CLASSIFICATION OF T\TEUROMUSCIILAR DISORDERSI BASED ON ELECTROMYOGRAPTTY (EMG) SIGNALS

Electromyography (EMG) signals are the measune of activity in the muscles. The motion of the muscles will be generated and recorded using skin surface electrodes. EMG signals can be found from anywhere on the exterior of human's body such as biceps, triceps, shoulder, arm, hand, leg. The aim of this project is to identifr the neuromuscular diseases based on EMG signals by means of classification. The ncuromuscular diseases that have been identified are healthy, myopathy and neuropathy. The signals weIE taken and analyzed from EMG lab database to become datasets for classification system. The classification was carried out using Artilicial Neural Network. In this project, there are two techniques that used to classifr three different types of muscular disorders such as Multilayer Perceptron (MLP) and Wavelet Neural Network (WlIhI). And the input that applied to these systems using feature extraction from EMC signals. In time domain, five feature extraction techniques that used to exmct the sample of signal such as Autoregressive (AR), Root mean square (RMS), Zero crossing (zc), waveform length (wL) and Mean Absolute Value (MA$. The comparison between different techniques will be included based on the accuracy of the result. The input data has been used in Multilayer Perceptron (MtP) to train the classification system. Besides that, frequency domain was used for extracting the useful information from EMG signal for Wavelet neural network (wl[Nr) such as Power Spectrum Density (pSD), both systems were hained and the test performances were examined after training to provide the best result

VIETNAMESE ENGLISH-MAJORED STUDENTS’ USE OF LISTENING STRATEGIES

The crucial role of listening skill in language learning has been well acknowledged, yet attention to this skill remains modest. Numerous studies investigating learners’ listening performance have identified listening strategies as a key factor contributing to the success of effective listeners. This study, using a Likert-scale questionnaire, examined the listening strategies employed by 81 Vietnamese English-majored students, who were divided into two groups - effective and less effective listeners based on an IELTS proficiency test. Findings showed that listening strategies were used at a relatively high level with the metacognitive group employed most frequently compared to cognitive and socio-affective strategies. Lowering anxiety, predicting and planning, resourcing, repetition, and cooperation were found most commonly employed individual strategies. Although no significant differences were found between the groups’ use of the three overarching strategy categories, several discrepancies were identified concerning their use of individual strategies, which provides important implications for listening pedagogical adjustments in this particular context. Article visualizations

The Impact of Digital Transformation in Higher Education: The Case Study from Vietnam

The education sector in general and higher education, in particular, has paid great attention to digital transformation into teaching. Digital transformation is not only a general development trend but also helps to change the traditional teaching method to a new active teaching method that helps learners to be active and creative when participating in the learning process. The traditional classroom model, which has existed for a long time, is gradually shifting to classrooms that apply information technology to teaching and learning to reduce lectures and transfer knowledge to develop the initiative, creativity, and capacity of students to learn. Create opportunities for learners to learn anytime, anywhere, learners can be active in the learning process, contributing to building a learning society. The article clarifies the impact of digital transformation on higher education in Vietnam. From there, offer an oriented solution to help digital transformation become an effective tool in higher education in Vietnam today

Multitasking Correlation Network for Depth Information Reconstruction

In this paper, we propose a novel multi-tasking network for stereo matching. The proposed network is trained to approximate similarity functions in statistics and linear algebra such as correlation coefficient, distance correlation and cosine similarity. By doing this, the proposed method decreases the amount of time needed to calculate the disparity map by using CNN's ability to calculate multiple pairs of image patches at the same time. We then compare the execution time and overall accuracy between the traditional method using functions and our method. The results show the model's ability to mimic the traditional method's performance while taking considerably less time to perform the task

ASEAN- Australia Trade Relations by Stages of Processing: Comparative Research

ASEAN and Australia became the dialogue partner in 1974. Over 46 years of cooperation and development, ASEAN- Australia relations have achieved many good results, especially in recent years when two sides participate in several free trade agreements (FTAs) at bilateral levels and multilateral levels. In 2014, Australia and ASEAN formally became strategic partners, recognizing the importance of the relationship in building mutual benefits. Participation in FTAs and relationship upgrading has helped bilateral trade growth. In 2018-2019, Australia was the seventh-largest trading partner of ASEAN and ASEAN was one of Australia's top three trading partner, however, the two sides face many challenges such as the difficulties after the global crisis since 2009, the "spaghetti bowl" phenomenon of joining many agreements at the same time. Thus, to solve these problems, the production and exporting of comparative advantage goods by the stages of processing to participate in an ideal regional supply chain are very important. Based on the comparative theory and supply chain perspectives The paper compared revealed comparative advantage indexes of ASEAN countries and Australia from 2009 through 2018 to find which country in ASEAN is Australia's suitable partner from which provided the ideal effective supply chain distribution. After that, through trade value comparison, the paper examed if ASEAN countries and Australia specialized in production by stages of processing. From the results, appropriate recommendations were provided to enhance the trade relations between ASEAN and Australia

MORE ENVIRONMENTAL FRIENDLY METHOD OF LEAD RECYCLING FROM WASTE BATTERY PASTE-AN ELECTROCHEMICAL INVESTIGATION

Joint Research on Environmental Science and Technology for the Eart

CLASSIFICATION OF T\TEUROMUSCIILAR DISORDERSI BASED ON ELECTROMYOGRAPTTY (EMG) SIGNALS

Electromyography (EMG) signals are the measune of activity in the muscles. The motion of the muscles will be generated and recorded using skin surface electrodes. EMG signals can be found from anywhere on the exterior of human's body such as biceps, triceps, shoulder, arm, hand, leg. The aim of this project is to identifr the neuromuscular diseases based on EMG signals by means of classification. The ncuromuscular diseases that have been identified are healthy, myopathy and neuropathy. The signals weIE taken and analyzed from EMG lab database to become datasets for classification system. The classification was carried out using Artilicial Neural Network. In this project, there are two techniques that used to classifr three different types of muscular disorders such as Multilayer Perceptron (MLP) and Wavelet Neural Network (WlIhI). And the input that applied to these systems using feature extraction from EMC signals. In time domain, five feature extraction techniques that used to exmct the sample of signal such as Autoregressive (AR), Root mean square (RMS), Zero crossing (zc), waveform length (wL) and Mean Absolute Value (MA$. The comparison between different techniques will be included based on the accuracy of the result. The input data has been used in Multilayer Perceptron (MtP) to train the classification system. Besides that, frequency domain was used for extracting the useful information from EMG signal for Wavelet neural network (wl[Nr) such as Power Spectrum Density (pSD), both systems were hained and the test performances were examined after training to provide the best result