Search results for: Ganoderma Infection pattern recognition
1259 A Constructive Analysis of the Formation of LGBTQ Families: Where Utopia and Reality Meet
Authors: Panagiotis Pentaris
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The issue of social and legal recognition of LGBTQ families is of high importance when exploring the possibility of a family. Of equal importance is the fact that both society and the individual contribute to the overall recognition of LGBTQ families. This paper is a conceptual discussion, by methodology, of both sides; it uses a method of constructive analysis to expound on this issue. This method’s aim is to broaden conceptual theory, and introduce a new relationship between concepts that were previously not associated by evidence. This exploration has found that LGBTQ realities from an international perspective may differ and both legal and social rights are critical toward self-consciousness and the formation of a family. This paper asserts that internalised and historic oppression of LGBTQ individuals, places them, not always and not in all places, in a disadvantageous position as far as engaging with the potential of forming a family goes. The paper concludes that lack of social recognition and internalised oppression are key barriers regarding LGBTQ families.
Keywords: Family, gay, LGBTQ, self-worth, social rights.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21151258 String Matching using Inverted Lists
Authors: Chouvalit Khancome, Veera Boonjing
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This paper proposes a new solution to string matching problem. This solution constructs an inverted list representing a string pattern to be searched for. It then uses a new algorithm to process an input string in a single pass. The preprocessing phase takes 1) time complexity O(m) 2) space complexity O(1) where m is the length of pattern. The searching phase time complexity takes 1) O(m+α ) in average case 2) O(n/m) in the best case and 3) O(n) in the worst case, where α is the number of comparing leading to mismatch and n is the length of input text.
Keywords: String matching, inverted list, inverted index, pattern, algorithm.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15421257 Identification of COVID-SARS Variants Based on Lactate Test Results
Authors: Zoltan Horvath, Dora Nagy
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In this research, it was examined whether individual COVID variants cause differences in the lactate curve of cyclists. After all, the virus variants attacked different organs in our body during the infections. During our tests, we used a traditional lactate step test, the results of which were compared with the values before the infection. In the tests, it has been proven that different virus variants show unique lactate curves. In this way, based on the lactate curve, it is possible to identify which variant caused the disease. Thanks to this, the return time has been shorten, because we can apply the best return protocol after infection to the competitors.
Keywords: SARS-CoV-2, lactate step test, virus mutation, lactate profile.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 631256 Identification of Binding Proteins That Interact with BVDV E2 Protein in Bovine Trophoblast Cell
Authors: Yan Ren, Fei Guo, Jun Qiao, Shengwei Hu, Hui Zhang, Yuanzhi Wang, Pengyan Wang, Jinliang Sheng, Xinli Gu, Xiaojun Liu, Chuangfu Chen
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Bovine viral diarrhea virus (BVDV) can cause lifelong persistent infection. One reason for the phenomena is attributed to BVDV infection to placenta tissue. However the mechanisms that BVDV invades into placenta tissue remain unclear. To clarify the molecular mechanisms, we investigated the possible means that BVDV entered into bovine trophoblast cells (TPC). Yeast two-hybrid system was used to identify proteins extracted from TPC, which interact with BVDV envelope glycoprotein E2. A PGbkt7-E2 yeast expression vector and TPC cDNA library were constructed. Through two rounds of screening, three positive clones were identified. Sequencing analysis indicated that all the three positive clones encoded the same protein clathrin. Physical interaction between clathrin and BVDV E2 protein was further confirmed by coimmunoprecipitation experiments. This result suggested that the clathrin might play a critical role in the process of BVDV entry into placenta tissue and might be a novel antiviral target for preventing BVDV infection.Keywords: Bovine viral diarrhea virus, clathrin, glycoprotein E2, yeast two-hybrid system.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19841255 Characterization of 3D-MRP for Analyzing of Brain Balancing Index (BBI) Pattern
Authors: N. Fuad, M. N. Taib, R. Jailani, M. E. Marwan
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This paper discusses on power spectral density (PSD) characteristics which are extracted from three-dimensional (3D) electroencephalogram (EEG) models. The EEG signal recording was conducted on 150 healthy subjects. Development of 3D EEG models involves pre-processing of raw EEG signals and construction of spectrogram images. Then, the values of maximum PSD were extracted as features from the model. These features are analyzed using mean relative power (MRP) and different mean relative power (DMRP) technique to observe the pattern among different brain balancing indexes. The results showed that by implementing these techniques, the pattern of brain balancing indexes can be clearly observed. Some patterns are indicates between index 1 to index 5 for left frontal (LF) and right frontal (RF).
