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Remotely sensed data is only a key source of detection of Earth's surface changes or Land-Use/Land-Cover (LULC) monitoring. During past decades, a series of effective change detection techniques such as Principal Component Analysis (PCA), Change Vector Analysis (CVA) and Post Classification Comparison (PCC), have been developed to observe the LULC vicissitudes. All aforesaid techniques performed very...
This paper induces the prominence of variegated machine learning techniques adapted so far for the identifying different network attacks and suggests a preferable Intrusion Detection System (IDS) with the available system resources while optimizing the speed and accuracy. With booming number of intruders and hackers in todays vast and sophisticated computerized world, it is unceasingly challenging...
This paper presents a supervised classification framework that integrates discrete wavelet transform (DWT) based spectral and textural features for the urban land cover classification using hyperspectral data. Investigations involved application of 1-D DWT along the wavelength dimension of the hyperspectral data followed by 2-D DWT along spatial dimensions for spectral and texture feature extraction...
Biometrie is one type of physical characteristics that can be used for person identification as it has uniqueness. Palm vein is one of individual biometrics that attracts researcher attention recently. The advantages of palm vein compared to others are that palm vein represent if someone is still alive or dead, palm vein is hidden under the human skin, and palm vein is quite impossible to be imitated...
The analysis of facial appearance is significant to an early diagnosis of medical genetic diseases. The fast development of image processing and machine learning techniques facilitates the detection of facial dysmorphic features. This paper is a survey of the recent studies developed for the screening of genetic abnormalities across the facial features obtained from two dimensional and three dimensional...
Facial expression is a prominent posture beneath the skin of the face. They are the way of communication in humans which convey many things non-verbally. During the past years face recognition has received significant attention as one of the most important applications of image understanding and analysis. Many algorithms have been implemented on different static and non-static conditions. Static conditions...
Gene expression data generated from microarray experiments are characterized by large number of genes or dimensions. Informative gene selection for performing clustering to discover useful phenotypes is a major issue as there is no class information available. In this paper, we propose a wrapper based feature selection approach to perform sample based clustering on gene expression data. The proposed...
With the widespread use of Internet, the possibilities of exposing confidential data to invaders or attackers increases. Intrusion Detection System (IDS) is used for detecting various intrusions in network environment and to prevent data from malicious attackers. In this paper, a combined algorithm based on Principal Component Analysis (PCA) and Core Vector Machine (CVM), which is an extremely fast...
To employ and develop the performance of the dimensionality reduction for microarray data there is need of good dimension reduction technique. High-dimensional data bring great challenges in terms of computational complexity and classification performance. Therefore, it is necessary to effectively compress in a low-dimensional feature space from high dimensional feature space to design a learner with...
The mismatch between the training data and the test data distributions is a challenging issue while designing many practical computer vision systems. In this paper, we propose a domain adaptation technique to tackle this issue. We are interested in a domain adaptation scenario where source domain has large amount of labeled examples and the target domain has large amount of unlabeled examples. We...
This paper motivates the use of combination of mel frequency cepstral coefficients (MFCC) and its delta derivatives (DMFCC and DDMFCC) calculated using mel spaced Gaussian filter banks for text independent speaker recognition. MFCC modeled on the human auditory system shows robustness against noise and session changes and hence has become synonymous with speaker recognition. Our main aim is to test...
This paper deals with the recognition of Telugu characters on palm leaf using statistical features. Handwritten character recognition has various applications in post offices, reading aids for blind, library automation and multimedia design. Palm leaf manuscripts contain religious texts and a host of subjects such as art, medicine, music, astrology, law and astronomy. There is an inherent 3D feature...
With the exponential growth of storage of digital images, retrieval has become an impending issue. Such large collection of data takes a considerable amount of time in retrieving images apart from picking relevant images with respect to the query. Despite advancements in introducing effective features, the search time still remains larger. In such scenario the search time could be minimized by categorizing...
The advancement of medical image digitization and storage is growing day by day and it has resulted in increasing demands for efficient medical image retrieval system. CBIR refers to the retrieval of similar images based on the given query image. Today, Computer Aided Detection/Diagnosis (CAD) schemes that uses CBIR has been attracting research interest. The mammography is the key imagery for early...
Wireless Sensor Network (WSN) is fixed in many sensing environments to capture and monitor events. The sensing values come from sensor device that may contain noise, missing values, and redundant features. Noise, missing values and redundant features should be removed from the streamed data using an efficient preprocessing mechanism and then preprocessed data can be provided for further processing...
This paper presents a pattern recognition method for multi-class classification of Parkinson's disease based on PCA, LDA and SVM. 22 voice features which are extracted and reduced using PCA and LDA. SVM is then used during the classification step. The classification accuracy between single features and PCA and LDA features are presented and the results show that the PCA features have greater accuracy...
In recent years, electronic commerce and online social networks (OSNs) have experienced fast growth, and as a result, recommendation systems (RSs) have become extremely common. Accuracy and robustness are important performance indexes that characterize customized information or suggestions provided by RSs. However, nefarious users may be present, and they can distort information within the RSs by...
We propose a novel approach for unsupervised visual domain adaptation that exploits auxiliary information in a target domain. The key idea is to embed data in the target domain into a subspace where samples are better organized, expecting auxiliary information to serve as a somewhat semantically related signal. Specifically, we apply partial least squares (PLS) to RGB image features and corresponding...
Based on principal component analysis (PCA) and support vector machine (SVM), a new method for the fault diagnosis of TE Process is proposed. The fault recognition based on kernel principal component analysis (KPCA) is analyzed and SVM is employed as a classifier for fault classification. To establish a more efficient SVM model, genetic algorithm (GA) is used to determine the optimal kernel parameter...
Effective robotic grasping and manipulation requires knowledge about the surface properties of an object and the environment in which it is located. Physical contact with materials using tactile sensors can enable the retrieval of detailed information about the material, i.e. compressibility, surface texture and thermal properties. This paper describes a system used to classify a wide range of materials...
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