Search results for: PD Pattern recognition

Authors: Inam Shamsher, Zaheer Ahmad, Jehanzeb Khan Orakzai, Awais Adnan

Abstract:

This paper deals with an Optical Character Recognition system for printed Urdu, a popular Pakistani/Indian script and is the third largest understandable language in the world, especially in the subcontinent but fewer efforts are made to make it understandable to computers. Lot of work has been done in the field of literature and Islamic studies in Urdu, which has to be computerized. In the proposed system individual characters are recognized using our own proposed method/ algorithms. The feature detection methods are simple and robust. Supervised learning is used to train the feed forward neural network. A prototype of the system has been tested on printed Urdu characters and currently achieves 98.3% character level accuracy on average .Although the system is script/ language independent but we have designed it for Urdu characters only.

Keywords: Algorithm, Feed Forward Neural Networks, Supervised learning, Pattern Matching.

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Authors: Yishu Gong, Liangliang Yang, Jianyu Zhang, Zhengyu Chen, Sihong He, Xusheng Zhang, Wei Zhang

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Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide and is characterized by cognitive decline and behavioral changes. People living with Alzheimer’s disease often find it hard to complete routine tasks. However, there are limited objective assessments that aim to quantify the difficulty of certain tasks for AD patients compared to non-AD people. In this study, we propose to use speech emotion recognition (SER), especially the frustration level as a potential biomarker for quantifying the difficulty patients experience when describing a picture. We build an SER model using data from the IEMOCAP dataset and apply the model to the DementiaBank data to detect the AD/non-AD group difference and perform longitudinal analysis to track the AD disease progression. Our results show that the frustration level detected from the SER model can possibly be used as a cost-effective tool for objective tracking of AD progression in addition to the Mini-Mental State Examination (MMSE) score.

Keywords: Alzheimer’s disease, Speech Emotion Recognition, longitudinal biomarker, machine learning.

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Authors: Wernhuar Tarng, Yuan-Yuan Chen, Chien-Lung Li, Kun-Rong Hsie, Mingteh Chen

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An emotional speech recognition system for the applications on smart phones was proposed in this study to combine with 3G mobile communications and social networks to provide users and their groups with more interaction and care. This study developed a mechanism using the support vector machines (SVM) to recognize the emotions of speech such as happiness, anger, sadness and normal. The mechanism uses a hierarchical classifier to adjust the weights of acoustic features and divides various parameters into the categories of energy and frequency for training. In this study, 28 commonly used acoustic features including pitch and volume were proposed for training. In addition, a time-frequency parameter obtained by continuous wavelet transforms was also used to identify the accent and intonation in a sentence during the recognition process. The Berlin Database of Emotional Speech was used by dividing the speech into male and female data sets for training. According to the experimental results, the accuracies of male and female test sets were increased by 4.6% and 5.2% respectively after using the time-frequency parameter for classifying happy and angry emotions. For the classification of all emotions, the average accuracy, including male and female data, was 63.5% for the test set and 90.9% for the whole data set.

Keywords: Smart phones, emotional speech recognition, socialnetworks, support vector machines, time-frequency parameter, Mel-scale frequency cepstral coefficients (MFCC).

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Authors: H. El Ayech, A. Trabelsi

Abstract:

In this article we are going to discuss the improvement of the multi classes- classification problem using multi layer Perceptron. The considered approach consists in breaking down the n-class problem into two-classes- subproblems. The training of each two-class subproblem is made independently; as for the phase of test, we are going to confront a vector that we want to classify to all two classes- models, the elected class will be the strongest one that won-t lose any competition with the other classes. Rates of recognition gotten with the multi class-s approach by two-class-s decomposition are clearly better that those gotten by the simple multi class-s approach.

Keywords: Artificial neural network, letter-recognition, Multi class Classification, Multi Layer Perceptron.

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Authors: Brigitte Rafael, Stefan M. Oertl

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Music segmentation is a key issue in music information retrieval (MIR) as it provides an insight into the internal structure of a composition. Structural information about a composition can improve several tasks related to MIR such as searching and browsing large music collections, visualizing musical structure, lyric alignment, and music summarization. The authors of this paper present the MTSSM framework, a twolayer framework for the multi-track segmentation of symbolic music. The strength of this framework lies in the combination of existing methods for local track segmentation and the application of global structure information spanning via multiple tracks. The first layer of the MTSSM uses various string matching techniques to detect the best candidate segmentations for each track of a multi-track composition independently. The second layer combines all single track results and determines the best segmentation for each track in respect to the global structure of the composition.