Keywords: Power spectral density, 3D EEG model, brain balancing, mean relative power, different mean relative power.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18991254 Recognition of Noisy Words Using the Time Delay Neural Networks Approach
Authors: Khenfer-Koummich Fatima, Mesbahi Larbi, Hendel Fatiha
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This paper presents a recognition system for isolated words like robot commands. It’s carried out by Time Delay Neural Networks; TDNN. To teleoperate a robot for specific tasks as turn, close, etc… In industrial environment and taking into account the noise coming from the machine. The choice of TDNN is based on its generalization in terms of accuracy, in more it acts as a filter that allows the passage of certain desirable frequency characteristics of speech; the goal is to determine the parameters of this filter for making an adaptable system to the variability of speech signal and to noise especially, for this the back propagation technique was used in learning phase. The approach was applied on commands pronounced in two languages separately: The French and Arabic. The results for two test bases of 300 spoken words for each one are 87%, 97.6% in neutral environment and 77.67%, 92.67% when the white Gaussian noisy was added with a SNR of 35 dB.
Keywords: Neural networks, Noise, Speech Recognition.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19241253 Reconstruction of the Most Energetic Modes in a Fully Developed Turbulent Channel Flow with Density Variation
Authors: Elteyeb Eljack, Takashi Ohta
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Proper orthogonal decomposition (POD) is used to reconstruct spatio-temporal data of a fully developed turbulent channel flow with density variation at Reynolds number of 150, based on the friction velocity and the channel half-width, and Prandtl number of 0.71. To apply POD to the fully developed turbulent channel flow with density variation, the flow field (velocities, density, and temperature) is scaled by the corresponding root mean square values (rms) so that the flow field becomes dimensionless. A five-vector POD problem is solved numerically. The reconstructed second-order moments of velocity, temperature, and density from POD eigenfunctions compare favorably to the original Direct Numerical Simulation (DNS) data.
Keywords: Pattern Recognition, POD, Coherent Structures, Low dimensional modelling.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13641252 Face Recognition with PCA and KPCA using Elman Neural Network and SVM
Authors: Hossein Esbati, Jalil Shirazi
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In this paper, in order to categorize ORL database face pictures, principle Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA) methods by using Elman neural network and Support Vector Machine (SVM) categorization methods are used. Elman network as a recurrent neural network is proposed for modeling storage systems and also it is used for reviewing the effect of using PCA numbers on system categorization precision rate and database pictures categorization time. Categorization stages are conducted with various components numbers and the obtained results of both Elman neural network categorization and support vector machine are compared. In optimum manner 97.41% recognition accuracy is obtained.Keywords: Face recognition, Principal Component Analysis, Kernel Principal Component Analysis, Neural network, Support Vector Machine.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19131251 A New Vector Quantization Front-End Process for Discrete HMM Speech Recognition System
Authors: M. Debyeche, J.P Haton, A. Houacine
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The paper presents a complete discrete statistical framework, based on a novel vector quantization (VQ) front-end process. This new VQ approach performs an optimal distribution of VQ codebook components on HMM states. This technique that we named the distributed vector quantization (DVQ) of hidden Markov models, succeeds in unifying acoustic micro-structure and phonetic macro-structure, when the estimation of HMM parameters is performed. The DVQ technique is implemented through two variants. The first variant uses the K-means algorithm (K-means- DVQ) to optimize the VQ, while the second variant exploits the benefits of the classification behavior of neural networks (NN-DVQ) for the same purpose. The proposed variants are compared with the HMM-based baseline system by experiments of specific Arabic consonants recognition. The results show that the distributed vector quantization technique increase the performance of the discrete HMM system.
