scholarly journals Addressing Biodisaster X Threats With Artificial Intelligence and 6G Technologies: Literature Review and Critical Insights (Preprint)

2020 ◽  
Author(s):  
Zhaohui Su ◽  
Dean McDonnell ◽  
Barry L Bentley ◽  
Jiguang He ◽  
Feng Shi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Information Technologies ◽  
Population Level ◽  
Cost Effective ◽  
Well Being ◽  
Economic Consequences ◽  
Unknown Risk ◽  
Remote Design ◽  
Shed Light

BACKGROUND With advances in science and technology, biotechnology is becoming more accessible to people of all demographics. These advances inevitably hold the promise to improve personal and population well-being and welfare substantially. It is paradoxical that while greater access to biotechnology on a population level has many advantages, it may also increase the likelihood and frequency of biodisasters due to accidental or malicious use. Similar to “Disease X” (describing unknown naturally emerging pathogenic diseases with a pandemic potential), we term this unknown risk from biotechnologies “Biodisaster X.” To date, no studies have examined the potential role of information technologies in preventing and mitigating Biodisaster X. OBJECTIVE This study aimed to explore (1) what Biodisaster X might entail and (2) solutions that use artificial intelligence (AI) and emerging 6G technologies to help monitor and manage Biodisaster X threats. METHODS A review of the literature on applying AI and 6G technologies for monitoring and managing biodisasters was conducted on PubMed, using articles published from database inception through to November 16, 2020. RESULTS Our findings show that Biodisaster X has the potential to upend lives and livelihoods and destroy economies, essentially posing a looming risk for civilizations worldwide. To shed light on Biodisaster X threats, we detailed effective AI and 6G-enabled strategies, ranging from natural language processing to deep learning–based image analysis to address issues ranging from early Biodisaster X detection (eg, identification of suspicious behaviors), remote design and development of pharmaceuticals (eg, treatment development), and public health interventions (eg, reactive shelter-at-home mandate enforcement), as well as disaster recovery (eg, sentiment analysis of social media posts to shed light on the public’s feelings and readiness for recovery building). CONCLUSIONS Biodisaster X is a looming but avoidable catastrophe. Considering the potential human and economic consequences Biodisaster X could cause, actions that can effectively monitor and manage Biodisaster X threats must be taken promptly and proactively. Rather than solely depending on overstretched professional attention of health experts and government officials, it is perhaps more cost-effective and practical to deploy technology-based solutions to prevent and control Biodisaster X threats. This study discusses what Biodisaster X could entail and emphasizes the importance of monitoring and managing Biodisaster X threats by AI techniques and 6G technologies. Future studies could explore how the convergence of AI and 6G systems may further advance the preparedness for high-impact, less likely events beyond Biodisaster X.

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

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.

scholarly journals Emerging Opportunities Provided by Technology to Advance Research in Child Health Globally

2020 ◽  
Vol 7 ◽  
pp. 2333794X2091757
Author(s):  
Alastair van Heerden ◽  
Jukka Leppanen ◽  
Mary Jane Rotheram-Borus ◽  
Carol M. Worthman ◽  
Brandon A. Kohrt ◽  
...  
Keyword(s):  
Child Health ◽  
Affective Computing ◽  
Population Level ◽  
Cost Effective ◽  
Well Being ◽  
Longitudinal Assessment ◽  
Health And Development ◽  
Advance Research ◽  
Development Goals ◽  
The Media

Current approaches to longitudinal assessment of children’s developmental and psychological well-being, as mandated in the United Nations Sustainable Development Goals, are expensive and time consuming. Substantive understanding of global progress toward these goals will require a suite of new robust, cost-effective research tools designed to assess key developmental processes in diverse settings. While first steps have been taken toward this end through efforts such as the National Institutes of Health’s Toolbox, experience-near approaches including naturalistic observation have remained too costly and time consuming to scale to the population level. This perspective presents 4 emerging technologies with high potential for advancing the field of child health and development research, namely (1) affective computing, (2) ubiquitous computing, (3) eye tracking, and (4) machine learning. By drawing attention of scientists, policy makers, investors/funders, and the media to the applications and potential risks of these emerging opportunities, we hope to inspire a fresh wave of innovation and new solutions to the global challenges faced by children and their families.

Role of Emerging Technologies in COVID 19: Analyses, Predictions, and Future Countermeasures (Preprint)

2020 ◽  
Author(s):  
Dr. Rekha G
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Internet Of Things ◽  
Language Processing ◽  
Emerging Technologies ◽  
Point Of Care ◽  
Cost Effective ◽  
Current Status ◽  
Future Research ◽  
Smart Healthcare

