Prediction of Learning Disabilities in School Age Children using SVM and Decision Tree

This paper highlights the prediction of Learning Disabilities (LD) in school-age children using two classification methods, Support Vector Machine (SVM) and Decision Tree (DT), with an emphasis on applications of data mining. About 10% of children enrolled in school have a learning disability. Learning disability prediction in school age children is a very complicated task because it tends to be identified in elementary school where there is no one sign to be identified. By using any of the two classification methods, SVM and DT, we can easily and accurately predict LD in any child. Also, we can determine the merits and demerits of these two classifiers and the best one can be selected for the use in the relevant field. In this study, Sequential Minimal Optimization (SMO) algorithm is used in performing SVM and J48 algorithm is used in constructing decision trees.

A Comparative study of HMM and SVM in Malayalam Digit Recognition

A primary medium for the human beings to communicate through language is Speech. Automatic Speech Recognition is wide spread today. Recognizing single digits is vital to a number of applications such as voice dialling of telephone numbers, automatic data entry, credit card entry, PIN (personal identification number) entry, entry of access codes for transactions, etc. In this paper we present a comparative study of SVM (Support Vector Machine) and HMM (Hidden Markov Model) to recognize and identify the digits used in Malayalam speech.

Characteristics of Upper Troposphere and Lower Stratosphere in Relation to Asian Summer Monsoon

The present work is an attempt to understand the characteristics of the upper troposphere and lower stratosphere over the Asian summer monsoon region, more specifically over the Indian subcontinent. Mainly three important parameters are taken such as zonal wind, temperature and ozone over the UT/LS of the Asian summer monsoon region. It made a detailed study of its interannual variability and characteristics of theses parameters during the Indian summer monsoon period. Monthly values of zonal wind and temperature from the NCEP/NCAR reanalysis for the period 1960-2002 are used for the present study. Also the daily overpass total ozone data for the 12 Indian stations (from low latitude to high latitudes) from the TOMS Nimbus 7 satellite for the period 1979 to 1992 were also used to understand the total ozone variation over the Indian region. The study reveals that if QBO phases in the stratosphere is easterly or weak westerly then the respective monsoon is found to be DRY or below Normal . On the other hand, if the phase is westerly or weak easterly the respective Indian summer monsoon is noted as a WET year. This connection of stratospheric QBO phases and Indian summer monsoon gives more insight in to the long-term predictions of Indian summer monsoon rainfall. Wavelet analysis and EOF methods are the two advanced statistical techniques used in the present study to explore more information of the zonal wind that from the smaller scale to higher scale variability over the Asian summer monsoon region. The interannual variability of temperature for different stratospheric and tropospheric levels over the Asian summer monsoon region have been studied. An attempt has been made to understand the total ozone characteristics and its interannual variablilty over 12 Indian stations spread from south latitudes to north latitudes. Finally it found that the upper troposphere and lower stratosphere contribute significantly to monsoon variability and climate changes. It is also observed that there exists a link between the stratospheric QBO and Indian summer monsoon