Keywords: Hidden Markov Model, Vector Quantization, Neural Network, Speech Recognition, Arabic Language
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20431250 The Labeled Classification and its Application
Authors: M. Nemissi, H. Seridi, H. Akdag
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This paper presents and evaluates a new classification method that aims to improve classifiers performances and speed up their training process. The proposed approach, called labeled classification, seeks to improve convergence of the BP (Back propagation) algorithm through the addition of an extra feature (labels) to all training examples. To classify every new example, tests will be carried out each label. The simplicity of implementation is the main advantage of this approach because no modifications are required in the training algorithms. Therefore, it can be used with others techniques of acceleration and stabilization. In this work, two models of the labeled classification are proposed: the LMLP (Labeled Multi Layered Perceptron) and the LNFC (Labeled Neuro Fuzzy Classifier). These models are tested using Iris, wine, texture and human thigh databases to evaluate their performances.Keywords: Artificial neural networks, Fusion of neural networkfuzzysystems, Learning theory, Pattern recognition.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13991249 The Influence of Job Recognition and Job Motivation on Organizational Commitment in Public Sector: The Mediation Role of Employee Engagement
Authors: Muhammad Tayyab, Saba Saira
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It is an established fact that organizations across the globe consider employees as their assets and try to advance their well-being. However, the local firms of developing countries are mostly profit oriented and do not have much concern about their employees’ engagement or commitment. Like other developing countries, the local organizations of Pakistan are also less concerned about the well-being of their employees. Especially public sector organizations lack concern regarding engagement, satisfaction or commitment of the employees. Therefore, this study aimed at investigating the impact of job recognition and job motivation on organizational commitment in the mediation role of employee engagement. The data were collected from land record officers of board of revenue, Punjab, Pakistan. Structured questionnaire was used to collect data through physically visiting land record officers and also through the internet. A total of 318 land record officers’ responses were finalized to perform data analysis. The data were analyzed through confirmatory factor analysis and structural equation modeling technique. The findings revealed that job recognition and job motivation have direct as well as indirect positive and significant impact on organizational commitment. The limitations, practical implications and future research indications are also explained.Keywords: Job motivation, job recognition, employee engagement, employee commitment, public sector, land record officers.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 8171248 Target Detection with Improved Image Texture Feature Coding Method and Support Vector Machine
Authors: R. Xu, X. Zhao, X. Li, C. Kwan, C.-I Chang
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An image texture analysis and target recognition approach of using an improved image texture feature coding method (TFCM) and Support Vector Machine (SVM) for target detection is presented. With our proposed target detection framework, targets of interest can be detected accurately. Cascade-Sliding-Window technique was also developed for automated target localization. Application to mammogram showed that over 88% of normal mammograms and 80% of abnormal mammograms can be correctly identified. The approach was also successfully applied to Synthetic Aperture Radar (SAR) and Ground Penetrating Radar (GPR) images for target detection.
Keywords: Image texture analysis, feature extraction, target detection, pattern classification.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17631247 Evolutionary Eigenspace Learning using CCIPCA and IPCA for Face Recognition
Authors: Ghazy M.R. Assassa, Mona F. M. Mursi, Hatim A. Aboalsamh
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Traditional principal components analysis (PCA) techniques for face recognition are based on batch-mode training using a pre-available image set. Real world applications require that the training set be dynamic of evolving nature where within the framework of continuous learning, new training images are continuously added to the original set; this would trigger a costly continuous re-computation of the eigen space representation via repeating an entire batch-based training that includes the old and new images. Incremental PCA methods allow adding new images and updating the PCA representation. In this paper, two incremental PCA approaches, CCIPCA and IPCA, are examined and compared. Besides, different learning and testing strategies are proposed and applied to the two algorithms. The results suggest that batch PCA is inferior to both incremental approaches, and that all CCIPCAs are practically equivalent.Keywords: Candid covariance-free incremental principal components analysis (CCIPCA), face recognition, incremental principal components analysis (IPCA).
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18111246 Teager-Huang Analysis Applied to Sonar Target Recognition
Authors: J.-C. Cexus, A.O. Boudraa
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In this paper, a new approach for target recognition based on the Empirical mode decomposition (EMD) algorithm of Huang etal. [11] and the energy tracking operator of Teager [13]-[14] is introduced. The conjunction of these two methods is called Teager-Huang analysis. This approach is well suited for nonstationary signals analysis. The impulse response (IR) of target is first band pass filtered into subsignals (components) called Intrinsic mode functions (IMFs) with well defined Instantaneous frequency (IF) and Instantaneous amplitude (IA). Each IMF is a zero-mean AM-FM component. In second step, the energy of each IMF is tracked using the Teager energy operator (TEO). IF and IA, useful to describe the time-varying characteristics of the signal, are estimated using the Energy separation algorithm (ESA) algorithm of Maragos et al .[16]-[17]. In third step, a set of features such as skewness and kurtosis are extracted from the IF, IA and IMF energy functions. The Teager-Huang analysis is tested on set of synthetic IRs of Sonar targets with different physical characteristics (density, velocity, shape,? ). PCA is first applied to features to discriminate between manufactured and natural targets. The manufactured patterns are classified into spheres and cylinders. One hundred percent of correct recognition is achieved with twenty three echoes where sixteen IRs, used for training, are free noise and seven IRs, used for testing phase, are corrupted with white Gaussian noise.
