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Recently, the problem of the intrusion detection has been largely studied by the computer and networks security communities. Then, the Intrusion Detection System (IDS) becomes a interest topic in research and in particular in machine learning and data mining. In order to improve the classification accuracy and to reduce high false alarm rate from the classical data base like KDD99 or others. In this...
This paper explains the gender recognition system through a human facial image by using the basic method of Principal Component Analysis (PCA) combined with Linear Discriminant Analysis (LDA). PCA+LDA method performance can be improved by improvising the preprocessing techniques such as resizing the image, equalizing the histogram, and removing the variation of the image background by adding oval...
At present, it is a great challenge that solving high-dimension and text sparsity problems in short text classification. To resolve these problems, this paper proposes a method which takes the correlation between lexical items and tags before completing Latent Dirichlet Allocation(LDA) topic model. Meanwhile, this paper adjusts parameters of Support Vector Machine(SVM) to find the optimal values by...
The hand gesture recognition is applied in many kinds of technology such as mobile phone applications, wearable wireless devices, sports detection, or video game. In this paper, we recorded signals of eight kinds of hand movements into computer using wearable wireless device with nine-axis sensor (including accelerometer, gyroscope and magnetometer sensors) worn on the wrist, then recognized gestures...
Schizophrenia is a mental disorder which has underlying neurological deficits. Schizophrenic patients have low social acceptance as well as a higher death rate. Initiation of schizophrenic symptoms are associated with a wide range of cognitive deficits. Impaired working memory performance is a basic characteristics of understanding schizophrenia. The performance impairment increases significantly...
A wide range of research exists on fMRI imaging and psychological assessment based memory and/or learning studies. However, absence of literature is observed in fNIRs based memory and learning research. This paper provides a novel study of prefrontal hemodynamic changes of subjects engaged in multiple trial paired-associate learning. The direct measure of prefrontal hemodynamic is collected by fNIRs...
Biometric is emerging area in the computer science for the secure various systems. Day to day life peoples are preferred to use, robust and highly acceptable security system which can surpass the human errors. Many scientists are engaged to develop a strong biometric system, but there are a lot of challenges in the real time application. It is observed and found that researchers are only working on...
Face recognition is a quintessential biometric technique. It still remains challenging to accurately characterize the identity related features in face images. In this paper, we propose a novel classification method based on Kernel Fisher Discriminant Analysis using the distinctiveness of Gabor features and the robustness of ordinal measures. These parameters are derived from magnitude, phase, real...
In this paper, the PEN3 electronic nose system was used to collect six kinds of fruit odor data. The smell print of each fruit is composed of 10 sensor data collected by the electronic nose. The combination of different sensor data was selected to classify the kinds of fruits. The principal component analysis (PCA) and linear discriminant analysis (LDA) were used to classify the data. Then, a combination...
In this paper, a new object recognition framework is presented. The framework includes a variety of object recognition approaches based on Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and the K-nearest neighbor (K-NN). A color image vector representation model is also introduced. Based on the representation model, color Eigenspace is constructed using PCA and LDA for feature...
Feature extraction addresses the problem of finding the most compact and informative set of features. To maximize the effectiveness of each single feature extraction algorithm and to develop an efficient intrusion detection system, an ensemble of Linear Discriminant Analysis (LDA) and Principle Component Analysis (PCA) feature extraction algorithms is implemented. This ensemble PCA-LDA method has...
This paper presents an audio event classification algorithm which automatically classifies an audio event as footstep, glass breaking, gunshot or scream mainly for surveillance applications. First, the Gabor feature of the audio spectrogram is extracted, there are two kinds of Gabor features, namely global Gabor feature and local Gabor feature. Then we use Principal Components Analysis (PCA) and Linear...
Increasing the number of sensors in a gas identification system generally improves its performance as this will add extra features for analysis. However, this affects the computational complexity, especially if the identification algorithm is to be implemented on a hardware platform. Therefore, feature reduction is required to extract the most important information from the sensors for processing...
Face recognition plays an important role in biometric based person authentication systems. Various algorithms have been proposed in last three decades. This paper presents an efficient face recognition method based on combination of linear discriminant analysis (LDA) and a 2-channel wavelet filter bank. The 2-channel wavelet filter bank is designed using factorization of generalized half band polynomial...
One of the major challenges in face recognition is that related to the differences in orientation or pose, the variations of illumination, the facial expressions, the occlusions and aging. In this paper, we propose an efficient method for face recognition in an uncontrolled environment where we fuse Gabor wavelets and Local Binary Patterns (LBP) in the feature extraction phase. Then, we apply the...
Protecting the privacy of user-identification data is fundamental to protect the information systems from attacks and vulnerabilities. Providing access to such data only to the limited and legitimate users is the key motivation for ‘Biometrics’. In ‘Biometric Systems’ confirming a user's claim of his/her identity reliably, is more important than focusing on ‘what he/she really possesses’ or ‘what...
Palm Print based biometric systems have attracted much attention in various security applications because palm prints are rich with unique line, point and texture patterns which even low resolution palm scanners can easily capture. This paper presents a texture based palm print recognition method which employ 2D Gabor filter to extract texture information from the central part of hand and use subspace...
This study considers the problem of in-depth document analysis. We propose a new document analysis method, named Multi-Dimensional Linear Discriminant Analysis (MDL-DA), which enables us to formulate an efficient class specific semantic representation of local information from a document with respect to term associations and spatial distributions. MDL-DA works by firstly partitioning each document...
This research aims at studying the recognition accuracy and execution time that are affected by different dimensionality reduction methods applied to the biometric image data. We comparatively study the fingerprint, face images, and handwritten signature data that are pre-processed with the two statistical based dimensionality reduction methods: principal component analysis (PCA) and linear discriminant...
Sign language is a way to communicate for deaf people, which hand shapes are used instead of sound patterns. In this paper, we present a method for recognizing alphabet and numbers in American sign language based on saliency detection of the image. After detecting saliency, the images were processed by Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), in order to reduce dimensions...
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