Keywords: Pattern Recognition, Music Information Retrieval, Machine Learning.

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Authors: Neeta Awasthy, J.P.Saini, D.S.Chauhan

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ICA which is generally used for blind source separation problem has been tested for feature extraction in Speech recognition system to replace the phoneme based approach of MFCC. Applying the Cepstral coefficients generated to ICA as preprocessing has developed a new signal processing approach. This gives much better results against MFCC and ICA separately, both for word and speaker recognition. The mixing matrix A is different before and after MFCC as expected. As Mel is a nonlinear scale. However, cepstrals generated from Linear Predictive Coefficient being independent prove to be the right candidate for ICA. Matlab is the tool used for all comparisons. The database used is samples of ISOLET.

Keywords: Cepstral Coefficient, Distance measures, Independent Component Analysis, Linear Predictive Coefficients.

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Authors: Renáta Iváncsy, István Vajk

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The various types of frequent pattern discovery problem, namely, the frequent itemset, sequence and graph mining problems are solved in different ways which are, however, in certain aspects similar. The main approach of discovering such patterns can be classified into two main classes, namely, in the class of the levelwise methods and in that of the database projection-based methods. The level-wise algorithms use in general clever indexing structures for discovering the patterns. In this paper a new approach is proposed for discovering frequent sequences and tree-like patterns efficiently that is based on the level-wise issue. Because the level-wise algorithms spend a lot of time for the subpattern testing problem, the new approach introduces the idea of using automaton theory to solve this problem.

Keywords: Frequent pattern discovery, graph mining, pushdownautomaton, sequence mining, state machine, tree mining.

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Authors: Akram M. Othman, May H. Riadh

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This research work is aimed at speech recognition using scaly neural networks. A small vocabulary of 11 words were established first, these words are “word, file, open, print, exit, edit, cut, copy, paste, doc1, doc2". These chosen words involved with executing some computer functions such as opening a file, print certain text document, cutting, copying, pasting, editing and exit. It introduced to the computer then subjected to feature extraction process using LPC (linear prediction coefficients). These features are used as input to an artificial neural network in speaker dependent mode. Half of the words are used for training the artificial neural network and the other half are used for testing the system; those are used for information retrieval. The system components are consist of three parts, speech processing and feature extraction, training and testing by using neural networks and information retrieval. The retrieve process proved to be 79.5-88% successful, which is quite acceptable, considering the variation to surrounding, state of the person, and the microphone type.

Keywords: Feature extraction, Liner prediction coefficients, neural network, Speech Recognition, Scaly ANN.

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Authors: Muhammad Raza Ali, Tim Morris

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Skin color is an important visual cue for computer vision systems involving human users. In this paper we combine skin color and optical flow for detection and tracking of skin regions. We apply these techniques to gesture recognition with encouraging results. We propose a novel skin similarity measure. For grouping detected skin regions we propose a novel skin region grouping mechanism. The proposed techniques work with any number of skin regions making them suitable for a multiuser scenario.

Keywords: Bayesian tracking, chromaticity space, optical flowgesture recognition

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Authors: Ying Zhao

Abstract:

Keystroke authentication is a new access control system to identify legitimate users via their typing behavior. In this paper, machine learning techniques are adapted for keystroke authentication. Seven learning methods are used to build models to differentiate user keystroke patterns. The selected classification methods are Decision Tree, Naive Bayesian, Instance Based Learning, Decision Table, One Rule, Random Tree and K-star. Among these methods, three of them are studied in more details. The results show that machine learning is a feasible alternative for keystroke authentication. Compared to the conventional Nearest Neighbour method in the recent research, learning methods especially Decision Tree can be more accurate. In addition, the experiment results reveal that 3-Grams is more accurate than 2-Grams and 4-Grams for feature extraction. Also, combination of attributes tend to result higher accuracy.

