Blended teaching strategy becomes an integral part of the 21st-century education system to meet the industry 4.0 needs. As not only the online system can create the best system from the points of view of effectiveness and expending cost, but also the traditional teaching style cannot meet the higher level of industry needs. Therefore, the combination of the advancement of technology and the effective design of teaching theory appears in different blended systems to promote education level. This system also proposes a new model to initiate the teaching style that can supplement the requirements of this education era based on FLIP learning terms. The system is built by blending the online and face-to-face strategies using communication technology and multimedia components at pre-class, online test, and in-class times based on stakeholders' satisfaction with the system. The outcome intends to build an effective education system that facilitates the developing country, the Myanmar situation. Moreover, the research methodology goal includes improving the problem-solving ability and performance results of the university students and to increase the self-reflection of all participants.
Speech is one of the most natural and fundamental means of human computer interaction and the state of human emotion is important in various domains. The recognition of human emotion is become essential in real world application, but speed signal is interrupted with various noises from the real world environments and the recognition performance is reduced by these additional signals of noise and emotion. Therefore this paper focuses to develop emotion recognition system for the noisy signal in the real world environment. Minimum Mean Square Error, MMSE is used as the enhancement technique, Mel-frequency Cepstrum Coefficients (MFCC) features are extracted from the speech signals and the state of the arts classifiers used to recognize the emotional state of the signals. To show the robustness of the proposed system, the experimental results are carried out by using the standard speech emotion database, IEMOCAP, under various SNRs level from 0db to 15db of real world background noise. The results are evaluated for seven emotions and the comparisons are prepared and discussed for various classifiers and for various emotions. The results indicate which classifier is the best for which emotion to facilitate in real world environment, especially in noisiest condition like in sport event.
As large quantity of document images is getting archived by the digital libraries, an efficient strategy that can convert Myanmar document image into machine understandable text format is needed. And Myanmar language contains many words, and most of them are similar, especially for small fonts, the accuracy of the Optical Character Recognition, OCR system for Myanmar may be low. Therefore, this paper designs an OCR system for Myanmar Printed Document (OCRMPD) with several proposed methods that can automatically convert Myanmar printed text to machine understandable text. In order to get more accurate system, enhance the input image by removing noise and making some correction on variants. A method for isolation of the character image is proposed by using connected component analysis for wrongly segmented characters produced by projection only. Finally, hierarchical mechanism is used for SVM classifier for recognition of the character image. The proposed algorithms have been tested on a variety of Myanmar printed documents and the results of the experiments indicate that the methods can increase the segmentation accuracy as well as recognition rates.
This paper proposes a new feature extraction method for off-line recognition of Myanmar printed documents. One of the most important factors to achieve high recognition performance in Optical Character Recognition (OCR) system is the selection of the feature extraction methods. Different types of existing OCR systems used various feature extraction methods because of the diversity of the scripts’ natures. One major contribution of the work in this paper is the design of logically rigorous coding based features. To show the effectiveness of the proposed method, this paper assumed the documents are successfully segmented into characters and extracted features from these isolated Myanmar characters. These features are extracted using structural analysis of the Myanmar scripts. The experimental results have been carried out using the Support Vector Machine (SVM) classifier and compare the pervious proposed feature extraction method.
Keyword search is the dominant information discovery method in Information Retrieval (IR) systems and search engines on the Web. Nowadays, there is an increase amount of data stored in structured databases (Relational Databases). Searching on traditional database management system is done through customized applications which are closely tied to the database schema. Traditional database management systems do not support keyword-based search. Keyword search techniques on the Web cannot directly be applied to databases because the data on the internet and database are in different forms. Therefore, a keyword-based search system for relational database is proposed. The goal is to provide the users, who are not familiar with query language and the schema of the database, to search the relational database with ease. The main contributions of this system compose of indexing of records in database, keyword matching, scoring and filtering of the relevant answer. As long as the database table records can be extended, this system can be easily extendable for further searching records from tables. The experimental results on a real database demonstrate that our method results in improvement in terms of retrieval efficiency as compared to previous strategies.
With the rapid development and requirement of application with Artificial Intelligent (AI) technologies, the researches related to human-computer interaction are always active and the emotional status of the users is very essential for most of the environment. Facial Emotion Recognition, FER is one of the important visual information providers for the AI systems. This paper proposes a FER system using an effective feature extraction methodology and classification technologies. Local features of the face are more effective features for recognition and Scale Invariant Feature Transform, SIFT can give a better representation of the face. The bag of the visual word (BOVW) is the good encoding method and the advancement of that model Vector of Locally Aggregate Descriptor, VLAD provides the better encoder for SIFT features and used these benefits for feature extraction environments. The power of SVM includes unknown class recognition problems and this advantage is used for classification. This system used the standard basement JAFEE dataset to measure the success of the proposed methods and prepared to compare with other systems. The proposed system achieves the better result when it compared with some of the other previous systems because of the combination of effective feature extraction and encoding method.
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