Bidirectional Residual LSTM-based Human Activity Recognition

Abstract

The Residual Long Short Term Memory (LSTM) deep learning approach is attracting attension of many researchers due to its efficiency when trained on high dimensional datasets. Nowadays, Human Activity Recognition (HAR) has come with enormous challenges that have to be addressed. In addressing such a problem, one can think of developing an application that can help the elderly people as an assistant when it works in collaboration with other timely technologies such as wearable devices with the help of IoT. Many research works are using a standard dataset in evaluating their proposed method in this regard. The dataset comes with its own challenge such as imbalanced classes. In this work, we propose to apply different machine learning techniques to address the specified problems and the method is validated on a standard dataset. To validate the proposed method, we evaluated using different standard metrics such as classification accuracy, precision, recall, f1-score, and Receiver Operating Characteristic (ROC) curve. The proposed method achieves an Area Under Curve (AUC) of 100%, 97.66% of accuracy, 91.59% precision,  93.75% of recall and 92.66% of F1-score respectively.

A new approach in identifying the psychological impact of COVID-19 on university student’s academic performance

“…Recent reports from various continents continue to indicate a daily increase in the number of new cases and mortality due to COVID-19. A total of 90,771,208 confirmed cases of COVID-19 were reported and its death toll reached about 1,944,768 by the beginning of 2021 [1] , [2] , [5] , [6] . The COVID-19 pandemic influenced all areas of human life; education, research, sports, amusement, transportation, entertainment, worship, social gathering/interactions, economy, businesses, and legislative issues [7] .…”

“…Moreover, strengths, weaknesses, opportunities, and challenges of online learning during the pandemic needs to be studied. Currently, Machine Learning (ML) approaches are commonly used to help solving real life problems based on statistical data [5] , [9] . In this paper, an ML approach was adopted to examine the mental and psychosomatic impacts of online learning on students in the time of COVID-19 pandemic.…”

Metaheuristic Optimization Algorithm for Signals Classification of Electroencephalography Channels

“…The optimization technique is widely used in various fields of study, including computer science, engineering [11], health [12], agriculture, and feature selection [13]. The primary aim of optimization is to choose the optimal solution to a given problem among the available solutions that match the problem description.…”

Applications of AI and IoT in COVID-19 Vaccine and Its Impact on Social Life

“…A deep learning approach such as LSTM has been used in COVID-19 comments to understand people opinions and sentiments toward covid-19 sentiment analysis. A comprehensive approach based on natural language processing is introduced in order to extract and evaluate reviews on Reddit related to COVID-19 [30]. For sentiment analysis of the collected reviews, they utilized the LSTM model and identified trending topics related to COVID-19 [31][32][33].…”