Investigations on Structural response of Water Backed Perforated plates

The study envisaged herein contains the numerical investigations on Perforated Plate (PP) as well as numerical and experimental investigations on Perforated Plate with Lining (PPL) which has a variety of applications in underwater engineering especially related to defence applications. Finite element method has been adopted as the tool for analysis of PP and PPL. The commercial software ANSYS has been used for static and free vibration response evaluation, whereas ANSYS LS-DYNA has been used for shock analysis. SHELL63, SHELL93, SOLID45, SOLSH190, BEAM188 and FLUID30 finite elements available in the ANSYS library as well as SHELL193 and SOLID194 available in the ANSYS LS-DYNA library have been made use of. Unit cell of the PP and PPL which is a miniature of the original plate with 16 perforations have been used. Based upon the convergence characteristics, the utility of SHELL63 element for the analysis of PP and PPL, and the required mesh density are brought out. The effect of perforation, geometry and orientation of perforation, boundary conditions and lining plate are investigated for various configurations. Stress concentration and deflection factor are also studied. Based on these investigations, stadium geometry perforation with horizontal orientation is recommended for further analysis.Linear and nonlinear static analysis of PP and PPL subjected to unit normal pressure has been carried out besides the free vibration analysis. Shock analysis has also been carried out on these structural components. The analytical model measures 0.9m x 0.9m with stiffener of 0.3m interval. The influence of finite element, boundary conditions, and lining plate on linear static response has been estimated and presented. Comparison of behavior of PP and PPL in the nonlinear strain regime has been made using geometric nonlinear analysis. Free vibration analysis of the PP and PPL has been carried out ‘in vacuum’ condition and in water backed condition, and the influence of water backed condition and effect of perforation on natural frequency have been investigated.Based upon the studies on the vibration characteristics of NPP, PP and PPL in water backed condition and ‘in vacuum’ condition, the reduction in the natural frequency of the plate in immersed condition has been rightly brought out. The necessity to introduce the effect of water medium in the analysis of water backed underwater structure has been highlighted.Shock analysis of PP and PPL for three explosives viz., PEK, TNT and C4 has been carried out and deflection and stresses on plate as well as free field pressure have been estimated using ANSYS LS-DYNA. The effect of perforations and the effect of lining plate have been predicted. Experimental investigations of the measurement of free field pressure using PPL have been conducted in a shock tank. Free field pressure has been measured and has been validated with finite element analysis results. Besides, an experiment has been carried out on PPL, for the comparison of the static deflection predicted by finite element analysis.The distribution of the free field pressure and the estimation of differential pressure from experimentation and the provision for treating the differential pressure as the resistance, as a part of the design load for PPL, has been brought out.

Legal Control of Industrial Pollution

Industries constitute the main spring of development. Without industrial development no country could reach a stage in which a decent living for its citizens would be achieved. Increasing production to meet the basic needs of society augmented scientific invention and machine oriented industrial order.Environmental pollution ls a burning global issue. It is more serious and dangerous than terrorism. Started with the discovery of fire and development of civilization. Pollution went unnoticed throughout the centuries of human growth until its adverse effects on human environment become explicit.National concern tor environment started in our country only atter the cause of protection of environment received global attention. At present legal control ot industrial pollution is in a scattered framework of piece meal processes with overlapping provisions and authorities.Environmental protection- should be an item not only in the concurrent list of schedule 7 to the Constitution but also in the list of matters entrusted to the panchayati institutions in the Schedule 11. It is heartening to note that so far as municipalities are concerned the Constitution of India lives up to the expectation. In the wake of New Industrial Policy based on liberalisation a long list of small scale industries fall outside the purview of environmental clearance. The Indian concept of environmental im»act assessment introduced under the Environment Act by notification excludes the entire gamut of small scale industries and r.elates only to scheduled industries covered by the notifica~ion. Most of them are subjected to ETA only it the investment goes above ~.50 crores. This provision dilutes the impact assessment considerably A mandatory impact assessment with public partiCipation and with provision for a review by specialized environmental courts will eliminate the possible evils of this judicial passiveness.

