Artificial Intelligence for the Novel Corona Virus (COVID-19) Pandemic

COVID-19 outbreak has created havoc around the world and has brought life to a disturbing halt claiming thousands of lives worldwide and infected cases rising every day. With technological advancements in Artificial Intelligence (AI), AI-based platforms can be used to deal with COVID-19 pandemic and accelerate the processes ranging from crowd surveillance to medical diagnosis. This paper renders a response to battle the virus through various AI techniques by making use of its subsets such as Machine Learning (ML), Deep learning (DL) and Natural Language Processing (NLP). A survey of promising AI methods which could be used in various applications to facilitate the processes in this pandemic along potential of AI and challenges imposed are discussed thoroughly. This paper relies on the findings of the most recent research publications and journals on COVID-19 and suggests numerous relevant strategies. A case study on the impact of COVID-19 in various economic sectors is also discussed. The potential research challenges and future directions are also presented in the paper.

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Artificial intelligence for Novel Corona Virus (COVID-19) Pandemic

International Journal of E-Health and Medical Communications ◽

10.4018/ijehmc.20220701oa05 ◽

2022 ◽

Vol 13 (2) ◽

pp. 0-0

Keyword(s):

Artificial Intelligence ◽

Language Processing ◽

Medical Diagnosis ◽

Economic Sectors ◽

Future Directions ◽

Research Challenges ◽

The World ◽

Crowd Surveillance ◽

The Impact

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Association and treatment of diabetes in patients affected by Covid-19

International Journal of Research in Pharmaceutical Sciences ◽

10.26452/ijrps.v11ispl1.3591 ◽

2020 ◽

Vol 11 (SPL1) ◽

pp. 1198-1201

Author(s):

Syed Yasir Afaque

Keyword(s):

Diabetes Mellitus ◽

Prognostic Value ◽

Heart Diseases ◽

The Novel ◽

The World ◽

Treatment Of Diabetes ◽

Coronavirus Infection ◽

Composite Outcomes ◽

Novel Coronavirus ◽

The Impact

In December 2019, a unique coronavirus infection, SARS-CoV-2, was first identified in the province of Wuhan in China. Since then, it spread rapidly all over the world and has been responsible for a large number of morbidity and mortality among humans. According to a latest study, Diabetes mellitus, heart diseases, Hypertension etc. are being considered important risk factors for the development of this infection and is also associated with unfavorable outcomes in these patients. There is little evidence concerning the trail back of these patients possibly because of a small number of participants and people who experienced primary composite outcomes (such as admission in the ICU, usage of machine-driven ventilation or even fatality of these patients). Until now, there are no academic findings that have proven independent prognostic value of diabetes on death in the novel Coronavirus patients. However, there are several conjectures linking Diabetes with the impact as well as progression of COVID-19 in these patients. The aim of this review is to acknowledge about the association amongst Diabetes and the novel Coronavirus and the result of the infection in such patients.

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Putting the world back to work: An expert system using big data and artificial intelligence in combating the spread of COVID-19 and similar contagious diseases

Work ◽

10.3233/wor-203309 ◽

2020 ◽

Vol 67 (3) ◽

pp. 557-572

Author(s):

Said Tkatek ◽

Amine Belmzoukia ◽

Said Nafai ◽

Jaafar Abouchabaka ◽

Youssef Ibnou-ratib

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Expert System ◽

Population Monitoring ◽

High Definition ◽

Contagious Diseases ◽

The World ◽

General Guide ◽

The Impact ◽

Back To Work

BACKGROUND: To combat COVID-19, curb the pandemic, and manage containment, governments around the world are turning to data collection and population monitoring for analysis and prediction. The massive data generated through the use of big data and artificial intelligence can play an important role in addressing this unprecedented global health and economic crisis. OBJECTIVES: The objective of this work is to develop an expert system that combines several solutions to combat COVID-19. The main solution is based on a new developed software called General Guide (GG) application. This expert system allows us to explore, monitor, forecast, and optimize the data collected in order to take an efficient decision to ensure the safety of citizens, forecast, and slow down the spread’s rate of COVID-19. It will also facilitate countries’ interventions and optimize resources. Moreover, other solutions can be integrated into this expert system, such as the automatic vehicle and passenger sanitizing system equipped with a thermal and smart High Definition (HD) cameras and multi-purpose drones which offer many services. All of these solutions will facilitate lifting COVID-19 restrictions and minimize the impact of this pandemic. METHODS: The methods used in this expert system will assist in designing and analyzing the model based on big data and artificial intelligence (machine learning). This can enhance countries’ abilities and tools in monitoring, combating, and predicting the spread of COVID-19. RESULTS: The results obtained by this prediction process and the use of the above mentioned solutions will help monitor, predict, generate indicators, and make operational decisions to stop the spread of COVID-19. CONCLUSIONS: This developed expert system can assist in stopping the spread of COVID-19 globally and putting the world back to work.