Keywords: Target recognition, Empirical mode decomposition, Teager-Kaiser energy operator, Features extraction.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22661245 Belief Theory-Based Classifiers Comparison for Static Human Body Postures Recognition in Video
Authors: V. Girondel, L. Bonnaud, A. Caplier, M. Rombaut
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This paper presents various classifiers results from a system that can automatically recognize four different static human body postures in video sequences. The considered postures are standing, sitting, squatting, and lying. The three classifiers considered are a naïve one and two based on the belief theory. The belief theory-based classifiers use either a classic or restricted plausibility criterion to make a decision after data fusion. The data come from the people 2D segmentation and from their face localization. Measurements consist in distances relative to a reference posture. The efficiency and the limits of the different classifiers on the recognition system are highlighted thanks to the analysis of a great number of results. This system allows real-time processing.
Keywords: Belief theory, classifiers comparison, data fusion, human motion analysis, real-time processing, static posture recognition.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15021244 Applying Sequential Pattern Mining to Generate Block for Scheduling Problems
Authors: Meng-Hui Chen, Chen-Yu Kao, Chia-Yu Hsu, Pei-Chann Chang
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The main idea in this paper is using sequential pattern mining to find the information which is helpful for finding high performance solutions. By combining this information, it is defined as blocks. Using the blocks to generate artificial chromosomes (ACs) could improve the structure of solutions. Estimation of Distribution Algorithms (EDAs) is adapted to solve the combinatorial problems. Nevertheless many of these approaches are advantageous for this application, but only some of them are used to enhance the efficiency of application. Generating ACs uses patterns and EDAs could increase the diversity. According to the experimental result, the algorithm which we proposed has a better performance to solve the permutation flow-shop problems.
Keywords: Combinatorial problems, Sequential Pattern Mining, Estimation of Distribution Algorithms, Artificial Chromosomes.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17061243 A Novel Modified Adaptive Fuzzy Inference Engine and Its Application to Pattern Classification
Authors: J. Hossen, A. Rahman, K. Samsudin, F. Rokhani, S. Sayeed, R. Hasan
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The Neuro-Fuzzy hybridization scheme has become of research interest in pattern classification over the past decade. The present paper proposes a novel Modified Adaptive Fuzzy Inference Engine (MAFIE) for pattern classification. A modified Apriori algorithm technique is utilized to reduce a minimal set of decision rules based on input output data sets. A TSK type fuzzy inference system is constructed by the automatic generation of membership functions and rules by the fuzzy c-means clustering and Apriori algorithm technique, respectively. The generated adaptive fuzzy inference engine is adjusted by the least-squares fit and a conjugate gradient descent algorithm towards better performance with a minimal set of rules. The proposed MAFIE is able to reduce the number of rules which increases exponentially when more input variables are involved. The performance of the proposed MAFIE is compared with other existing applications of pattern classification schemes using Fisher-s Iris and Wisconsin breast cancer data sets and shown to be very competitive.Keywords: Apriori algorithm, Fuzzy C-means, MAFIE, TSK
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19171242 Input Data Balancing in a Neural Network PM-10 Forecasting System
Authors: Suk-Hyun Yu, Heeyong Kwon
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Recently PM-10 has become a social and global issue. It is one of major air pollutants which affect human health. Therefore, it needs to be forecasted rapidly and precisely. However, PM-10 comes from various emission sources, and its level of concentration is largely dependent on meteorological and geographical factors of local and global region, so the forecasting of PM-10 concentration is very difficult. Neural network model can be used in the case. But, there are few cases of high concentration PM-10. It makes the learning of the neural network model difficult. In this paper, we suggest a simple input balancing method when the data distribution is uneven. It is based on the probability of appearance of the data. Experimental results show that the input balancing makes the neural networks’ learning easy and improves the forecasting rates.