Keywords: Keystroke Authentication, Pattern recognition, MachineLearning, Instance-based Learning, Bayesian, Decision Tree.

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Authors: Banashree N. P., Andhe Dharani, R. Vasanta, P. S. Satyanarayana

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The applications on numbers are across-the-board that there is much scope for study. The chic of writing numbers is diverse and comes in a variety of form, size and fonts. Identification of Indian languages scripts is challenging problems. In Optical Character Recognition [OCR], machine printed or handwritten characters/numerals are recognized. There are plentiful approaches that deal with problem of detection of numerals/character depending on the sort of feature extracted and different way of extracting them. This paper proposes a recognition scheme for handwritten Hindi (devnagiri) numerals; most admired one in Indian subcontinent our work focused on a technique in feature extraction i.e. Local-based approach, a method using 16-segment display concept, which is extracted from halftoned images & Binary images of isolated numerals. These feature vectors are fed to neural classifier model that has been trained to recognize a Hindi numeral. The archetype of system has been tested on varieties of image of numerals. Experimentation result shows that recognition rate of halftoned images is 98 % compared to binary images (95%).

Keywords: OCR, Halftoning, Neural classifier, 16-segmentdisplay concept.

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Authors: Chunming Xu

Abstract:

Sparse representation which can represent high dimensional data effectively has been successfully used in computer vision and pattern recognition problems. However, it doesn-t consider the label information of data samples. To overcome this limitation, we develop a novel dimensionality reduction algorithm namely dscriminatively regularized sparse subspace learning(DR-SSL) in this paper. The proposed DR-SSL algorithm can not only make use of the sparse representation to model the data, but also can effective employ the label information to guide the procedure of dimensionality reduction. In addition,the presented algorithm can effectively deal with the out-of-sample problem.The experiments on gene-expression data sets show that the proposed algorithm is an effective tool for dimensionality reduction and gene-expression data classification.

Keywords: sparse representation, dimensionality reduction, labelinformation, sparse subspace learning, gene-expression data classification.

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Authors: Abhijit Mitra, Bhargav Kumar Mitra, Biswajoy Chatterjee

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We present a novel scheme to recognize isolated speech signals using certain statistical parameters derived from those signals. The determination of the statistical estimates is based on extracted signal information rather than the original signal information in order to reduce the computational complexity. Subtle details of these estimates, after extracting the speech signal from ambience noise, are first exploited to segregate the polysyllabic words from the monosyllabic ones. Precise recognition of each distinct word is then carried out by analyzing the histogram, obtained from these information.

Keywords: Isolated speech signals, Block overlapping technique, Positive peaks, Histogram analysis.

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Authors: Sufian Ashraf Mazhari, Surendra Kumar

Abstract:

This paper compares the heuristic Global Search Techniques; Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Generalized Pattern Search, genetic algorithm hybridized with Nelder–Mead and Generalized pattern search technique for tuning of fuzzy PID controller for Puma 560. Since the actual control is in joint space ,inverse kinematics is used to generate various joint angles correspoding to desired cartesian space trajectory. Efficient dynamics and kinematics are modeled on Matlab which takes very less simulation time. Performances of all the tuning methods with and without disturbance are compared in terms of ITSE in joint space and ISE in cartesian space for spiral trajectory tracking. Genetic Algorithm hybridized with Generalized Pattern Search is showing best performance.

Keywords: Controller tuning, Fuzzy Control, Genetic Algorithm, Heuristic search, Robot control.

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Authors: Ramin Javadzadeh., Zahra Afsahi, MohammadReza Meybodi

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The Artificial immune systems algorithms are Meta heuristic optimization method, which are used for clustering and pattern recognition applications are abundantly. These algorithms in multimodal optimization problems are more efficient than genetic algorithms. A major drawback in these algorithms is their slow convergence to global optimum and their weak stability can be considered in various running of these algorithms. In this paper, improved Artificial Immune System Algorithm is introduced for the first time to overcome its problems of artificial immune system. That use of the small size of a local search around the memory antibodies is used for improving the algorithm efficiently. The credibility of the proposed approach is evaluated by simulations, and it is shown that the proposed approach achieves better results can be achieved compared to the standard artificial immune system algorithms

Keywords: Artificial immune system, Cellular Automata, Cellular learning automata, Cellular learning automata, , Local search, Optimization.

