ABSTRACT
Ventilators have become the need of the hour in view of the pandemic COVID-19. Hospitals around the world have faced difficulty in managing the same. Non-Invasive Ventilation (NIV) involves offering breathing support in the form of a mask which can be a nasal or a face mask or a helmet. The proposed work analyses the design aspects of a helmet-based NIV and its effective management through the usage of a dedicated website capable of communicating with the ventilator directly. The entire analysis is carried out using the simulated ventilator model on Simulink. The communication aspects are tested by conveying necessary information to ThingSpeak, an IoT based analytics platform, which can be accessed by the user through a website. The website can be used to check availability of ventilators at places. © 2022 IEEE.
ABSTRACT
Creative industries were thought to be the most difficult avenue for Computer Science to enter and to perform well at. Fashion is an integral part of day to day life, one necessary both for displaying style, feelings and conveying artistic emotions, and for simply serving the purely functional purpose of keeping our bodies warm and protected from external factors. The Covid-19 pandemic has accelerated several trends that had been forming in the clothing and textile industry. With the large-scale adoption of Artificial Intelligence (AI) and Deep Learning technologies, the fashion industry is at a turning point. AI is now in charge of supervising the supply chain, manufacturing, delivery, marketing and targeted advertising for clothes and wearable and could soon replace designers too. Clothing design for purely digital environments such as the Metaverse, different games and other on-line specific activities is a niche with a huge potential for market growth. This article wishes to explain the way in which Big Data and Machine Learning are used to solve important issues in the fashion industry in the post-Covid context and to explore the future of clothing and apparel design via artificial generative design. We aim to explore the new opportunities offered to the development of the fashion industry and textile patterns by using of the generative models. The article focuses especially on Generative Adversarial Networks (GAN) but also briefly analyzes other generative models, their advantages and shortcomings. To this regard, we undertook several experiments that highlighted some disadvantages of GANs. Finally, we suggest future research niches and possible hindrances that an end user might face when trying to generate their own fashion models using generative deep learning technologies. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Clustering has been widely studied to group data into clusters. Several methods have been used including Maximum Likelihood (ML), Information Criterion by Akaike (AIC), and Bayesian Information Criterion (BIC) by Schwarz. In this paper, Minimum Message Length (MML) is applied to the clustering of mutual funds data. In this application, data are assumed to come from multivariate correlated Gaussian distribution. For this, MML principle needs to be numerically approximated. The modeling results are contrasted with those obtained using alternative methods, in terms of probability-bit costings and clustering structures. The experiment's findings demonstrate that, in terms of the fitted probability bit costings, MML clustering provided a more trustworthy model than AIC and BIC with significantly less bits required in conveying the data given the model. MML clustering also handled overlapping clusters better compared to modeling using the combination of ML with AIC and BIC. Furthermore, mutual funds trading have shown changes of movements during the pandemic Covid-19 with performances of mutual funds tend to be decreasing across funds, especially during the first 15 months of the period. Only several funds were grouped differently compared to most funds analyzed. The latter have shown effect of pandemic Covid-19 the most with lower returns compared to the returns of most funds. © 2022 IEEE.
ABSTRACT
Misinformation presented in different modalities about the COVID-19 pandemic has been prevalent. One approach to reducing the negative effects of misinformation is through corrective information. However, it is possible that people develop counter-attitude towards the corrective information and reaffirm their belief in misinformation, called the boomerang effect. Fewer studies examined how different modes of corrective information about COVID-19 may address the boomerang effect. With a 3-by-3 between-subject experiment design (n = 210), we first presented one of the three modalities of misinformation (text, image, video) to the participants, followed by one of the three modalities of corrective information (text, image, video) to examine the effect of the corrective information. The results showed that there was no boomerang effect after correction in all modalities, indicating that all corrective information successfully reduced participants' perceived credibility and potential action for misinformation. In the post-hoc analysis, the correction in the video mode worked best on text misinformation. Our results also suggested that image misinformation worked least effectively in terms of conveying misinformation. © 2022 Owner/Author.