Keywords: AI, air quality prediction, neural networks, pattern recognition, PM-10.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 8211241 Generation of Sets of Synthetic Classifiers for the Evaluation of Abstract-Level Combination Methods
Authors: N. Greco, S. Impedovo, R.Modugno, G. Pirlo
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This paper presents a new technique for generating sets of synthetic classifiers to evaluate abstract-level combination methods. The sets differ in terms of both recognition rates of the individual classifiers and degree of similarity. For this purpose, each abstract-level classifier is considered as a random variable producing one class label as the output for an input pattern. From the initial set of classifiers, new slightly different sets are generated by applying specific operators, which are defined at the purpose. Finally, the sets of synthetic classifiers have been used to estimate the performance of combination methods for abstract-level classifiers. The experimental results demonstrate the effectiveness of the proposed approach.
Keywords: Abstract-level Classifier, Dempster-Shafer Rule, Multi-expert Systems, Similarity Index, System Evaluation
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14731240 Investigation of the Tattooed Skin by OCT
Authors: Young Geun Kim, Tae Woo Lee, Changmin Yeo, Jung min Yoo, Yeo Jin Kang, Tack-Joong Kim, Byungjo Jung, Ji Hun Cha, Chan Hoi Hur, Dong-Sup Kim, Ki Jung Park, Han Sung Kim
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The intention of this lessons is to assess the probability of optical coherence tomography (OCT) for biometric recognition. The OCT is the foundation on an optical signal acquisition and processing method and has the micrometer-resolution. In this study, we used the porcine skin for verifying the abovementioned means. The porcine tissue was sound acknowledged for structural and immunohistochemical similarity with human skin, so it could be suitable for pre-clinical trial as investigational specimen. For this reason, it was tattooed by the tattoo machine with the tattoo-pigment. We detected the pattern of the tattooed skin by the OCT according to needle speed. The result was consistent with the histology images. This result showed that the OCT was effective to examine the tattooed skin section noninvasively. It might be available to identify morphological changes inside the skin.Keywords: mechanical skin damage, optical coherence tomography, tattooed skin
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17511239 Test Data Compression Using a Hybrid of Bitmask Dictionary and 2n Pattern Runlength Coding Methods
Authors: C. Kalamani, K. Paramasivam
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In VLSI, testing plays an important role. Major problem in testing are test data volume and test power. The important solution to reduce test data volume and test time is test data compression. The Proposed technique combines the bit maskdictionary and 2n pattern run length-coding method and provides a substantial improvement in the compression efficiency without introducing any additional decompression penalty. This method has been implemented using Mat lab and HDL Language to reduce test data volume and memory requirements. This method is applied on various benchmark test sets and compared the results with other existing methods. The proposed technique can achieve a compression ratio up to 86%.Keywords: Bit Mask dictionary, 2n pattern run length code, system-on-chip, SOC, test data compression.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19071238 Low Resolution Single Neural Network Based Face Recognition
Authors: Jahan Zeb, Muhammad Younus Javed, Usman Qayyum
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This research paper deals with the implementation of face recognition using neural network (recognition classifier) on low-resolution images. The proposed system contains two parts, preprocessing and face classification. The preprocessing part converts original images into blurry image using average filter and equalizes the histogram of those image (lighting normalization). The bi-cubic interpolation function is applied onto equalized image to get resized image. The resized image is actually low-resolution image providing faster processing for training and testing. The preprocessed image becomes the input to neural network classifier, which uses back-propagation algorithm to recognize the familiar faces. The crux of proposed algorithm is its beauty to use single neural network as classifier, which produces straightforward approach towards face recognition. The single neural network consists of three layers with Log sigmoid, Hyperbolic tangent sigmoid and Linear transfer function respectively. The training function, which is incorporated in our work, is Gradient descent with momentum (adaptive learning rate) back propagation. The proposed algorithm was trained on ORL (Olivetti Research Laboratory) database with 5 training images. The empirical results provide the accuracy of 94.50%, 93.00% and 90.25% for 20, 30 and 40 subjects respectively, with time delay of 0.0934 sec per image.Keywords: Average filtering, Bicubic Interpolation, Neurons, vectorization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17401237 Electroencephalography-Based Intention Recognition and Consensus Assessment during Emergency Response
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After natural and man-made disasters, robots can bypass the danger, expedite the search, and acquire unprecedented situational awareness to design rescue plans. Brain-computer interface is a promising option to overcome the limitations of tedious manual control and operation of robots in the urgent search-and-rescue tasks. This study aims to test the feasibility of using electroencephalography (EEG) signals to decode human intentions and detect the level of consensus on robot-provided information. EEG signals were classified using machine-learning and deep-learning methods to discriminate search intentions and agreement perceptions. The results show that the average classification accuracy for intention recognition and consensus assessment is 67% and 72%, respectively, proving the potential of incorporating recognizable users’ bioelectrical responses into advanced robot-assisted systems for emergency response.
