The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Faces express many social indications, including gender, ethnicity, age, expression and identity, most of them have drawn thriving attention from various research communities, for instance neuroscience, computer science and psychology. In this paper, we propose a new approach to classify gender and ethnicity by merging both texture and shape features extracted from face images. Gabor filter is used...
This article introduces a new approach for identification of tea sample using pulse voltammetry method in an electronic tongue based instrumentation. The classifier system consists of a principle component (PCA) based feature extraction module followed by support vector machine based discrimination. The PCA score of unknown tea sample is undergone through different pair-wise (binary) classification...
The sign language considered as the main language for deaf and dumb people. So, a translator is needed when a normal person wants to talk with a deaf or dumb person. In this paper, we present a framework for recognizing Bangla Sign Language (BSL) using Support Vector Machine. The Bangla hand sign alphabets for both vowels and consonants have been used to train and test the recognition system. Bangla...
Remaining useful life (RUL) prognostics is a core problem in prognostics and health management (PHM). Accurate RUL prediction is crucial not only to the verification of mission goals but also to failure prevention and maintenance decision in a more effective and efficient manner. However, the substantial nonlinearity is one of most important challenges in deterioration modeling and RUL estimation...
We introduce a method that incorporates robustness to one of the main building blocks of sparse modeling: dictionary learning. Particularly, we exploit correntropy to compute the principal components in cases where outliers might be detrimental without proper care. This is further added to one of the most utilized dictionary learning tools: K-SVD; the result is Correntropy K-SVD, or CK-SVD, a method...
Diabetes is a leading health problem inthe developed world. The recent surge of wealth inQatar has made it one of the most vulnerable nationsto diabetes and related diseases. Recent technologicaladvances in 1H nuclear magnetic resonance (NMR) spectroscopy techniques for metabolomics profilingoffer a great opportunity for biomarkers discovery tobetter understand the disease. Using this technology,...
Human iris can be used for detecting organ disorders based on iridology science. Nowadays, iridology diagnosis can be done automatically by computer using artificial intelligence approach. This research focused on cardiac diagnosis based on left iris map on clockwise direction around 2:00 to 3:00. The Principal Component Analysis (PCA) is used for feature extraction while the Support Vector Machine...
A personal or enterprise collection of a large set of face images may contain many types of tags used for querying the collection. Often the tags have many irrelevant content that may not reflect the image content in terms of the facial characteristics. In this paper, we propose a data curation method to filter out the irrelevant face images using a face recognition based subgraph identification....
Deep learning is well known as a method to extract hierarchical representations of data. In this paper a novel unsupervised deep learning based methodology, named Local Binary Pattern Network (LBPNet), is proposed to efficiently extract and compare high-level over-complete features in multilayer hierarchy. The LBPNet retains the same topology of Convolutional Neural Network (CNN) — one of the most...
Dimensionality reduction is an important issue in information processing and has popular applications in many fields, where locally linear embedding (LLE) is widely used due to accuracy and simple to implement. However, LLE is lack of robustness, and sensitive to local structure that can't preserve neighborhood character sometimes. Instead, Laplacian eigenmaps (LE) can overcome these weaknesses. In...
Non-structural protein (NS1) has been conceded as one of the biomarkers for flavivirus that causes diseases with life threatening consequences. NS1 is an antigen that allows detection of the illness at febrile stage, mostly from blood samples currently. Our work here intends to define an optimum model for PCA-SVM with MLP kernel for classification of flavivirus biomarker, NS1 molecule, from SERS spectra...
In this paper, an improved Kernel Fisher Discriminant (KFD) method is used in face recognition. A Generalized Kernel Fisher Discriminant Analysis (GKFD) is proposed to make the most of two kinds of discriminant information in “double discriminant subspaces”. It can also uniform the discriminant functions in two subspaces of DSDA. Experimental results on ORL face database show the feasibility of the...
Nowadays, a consequence of data overload is that world's technology capacity to collect, communicate, and store large volumes of data is increasing faster than human analysis skills. Such an issue has motivated the development of graphic ways to visually represent and analyze high-dimensional data. Particularly, in this work, we propose a graphical interface that allow the combination of dimensionality...
Euler-Principal Component Analysis (e-PCA) has been recently proposed and successfully applied to the classification frame works. By utilizing the robust dissimilarity measure e-PCA demonstrates better performance than standard PCA while dealing with nonlinear component analysis and suppressing outliers. In this letter, we define a two-Dimensional Euler-Principal Component Analysis (e-2DPCA) framework...
Fingerprinting localization is to estimate a mobile terminal's location using its online received signal strength (RSS) measurement and offline RSS database originated from multiple access points (APs). Kernel-based fingerprinting localization is such a competitive algorithm. However, all training data need to be considered in its offline model learning stage. This render high risks for overfitting...
The detection and identification of the kinds of ships, i.e., warship or merchant ship, is of great interest for military use. Ships are usually detected and recognized based on ship physical fields, and the commonly used ship physical fields include sound field, magnetic field, hydraulic pressure field, electric field, gravity field, etc, which all contain plenty of discriminative information. However,...
Short, transient radio-frequency interference (RFI) events could threaten the quality of astronomical observations made by new and planned radio telescopes such as MeerKAT, the SKA and HERA in the radio quiet reserve in South Africa. Because they are so short, often of the order of microseconds long, these events are difficult to detect and identify in the time-frequency plots typically produced by...
Traditional nonnegative matrix factorization (NMF) is an unsupervised method for linear feature extraction. Recently, NMF with block strategy is shown to be able to extract more sparse and discriminative information of the images. To enhance the discriminative power of NMF, this paper proposes a block kernel nonnegative matrix factorization (BKNMF) based on the kernel theory and block technique. Kernel...
Application of the particle swarm optimization (PSO) is studied for the prediction of passenger car 100 km fuel consumption, and the sample data is processed by the principal component analysis. The PSO is introduced to optimize the kernel function parameter and penalty factor parameter, which are predicted by the least squares support vector regression (LSSVR). In order to overcome the weakness of...
This paper studies how to sample load more realistically and efficiently for security constraint unit commitment (SCUC) problems in order to achieve a high degree of robustness of the unit commitment (UC) solution. For example, given the UC solution, 95% of load profiles can be supplied. Principal component analysis (PCA) is introduced to find a clear feature of the historical load in two-dimensional...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.