Cough Detection for Prevention Against the COVID-19 Pandemic

This paper proposes a novel and robust technique for remote cough recognition for COVID-19 detection. This technique is based on sound and image analysis. The objective is to create a real-time system combining artificial intelligence (AI) algorithms, embedded systems, and network of sensors to detect COVID-19-specific cough and identify the person who coughed. Remote acquisition and analysis of sounds and images allow the system to perform both detection and classification of the detected cough using AI algorithms and image processing to identify the coughing person. This will give the ability to distinguish between a normal person and a person carrying the COVID-19 virus. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Detection of COVID-19 using ResNet50, VGG19, MobileNet, and Forecasting, using Logistic Regression, Prophet, and SEIRD Model

Artificial Intelligence can quickly identify hazardous viral strains in humans. To detect COVID-19 symptoms, AI algorithms can be used to train to examine medical images like X-rays and CT scans. This can help healthcare providers to diagnose the disease more accurately and quickly. AI helps examine data on the spread of COVID-19 andmake predictions about how it will likely spread in the future. Machine learning algorithms known as Convolutional Neural Networks (CNN) are highly effective at evaluating images. As a result, CNN could assist in the early detection of COVID-19 by evaluating medical images like X-rays and CT scans to spot the disease's symptoms. This article's main aim is to provide brief information on some of the CNN models to detect and forecast COVID-19. The models were purely trained with Chest X-ray images of different categorized patients. The COVID-19 prediction models like ResNet50, VGG19, and MobileNet give accuracies of 98.50%, 97.68%, and 93.94%, respectively. On the other hand, forecasting also plays a vital role in reducing the pandemic because it helps us to analyze the risk and plan a solution to avoid it. The model is trained with some forecasting techniques like Prophet, LogisticRegression, and S EIRD model based on a text-based dataset that contains parameters such as the number of people infected per day recovered per day an d many more for visualizing the trends in forecasting, which help in decision-making to analyze risks and plan solutions to prevent the further spread of the disease. © 2023 IEEE.

Understanding experiences of food-delivery-platform workers under algorithmic management using topic modeling

With the emergence of COVID-19, the food-delivery market has grown and now employs large numbers of people as drivers. The independent contractors enjoy flexibility, but their schedules are at the mercy of algorithmic management. This study explores the experiences of food-delivery-platform workers with algorithmic management and their perceptions in response to algorithmic decisions. A collection of 1046 posts containing "AI” (for artificial intelligence) and "algorithm” made to online communities frequently used by platform workers is subjected to keyword analysis, semantic network analysis, and topic modeling using latent Dirichlet allocation. Fifteen latent topics are classified into AI matching, driver behavior, and platform system, and discussed using representative posts from online communities. The results highlight the advancement of AI technology and collaboration strategies used by platform operators to generate trust in algorithmic decisions among food-delivery-platform workers. In addition, this study uses using topic modeling to comprehensively and objectively explore how food-delivery-platform workers with experience working under AI algorithmic management perceive that management. © 2023 Elsevier Inc.

An Efficient Method for Addressing COVID-19 Proximity Related Issues in Autonomous Shuttles Public Transportation

The COVID-19 pandemic has created significant restrictions to passenger mobility through public transportation. Several proximity rules have been applied to ensure sufficient distance between passengers and mitigate contamination. In conventional transportation, abiding by the rules can be ensured by the driver of the vehicle. However, this is not obvious in Autonomous Vehicles (AVs) public transportation systems, since there is no driver to monitor these special circumstances. Since, AVs constitute an emerging mobility infrastructure, it is obvious that creating a system that can provide a sense of safety to the passenger, when the driver is absent, is a challenging task. Several studies employ computer vision and deep learning techniques to increase safety in unsupervised environments. In this work, an image-based approach, supported by novel AI algorithms, is proposed as a service to increase the COVID-19 safety rules adherence of the passengers inside an autonomous shuttle. The proposed real-time service, can detect deviations from proximity rules and notify the authorized personnel, while it is possible to be further extended in other application domains, where automated proximity assessment is critical. © 2022, IFIP International Federation for Information Processing.

Using the SQuaRE series as a guarantee for GDPR compliance

In a context where the availability of information represents the opportunity for companies to gain a competitive advantage in the market through the use of sophisticated AI algorithms, data quality assumes a strategic role. With this paper we want to show that the adoption of an international quality measurement standard such as the one present in the SQuaRE series can on the one hand improve the ethical aspect of machine learning algorithms and on the other hand meet the requirements imposed by the European Community regarding the protection of personal data of citizens in Member States (GDPR). Indeed, although the attention to the protection of personal data is mainly directed towards the aspects of security and confidentiality, in a holistic view we should also evaluate the risks arising from the absence of quality in the data. In this context, we consider consistent and of reference for the international community the choice of the Italian legislator made for the Public Administrations. Since 2013 the Agency for Digital Italy (AgID) has suggested the adoption of ISO/IEC 25012 for public administrations in charge of managing databases of national interest. In the article, we propose a methodological approach that ensures the governance of data quality and some open questions regarding the homogeneity of the selected measures. © 2021 for this paper by its authors

A Review of Spam Detection in Social Media

With significant usage of social media to socialize in virtual environments, bad actors are now able to use these platforms to spread their maUcious activities such as hate speech, spam, and even phishing to very large crowds. Especially, Twitter is suitable for these types of activities because it is one of the most common social media platforms for microblogging with millions of active users. Moreover, since the end of 2019, Covid-19 has changed the lives of individuals in many ways. While it increased social media usage due to free time, the number of cyber-attacks soared too. To prevent these activities, detection is a very crucial phase. Thus, the main goal of this study is to review the state-of-art in the detection of malicious content and the contribution of AI algorithms for detecting spam and scams effectively in social media. © 2021 IEEE

A Multimodal Misinformation Detector for COVID-19 Short Videos on TikTok

This paper studies an emerging and important problem of identifying misleading COVID-19 short videos where the misleading content is jointly expressed in the visual, audio, and textual content of videos. Existing solutions for misleading video detection mainly focus on the authenticity of videos or audios against AI algorithms (e.g., deepfake) or video manipulation, and are insufficient to address our problem where most videos are user-generated and intentionally edited. Two critical challenges exist in solving our problem: i) how to effectively extract information from the distractive and manipulated visual content in TikTok videos? ii) How to efficiently aggregate heterogeneous information across different modalities in short videos? To address the above challenges, we develop TikTec, a multimodal misinformation detection framework that explicitly exploits the captions to accurately capture the key information from the distractive video content, and effectively learns the composed misinformation that is jointly conveyed by the visual and audio content. We evaluate TikTec on a real-world COVID- 19 video dataset collected from TikTok. Evaluation results show that TikTec achieves significant performance gains compared to state-of-the-art baselines in accurately detecting misleading COVID-19 short videos. © 2021 IEEE.