Feature fusion H-ELM based learned features and hand-crafted features for human activity recognition

Recognizing human activities is one of the main goals of human-centered intelligent systems. Smartphone sensors produce a continuous sequence of observations. These observations are noisy, unstructured and high dimensional. Therefore, efficient features have to be extracted in order to perform accurate classification. This paper proposes a combination of Hierarchical and kernel Extreme Learning Machine (HK-ELM) methods to learn features and map them to specific classes in a short time. Moreover, a feature fusion approach is proposed to combine H-ELM based learned features with hand-crafted ones. Our proposed method was found to outperform state-of-the-art in terms of accuracy and training time. It gives accuracy of 97.62 % and takes 3.4 seconds as a training time by using a normal Central Processing Unit (CPU)

Glass break detection system using deep auto encoders with fuzzy rules induction algorithm

Main uses of glass windows in commercial and residential buildings are prevalent. While a glass-based material has its advantages, it also poses security risks. Therefore, glass break detectors play an important role in security protection for offices and residential buildings. Conventional vibration-based and acoustic-based glass break detectors are designed to detect predetermined temporal and frequency feature thresholds of glass breakage sound signals. This leads to the inability to differentiate glass break from environmental sounds (such as the sound of striking objects, heavy sounds and shouted sounds) that are similar in their amplitude threshold and frequency pattern. Machine learning based acoustic audio classification has been popular in security surveillance applications. Researchers are interested in this research area, and different approaches have been proposed for anomaly event detection (such as gunshots, glass breakage sounds, etc.). This paper proposes a new design of a glass break detection algorithm based on Fuzzy Deep Auto-encoder Neural Network. The algorithm reduces false alarms and improves detection accuracy. Experimental results indicate that proposed fuzzy deep auto-encoder network system attained 95.5% correct detection for the proposed audio dataset

Construction of Diaphragm Wall Support Underground Car Park in Historical Area of Bangkok

Geotechnical aspects in construction of diaphragm-wall-support 2 level underground car park building, located in the historically and culturally significant area of Bangkok is presented in this paper. Results of the preliminary analyses showed that the deflection of the thin diaphragm wall of 0.60 m width would be large if it was to be fully cantilevered to fulfill the architectural and utility aspects of the car park structure. It was therefore decided to use buttress to minimize the diaphragm wall deflection. Performance of buttressed-support diaphragm wall is demonstrated based on the inclinometer monitoring results. Intensive modification of construction sequence in actual work execution with “value engineering options” different from tender stage design is demonstrated along with application of observational method

Comparison of machine learning classifiers for dimensionally reduced fMRI data using random projection and principal component analysis

Machine learning has opened up the opportunity for understanding how the brain works. In this paper, functional magnetic resonance imaging (fMRI) data are analyzed with reduced dimension.We have carried out a performance comparison of random projection (RP) and principal component analysis (PCA) with different number of components of fMRI data. In addition to that, six different types of machine learning algorithm have been used. In particular, the Haxby dataset is chosen for our experiment. The dataset comprises 9 classes for object recognition. 10-fold cross validation step has been employed. We have discovered that RP outperforms PCA when the former is paired with logistic regression, Gaussian Naive Bayes and linear support vector machine. The best pair for this study was found to be PCA and k-nearest neighbors. Nevertheless, each algorithm was found to have its own strengths for fMRI classification approach

Ethnic Minority Education in Myanmar

This observe explores ethnic minority education in Myanmar through a quantitative evaluation of demographic traits, academic overall performance, get entry to to instructional sources, and perceptions of the getting to know surroundings amongst three hundred participants. The findings reveal a balanced gender distribution and variations in ethnic illustration, emphasizing the want for focused interventions. Despite stereotypes, the moderately excessive imply GPA underscores educational achievements among ethnic minority students. Positive perceptions of library availability advise a robust basis, while demanding situations in computer get entry to factor to areas for development. The take a look at highlights the significance of fostering inclusive and culturally responsive mastering environments to address nuanced perspectives. As Myanmar ambitions for national harmony and sustainable improvement, those findings make a contribution to shaping rules and interventions for an equitable schooling device that embraces the wealthy diversity of its ethnic minority students