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Authors: Tossaporn Kachanubal, Somkait Udomhunsakul

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In this paper, we proposed a method to classify each type of natural rock texture. Our goal is to classify 26 classes of rock textures. First, we extract five features of each class by using principle component analysis combining with the use of applied spatial frequency measurement. Next, the effective node number of neural network was tested. We used the most effective neural network in classification process. The results from this system yield quite high in recognition rate. It is shown that high recognition rate can be achieved in separation of 26 stone classes.

Keywords: Texture classification, SFM, neural network, rock texture classification.

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Authors: Ber-Lin Yu, Ting-Zhu Huang

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If there exists a nonempty, proper subset S of the set of all (n + 1)(n + 2)/2 inertias such that S Ôèå i(A) is sufficient for any n × n zero-nonzero pattern A to be inertially arbitrary, then S is called a critical set of inertias for zero-nonzero patterns of order n. If no proper subset of S is a critical set, then S is called a minimal critical set of inertias. In [3], Kim, Olesky and Driessche identified all minimal critical sets of inertias for 2 × 2 zero-nonzero patterns. Identifying all minimal critical sets of inertias for n × n zero-nonzero patterns with n ≥ 3 is posed as an open question in [3]. In this paper, all minimal critical sets of inertias for 3 × 3 zero-nonzero patterns are identified. It is shown that the sets {(0, 0, 3), (3, 0, 0)}, {(0, 0, 3), (0, 3, 0)}, {(0, 0, 3), (0, 1, 2)}, {(0, 0, 3), (1, 0, 2)}, {(0, 0, 3), (2, 0, 1)} and {(0, 0, 3), (0, 2, 1)} are the only minimal critical sets of inertias for 3 × 3 irreducible zerononzero patterns.

Keywords: Permutation digraph, zero-nonzero pattern, irreducible pattern, critical set of inertias, inertially arbitrary.

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Authors: J. Kavitha, S. Sowmyayani, P. Arockia Jansi Rani

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In this paper, a shot boundary detection method is presented using octagon square search pattern. The color, edge, motion and texture features of each frame are extracted and used in shot boundary detection. The motion feature is extracted using octagon square search pattern. Then, the transition detection method is capable of detecting the shot or non-shot boundaries in the video using the feature weight values. Experimental results are evaluated in TRECVID video test set containing various types of shot transition with lighting effects, object and camera movement within the shots. Further, this paper compares the experimental results of the proposed method with existing methods. It shows that the proposed method outperforms the state-of-art methods for shot boundary detection.

Keywords: Content-based indexing and retrieval, cut transition detection, discrete wavelet transform, shot boundary detection, video source.

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Authors: Jan Flusser

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This paper aims to present a survey of object recognition/classification methods based on image moments. We review various types of moments (geometric moments, complex moments) and moment-based invariants with respect to various image degradations and distortions (rotation, scaling, affine transform, image blurring, etc.) which can be used as shape descriptors for classification. We explain a general theory how to construct these invariants and show also a few of them in explicit forms. We review efficient numerical algorithms that can be used for moment computation and demonstrate practical examples of using moment invariants in real applications.

Keywords: Object recognition, degraded images, moments, moment invariants, geometric invariants, invariants to convolution, moment computation.

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Authors: Yanhui Zhang, Wenyu Yang

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One of the difficulties of the vibration-based damage identification methods is the nonuniqueness of the results of damage identification. The different damage locations and severity may cause the identical response signal, which is even more severe for detection of the multiple damage. This paper proposes a new strategy for damage detection to avoid this nonuniqueness. This strategy firstly determines the approximates damage area based on the statistical pattern recognition method using the dynamic strain signal measured by the distributed fiber Bragg grating, and then accurately evaluates the damage information based on the Bayesian model updating method using the experimental modal data. The stochastic simulation method is then used to compute the high-dimensional integral in the Bayesian problem. Finally, an experiment of the plate structure, simulating one part of mechanical structure, is used to verify the effectiveness of this approach.

Keywords: Bayesian method, damage detection, fiber Bragg grating, structural health monitoring.

