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Computational and machine learning techniques have been applied in identifying biomarkers and constructing predictive models for diagnosis of hypertension. Strategies such as improved classification rules based on decision trees have been proposed. Other techniques such as Fuzzy Expert Systems (FES) and Neuro-Fuzzy Systems (NFS) have recently been applied. However, these methods lack the ability to...
Emotional speech recognition is an interesting application that is able to recognize different emotional states from speech signal. In Human-Robot Interaction (HRI), emotion recognition is being applied on intelligent robots so that they can understand emotional states of user and interact in a more human-like manner. However, it is not easy to apply emotion recognition algorithms in real applications...
Devnagari (Marathi) is an Indo-Aryan language and has a number of speakers all around the world. Marathi language has gained acceptability in the media & communication and therefore deserves to have a place in the growing field of automatic speech recognition. This manuscript describes the automatic speech recognition system that recognizes Marathi phoneme using Continuous Density Hidden Markov...
To unfold a solution for the detection of metamorphic viruses (obfuscated malware), we propose a non signature based approach using feature selection techniques such as Categorical Proportional Difference (CPD), Weight of Evidence of Text (WET), Term Frequency-Inverse Document Frequency (TF-IDF) and Term Frequency-Inverse Document Frequency-Class Frequency (TF-IDF-CF). Feature selection methods are...
A new acoustic model based on deep neural network (DNN) has been introduced recently and outperforms the conventional Gaussian mixture model (GMM) in speech recognition on several tasks. However, the number of parameters required by a DNN model is much larger than that of its counterpart. The excessive cost of computation cumbers the implementation of DNN in many scenarios. In this paper, a DNN-based...
This paper presents a multi-objective optimization approach to select key machining features for improving the predictive accuracy of virtual metrology. Along increasing of complicated machining features, supervised optimization methods can be applied to select the significant features; however, these methods are inapplicable when the number of selected features are far greater than the number of...
Image segmentation is a challenging task that has several applications in domains like medical imaging and surveillance. Among the various approaches proposed for this task, unsupervised methods have the advantage of being able to segment images without any assistance from the user. However, such methods often suffer from long runtimes and tend to be sensitive to the choice of parameters. Because...
The need for fast retrieving images has recently increased tremendously in many application areas. SIFT-like local descriptor-based matching is widely adopted and has achieved state-of-the-art performance. However, it becomes inefficient when computational and storage resources are limited. Besides, local descriptor-based methods may suffer difficulties when an image pair contains multiple similar...
Human action recognition using depth information is a trending technology especially in human computer interaction. Depth information may provide more robust features to increase accuracy of action recognition. This paper presents an approach to recognize basic human actions using the depth information from RGB-D sensors. Features obtained from a trained skeletal model and raw depth data are studied...
Virus writers make their viruses undetectable by using obfuscation methods, which ends in metamorphic viruses. We propose a method named detection circle which is based on the hidden Markov Model theory. We have used three elements to characterize a family of viruses: string occurrence probability, specifically-located character occurrence probability, and the amount of virus similarities. For the...
In this paper, we propose multiple facial action unit recognition by modeling their relations from both features and target labels. First, a multi-task feature learning method is adopted to divide action unit recognition tasks into several groups, and then learn the shared features for each group. Second, a Bayesian network is used to model the co-existent and mutual-exclusive semantic relations among...
To select an object requested by human voice among several unknown objects is one of the important tasks for household robots that assist human's daily lives. In this paper, we propose a method that can achieve this task. Using image models which are constructed by Web images collected from the results of speech recognition, the proposed method enables a robot to select an object specified by a speech...
Most current approaches in action recognition face difficulties that cannot handle recognition of multiple actions, fusion of multiple features, and recognition of action in frame by frame model, incremental learning of new action samples and application of position information of space-time interest points to improve performance simultaneously. In this paper, we propose a novel approach based on...
In the field of gestural action recognition, many studies have focused on dimensionality reduction along the spatial axis, to reduce both the variability of gestural sequences expressed in the reduced space, and the computational complexity of their processing. It is noticeable that very few of these methods have explicitly addressed the dimensionality reduction along the time axis. This is however...
In recent years, the desire and need to understand streaming data has been increasing. Along with the constant flow of data, it is critical to classify and segment the observations on-the-fly without being limited to a rigid number of classes. In other words, the system needs to be adaptive to the streaming data and capable of updating its parameters to comply with natural changes. This interesting...
This paper investigates the spatial and temporal dynamics in multi-channel electrocardiographic (ECoG) time series signals using Coupled Hidden Markov Model (CHMM). The signals are recorded in a hand motion control task, when the subject uses a joystick to move a cursor appearing on the screen to hit a virtual target. We detect signal onset using two heuristic schemes based on the experiment process...
Activity recognition in smart homes plays an important role in healthcare by maintaining the well being of elderly and patients through remote monitoring and assisted technologies. In this paper, we propose a two level classification approach for activity recognition by utilizing the information obtained from the sensors deployed in a smart home. In order to separates the similar activities from the...
Spontaneous facial expression recognition using temporal patterns is a relatively unexplored area in facial image analysis. Several factors such as head orientation, co-occurrence and presence of subtle facial action units (AUs), and time variability of AUs make the problem more challenging. This paper presents a methodology to model and automatically recognize the intensity of spontaneous AUs in...
This paper proposes a markerless system whose purpose is to help preventing falls of elderly people at home. To track human movements, the Microsoft Kinect camera is used which allows to acquire at the same time a RGB image and a depth image. Several articles show that the analysis of some gait parameters could allow fall risk assessment. We developed a system which extracts three gait parameters...
In this paper, we propose a Wi-Fi positioning method based on Deep Learning (DL). To deal with the variant and unpredictable wireless signals, the positioning is casted in a four-layer Deep Neural Network (DNN) structure that is capable of learning reliable features from a large set of noisy samples and avoids the need for hand-engineering. Also, to maintain the temporal coherence, a Hidden Markov...
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