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Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study

European Radiology ◽

10.1007/s00330-021-07992-w ◽

2021 ◽

Author(s):

Suzanne L. van Winkel ◽

Alejandro Rodríguez-Ruiz ◽

Linda Appelman ◽

Albert Gubern-Mérida ◽

Nico Karssemeijer ◽

...

Keyword(s):

Breast Cancer ◽

Artificial Intelligence ◽

Cancer Detection ◽

Support System ◽

Reading Time ◽

Digital Breast Tomosynthesis ◽

Breast Tomosynthesis ◽

Screening Programs ◽

The Impact

Abstract Objectives Digital breast tomosynthesis (DBT) increases sensitivity of mammography and is increasingly implemented in breast cancer screening. However, the large volume of images increases the risk of reading errors and reading time. This study aims to investigate whether the accuracy of breast radiologists reading wide-angle DBT increases with the aid of an artificial intelligence (AI) support system. Also, the impact on reading time was assessed and the stand-alone performance of the AI system in the detection of malignancies was compared to the average radiologist. Methods A multi-reader multi-case study was performed with 240 bilateral DBT exams (71 breasts with cancer lesions, 70 breasts with benign findings, 339 normal breasts). Exams were interpreted by 18 radiologists, with and without AI support, providing cancer suspicion scores per breast. Using AI support, radiologists were shown examination-based and region-based cancer likelihood scores. Area under the receiver operating characteristic curve (AUC) and reading time per exam were compared between reading conditions using mixed-models analysis of variance. Results On average, the AUC was higher using AI support (0.863 vs 0.833; p = 0.0025). Using AI support, reading time per DBT exam was reduced (p < 0.001) from 41 (95% CI = 39–42 s) to 36 s (95% CI = 35– 37 s). The AUC of the stand-alone AI system was non-inferior to the AUC of the average radiologist (+0.007, p = 0.8115). Conclusions Radiologists improved their cancer detection and reduced reading time when evaluating DBT examinations using an AI reading support system. Key Points • Radiologists improved their cancer detection accuracy in digital breast tomosynthesis (DBT) when using an AI system for support, while simultaneously reducing reading time. • The stand-alone breast cancer detection performance of an AI system is non-inferior to the average performance of radiologists for reading digital breast tomosynthesis exams. • The use of an AI support system could make advanced and more reliable imaging techniques more accessible and could allow for more cost-effective breast screening programs with DBT.

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A case study of noise pollution levels during the restrictions period due to COVID-19.

INTER-NOISE and NOISE-CON Congress and Conference Proceedings ◽

10.3397/in-2021-1342 ◽

2021 ◽

Vol 263 (6) ◽

pp. 206-214

Author(s):

David Montes-González ◽

Juan Miguel Barrigón-Morillas ◽

Ana Cristina Bejarano-Quintas ◽

Manuel Parejo-Pizarro ◽

Guillermo Rey-Gozalo ◽

...

Keyword(s):

Noise Pollution ◽

Urban Environments ◽

Control Measures ◽

Acoustic Measurements ◽

Pollution Levels ◽

The World ◽

The Impact ◽

The City

The pandemic of coronavirus disease (COVID-19) led to the need for drastic control measures around the world to reduce the impact on the health of the population. The confinement of people in their homes resulted in a significant reduction in human activity at every level (economic, social, industrial, etc.), which was reflected in a decrease in environmental pollution levels. Studying the evolution of parameters, such as the level of environmental noise caused by vehicle traffic in urban environments, makes it possible to assess the impact of this type of measure. This paper presents a case study of the acoustic situation in Cáceres (Spain) during the restriction period by means of long-term acoustic measurements at various points of the city.