PERFORMANCE OF DIFFERENT CLASSIFIERS IN SPEECH RECOGNITION

Speech is the most natural means of communication among human beings and speech processing and recognition are intensive areas of research for the last five decades. Since speech recognition is a pattern recognition problem, classification is an important part of any speech recognition system. In this work, a speech recognition system is developed for recognizing speaker independent spoken digits in Malayalam. Voice signals are sampled directly from the microphone. The proposed method is implemented for 1000 speakers uttering 10 digits each. Since the speech signals are affected by background noise, the signals are tuned by removing the noise from it using wavelet denoising method based on Soft Thresholding. Here, the features from the signals are extracted using Discrete Wavelet Transforms (DWT) because they are well suitable for processing non-stationary signals like speech. This is due to their multi- resolutional, multi-scale analysis characteristics. Speech recognition is a multiclass classification problem. So, the feature vector set obtained are classified using three classifiers namely, Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Naive Bayes classifiers which are capable of handling multiclasses. During classification stage, the input feature vector data is trained using information relating to known patterns and then they are tested using the test data set. The performances of all these classifiers are evaluated based on recognition accuracy. All the three methods produced good recognition accuracy. DWT and ANN produced a recognition accuracy of 89%, SVM and DWT combination produced an accuracy of 86.6% and Naive Bayes and DWT combination produced an accuracy of 83.5%. ANN is found to be better among the three methods.

Prediction of Key Symptoms of Learning Disabilities in School-Age Children Using Rough Sets

This paper highlights the prediction of learning disabilities (LD) in school-age children using rough set theory (RST) with an emphasis on application of data mining. In rough sets, data analysis start from a data table called an information system, which contains data about objects of interest, characterized in terms of attributes. These attributes consist of the properties of learning disabilities. By finding the relationship between these attributes, the redundant attributes can be eliminated and core attributes determined. Also, rule mining is performed in rough sets using the algorithm LEM1. The prediction of LD is accurately done by using Rosetta, the rough set tool kit for analysis of data. The result obtained from this study is compared with the output of a similar study conducted by us using Support Vector Machine (SVM) with Sequential Minimal Optimisation (SMO) algorithm. It is found that, using the concepts of reduct and global covering, we can easily predict the learning disabilities in children

A System for Offline Recognition of Handwritten Characters in Malayalam Script

In this paper, we propose a handwritten character recognition system for Malayalam language. The feature extraction phase consists of gradient and curvature calculation and dimensionality reduction using Principal Component Analysis. Directional information from the arc tangent of gradient is used as gradient feature. Strength of gradient in curvature direction is used as the curvature feature. The proposed system uses a combination of gradient and curvature feature in reduced dimension as the feature vector. For classification, discriminative power of Support Vector Machine (SVM) is evaluated. The results reveal that SVM with Radial Basis Function (RBF) kernel yield the best performance with 96.28% and 97.96% of accuracy in two different datasets. This is the highest accuracy ever reported on these datasets

Spectral clustering independent component analysis for tissue classification from brain MRI

A spectral angle based feature extraction method, Spectral Clustering Independent Component Analysis (SC-ICA), is proposed in this work to improve the brain tissue classification from Magnetic Resonance Images (MRI). SC-ICA provides equal priority to global and local features; thereby it tries to resolve the inefficiency of conventional approaches in abnormal tissue extraction. First, input multispectral MRI is divided into different clusters by a spectral distance based clustering. Then, Independent Component Analysis (ICA) is applied on the clustered data, in conjunction with Support Vector Machines (SVM) for brain tissue analysis. Normal and abnormal datasets, consisting of real and synthetic T1-weighted, T2-weighted and proton density/fluid-attenuated inversion recovery images, were used to evaluate the performance of the new method. Comparative analysis with ICA based SVM and other conventional classifiers established the stability and efficiency of SC-ICA based classification, especially in reproduction of small abnormalities. Clinical abnormal case analysis demonstrated it through the highest Tanimoto Index/accuracy values, 0.75/98.8%, observed against ICA based SVM results, 0.17/96.1%, for reproduced lesions. Experimental results recommend the proposed method as a promising approach in clinical and pathological studies of brain diseases

Influence of dopants on the properties of maghemite

Preparatiun or aci cular maghemi te containing dopan ls li ke Mg. i and Gd and their characleris,llion us ing different analytica l techniques have been reported. These in vestigat ions reveal that the addition of dopants li ke Mg. Ni and Gd modifies the magnetic properties without cfrecting any structural changes. The opt ical bandgaps of these doped compositions have ,li S() hecn determ ined. Evidence is ,li so av,li lab le from spectroscopic investi gations suggest ing thalmaghcllli te prepared vi,l the ()xa l,ltc precursor route does nm exhi bit a hydrogen fe rrite structure