Keywords: Consensus assessment, electroencephalogram, EEG, emergency response, human-robot collaboration, intention recognition, search and rescue.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3161236 Some Immunological Characteristics of Tick- Borne Encephalitis in Perm Region
Authors: Some Immunological Characteristics of Tick- Borne Encephalitis in Perm Region
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It is shown that the relationship of tick-borne encephalitis virus with the human body comes in two ways, the development of acute infection with the outcome in convalescence and long stay by the virus in the body, its persistence in the nervous tissue with periodic reactivation and prolonged circulating immunoglobulin M. In spite of the fact that tick-borne encephalitis virus has a tropism for nerve tissue, involvement in the process of blood cells is an integral component of the infection. Comprehensive study of the relation of factors of innate and adaptive immunity in the tick-borne encephalitis providing insight into the features of chronic disease.Keywords: Tick-borne encephalitis, phagocytic activity, a progressive.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14521235 Facial Expression Phoenix (FePh): An Annotated Sequenced Dataset for Facial and Emotion-Specified Expressions in Sign Language
Authors: Marie Alaghband, Niloofar Yousefi, Ivan Garibay
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Facial expressions are important parts of both gesture and sign language recognition systems. Despite the recent advances in both fields, annotated facial expression datasets in the context of sign language are still scarce resources. In this manuscript, we introduce an annotated sequenced facial expression dataset in the context of sign language, comprising over 3000 facial images extracted from the daily news and weather forecast of the public tv-station PHOENIX. Unlike the majority of currently existing facial expression datasets, FePh provides sequenced semi-blurry facial images with different head poses, orientations, and movements. In addition, in the majority of images, identities are mouthing the words, which makes the data more challenging. To annotate this dataset we consider primary, secondary, and tertiary dyads of seven basic emotions of "sad", "surprise", "fear", "angry", "neutral", "disgust", and "happy". We also considered the "None" class if the image’s facial expression could not be described by any of the aforementioned emotions. Although we provide FePh as a facial expression dataset of signers in sign language, it has a wider application in gesture recognition and Human Computer Interaction (HCI) systems.Keywords: Annotated Facial Expression Dataset, Sign Language Recognition, Gesture Recognition, Sequenced Facial Expression Dataset.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 6991234 A Two-Stage Adaptation towards Automatic Speech Recognition System for Malay-Speaking Children
Authors: Mumtaz Begum Mustafa, Siti Salwah Salim, Feizal Dani Rahman
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Recently, Automatic Speech Recognition (ASR) systems were used to assist children in language acquisition as it has the ability to detect human speech signal. Despite the benefits offered by the ASR system, there is a lack of ASR systems for Malay-speaking children. One of the contributing factors for this is the lack of continuous speech database for the target users. Though cross-lingual adaptation is a common solution for developing ASR systems for under-resourced language, it is not viable for children as there are very limited speech databases as a source model. In this research, we propose a two-stage adaptation for the development of ASR system for Malay-speaking children using a very limited database. The two stage adaptation comprises the cross-lingual adaptation (first stage) and cross-age adaptation. For the first stage, a well-known speech database that is phonetically rich and balanced, is adapted to the medium-sized Malay adults using supervised MLLR. The second stage adaptation uses the speech acoustic model generated from the first adaptation, and the target database is a small-sized database of the target users. We have measured the performance of the proposed technique using word error rate, and then compare them with the conventional benchmark adaptation. The two stage adaptation proposed in this research has better recognition accuracy as compared to the benchmark adaptation in recognizing children’s speech.
