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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....
With the increase of the scale and complexity of the industrial process, the requirements for process safety and reliability are further improved. In order to detect the equipment failure accurately and timely, a fault detection method based on continuous hidden Markov model (CHMM) is proposed. The principal component analysis (PCA) method is used to extract the characteristic data of the process...
Recognition of human actions by using wearable sensors has become an important research field. Segmentation to sensor data is a vital issue in reconstructing and understanding human daily actions, and strongly affects the accuracy of human actions recognition. Traditional online segmentation approaches are mostly designed for one-dimensional sensor data, which greatly limits these approaches to multi-dimensional...
System availability is one of the major requirements expected from systems in the trading domain. In order to prevent system outages that can deteriorate system availability, anomaly detection must be able to assess the status of the system and detect anomalies that can lead to failures on a real-time basis. This paper presents a framework for anomaly detection for complex trading systems based on...
Due to the confusion of fault-prone software modules and non-fault-prone ones, and the limit of traditional mothed such as LDA and PCA, the performance of software defect prediction model is difficult to improve. In this paper, we present GMCRF, a method based on dimensionality reduction technique and conditional random field (CRF) for software defect prediction. In our proposed method, firstly, we...
In this paper, we propose the novel remote sensing image classification algorithm based on the PCA and hidden Markov random field theory. Remote sensing image classification method based on the pattern recognition theory at home and abroad, many experts have done a lot of research work, machine learning has been in study of remote sensing image classification and information extraction has been widely...
We propose a visual speech parametrization based on histogram of oriented gradients (HOG) for the task of lipreading from frontal face videos. Inspired by the success of spatiotemporal local binary patterns, the features are designed to capture dynamic information contained in the input video sequence by combining HOG descriptors extracted from three orthogonal planes that span x, y and t axes. We...
Tactile sensing has recently attracted significant research interest in robotics. Despite the fact that tactile sensors provide temporal sequences of readings, state-of-the-art material recognition approaches are episodic, i.e. a whole sequence of readings is processed to identify the material. Based on vibration frequency response, this work presents an online identification technique using recursive...
One of the desirable features in a ground surveillance radar is to provide information about what a detected person is doing. This would give a law enforcement organization ability to detect suspicious activities remotely and act accordingly. Previously, micro-Doppler radar signatures from humans were shown to have the necessary features to make that distinction. Typically, micro-Doppler signal spectrograms...
This paper develops an Audio-Visual Speech Recognition (AVSR) method, by (1) exploring high-performance visual features, (2) applying audio and visual deep bottleneck features to improve AVSR performance, and (3) investigating effectiveness of voice activity detection in a visual modality. In our approach, many kinds of visual features are incorporated, subsequently converted into bottleneck features...
Nowadays, hand gesture is one of main considerations for hearing impaired people because they use sign language to communicate with each other and to normal people. In general, the normal people have difficulties with sign language therefore they need an interpreter supporting communication. Then the automatic hand gesture recognition system is needed to help hearing impaired people integrating into...
Automatic personal identification from their physical and behavioral traits, called biometrics technologies, is now needed in many fields such as: surveillance systems, access control systems, physical buildings and many more applications. In this paper, we propose an efficient online personal identification system based on Multi-Spectral Palmprint images (MSP) using Hidden Markov Model (HMM) and...
The paper compares the use of Principal Component Analysis (PCA) to Information Gain (IG) as a feature selection method for improving the classification of Influenza-A antiviral resistance. Neural networks were used as the classification method of choice. The experiment was conducted on cDNA viral segments of Influenza-A belonging to the H1N1 strain. Sequences from each segment were further divided...
Complexities in the facial recognition increases because of real time image acquisition, which is generally not performed by an expert. Because of the inappropriate focus, the positional aspect of the input image can be different. In this work, a directional aspect based structural analysis is provided for generating the Local binary pattern. For a single face about 60 different binary patterns are...
Researches have shown accent classification can be improved by integrating semantic information into pure acoustic approach. In this work, we combine phonetic knowledge, such as vowels, with enhanced acoustic features to build an improved accent classification system. The classifier is based on Gaussian Mixture Model-Universal Background Model (GMM-UBM), with normalized Perceptual Linear Predictive...
This paper proposes methods of using restricted Boltzmann machines (RBM) to generate the sequence of lip images for visual speech synthesis. The aim of our proposed methods is to alleviate the over-smoothing effect of the conventional hidden Markov model (HMM) based statistical approach for lip synthesis. Two model structures using RBMs to model and generate lip movements are investigated in this...
Speech and hand gestures form a composite communicative signal that boosts the naturalness and affectiveness of the communication. We present a multimodal framework for joint analysis of continuous affect, speech prosody and hand gestures towards automatic synthesis of realistic hand gestures from spontaneous speech using the hidden semi-Markov models (HSMMs). To the best of our knowledge, this is...
In this paper, we investigate how to efficiently extract informative features of high-dimensional data through noisy channels. Specifically, we decompose the feature space of the data into a sequence of score functions with decreasing information volumes, such that different scores are uncorrelated. From this decomposition, the features of the data become a sequence of score functions such that the...
Classification of moving objects for video surveillance applications still remains a challenging problem due to the video inherently changing conditions such as lighting or resolution. This paper proposes a new approach for vehicle/pedestrian object classification based on the learning of a static kNN classifier, a dynamic Hidden Markov Model (HMM)-based classifier, and the definition of a fusion...
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...
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