The huge increase amount of Cyber Attacks in computer networks emerge essential requirements of intrusion detection system, IDS to monitors the cybercriminals. The inefficient or unreliable IDS can decrease the performance of security services and today world applications and make the ongoing challenges on the Cyber Security and Data mining fields. This paper proposed a new detection system for the cyber-attacks with the ensemble classification of efficient cost sensitive decision trees, CSForest classifier and the least numbers of most relevant features are selected as the additional mechanism to reduce the cost. The standard dataset, NSL-KDD, IDS is used to appraise the results and compare the previous existing systems and state-of-the-art methods. The proposed system outperforms the other existing systems and can be public a new benchmark record for the NSL-KDD datasets of intrusion detection system. The proposed combination of choosing the appropriate classifier and the selection of perfect features mechanism can produce the cost-efficient IDS system for the security world.

Face Recognition plays a major role in the new modern information technology era for security purposes in biometric modalities and has still various challenges in many applications of computer vision systems. Consequently, it is a hot topic research area for both industrial and academic environments and was developed with many innovative ideas to improve accuracy and robustness. Therefore, this paper proposes a recognition system for facial images by using Deep learning strategies to detect a face, extract features, and recognize. The standard facial dataset, FEI is used to prove the effectiveness of the proposed system and compare it with the other previous research works, and the experiments are carried out for different detection methods. The results show that the improved accuracy and reduce time complexity can provide from this system, which is the advantage of the Convolution Neural Network (CNN) than other some of the previous works.

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.

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.

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.