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Hand-shape recognition is an important problem in computer vision with significant societal impact. In this work, we introduce a new image dataset for Irish Sign Language (ISL) recognition and we compare between two recognition approaches. The dataset was collected by filming human subjects performing ISL hand-shapes and movements. Then, we extracted frames from the videos. This produced a total of...
Distance or similarity measures are essence components used by distance-based recognition techniques. Since the Euclidean distance function is the most widely used distance metric in PCA and LDA recognition systems , no empirical study examines the recognition performance based on these two methods by using different distance functions, especially for biometric authentication domain problems. The...
Deep learning based hyperspectral image (HSI) classification have recently shown promising performance. However, complex network architecture, tedious training process and effective utilization of spatial/contextual information in deep network limits the application and performance of deep learning. In this paper, for an effective spectral-spatial feature extraction , an improved deep network, spatial...
Countering network threats, particularly intrusions, is a challenging area of research in the field of information security. Intruders use sophisticated mechanisms to hide the attack payload and break the detection techniques. To overcome that, various unsupervised learning approaches from the field of machine learning and pattern recognition have been employed. The most popularly used method is Principal...
Video surveillance systems have enabled the monitoring of complex events in several places, such as airports, banks, streets, schools, industries, among others. Due to the massive amount of multimedia data acquired by video cameras, traditional visual inspection by human operators is a very tedious and time consuming task, whose performance is affected by fatigue and stress. A challenge is to develop...
With millions of people suffering from dementia worldwide, the global prevalence of dementia has a significant impact on the patients' lives, their caregivers' physical and emotional states, and the global economy. Early diagnosis of dementia helps in finding suitable therapies that reduce or even prevent further deterioration of patients' cognitive abilities. MRI scans are shown to be the most effective...
With the extensive application of machine learning algorithms in bioinformatics, more and more computer researchers are beginning to focus on this field. Polyadenylation of messenger RNA (mRNA) is one of the key steps of gene expression in eukaryotes, polyadenylation site marks the end of transcription, it is of great significance to explore prediction of the site of gene sequences encoding gene....
Biometrics is an active research field that is increasingly being integrated into current technology. As a result, more and more people are becoming familiar with biometric technics such as fingerprint or facial recognition. Nevertheless, there are innovative techniques such as ear-based biometrics which are not very well known yet because they are at initial stages of research. In this work, an ear...
This paper proposes an effective fusion scheme for extracting more discriminative information from bimodal biometrics at data, feature and decision levels. In all these three levels of fusion, information from both face andfingerprint image of a single subject are fused to effectively represent it in a more discriminative ways. For all these three approaches, a combination of wavelet and principal...
Some of the best current face recognition approaches use feature extraction techniques based on either Principle Component Analysis (PCA), Local Binary Patterns (LBP), Autoencoder (non-linear PCA), etc. While each of these feature techniques works fairly well, we propose to combine multiple feature extractors with deep learning in a system so that the overall face recognition accuracy can be improved...
Gender classification play a significant role in recognition performance. For the purpose of visual surveillance, gender is considered as an important factor. In this paper a hybrid approach is proposed by fusing Gait Energy Image (GEI) with spatio temporal parameters for the gender classification. The dataset used is CASIA B which comprises of 118 subjects (89 males and 29 females). The proposed...
In this paper, we propose a method to classify K-pop dance based on motion data obtained from Kinect V2 for research of motion classification and development of anti-plagiarism system. To do this, 200-point dances of K-pop are acquired. Dance motions from 40 amateur dancers are acquired to construct a total of 400 data. The proposed classification method consists of three steps. First, we obtain 13...
Fully automated defect detection and classification of automobile components are crucial for solving quality and efficiency problems for automotive manufacturers, due to the rising wage, production costs and warranty claims. However, metrological deviations in form still represent unsolved problems using state-of-the-art techniques, especially for forged or casted components with complex geometry...
With the development of machine learning techniques, artificial intelligence applications in medicine are becoming hot topic in health information systems. In this research, we construct a new basic heart failure disease database which contains 1715 patients and 400 features. Then, we propose a new machine learning method called Polynomial Smooth Support Vector Machine(PSSVM) to help doctors diagnose...
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...
High dimensionality of feature space is a problem in supervised machine learning. Redundant or superfluous features either slow down the training process or dilute the quality of classification. Many methods are available in literature for dimensionality reduction. Earlier studies explored a discernibility matrix (DM) based reduct calculation for dimensionality reduction. Discernibility matrix works...
The paper focuses on using stacking and rotation-based technique to improve performance and generalization ability of the machine learning classification with data reduction. The aim of data reduction technique is decreasing the quantity of information required to learn a high quality classifiers, especially when the data are huge. The paper shows that merging both stacking and rotation-based ensemble...
Essentially, opinion reviews are a valuable and trustworthy source of information for the readers. However, regarding the business purposes, a huge number of deceptive opinions are intentionally posted on the Web. In order to keep opinion reviews as a precious and trusted resource, we propose a method which focuses on detecting positive and negative deceptive opinions. In this paper, we explore the...
We propose a novel domain adaptation method for deep learning that combines adaptive batch normalization to produce a common feature-space between domains and label transfer with subspace alignment on deep features. The first step of our method automatically conditions the features from the source/target domain to have similar statistical distributions by normalizing the activations in each layer...
A patient-specific seizure detection system for Nocturnal Frontal Lobe Epilepsy (NFLE) is proposed. Data of several patients affected by NFLE, extracted from the EPILEPSIAE database, have been used for this study. As every patient possesses different physiological characteristics, several simulations were performed in order to find the best features to be extracted from electroencephalogram (EEG)...
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