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Authors: Girija Chetty, Monica Singh

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In this paper we propose a novel approach for ascertaining human identity based on fusion of profile face and gait biometric cues The identification approach based on feature learning in PCA-LDA subspace, and classification using multivariate Bayesian classifiers allows significant improvement in recognition accuracy for low resolution surveillance video scenarios. The experimental evaluation of the proposed identification scheme on a publicly available database [2] showed that the fusion of face and gait cues in joint PCA-LDA space turns out to be a powerful method for capturing the inherent multimodality in walking gait patterns, and at the same time discriminating the person identity..

Keywords: Biometrics, gait recognition, PCA, LDA, Eigenface, Fisherface, Multivariate Gaussian Classifier

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Authors: Jamuna Bhandari

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String matching also known as pattern matching is one of primary concept for network security. In this area the effectiveness and efficiency of string matching algorithms is important for applications in network security such as network intrusion detection, virus detection, signature matching and web content filtering system. This paper presents brief review on some of string matching techniques used for network security.

Keywords: Filtering, honeypot, network telescope, pattern, string, signature.

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Authors: Seo-Hee Kim, Kee-Won Kim, Seung-Hoon Kim

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The most important elements of a novel are the characters, events and background. The background represents the time, place and situation that character appears, and conveys event and atmosphere more realistically. If readers have the proper knowledge about background of novels, it may be helpful for understanding the atmosphere of a novel and choosing a novel that readers want to read. In this paper, we are targeting Korean historical novels because spatio-temporal background especially performs an important role in historical novels among the genre of Korean novels. To the best of our knowledge, we could not find previous study that was aimed at Korean novels. In this paper, we build a Korean historical national dictionary. Our dictionary has historical places and temple names of kings over many generations as well as currently existing spatial words or temporal words in Korean history. We also present a method for recognizing spatio-temporal background based on patterns of phrasal words in Korean sentences. Our rules utilize postposition for spatial background recognition and temple names for temporal background recognition. The knowledge of the recognized background can help readers to understand the flow of events and atmosphere, and can use to visualize the elements of novels.

Keywords: Data mining, Korean historical novels, Korean linguistic feature, spatio-temporal background.

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Authors: Yuzana, Khin Marlar Tun

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Personal name matching system is the core of essential task in national citizen database, text and web mining, information retrieval, online library system, e-commerce and record linkage system. It has necessitated to the all embracing research in the vicinity of name matching. Traditional name matching methods are suitable for English and other Latin based language. Asian languages which have no word boundary such as Myanmar language still requires sounds alike matching system in Unicode based application. Hence we proposed matching algorithm to get analogous sounds alike (phonetic) pattern that is convenient for Myanmar character spelling. According to the nature of Myanmar character, we consider for word boundary fragmentation, collation of character. Thus we use pattern conversion algorithm which fabricates words in pattern with fragmented and collated. We create the Myanmar sounds alike phonetic group to help in the phonetic matching. The experimental results show that fragmentation accuracy in 99.32% and processing time in 1.72 ms.

Keywords: natural language processing, name matching, phonetic matching

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Authors: Xu Han, Shanxiong Chen, Shiyu Zhu, Xiaoyu Lin, Fujia Zhao, Dingwang Wang

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Character detection is an important issue for character recognition of ancient Yi books. The accuracy of detection directly affects the recognition effect of ancient Yi books. Considering the complex layout, the lack of standard typesetting and the mixed arrangement between images and texts, we propose a character detection method for ancient Yi books based on connected components and regressive character segmentation. First, the scanned images of ancient Yi books are preprocessed with nonlocal mean filtering, and then a modified local adaptive threshold binarization algorithm is used to obtain the binary images to segment the foreground and background for the images. Second, the non-text areas are removed by the method based on connected components. Finally, the single character in the ancient Yi books is segmented by our method. The experimental results show that the method can effectively separate the text areas and non-text areas for ancient Yi books and achieve higher accuracy and recall rate in the experiment of character detection, and effectively solve the problem of character detection and segmentation in character recognition of ancient books.

Keywords: Computing methodologies, interest point, salient region detections, image segmentation.

