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Psychological testing is a process to know a human feeling or thought in a normal situation or a pressure situation, to know personal behavior. A psychological evaluation technique that generally uses questionnaires wrote by a specialist investigating personal behavior. In this research, we motivate on applying hidden markov models to recognize the personal behavior of human from doing the psychological...
Nowadays, deep learning is very popular in a variety of research field due to its outperformance over the existing machine learning methods and its high generality over raw inputs. According to recent surveys, deep learning can give high performance in visual object recognition system. Human Action Recognition (HAR) is a promising research area over the computer vision research field due to its enormous...
In this study, we will present a rule based fuzzy gesture recognition system where a user will interact with a spherical robot with hand gestures performed with a smart phone and the droid will respond by imitating this movements. In this context, we will take up the Gesture Recognition, Fuzzy Logic and Internet of Things (IoT) frameworks to construct such a Human-Machine Interface (HMI). In the proposed...
Keystroke dynamics, which is a biometric characteristic that depends on typing style of users. In the past thirty years, dozens of classifiers have been proposed for distinguishing people using keystroke dynamics; many have obtained excellent results in evaluation. However, a more common case is that only normal instances are available and none of the rare classes are observed. It leads us to use...
The trend for about twenty years, the research regarding the number of states in Hidden Markov Model (HMM) was mainly aimed at increasing it in order to ensure the robustness of the face recognition system. In this paper, a novel face recognition method is presented based on one state of discrete HMM, where it seemed impossible in the past. Contrary to other approaches that use the three parameters...
In this paper, a blind bandwidth extension algorithm for music signals has been proposed. This method applies the K-means algorithm to firstly cluster audio data in the feature space, and constructs multiple envelope predictors for each cluster accordingly using Support Vector Regression (SVR). A set of well-established audio features for Music Information Retrieval (MIR) has been used to characterize...
Finding an effective way to represent human actions is yet an open problem because it usually requires taking evidences extracted from various temporal resolutions into account. A conventional way of representing an action employs temporally ordered fine-grained movements, e.g., key poses or subtle motions. Many existing approaches model actions by directly learning the transitional relationships...
This paper explores the long short-term memory (LSTM) recurrent neural network for human action recognition from micro-Doppler signatures. The recurrent neural network model is evaluated using the Johns Hopkins MultiModal Action (JHUMMA) dataset. In testing we use only the active acoustic micro-Doppler signatures. We compare classification performed using hidden Markov model (HMM) systems trained...
Medical records of Traditional Chinese Medicine (TCM) are usually free text and unstructured data, how to extract medical terms from TCM medical records based on conditional random fields is an interesting problem. TCM medical records obtained from dermatology in Guangdong Provincial Hospital of Chinese Medicine are segmented to single words and labeled with grammatical properties of words by TCM...
We present a novel approach towards web video classification and recounting that uses video segments to model an event. This approach overcomes the limitations faced by the classical video-level models such as modeling semantics, identifying informative segments in a video and background segment suppression. We posit that segment-based models are able to identify both the frequently-occurring and...
Inspired by Gustave Lebon's idea of crowds as single-minded entities, we present a novel approach to describe the behavior of a crowd as a single entity, based on the global movement of the entire aggregate of people conforming the crowd. The present work significantly differs from existing literature where the behavior of single individuals within the crowd are the building blocks to describe crowd...
Real handwriting authentication systems need a robust writer identification over a long time period. The paper analyzes signature sessions of the ATV-Signature Long Term Database (ATV-SLT DB). The database contains 6 sessions generated by 27 users over 15 month. The quality change of the verification results over a period of 15 month is examined. 64static and dynamic biometric features from the ATV-SLT...
The Carnegie Mellon University's (CMU) Sphinx framework is increasingly used for the Arabic speech recognition in general and applied to the Holy Quran in particular. Generating the language model includes a tedious task of preparing the transcriptions for all the data. In this paper, we investigate the fault-tolerance of the automatically generated language model as compared to a corrected and uncorrected...
In the past decade, the Internet has been widely used in everyday life. Different types of mobile broadband applications are created and require an increasing amount of network resources. However, Internet service providers must maximize the use of these limited resources to provide users with different levels of quality of service. The first step toward traffic engineering is to perform traffic classification...
Currently, studies on learning relationship between objects focus on the text domain. There are a few researchers who focus on relationship learning between objects in other domains. In these researches, they have tried to represent the qualitative description of structure of objects, and the symbolic relationship between them. This output provides symbolic meaning to the inter-object relationships...
Recently, the speaker code based adaptation has been successfully expanded to recurrent neural networks using bidirectional Long Short-Term Memory (BLSTM-RNN) [1]. Experiments on the small-scale TIMIT task have demonstrated that the speaker code based adaptation is also valid for BLSTM-RNN. In this paper, we evaluate this method on large-scale task and introduce an error normalization method to balance...
This paper presents a comparison of two classifier methods based on accuracy level in Indonesian speaker recognition for unclear pronunciation problem in a word, simple sentences, and complete sentences. The first method is Vector Quantization (VQ) based on distortion distance and the second method is Hidden Markov Model (HMM) based on the probability value of the data is observed. Based on the experiments,...
This paper propose a novel fault diagnosis of bearings approach based on sparse representation. Three steps are conducted to classify the fault types. In the dictionary learning step, dictionary is learned using training set with known fault types; in the sparse coding step, testing samples with unknown fault types are represented through spares representation model with sub-dictionaries extracted...
This paper explains the implementation of the phonetic level speech recognition system for Punjabi language because it is a highly prosodic language. Here Hidden Markov Toolkit (HTK) is used. First step is data collection and nine hours data is collected in read speech mode. Second step is data preparation, in which hmmlist, grammar and dictionary files are created. Once the data is prepared, 75%...
A frequency count based two stage classification approach is proposed by combining generative and discriminative modeling principles for online handwritten character recognition. The first stage classifier based on Hidden Markov Model (HMM) returns top-K ranking characters out of the total N classes. In the second stage, pairwise classifiers for K(K − 1)/2 unique combinations of top-K characters using...
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