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The brain is one of the vital organ of the body where it is the custodian of the involuntary and voluntary actions like walking, vision, memory. Now a days the most common brain disorders are Alzheimer's disease, Epilepsy (paralysis or stroke), tumors, brain tumors. Early diagnosis and proper treatment of brain tumors is required. The Computer Aided Diagnostic tools (CAD) can be used by the doctor...
As the high-tech production system gets more complex, Equipment Condition Diagnosis (ECD) in semiconductor manufacturing for Fault Detection and Classification (FDC) is becoming more and more challenging than ever. This paper uses well-known machine learning techniques such as Support Vector Machine (SVM), K-Means clustering and Self-Organizing Map (SOM) to develop an efficient ECD model. The process...
While the current supernova (SN) photometric classification system is based on high resolution spectroscopic observations, the next generation of large scale surveys will be based on photometric light curves of supernovae gathered at an unprecedented rate. Developing an efficient method for SN photometric classification is critical to cope with the rapid growth of data volumes in current astronomical...
Demand side management (DSM) is a key mechanism to make smart grids cost efficient using electricity price forecasting issue. Price forecasting method takes the big price data into account, and gives estimates of the future electricity price. However, most of existing price forecasting methods cannot avoid redundancy at feature selection and lack of an integrated framework that coordinates the steps...
Two techniques to further enhance the efficiency of Evolutionary Algorithms (EAs), even those which have already been accelerated by implementing surrogate evaluation models or metamodels to overcome a great amount of costly evaluations, are presented. Both rely upon the use of a Kernel Principal Component Analysis (Kernel PCA or KPCA) of the design space, as this reflects upon the offspring population...
In this paper, we pose and address some of the unique challenges in the analysis of scientific Big Data on supercomputing platforms. Our approach identifies, implements and scales numerical kernels that are critical to the instantiation of theory-inspired analytic workflows on modern computing architectures. We present the benefits of scalable kernels towards constructing algorithms such as principal...
Outlier detection or anomaly detection is an important and challenging issue in data mining, even so in the domain of energy data mining where data are often collected in large amounts but with little labeled information. This paper presents a couple of online outlier detection algorithms based on principal component analysis. Novel algorithmic treatments are introduced to build incremental PCA and...
This paper proposed a hybrid feature extraction method to improve the correct recognition rate of a handwritten digit recognition device based on temperature sensor array. The hybrid features are based on the temperature changes of the temperature sensor array during the process of handwriting, and the Principal Component Analysis (PCA) method is used for choosing the principal component of the features...
Spatio-temporal features can extract style of walking in a video sequence and are used to model human's motion for specified recognition tasks. The generic model of spatio-temporal feature is derived from spatial filtering of the video followed by a temporal filtering. Recently, several types of filtering kernels are proposed for human's motion analysis. However, utilizing a suitable filtering approach...
In this research, the application of machine learning approach specifically support vector machine along with principal component analysis and linear discriminant analysis as feature extractions are evaluated and validated in discriminating gait features between normal subjects and autism children. Gait features of 32 normal and 12 autism children were recorded and analyzed using VICON motion analysis...
Parkinson's disease (PD) and essential tremor (ET) are two kinds of tremor disorders which always confusing doctors in clinical diagnosis. Early experiments on structural MRI have already shown that Parkinson's disease can cause pathological changes in the brain region named Caudate_R (a part of Basal ganglia) while essential tremor cannot. Although there are many research work on the classification...
One of the central problems in machine learning and pattern recognition is how to deal with high-dimensional data either for visualization or for classification and clustering. Most of dimensionality reduction technologies, designed to cope with the curse of dimensionality, are based on Euclidean distance metric. In this work, we propose an unsupervised nonlinear dimensionality reduction method which...
This work presents a dimensionality reduction (DR) framework that enables users to perform either the selection or mixture of DR methods by means of an interactive model, here named Geo-Desic approach. Such a model consists of linear combination of kernel-based representations of DR methods, wherein the corresponding coefficients are related to coordinated latitude and longitude inside of the world...
Object recognition on large-scale video has recently attracted considerable research interest due to the huge amount of data available on the Internet, surveillance systems, social media networks and autonomous vehicles. By representing large-scale videos as image sets, we can handle the complex data variations such as viewpoint, illumination, and pose. In this paper, we propose an efficient and robust...
This paper investigates the performance of two-class classification credit scoring data sets with low default ratios. The standard two-class parametric Gaussian and non-parametric Parzen classifiers are extended, using Bayes' rule, to include either a class imbalance or a Bernoulli prior. This is done with the aim of addressing the low default probability problem. Furthermore, the performance of Parzen...
Feature extraction plays an important role in machinery fault diagnosis and prognosis. The features extracted from time, frequency and time-frequency domains are widely investigated to describe the properties of overall signal from different perspectives, seldom considering the sequential characteristic of time-series signal in which the fault information may be embedded. This paper investigates a...
Near-Infrared spectroscopy (NIRS) is an emerging non-invasive brain computer interface (BCI) modality that measures changes in haemoglobin concentrations in the cortical tissue. To date most NIRS studies have used standard multiple subject/session dependent classifiers for neural signal decoding. Such approach is preferable to avoid large degree of variabilities in the acquired data that affects classifier...
Biometrics, due to their uniqueness and accuracy are being widely used for authentication and identification. In multimodal biometrics more than one trait of an individual is fused. Multimodal biometrics is of different types, multi algorithmic, multi instance and multi sensorial. This paper combines two biometric traits namely face and fingerprint. Face is being processed using two algorithms, PCA...
Palmprint recognition method is part of the biometric system that has a significant impact on the advancement of civilization, especially in the areas of sensing identity the person. To get the reliable system, the selection of actions to be taken include choosing a filter of skeleton method, selecting the scale orientation of Gabor method, and using appropriate a dimension reduction. The results...
Occluded images often affected the recognition rates in face recognition, thus the occlusion should be checked out and given a little weighting coefficient so as to weaken its impact on the recognition rate as much as possible. The traditional algorithms often use the reconstruction error operator based on principal component analysis (PCA) to estimate the weight for occluded face, which often need...
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