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Authors: Hamdi Amroun, Yacine Benziani, Mehdi Ammi

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In this paper, we want to determine whether the accurate prediction of nicotine concentration can be obtained by using a network of smart objects and an e-cigarette. The approach consists of, first, the recognition of factors influencing smoking cessation such as physical activity recognition and participant’s behaviors (using both smartphone and smartwatch), then the prediction of the configuration of the e-cigarette (in terms of nicotine concentration, power, and resistance of e-cigarette). The study uses a network of commonly connected objects; a smartwatch, a smartphone, and an e-cigarette transported by the participants during an uncontrolled experiment. The data obtained from sensors carried in the three devices were trained by a Long short-term memory algorithm (LSTM). Results show that our LSTM-based model allows predicting the configuration of the e-cigarette in terms of nicotine concentration, power, and resistance with a root mean square error percentage of 12.9%, 9.15%, and 11.84%, respectively. This study can help to better control consumption of nicotine and offer an intelligent configuration of the e-cigarette to users.

Keywords: Iot, activity recognition, automatic classification, unconstrained environment, deep neural networks.

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Authors: Besa Muslimi, Miriam A. M. Capretz, Jagath Samarabandu

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Artificial neural networks (ANN) have the ability to model input-output relationships from processing raw data. This characteristic makes them invaluable in industry domains where such knowledge is scarce at best. In the recent decades, in order to overcome the black-box characteristic of ANNs, researchers have attempted to extract the knowledge embedded within ANNs in the form of rules that can be used in inference systems. This paper presents a new technique that is able to extract a small set of rules from a two-layer ANN. The extracted rules yield high classification accuracy when implemented within a fuzzy inference system. The technique targets industry domains that possess less complex problems for which no expert knowledge exists and for which a simpler solution is preferred to a complex one. The proposed technique is more efficient, simple, and applicable than most of the previously proposed techniques.

Keywords: fuzzy rule extraction, fuzzy systems, knowledgeacquisition, pattern recognition, artificial neural networks.

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Authors: Carlos Machado, José A. Mendes

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Home Automation is a field that, among other subjects, is concerned with the comfort, security and energy requirements of private homes. The configuration of automatic functions in this type of houses is not always simple to its inhabitants requiring the initial setup and regular adjustments. In this work, the ubiquitous computing system vision is used, where the users- action patterns are captured, recorded and used to create the contextawareness that allows the self-configuration of the home automation system. The system will try to free the users from setup adjustments as the home tries to adapt to its inhabitants- real habits. In this paper it is described a completely automated process to determine the light state and act on them, taking in account the users- daily habits. Artificial Neural Network (ANN) is used as a pattern recognition method, classifying for each moment the light state. The work presented uses data from a real house where a family is actually living.

Keywords: ANN, Home Automation, Neural Systems, PatternRecognition, Ubiquitous Computing.

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Authors: Rachel C. F. Sun

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This study examined the predictive effects of moral competence, prosocial norms and positive behavior recognition on school misbehavior among Chinese junior secondary school students. Results of multiple regression analysis showed that students were more likely to misbehave in school when they had lower levels of moral competence and prosocial norms, and when they perceived their positive behavior being less likely recognized. Practical implications were discussed on how to guide students to make the right choices to behave appropriately in school. Implications for future research were also discussed.

Keywords: Moral competence, positive behavior recognition, prosocial norms, school misbehavior.

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Authors: Mourad Ykhlef, Hebah ElGibreen

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Mining Sequential Patterns in large databases has become an important data mining task with broad applications. It is an important task in data mining field, which describes potential sequenced relationships among items in a database. There are many different algorithms introduced for this task. Conventional algorithms can find the exact optimal Sequential Pattern rule but it takes a long time, particularly when they are applied on large databases. Nowadays, some evolutionary algorithms, such as Particle Swarm Optimization and Genetic Algorithm, were proposed and have been applied to solve this problem. This paper will introduce a new kind of hybrid evolutionary algorithm that combines Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) to mine Sequential Pattern, in order to improve the speed of evolutionary algorithms convergence. This algorithm is referred to as SP-GAPSO.

Keywords: Genetic Algorithm, Hybrid Evolutionary Algorithm, Particle Swarm Optimization algorithm, Sequential Pattern mining.

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