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Leveraging Twitter Using Artificial Intelligence to Explore Mental Health Insights in the UK During the COVID-19 Pandemic (Preprint)

10.2196/preprints.32449 ◽

2021 ◽

Author(s):

Christopher Marshall ◽

Kate Lanyi ◽

Rhiannon Green ◽

Georgie Wilkins ◽

Fiona Pearson ◽

...

Keyword(s):

Mental Health ◽

Artificial Intelligence ◽

Language Processing ◽

Mental Health Problems ◽

Well Being ◽

Research Activity ◽

Health Crisis ◽

Key Topics ◽

The Uk ◽

The Impact

BACKGROUND There is increasing need to explore the value of soft-intelligence, leveraged using the latest artificial intelligence (AI) and natural language processing (NLP) techniques, as a source of analysed evidence to support public health research activity and decision-making. OBJECTIVE The aim of this study was to further explore the value of soft-intelligence analysed using AI through a case study, which examined a large collection of UK tweets relating to mental health during the COVID-19 pandemic. METHODS A search strategy comprising a list of terms related to mental health, COVID-19, and lockdown restrictions was developed to prospectively collate relevant tweets via Twitter’s advanced search application programming interface over a 24-week period. We deployed a specialist NLP platform to explore tweet frequency and sentiment across the UK and identify key topics of discussion. A series of keyword filters were used to clean the initial data retrieved and also set up to track specific mental health problems. Qualitative document analysis was carried out to further explore and expand upon the results generated by the NLP platform. All collated tweets were anonymised RESULTS We identified and analysed 286,902 tweets posted from UK user accounts from 23 July 2020 to 6 January 2021. The average sentiment score was 50%, suggesting overall neutral sentiment across all tweets over the study period. Major fluctuations in volume and sentiment appeared to coincide with key changes to any local and/or national social-distancing measures. Tweets around mental health were polarising, discussed with both positive and negative sentiment. Key topics of consistent discussion over the study period included the impact of the pandemic on people’s mental health (both positively and negatively), fear and anxiety over lockdowns, and anger and mistrust toward the government. CONCLUSIONS Through the primary use of an AI-based NLP platform, we were able to rapidly mine and analyse emerging health-related insights from UK tweets into how the pandemic may be impacting people’s mental health and well-being. This type of real-time analysed evidence could act as a useful intelligence source that agencies, local leaders, and health care decision makers can potentially draw from, particularly during a health crisis.

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Comparison of Earthquake-Triggered Landslide Inventories: A Case Study of the 2015 Gorkha Earthquake, Nepal

Geosciences ◽

10.3390/geosciences9100437 ◽

2019 ◽

Vol 9 (10) ◽

pp. 437 ◽

Cited By ~ 6

Author(s):

Meena ◽

Tavakkoli Piralilou

Keyword(s):

Risk Mitigation ◽

Landslide Susceptibility Mapping ◽

Gorkha Earthquake ◽

Area Distribution ◽

2015 Gorkha Earthquake ◽

The World ◽

Mapping Techniques ◽

Event Based ◽

The Impact

Despite landslide inventories being compiled throughout the world every year at different scales, limited efforts have been made to critically compare them using various techniques or by different investigators. Event-based landslide inventories indicate the location, distribution, and detected boundaries of landslides caused by a single event, such as an earthquake or a rainstorm. Event-based landslide inventories are essential for landslide susceptibility mapping, hazard modeling, and further management of risk mitigation. In Nepal, there were several attempts to map landslides in detail after the Gorkha earthquake. Particularly after the main event on 25 April 2015, researchers around the world mapped the landslides induced by this earthquake. In this research, we compared four of these published inventories qualitatively and quantitatively using different techniques. Two principal methodologies, namely the cartographical degree of matching and frequency area distribution (FAD), were optimized and applied to evaluate inventory maps. We also showed the impact of using satellite imagery with different spatial resolutions on the landslide inventory generation by analyzing matches and mismatches between the inventories. The results of our work give an overview of the impact of methodology selection and outline the limitations and advantages of different remote sensing and mapping techniques for landslide inventorying.