Keywords: Automatic speech recognition system, children speech, adaptation, Malay.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17431233 Real-Time Specific Weed Recognition System Using Histogram Analysis
Authors: Irshad Ahmad, Abdul Muhamin Naeem, Muhammad Islam
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Information on weed distribution within the field is necessary to implement spatially variable herbicide application. Since hand labor is costly, an automated weed control system could be feasible. This paper deals with the development of an algorithm for real time specific weed recognition system based on Histogram Analysis of an image that is used for the weed classification. This algorithm is specifically developed to classify images into broad and narrow class for real-time selective herbicide application. The developed system has been tested on weeds in the lab, which have shown that the system to be very effectiveness in weed identification. Further the results show a very reliable performance on images of weeds taken under varying field conditions. The analysis of the results shows over 95 percent classification accuracy over 140 sample images (broad and narrow) with 70 samples from each category of weeds.Keywords: Image Processing, real-time recognition, Weeddetection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17601232 Spatial Mapping of Dengue Incidence: A Case Study in Hulu Langat District, Selangor, Malaysia
Authors: Er, A. C., Rosli, M. H., Asmahani A., Mohamad Naim M. R., Harsuzilawati M.
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Dengue is a mosquito-borne infection that has peaked to an alarming rate in recent decades. It can be found in tropical and sub-tropical climate. In Malaysia, dengue has been declared as one of the national health threat to the public. This study aimed to map the spatial distributions of dengue cases in the district of Hulu Langat, Selangor via a combination of Geographic Information System (GIS) and spatial statistic tools. Data related to dengue was gathered from the various government health agencies. The location of dengue cases was geocoded using a handheld GPS Juno SB Trimble. A total of 197 dengue cases occurring in 2003 were used in this study. Those data then was aggregated into sub-district level and then converted into GIS format. The study also used population or demographic data as well as the boundary of Hulu Langat. To assess the spatial distribution of dengue cases three spatial statistics method (Moran-s I, average nearest neighborhood (ANN) and kernel density estimation) were applied together with spatial analysis in the GIS environment. Those three indices were used to analyze the spatial distribution and average distance of dengue incidence and to locate the hot spot of dengue cases. The results indicated that the dengue cases was clustered (p < 0.01) when analyze using Moran-s I with z scores 5.03. The results from ANN analysis showed that the average nearest neighbor ratio is less than 1 which is 0.518755 (p < 0.0001). From this result, we can expect the dengue cases pattern in Hulu Langat district is exhibiting a cluster pattern. The z-score for dengue incidence within the district is -13.0525 (p < 0.0001). It was also found that the significant spatial autocorrelation of dengue incidences occurs at an average distance of 380.81 meters (p < 0.0001). Several locations especially residential area also had been identified as the hot spots of dengue cases in the district.
Keywords: Dengue, geographic information system (GIS), spatial analysis, spatial statistics
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 53341231 High Impedance Faults Detection Technique Based on Wavelet Transform
Authors: Ming-Ta Yang, Jin-Lung Guan, Jhy-Cherng Gu
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The purpose of this paper is to solve the problem of protecting aerial lines from high impedance faults (HIFs) in distribution systems. This investigation successfully applies 3I0 zero sequence current to solve HIF problems. The feature extraction system based on discrete wavelet transform (DWT) and the feature identification technique found on statistical confidence are then applied to discriminate effectively between the HIFs and the switch operations. Based on continuous wavelet transform (CWT) pattern recognition of HIFs is proposed, also. Staged fault testing results demonstrate that the proposed wavelet based algorithm is feasible performance well.Keywords: Continuous wavelet transform, discrete wavelet transform, high impedance faults, statistical confidence.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 23071230 A Computational Cost-Effective Clustering Algorithm in Multidimensional Space Using the Manhattan Metric: Application to the Global Terrorism Database
Authors: Semeh Ben Salem, Sami Naouali, Moetez Sallami
Abstract:
The increasing amount of collected data has limited the performance of the current analyzing algorithms. Thus, developing new cost-effective algorithms in terms of complexity, scalability, and accuracy raised significant interests. In this paper, a modified effective k-means based algorithm is developed and experimented. The new algorithm aims to reduce the computational load without significantly affecting the quality of the clusterings. The algorithm uses the City Block distance and a new stop criterion to guarantee the convergence. Conducted experiments on a real data set show its high performance when compared with the original k-means version.
Keywords: Pattern recognition, partitional clustering, K-means clustering, Manhattan distance, terrorism data analysis.
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