ABSTRACT
The recent spread of coronavirus (COVID-19) has meant that online conference presentations are becoming more and more frequent at national and international level. We believe that these online presentations will remain an option even after the pandemic has subsided. One of the challenges of online conference presentations is that it is difficult to convey nonverbal information such as gestures and the facial expressions of the presenter. In this paper, we propose the “Stage-like Presentation Method”, which involves projecting the whole body, and investigates how the presence or absence of nonverbal information from the presenter affects the audience. A comparison of the proposed method with two other presentation methods confirmed that the audience considered it the most effective. The method was used by seven people in actual conference presentations, and it was found that the audience’s impressions changed according to the details of the setting. This research confirmed that the Stage-like Presentation Method left the audience at online conferences with a good impression of presentations. It also suggests that audiences find visual nonverbal information useful. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Social media have been awash with news and discussions of the COVID-19 pandemic. It is phenomenal to observe that social media has been the focal venue for people to express their reactions, opinions, and interpretations of the pandemic, given the presence of mixed sources of real information and misinformation. Thus, it is essential to conduct professional assessments of the public views and their evolving nature. Our study aims to extract and assess insights into the reflections of sentiments and topics of the public on Twitter and their dynamics along the timeline of the Delta variant. It highlights the extraordinary influence Twitter, or similar major social media, would have on people to comprehend and decide how to cope with the pandemic. We present findings of extracted sentiments and topics from a large-scale dataset of COVID-related tweets collected for the recent phase of the Delta variant of the pandemic (July-September 2021). We utilized a variety of machine learning algorithms for topic modeling and testing the accuracy of sentiment analysis. Our study shows the dramatic dominance of a positive and objective sentiment rather than a negative and subjective sentiment as well as the shift of prevalent topics during the period of study. The findings indicate the importance of conveying real, rational, and accurate information instead of misinformation on social media to foster the public's awareness and preparedness for a major public emergency incident such as the pandemic. © 2022 IEEE.
ABSTRACT
Even as more people are getting vaccinated and measured steps with caution are taken to return towards normalcy, Long Covid still persists. Long Covid is a post-Covid condition in which patients still have symptoms for weeks or months after they have recovered from Covid-19. The Covid-19 epidemic and its accompanying societal mitigation methods, such as lockdowns, have resulted in a spike in people's usage of social media platforms like Twitter for conveying their views, opinions, and anxieties. As a result, in this paper, I have performed sentimental analysis on three sets of data that were collected relating to Long Covid, Long Covid in Kids and lastly Treating Long Covid. This was done to explore societal-scale reactions for the illness Long Covid. A total of 98386 tweets were extracted for the period of 11th December 2021 to 20th December 2021 using python's Tweepy package. After performing all the pre-processing on the tweets, a total of 15827 tweets were analyzed. AFFIN lexicon model was employed for performing sentimental analysis on the user's tweets. Visualizations in the form of bar charts, histograms, strip plots, box plots, pie charts, and word clouds have been created for gaining deeper insight into the sentiments of the tweets posted. The results showed that 44% of tweets are negative, 34 % of tweets are positive and 23 % of tweets are neutral for Long Covid. 39% of tweets are negative, 33 % of tweets are positive and 28% of tweets are neutral for the data set of Long Covid in Kids. These results show that negative sentiments outweigh positive sentiments relating to Long Covid. However, 41 % of tweets are positive, 32% of tweets are neutral and 27% of tweets are negative for the data set of treating Long Covid. This result portrays that people have more positive sentiments regarding treating Long Covid. © 2022 IEEE.
ABSTRACT
This study aims to determine the role of the Indonesian government in disseminating the disability vaccination program through social media, especially on Twitter. This research data is seen and analyzed in the social media accounts of @KemenkesRI, @KemensosRI, and @Kemkominfo. The method in this study uses Q-DAS (Qualitative Data Analysis Software) Nvivo 12 plus. The data obtained are tweets from the Twitter accounts of the Ministry of Social Affairs, Ministry of Health, and Ministry of Information Technology. This study found that the Ministry of Health was very intensive in disseminating information and distributing vaccination programs compared to the social media accounts of the Ministry of Social Affairs and the Ministry of Communication and Information. Dissemination of vaccination information for persons with disabilities on social media Twitter @ Ministry of Social Affairs in Vaccination Distribution information. Meanwhile, in conveying information, the Twitter social media account @Kemkominfo is more dominant in using symbols or hashtags. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
In Bangladesh, there is a shortage of legitimate nourishment data frameworks that can give fitting sustenance messages dependent on various rules for pregnant ladies and newborn children. Lack of healthy sustenance devastatingly affects people's wellbeing and prosperity and the monetary improvement of nations. Conversely, essential or tertiary health laborers couldn't offer vital assistance to them. With so many people becoming ill from the (COVID-19), poor weight control plans exacerbate pre-existing conditions, putting them at greater risk. Individuals living with chronic illnesses who have been diagnosed with COVID-19 must improve their mental health and count calories to ensure that they remain in good health. Look for direct and psychosocial support from suitably prepared wellbeing care experts, including community-based lay and peer guides. Venturing into nourishment counsel, advancing breastfeeding, and battling deception around COVID-19 transmission will offer assistance to protect the role of nutritious nourishment as a partner against sickness. Any health worker in Bangladesh can easily use this application. Our health laborers regularly neglect to convey legitimate nourishment data to moms. Such an instrument can be helpful in giving a proper method to show particular nourishment messages to mothers dependent on their wellbeing stages and dependent on their baby's age. The design of this application can provide a legitimate office for conveying sustenance messages to mothers and workers. This framework may have to be examined occasionally to meet the progression of client prerequisites and be applied properly. © 2021 IEEE.