Soft Computing Approach for Optimization of siRNA Efficiency Prediction for Post-transcriptional Gene Silencing

Post-transcriptional gene silencing by RNA interference is mediated by small interfering RNA called siRNA. This gene silencing mechanism can be exploited therapeutically to a wide variety of disease-associated targets, especially in AIDS, neurodegenerative diseases, cholesterol and cancer on mice with the hope of extending these approaches to treat humans. Over the recent past, a significant amount of work has been undertaken to understand the gene silencing mediated by exogenous siRNA. The design of efficient exogenous siRNA sequences is challenging because of many issues related to siRNA. While designing efficient siRNA, target mRNAs must be selected such that their corresponding siRNAs are likely to be efficient against that target and unlikely to accidentally silence other transcripts due to sequence similarity. So before doing gene silencing by siRNAs, it is essential to analyze their off-target effects in addition to their inhibition efficiency against a particular target. Hence designing exogenous siRNA with good knock-down efficiency and target specificity is an area of concern to be addressed. Some methods have been developed already by considering both inhibition efficiency and off-target possibility of siRNA against agene. Out of these methods, only a few have achieved good inhibition efficiency, specificity and sensitivity. The main focus of this thesis is to develop computational methods to optimize the efficiency of siRNA in terms of “inhibition capacity and off-target possibility” against target mRNAs with improved efficacy, which may be useful in the area of gene silencing and drug design for tumor development. This study aims to investigate the currently available siRNA prediction approaches and to devise a better computational approach to tackle the problem of siRNA efficacy by inhibition capacity and off-target possibility. The strength and limitations of the available approaches are investigated and taken into consideration for making improved solution. Thus the approaches proposed in this study extend some of the good scoring previous state of the art techniques by incorporating machine learning and statistical approaches and thermodynamic features like whole stacking energy to improve the prediction accuracy, inhibition efficiency, sensitivity and specificity. Here, we propose one Support Vector Machine (SVM) model, and two Artificial Neural Network (ANN) models for siRNA efficiency prediction. In SVM model, the classification property is used to classify whether the siRNA is efficient or inefficient in silencing a target gene. The first ANNmodel, named siRNA Designer, is used for optimizing the inhibition efficiency of siRNA against target genes. The second ANN model, named Optimized siRNA Designer, OpsiD, produces efficient siRNAs with high inhibition efficiency to degrade target genes with improved sensitivity-specificity, and identifies the off-target knockdown possibility of siRNA against non-target genes. The models are trained and tested against a large data set of siRNA sequences. The validations are conducted using Pearson Correlation Coefficient, Mathews Correlation Coefficient, Receiver Operating Characteristic analysis, Accuracy of prediction, Sensitivity and Specificity. It is found that the approach, OpsiD, is capable of predicting the inhibition capacity of siRNA against a target mRNA with improved results over the state of the art techniques. Also we are able to understand the influence of whole stacking energy on efficiency of siRNA. The model is further improved by including the ability to identify the “off-target possibility” of predicted siRNA on non-target genes. Thus the proposed model, OpsiD, can predict optimized siRNA by considering both “inhibition efficiency on target genes and off-target possibility on non-target genes”, with improved inhibition efficiency, specificity and sensitivity. Since we have taken efforts to optimize the siRNA efficacy in terms of “inhibition efficiency and offtarget possibility”, we hope that the risk of “off-target effect” while doing gene silencing in various bioinformatics fields can be overcome to a great extent. These findings may provide new insights into cancer diagnosis, prognosis and therapy by gene silencing. The approach may be found useful for designing exogenous siRNA for therapeutic applications and gene silencing techniques in different areas of bioinformatics.