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Gender and feminist considerations in Artificial Intelligence from a developing-world perspective, with India as a case study

10.31124/advance.17054936 ◽

2021 ◽

Author(s):

SANGHAMITRA CHOUDHURY ◽

Shailendra Kumar

Keyword(s):

Artificial Intelligence ◽

Developing Countries ◽

Developing World ◽

Developed Countries ◽

High Level ◽

The Impact ◽

The Relationship ◽

Precarious Situation ◽

And Robotics

<p>The relationship between women, technology manifestation, and likely prospects in the developing world is discussed in this manuscript. Using India as a case study, the paper goes on to discuss how ontology and epistemology views utilised in AI (Artificial Intelligence) and robotics will affect women's prospects in developing countries. Women in developing countries, notably in South Asia, are perceived as doing domestic work and are underrepresented in high-level professions. They are disproportionately underemployed and face prejudice in the workplace. The purpose of this study is to determine if the introduction of AI would exacerbate the already precarious situation of women in the developing world or if it would serve as a liberating force. While studies on the impact of AI on women have been undertaken in developed countries, there has been less research in developing countries. This manuscript attempts to fill that need.</p>

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Employment Opportunities And Human Resources Development In Digital Era: A Case Study In Industrial Sector

Journal of Indonesian Social Sciences and Humanities ◽

10.14203/jissh.v8i2.138 ◽

2018 ◽

Vol 8 (2) ◽

pp. 159-168

Author(s):

Devi Asiati ◽

Gutomo Bayu Aji ◽

Vanda Ningrum ◽

Ngadi Ngadi ◽

Triyono Triyono ◽

...

Keyword(s):

Industrial Sector ◽

Production Equipment ◽

Improvement Program ◽

Digital Era ◽

Local Resources ◽

The World ◽

The Government ◽

Machine Replacement ◽

The Impact

Transformation of digitalization in large industries has an impact on the automation of production equipment, including the replacement of production machines from conventional machines (manual) to digital machines. Meanwhile, automation of production equipment requires workers with higher skills, in fact the existing workforce does not have expertise in carrying out all-digital equipment. The impact is a reduction in labor (layoffs). Machine replacement is done in stages so that the reduction of workforce (PHK) in bulk is not visible. However, the inconsistency between the preparation in the world of education and the needs in the world of work continues to occur today. Until now, vocational development based on local resources has not been operating optimally and needs serious attention from the local government. The government on various occasions mentioned four leading sectors that will be strengthened in the development of vocational institutions, namely maritime, tourism, agriculture (food security), and the creative industry. In addition, the government is also developing a policy scheme for Skill Development Funds (SDF), which is a skills improvement program for workers affected by automation (PHK), including through Vocational Training Center (BLK).

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ARTIFICIAL INTELLIGENCE: IS IT A THREAT OR AN OPPORTUNITY BASED ON ITS LEGAL PERSONALITY AND CRIMINAL LIABILITY?

Journal of Information System and Technology Management ◽

10.35631//jistm.620001 ◽

2021 ◽

Vol 6 (20) ◽

pp. 01-09

Author(s):

Mark Louis ◽

Angelina Anne Fernandez ◽

Nazura Abdul Manap ◽

Shamini Kandasamy ◽

Sin Yee Lee

Keyword(s):

Artificial Intelligence ◽

Information Technology ◽

Criminal Liability ◽

Legal Person ◽

Human Intelligence ◽

Legal Research ◽

Legal Personality ◽

The World ◽

Technological World ◽

The Impact

Information technology is taking the world by storm. The technological world is changing rapidly and drastically. Human activities are taken over by robots and computers. The usage of computers and robots has increased productivity in various sectors. The emergence of artificial intelligence has stirred up many debates on both its importance and limitations. Artificial intelligence is directed to the usage of Information Technology in conducting tasks that normally require human intelligence. The expectation of artificial intelligence is high, nevertheless, artificial intelligence has its shortcomings namely the impact of artificial intelligence on the concept of a legal personality. The problem with artificial Intelligence is the debate on whether does it have a legal personality? And another problem is under what situation does the law treat artificial intelligence as an entity with its own rights and obligations. The objective of this article is to examine the various definitions of legal personality and whether artificial intelligence can become a legal person. The article will also examine the criminal liability of artificial intelligence when a crime has been committed. The methodology adopted is qualitative namely Doctrinal Legal Research by analyzing the relevant legal views from various journals on artificial intelligence. The study found out that artificial intelligence has its limitations in defining its legal personality and also in examining the criminal liability when a crime has been committed by robots.

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