UNSTRUCTURED In the resent decade, emerging technologies like Artificial Intelligence, Blockchain Technology, Cloud Computing , Internet of Things (IoT), etc., have changed people life a lot (in terms of living). Artificial Intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous successful stories. AI has also contributed to dealing with the coronavirus disease (COVID-19) pandemic, which is currently happening around the globe.We touch on a number of areas where AI plays as an essential component, from medical image processing, data analytics, text mining and natural language processing, the Internet of Things, to computational biology and medicine. For this, a summary of COVID-19 related data sources that are available for research purposes (for future researchers) is also presented.For that, all the tools, resources and datasets needed to facilitate AI research are also been reviewed. Also discussed about Machine Learning use cases for Drug Formulations, Treatment of Patients Suffering with COVID-19, how Artificial Intelligence and internet of things can be useful to develop Cost- effective and Rapid Point-of-Care Diagnostics. For example, uses of Internet of Medical Things for Smart Healthcare (primary focus on detecting COVID-19 symptoms, and alerts for other users) have been discussed in this work. In summary, this work providesuseful information about (potential of) AI methods, machine learning, internet of things, used in many applications like Medicare, COVID-19 outbreak and summarizes several critical roles of Artificial Intelligence (including machine learning and internet of things) research in this unprecedented battle.We also discuss several future Research directions, global impact of corona on internet of things and many applications. It is envisaged that this work will provide AI, and ML researchers and the wider community an overview of the current status of AI and ML applications and motivate researchers in harnessing AI potentials in the fight against COVID-19.

Artificial Intelligence for Emotion-Semantic Trending and People Emotion Detection During COVID-19 Social Isolation (Preprint)

2021 ◽  
Author(s):  
Hamed Jelodar
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Language Processing ◽  
Well Being ◽  
Emotional Reaction ◽  
Emotion Classification ◽  
Media Messages ◽  
Intelligence Models ◽  
Artificial Intelligence Models

BACKGROUND Given the limitations of medical diagnosis of early emotional change signs during the COVID-19 quarantine period, artificial intelligence models provide effective mechanisms in uncovering early signs, symptoms and escalating trend. OBJECTIVE The main purpose of this project is to demonstrate the effectiveness of Artificial Intelligence, and in particular Natural Language Processing and Machine Learning in detecting and analyzing emotions from tweets talking about COVID-19 social confinement. METHODS We developed a systematic framework that can be directly applied to COVID-19 related mood discovery, using eight types of emotional reaction and designing a deep learning model to uncover emotions based on the first wave of the pandemic public health restriction of mandatory social segregation. We argue that the framework can discover semantic trends of COVID-19 tweets during the first wave of the pandemic to predict new concerns that may be associated with furthering into the new waves of COVID-19 quarantine orders and other related public health regulations. RESULTS Our findings revealed Stay-At-Home restrictions result in people expressing on twitter both negative and positive based on emotional and semantics aspects. Moreover, the statistical results of the emotion classification is show that our framework based on CNN deep learning has predicted the emotion levels or target labels with more F1-socore than the LSTM model, which are 0.95% and 0.93%, respectively. However, these results have potential to impact public health policy decisions through monitoring trends of emotional feelings of those who are quarantined. CONCLUSIONS The research shows that the framework is effective in capturing the emotions and semantics trends in social media messages during the pandemic. Moreover, the framework can be applied to uncover reactions to similar public health policies that affect people’s well-being.

scholarly journals Leveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective

2021 ◽  
Author(s):  
Erik Hermann
Keyword(s):  
Artificial Intelligence ◽  
Well Being ◽  
Ethical Principles ◽  
Ethical Challenges ◽  
Ethical Perspective ◽  
Social Good ◽  
Stakeholder Perspective ◽  
Ai Ethics ◽  
Multi Stakeholder ◽  
Shed Light

AbstractArtificial intelligence (AI) is (re)shaping strategy, activities, interactions, and relationships in business and specifically in marketing. The drawback of the substantial opportunities AI systems and applications (will) provide in marketing are ethical controversies. Building on the literature on AI ethics, the authors systematically scrutinize the ethical challenges of deploying AI in marketing from a multi-stakeholder perspective. By revealing interdependencies and tensions between ethical principles, the authors shed light on the applicability of a purely principled, deontological approach to AI ethics in marketing. To reconcile some of these tensions and account for the AI-for-social-good perspective, the authors make suggestions of how AI in marketing can be leveraged to promote societal and environmental well-being.

CONTENT OF TEACHING THE ELEMENTS OF ARTIFICIAL INTELLIGENCE IN A SCHOOL INFORMATICS COURSE

2020 ◽  
pp. 3-10
Author(s):  
I. V. Levchenko
Keyword(s):  
Artificial Intelligence ◽  
General Education ◽  
Extracurricular Activities ◽  
Information Technologies ◽  
Learning Experience ◽  
9Th Grade ◽  
Modern Information ◽  
Further Development ◽  
Form Variant ◽  
Theoretical Foundations

The article considers the feasibility of integrating artificial intelligence technologies into school education and identifies a problem in identifying didactic elements in the field of artificial intelligence, which must be mastered in a school informatics course. The purpose of the article is to propose variant of the content of teaching the elements of artificial intelligence for the general education of schoolchildren as part of the curricular and extracurricular activities in informatics. An analysis of the psychological, pedagogical and scientific-methodical literature in the field of artificial intelligence made it possible to identify the appropriateness of teaching schoolchildren the elements of artificial intelligence in the framework of a comprehensive informatics course, as the theoretical foundations of modern information technologies. Summarizing and systematizing the learning experience of schoolchildren in the field of artificial intelligence made it possible to form variant of the content of teaching the elements of artificial intelligence, which can be implemented in a compulsory informatics course for 9th grade, as well as in elective classes. The results of the study are the theoretical basis for the further development of the components of the methodological system of teaching the elements of artificial intelligence in a school informatics course. The research materials may be useful to specialists in the field of teaching informatics and to informatics teachers.

Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence

AI Magazine ◽  
2019 ◽  
Vol 40 (3) ◽  
pp. 67-78
Author(s):  
Guy Barash ◽  
Mauricio Castillo-Effen ◽  
Niyati Chhaya ◽  
Peter Clark ◽  
Huáscar Espinoza ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Cyber Security ◽  
Question Answering ◽  
Intent Recognition ◽  
Affective Content ◽  
Learning Plan ◽  
Dialog System ◽  
Affective Content Analysis ◽  
Games And Simulations

The workshop program of the Association for the Advancement of Artificial Intelligence’s 33rd Conference on Artificial Intelligence (AAAI-19) was held in Honolulu, Hawaii, on Sunday and Monday, January 27–28, 2019. There were fifteen workshops in the program: Affective Content Analysis: Modeling Affect-in-Action, Agile Robotics for Industrial Automation Competition, Artificial Intelligence for Cyber Security, Artificial Intelligence Safety, Dialog System Technology Challenge, Engineering Dependable and Secure Machine Learning Systems, Games and Simulations for Artificial Intelligence, Health Intelligence, Knowledge Extraction from Games, Network Interpretability for Deep Learning, Plan, Activity, and Intent Recognition, Reasoning and Learning for Human-Machine Dialogues, Reasoning for Complex Question Answering, Recommender Systems Meet Natural Language Processing, Reinforcement Learning in Games, and Reproducible AI. This report contains brief summaries of the all the workshops that were held.

Press “A” for Artificial Intelligence in Agriculture: A Review

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Yogesh Awasthi
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Significant Role ◽  
Pattern Analysis ◽  
Cost Effective ◽  
Predictive Analysis ◽  
Accurate Information ◽  
Digital Era ◽  
Learning Techniques ◽  
Crucial Information

Agriculture is the backbone of the developing country. In old era agriculture was based on the experience which was shared by people to people but in this digital era technology play a very important and significant role in agriculture. Now agriculture become a business hub therefore farmers are focusing on precision farming. They introduced the technology in agriculture to define the accurate information about seed, soil, weather, disease and all factors which affecting the farming. Artificial Intelligence uses predictive analysis, image analysis, learning techniques and Pattern analysis to declare the best cost effective and maximum gain for the agriculturist. The aim of this paper is to provide the crucial information with the help of technology which a farmers can use to harvest the variety of crops as per the demand in world so that they can get maximum benefits.

Clinical utility of Shabda and Sparsha Pareeksha in evaluation of Prakruta and Vaikruta Garbhavastha

2016 ◽  
Vol 1 (2) ◽  
Author(s):  
Priyanka T K ◽  
V.N. K. Usha ◽  
Sucheta Kumari M
Keyword(s):  
Clinical Study ◽  
Clinical Utility ◽  
Neonatal Outcome ◽  
Predictive Accuracy ◽  
Cost Effective ◽  
Well Being ◽  
Prospective Clinical Study ◽  
Clinical Tool ◽  
Accuracy Rate ◽  
Fetal Pathology

Garbha is a conglomeration of biological mass with different strata including consciousness, needs an innovative clinical tool to evaluate its well being, which proves safe, potent, cost-effective and noninvasive. The idea of taking up this study was to sensitively predict the Prakrutavastha or well being w.r.t Garbha-pushti and ongoing Fetal Pathology, Vaikrutavastha w.s.r Garbhavyapads for a sharp interference to get a possible best neonatal outcome. The objective of this study was to calculate the predictive accuracy of evaluation of Garbhaspandanam on external Shabda and Sparsha Pareeksha. A Prospective Clinical study of Garbhaspandanam (FHS and FM) with external Shabda and Sparsha stimulation on maternal abdomen, from 24th week onwards was conducted in a cohort of 30 Singleton Pregnant women at Dept. of Prasuti Tantra and Stri Roga, S.D.M.C.A. Hospital, Udupi. Among the 9 cases in abnormal category, 2 cases had gone for IUD and one case though placed in abnormal category had responded relatively well to Shabda and Sparsha Pareeksha which may be due to the proper antenatal care and intervention given along with the patient’s Vatakara Nidana Parivarjana. Predictive Accuracy Rate on Shabda and Sparsha Pareeksha showed, FHS 70%, FM 76.7%; FHS 73.3%, FM 66.7% respectively. Shabda and Sparshapareeksha can be utilized as the Garbha - chetana - dyodakalakshana and can be performed as a routine antenatal bedside procedure, which can fairly detect the Prakruta and Vaikrutavastha of Garbha w.r.t Pushti. However larger prospective studies are required.

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