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.

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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.

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