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Identifying structure of genes in Human genomes highly depends upon accurate recognition of boundaries between exons and introns, i.e. splice sites. Hence, development of new methods for effective detection of splice sites is essential. DNA encoding approaches are used for feature extraction from gene sequences, while machine learning methods are used for classification of splice sites using those...
A protein is a long one-dimensional amino acid sequence. Some subsequences of this sequence are given names and β-strand in one such subsequence. β-strands are very common and two or more such strands within one protein can form a β-sheet in the secondary structure of proteins. In its natural form, a protein is a three-dimensional entity composed of β-sheets, α-helices, and other types of substructures...
With the increasing stress in working and studying, mental health becomes a major problem in the current social research. Generally, researchers can analyze psychological health states by using social perception behavior. The speech signal is an important research direction in this domain. It objectively assesses the mental health of social groups through the extraction and fusion of speech features...
Detecting pedestrians that are partially occluded remains a challenging problem due to variations and uncertainties of partial occlusion patterns. Following a commonly used framework of handling partial occlusions by part detection, we propose a multi-label learning approach to jointly learn part detectors to capture partial occlusion patterns. The part detectors share a set of decision trees via...
Adaptive learning is a promising approach to education, in which instructional methods and materials are selected according to the performance of students. In this manner, the learning process can be tailored to the needs and strengths of students in order to maximize efficiency. Advances in internet technology and portable devices has led to the development of e-learning platforms outside the traditional...
Motion analysis and tracking often relies on multimodal signals, e.g., video, depth map, motion capture (MoCap), due to the completeness of information they jointly provide. The joint analysis of multimodal signals requires to know the correct timing, i.e., the signals to be aligned. In this paper we propose an approach to automatically estimate the correct matching and alignment between a video and...
In almost every mental disorder, there are deficiencies in both structure and function of the brain. So the need for analyzing complementary modalities that project all aspects of the brain is rising. The most severe kind of these disorders is schizophrenia. The main cause of schizophrenia is still unknown. Therefore, analyzing resting-state fMRI (rs-fMRI) and structural MRI (sMRI) to investigate...
Every year, a number of the students who obtain their bachelor's degree attend a university in order to continue their graduate studies. Most universities attempt to encourage their top students to continue education in the same university. On the other hand, they receive applicants from other universities, and selecting the top students among these candidates without a thorough examination may not...
Feature ranking from video-wide temporal evolution brings reliable information for complex action recognition. However, a video may contain similar features in the sequence of frames which deliver unnecessary information to the ranking function. This paper proposes a method to improve the rank-pooling strategy which captures the optimized latent structure of the video sequence data. The optimization...
Long monitoring tasks without regular actions, are becoming increasingly common from aircraft pilots to train conductors as these systems grow more automated. These task contexts are challenging for the human operator because they require inputs at irregular and highly interspaced moments even though these actions are often critical. It has been shown that such conditions lead to divided and distracted...
Biometrie recognition of persons are widely explored nowadays to develop robust and trustworthy security systems. On account of the unique neural signature of each person, the brain activity recorded by Electroencephalogram (EEG) has recently been identified as a potential biometric trait. In this paper, we propose an online EEG-based biometric system which utilizes the activations of brain towards...
Use of online applications in day-to-day life is increasing. In parallel to this increase the threat to the security of these applications is also increasing. The security of these applications is breached by different cyber attacks. Denial-of-Service (DoS) is one such type of cyber attack. DoS makes the online application or the resources of the server unavailable to the intended users. For detecting...
With the significant increase of the network heterogeneity and the wide use of emerging video applications such as wireless sensor networks, video surveillance systems or remote sensing, the Distributed Scalable Video Coding (DSVC) is a potential solution for efficiently transmitting and storing video data due to its high compression efficiency and low encoding complexity capabilities. In DSVC framework,...
This paper takes advantages from probability theory and fuzzy modeling. We use probability theory to overcome some common problems in data based modeling methods. A probability based clustering method is proposed to partition the hidden features, and extract fuzzy rules with probability measurement. An optimization method are applied to train the consequent part of the fuzzy rules and the probability...
In the paper, we propose an effective long-term real-time tracking method to address the problem of robustness and tracking failure in visual tracking with UAVs. Most existing trackers only consider short-term tracking, therefore are unable to cope with partial and complete occlusion, which finally leads to object drifting or loss. Our method still follows the tracking-by-detection framework. However,...
Image classification is a method that distinguishes the different categories of targets based on the different features of image. The current problem usually is that the feature modeling of target has a great influence on recognition robustness. In order to solve this problem, a correlation-based method is presented to optimize the bag-of-visual-word (BOVW) model by reducing the dictionary size. The...
The performance of classification of various mental states using Electroencephalography (EEG) is often limited by the lack of information regarding the most discriminative channels and frequency bands. The paper proposes a Canonical Correlation Analysis (CCA) of EEG recorded during bilateral imagined hand movement. CCA determines linear transformation of EEG that is maximally correlated with a transformed...
This paper investigates feature selection method using filter Fast Correlation based Filter FCBF combined with Genetic Algorithm GA and particle swarm optimization PSO. In this paper two hybrid approaches based on filter method FCBF and Genetic algorithm (FCBF-GA) and filter FCBF with particle swarm (FCBF-PSO) are proposed. It has been found that the proposed method FCBF-PSO outperform the proposed...
Available sensing measurements in modern industrial process include two significant characteristics: distribution and autocorrelation. Different types of sensing measurements exhibit different characteristics. Moreover, different feature extraction methods are suitable for data with corresponding characteristics. This paper proposes a novel dual-step subspace partition method in order to establish...
This paper presents a personalized preference estimation method for video recommendation. Our method not only uses deep convolutional neural network (DCNN)-based video features but also transforms them based on user's viewing behavior in order to improve accuracy of preference estimation for a video. Specifically, we adopt supervised multi-view canonical correlation analysis (sMVCCA) in order to calculate...
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