Leveraging machine learning to analyze sentiment from COVID-19 tweets: A global perspective

Since the advent of the worldwide COVID-19 pandemic, analyzing public sentiment has become one of the major concerns for policy and decision-makers. While the priority is to curb the spread of the virus, mass population (user) sentiment analysis is equally important. Though sentiment analysis using different state-of-the-art technologies has been focused on during the COVID-19 pandemic, the reasons behind the variations in public sentiment are yet to be explored. Moreover, how user sentiment varies due to the COVID-19 pandemic from a cross-country perspective has been less focused on. Therefore, the objectives of this study are: to identify the most effective machine learning (ML) technique for classifying public sentiments, to analyze the variations of public sentiment across the globe, and to find the critical contributing factors to sentiment variations. To attain the objectives, 12,000 tweets, 3000 each from the USA, UK, and Bangladesh, were rigorously annotated by three independent reviewers. Based on the labeled tweets, four different boosting ML models, namely, CatBoost, gradient boost, AdaBoost, and XGBoost, are investigated. Next, the top performed ML model predicted sentiment of 300,000 data (100,000 from each country). The public perceptions have been analyzed based on the labeled data. As an outcome, the CatBoost model showed the highest (85.8 %) F1-score, followed by gradient boost (84.3%), AdaBoost (78.9 %), and XGBoost (83.1 %). Second, it was revealed that during the time of the COVID-19 pandemic, the sentiments of the people of the three countries mainly were negative, followed by positive and neutral. Finally, this study identified a few critical concerns that impact primarily varying public sentiment around the globe: lockdown, quarantine, hospital, mask, vaccine, and the like

Designing and developing a vision-based system to investigate the emotional effects of news on short sleep at noon: an experimental case study

Background: Sleep is a critical factor in maintaining good health, and its impact on various diseases has been recognized by scientists. Understanding sleep patterns and quality is crucial for investigating sleep-related disorders and their potential links to health conditions. The development of non-intrusive and contactless methods for analyzing sleep data is essential for accurate diagnosis and treatment. Methods: A novel system called the sleep visual analyzer (VSleep) was designed to analyze sleep movements and generate reports based on changes in body position angles. The system utilized camera data without requiring any physical contact with the body. A Python graphical user interface (GUI) section was developed to analyze body movements during sleep and present the data in an Excel format. To evaluate the effectiveness of the VSleep system, a case study was conducted. The participants' movements during daytime naps were recorded. The study also examined the impact of different types of news (positive, neutral, and negative) on sleep patterns. Results: The system successfully detected and recorded various angles formed by participants' bodies, providing detailed information about their sleep patterns. The results revealed distinct effects based on the news category, highlighting the potential impact of external factors on sleep quality and behaviors. Conclusions: The sleep visual analyzer (VSleep) demonstrated its efficacy in analyzing sleep-related data without the need for accessories. The VSleep system holds great potential for diagnosing and investigating sleep-related disorders. The proposed system is affordable, easy to use, portable, and a mobile application can be developed to perform the experiment and prepare the results

Detection of some major heart diseases using fractal analysis

Islam, N ORCiD: 0000-0002-5469-8104This paper presents a new method to analyze three specific heart diseases namely Atrial Premature Beat(APB), Left Bundle Branch Block (LBBB) and Premature Ventricular Contraction (PVC). The problem is introduced from the discussion of Fractal Dimension. Further, the fractal dimension is used to distinguish between the Electrocardiogram (ECG) signals of healthy person and persons with PVC, LBBB and APB from the raw ECG data. The work done in this paper can be divided into few steps. First step is the determination of the rescaled range of an ECG signal. Then there comes the necessity of calculating the slope of the rescaled range curve. Through this methodology we have established a range of fractal dimension for healthy person and persons with various heart diseases. The way towards determining the range of fractal dimension for those ECG data taken from MIT-BIH Arrhythmia Database has been explained. Again, the obtained range of fractal dimension is also presented here in a tabular fashion with proper analysis

Leveraging machine learning to analyze sentiment from COVID‐19 tweets: A global perspective

Abstract Since the advent of the worldwide COVID‐19 pandemic, analyzing public sentiment has become one of the major concerns for policy and decision‐makers. While the priority is to curb the spread of the virus, mass population (user) sentiment analysis is equally important. Though sentiment analysis using different state‐of‐the‐art technologies has been focused on during the COVID‐19 pandemic, the reasons behind the variations in public sentiment are yet to be explored. Moreover, how user sentiment varies due to the COVID‐19 pandemic from a cross‐country perspective has been less focused on. Therefore, the objectives of this study are: to identify the most effective machine learning (ML) technique for classifying public sentiments, to analyze the variations of public sentiment across the globe, and to find the critical contributing factors to sentiment variations. To attain the objectives, 12,000 tweets, 3000 each from the USA, UK, and Bangladesh, were rigorously annotated by three independent reviewers. Based on the labeled tweets, four different boosting ML models, namely, CatBoost, gradient boost, AdaBoost, and XGBoost, are investigated. Next, the top performed ML model predicted sentiment of 300,000 data (100,000 from each country). The public perceptions have been analyzed based on the labeled data. As an outcome, the CatBoost model showed the highest (85.8%) F1‐score, followed by gradient boost (84.3%), AdaBoost (78.9%), and XGBoost (83.1%). Second, it was revealed that during the time of the COVID‐19 pandemic, the sentiments of the people of the three countries mainly were negative, followed by positive and neutral. Finally, this study identified a few critical concerns that impact primarily varying public sentiment around the globe: lockdown, quarantine, hospital, mask, vaccine, and the like

A Multilingual Handwriting Learning System for Visually Impaired People

Visually impaired people have previously been brought into learning and educational systems through various forms of assistive technology, such as haptic feedback systems. Haptic systems generally need expensive equipment and support from sighted teachers. Moreover, the learning has always been carried out with letters of different alphabets mapped into some tactile pattern. Writing is a big concern for the visually impaired as most official work, like signing, is still carried out by conventional handwriting methods. Most of the existing systems are limited to teaching a single language’s alphabet and basic grammar or may not provide feedback to let the learners know of their learning progress. Therefore, the objectives of this research are to develop an efficient system that includes voice-over guidance to teach writing in multiple alphabets to visually impaired people and to evaluate the performance of the proposed system. As such, a system was developed for teaching multilingual alphabets to visually impaired people with voice instructions. With the aid of a voice-over guide, learners were able to write letters with a stylus on a graphics pad. The progress assessment of the learners is carried out by an image processing algorithm and scored by a machine learning (ML) model. The Random Forest model was used due to its high accuracy (f1-score of 99.8% on test data) among the existing ten different ML algorithms. Finally, the performance and usability of this system were evaluated through an empirical study replicated with 16 participants, including four teachers and twelve visually impaired people. It was found that visually impaired people made fewer attempts to learn handwriting with the proposed system than with the normal handwriting teaching system. 100% of the participants agreed to recommend the system in the future