%) and SVM (22%). PCA-SVM yields the highest in accuracy (99.9%), followed by PCA-ANN (98%) and LRA (97%). PCA-LDA and SVM score the highest in both sensitivity-specificity.Keywords: Raman Spectra, Surface Enhanced Raman Spectroscopy (SERS), Neural Network (NN), Support Vector Machine (SVM), Logistic Regression Analysis (LRA), Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA)

Domestic electrical appliances consumption: the case of Centre of Foundation Studies for Agricultural Science UPM students

The electrical consumption for domestic applications in Peninsular Malaysia is investigated in this paper. The study is performed among the homes of Physics students at Centre of Foundation Studies for Agricultural Science, Universiti Putra Malaysia (UPM). The approximately 500 homes in Peninsular Malaysia are selected in this study, where the electricity in this region is governed by Tenaga Nasional Berhad (TNB). The purpose of this study is to recognize the electrical usage for the various types of household electrical appliances, together with their cost which contribute to the amount of electric bill. As a result, this study will be able to determine which household appliances consumes the most energy and cost. Thus, the paper also includes several practical ways to conserve electricity

An assessment on the auction market in Selangor

The first auction legislation in Malaysia was enacted in the states of Penang and Malacca in 1906. The real estate market including auction market is considered an effective method in selling real estate properties (Chyi, 2015). Auction is one method in selling property in a quick way but this method is consider as force sales as most properties sold due to owner unable to pay the monthly mortgage to the bank. There are advantages of buying property in auction market where the buyer can get the property cheaper than market value and the property usually has a matured environment. The downside of purchasing an auction property is that the buyer is not allowed to view or inspect the inside of the property and sometimes the property is not vacant by the owner. The buyer need to conduct their own research first if they have the intention to buy auctioned property. Based on literatures, there are various considerations for buying auction properties such as the type of properties, the location, value, existing conditions, the existing facilities and infrastructure and many more. This aim of the study is to assess the issues and problems faced by buyers in housing auction market in Selangor. The objectives were to identify the preferred location among the respondents in selection their bidding units. It was found that among respondents, the most preferred location of auction properties was in Gombak. In term of issues and problem in buying auction properties, respondents claimed the issues are the long period of time in getting loan approved and released payment , the title and ownership issues, the units are still occupied by owner, the conditions of units and the overall cost that need to be prepare by buyers. It is hoped that this paper would guide decision maker and government in planning of the overall housing market in Malaysia as properties being auctioned is getting higher over time

An Assessment on the Housing Auction Market in Selangor

The first auction legislation in Malaysia was enacted in the states of Penang and Malacca in 1906. The real estate market including auction market is considered an effective method in selling real estate properties (Chyi, 2015). Auction is one method in selling property in a quick way but this method is consider as force sales as most properties sold due to owner unable to pay the monthly mortgage to the bank. There are advantages of buying property in auction market where the buyer can get the property cheaper than market value and the property usually has a matured environment. The downside of purchasing an auction property is that the buyer is not allowed to view or inspect the inside of the property and sometimes the property is not vacant by the owner. The buyer need to conduct their own research first if they have the intention to buy auctioned property. Based on literature, there are various considerations for buying auction properties such as the type of properties, the location, value, existing conditions, the existing facilities and infrastructure and many more. This aim of the study is to assess the issues and problems faced by buyers in housing auction market in Selangor. The objectives were to identify the preferred location among the respondents in selection their bidding units. It was found that among respondents, the most preferred location of auction properties was in Gombak. In term of issues and problem in buying auction properties, respondents claimed the issues are the long period of time in getting loan approved and released payment , the title and ownership issues, the units are still occupied by owner, the conditions of units and the overall cost that need to be prepare by buyers. It is hoped that this paper would guide decision maker and government in planning of the overall housing market in Malaysia as properties being auctioned is getting higher over time

Kajian Cadangan Penubuhan Semula Lembaga Kemajuan Pahang Tenggara (DARA) Dan Lembaga Kemajuan Wilayah Jengka (LKWJ)

Lembaga Kemajuan Pahang Tenggara (DARA) dan Lembaga Kemajuan Wilayah Jengka (LKWJ) dahulunya merupakan agensi di bawah Kementerian Pembangunan Luar Bandar (KPLB) yang ditubuhkan di bawah Akta Lembaga Kemajuan Pahang Tenggara 1972 (Akta 68) dan Akta Lembaga Kemajuan Wilayah Jengka 1983 (Akta 285). Penubuhan kedua-dua Lembaga Kemajuan Wilayah (LKW) ini merupakan satu strategi pembangunan terancang Kerajaan untuk mempercepatkan pembangunan wilayah mundur kearah mencapai keseimbangan pembangunan antara wilayah

Performance and emission characteristics of a spark ignition engine fuelled with butanol isomer-gasoline blends

The heavy reliance on petroleum-derived fuels such as gasoline in the transportation sector is one of the major causes of environmental pollution. For this reason, there is a critical need to develop cleaner alternative fuels. Butanol is an alcohol with four different isomers that can be blended with gasoline to produce cleaner alternative fuels because of their favourable physicochemical properties compared to ethanol. This study examined the effect of butanol isomer-gasoline blends on the performance and emission characteristics of a spark ignition engine. The butanol isomers; n-butanol, sec-butanol, tert-butanol and isobutanol are mixed with pure gasoline at a volume fraction of 20 vol%, and the physicochemical properties of these blends are measured. Tests are conducted on a SI engine at full throttle condition within an engine speed range of 1000–5000 rpm. The results show that there is a significant increase in the engine torque, brake power, brake specific fuel consumption and CO2 emissions with respect to those for pure gasoline. The butanol isomers-gasoline blends give slightly higher brake thermal efficiency and exhaust gas temperature than pure gasoline at higher engine speeds. The iBu20 blend (20 vol% of isobutanol in gasoline) gives the highest engine torque, brake power and brake thermal efficiency among all of the blends tested in this study. The isobutanol and n-butanol blend results in the lowest CO and HC emissions, respectively. In addition, all of the butanol isomer-gasoline blends yield lower NO emissions except for the isobutanol-gasoline blend