Metaverse is to build a virtual world that is both mapped and independent of the real world in cyberspace by using the improvement in the maturity of various digital technologies, such as virtual reality (VR), augmented reality (AR), big data, and 5G, which is important for the future development of a wide variety of professions, including education. The metaverse represents the latest stage of the development of visual immersion technology. Its essence is an online digital space parallel to the real world, which is becoming a practical field for the innovation and development of human society. The most prominent advantage of the English-teaching metaverse is that it can provide an immersive and interactive teaching field for teachers and students, simultaneously meeting the teaching and learning needs of teachers and students in both the physical world and virtual world. This study constructs experiential situational English-teaching scenario and convolutional neural networks (CNNs)–recurrent neural networks (RNNs) fusion models are proposed to recognize students’ emotion electroencephalogram (EEG) in experiential English teaching during the feature space of time domain, frequency domain, and spatial domain. Analyzing EEG data collected by OpenBCI EEG Electrode Cap Kit from students, experiential English-teaching scenario is designed into three types: sequential guidance, comprehensive exploration, and crowd-creation construction. Experimental data analysis of the three kinds of learning activities shows that metaverse-powered experiential situational English teaching can promote the improvement of students’ sense of interactivity, immersion, and cognition, and the accuracy and analysis time of CNN–RNN fusion model is much higher than that of baselines. This study can provide a nice reference for the emotion recognition of students under COVID-19.

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Metaverse-Powered Experiential Situational English-Teaching Design: An Emotion-Based Analysis Method

Programming for the Arduino

A new approach of ECG steganography and prediction using deep learning

Polycystic ovary syndrome (PCOS) is the most prevalent endocrinological abnormality and one of the primary causes of anovulatory infertility in women globally. The detection of multiple cysts using ovary ultrasonograpgy (USG) scans is one of the most reliable approach for making an accurate diagnosis of PCOS and creating an appropriate treatment plan to heal the patients with this syndrome. Instead of depending on error-prone manual identification, an intelligent computer-aided cyst detection system can be a viable approach. Therefore, in this research, an extended machine learning classification technique for PCOS prediction has been proposed, trained and tested over 594 ovary USG images; where the Convolutional Neural Network (CNN) incorporating different state-of-the-art techniques and transfer learning has been employed for feature extraction from the images; and then stacking ensemble machine learning technique using conventional models as base learners and bagging or boosting ensemble model as meta-learner have been used on that reduced feature set to classify between PCOS and non-PCOS ovaries. The proposed technique significantly enhances the accuracy while also reducing training execution time comparing with the other existing ML based techniques. Again, following the proposed extended technique, the best performing results are obtained by incorporating the "VGGNet16" pre-trained model with CNN architecture as feature extractor and then stacking ensemble model with the meta-learner being "XGBoost" model as image classifier with an accuracy of 99.89% for classification. 

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An extended machine learning technique for polycystic ovary syndrome detection using ovary ultrasound image

An automatic tumour growth prediction based segmentation using full resolution convolutional network for brain tumour

Deep neural network based missing data prediction of electrocardiogram signal using multiagent reinforcement learning

In this work, a multi-lead Electrocardiogram (ECG) data compression using principal component analysis (PCA) combined with a machine learning technique is proposed to achieve a high compression ratio (CR) with low reconstruction error (within 2% percentage root mean squared difference, or, PRD). The beat detection procedure was inspired by the Pan-Tompkins algorithm with some necessary modifications. A lead-wise PCA decomposition was performed for dimensionality reduction with a single beat from each lead at a time using a fixed energy reconstruction criteria. The optimal quantization levels of the principal components were allocated using multi-layer perceptron neural network (MLP-NN) using lead clinical features as the input. This MLP-NN was tuned offline by a particle swarm optimization (PSO) generated data for quantization level of coefficients of PC as the reference. The proposed technique was evaluated using 8 types of cardiac abnormalities record from multi-lead ECG data from the PTB Diagnostic ECG database, with an average CR, PRD and PRDN of 16.2, 1.47% and 1.84% respectively. The reconstructed records were clinically acceptable. The proposed technique provides superior performance than few recent published works on multilead ECG compression.

Compression of Multilead Electrocardiogram Using Principal Component Analysis and Machine Learning Approach

Quality Aware Compression of Multilead Electrocardiogram Signal using 2-mode Tucker Decomposition and Steganography

In this work, a first derivative based QRS detection followed by beat delineation using discrete wavelet transform (DWT) of single lead Electrocardiogram signal, has been proposed. The QRS zone is the most dominant zone within an ECG beat and accurate detection of R-peak helps in further identification of ECG characteristics points. The slope of QRS complex in higher with respect to other part within a beat and using first derivative of ECG signal by computing sample to sample differentiation, position of R-peak can easily be detected. After detection of R-peak, corresponding Q-peak, S-peak, P-wave, T-wave and onset and offset of each wave were identified by discrete wavelet transform. After testing this proposed algorithm on various ECG signals, an average positive predictivity and sensitivity of 99.89% and 99.93% were achieved respectively using 48 records of MIT-BIH Arrhythmia database, 99.98% and 99.977% were achieved respectively using 115 records of ptbdb database, 100% and 100% were achieved respectively using 30 records of Long Term ST database, 99.95% and 100% were achieved respectively using 22 records of St. Petersburg INCART Arrhythmia database.

Soumyendu Banerjee

Papers

A new approach of ECG steganography and prediction using deep learning

Deep neural network based missing data prediction of electrocardiogram signal using multiagent reinforcement learning

Compression of Multilead Electrocardiogram Using Principal Component Analysis and Machine Learning Approach

Quality Aware Compression of Multilead Electrocardiogram Signal using 2-mode Tucker Decomposition and Steganography

A First Derivative Based R-Peak Detection and DWT Based Beat Delineation Approach of Single Lead Electrocardiogram Signal