Machine Learning and Artificial Intelligence in Pediatric Research: Current State, Future Prospects, and Examples in Perioperative and Critical Care

2020 ◽  
Vol 221 ◽  
pp. S3-S10 ◽  
Author(s):  
Hannah Lonsdale ◽  
Ali Jalali ◽  
Luis Ahumada ◽  
Clyde Matava
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Critical Care ◽  
Pediatric Research ◽  
Future Prospects ◽  
Current State

Applications of Artificial Intelligence in the Realm of Business Intelligence

2021 ◽  
pp. 358-386
Author(s):  
Prakhar Mehrotra
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Time Series ◽  
Natural Language Processing ◽  
Language Processing ◽  
Business Intelligence ◽  
Machine Learning Techniques ◽  
Current State ◽  
Learning Techniques ◽  
High Level

The objective of this chapter is to discuss the integration of advancements made in the field of artificial intelligence into the existing business intelligence tools. Specifically, it discusses how the business intelligence tool can integrate time series analysis, supervised and unsupervised machine learning techniques and natural language processing in it and unlock deeper insights, make predictions, and execute strategic business action from within the tool itself. This chapter also provides a high-level overview of current state of the art AI techniques and provides examples in the realm of business intelligence. The eventual goal of this chapter is to leave readers thinking about what the future of business intelligence would look like and how enterprise can benefit by integrating AI in it.

scholarly journals Overview of current state of research on the application of artificial intelligence techniques for COVID-19

2021 ◽  
Vol 7 ◽  
pp. e564
Author(s):  
Vijay Kumar ◽  
Dilbag Singh ◽  
Manjit Kaur ◽  
Robertas Damaševičius
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Drug Interaction ◽  
Future Research ◽  
Rapid Testing ◽  
Learning Models ◽  
Open Area ◽  
Current State ◽  
State Of Research ◽  
Novel Drug

Background Until now, there are still a limited number of resources available to predict and diagnose COVID-19 disease. The design of novel drug-drug interaction for COVID-19 patients is an open area of research. Also, the development of the COVID-19 rapid testing kits is still a challenging task. Methodology This review focuses on two prime challenges caused by urgent needs to effectively address the challenges of the COVID-19 pandemic, i.e., the development of COVID-19 classification tools and drug discovery models for COVID-19 infected patients with the help of artificial intelligence (AI) based techniques such as machine learning and deep learning models. Results In this paper, various AI-based techniques are studied and evaluated by the means of applying these techniques for the prediction and diagnosis of COVID-19 disease. This study provides recommendations for future research and facilitates knowledge collection and formation on the application of the AI techniques for dealing with the COVID-19 epidemic and its consequences. Conclusions The AI techniques can be an effective tool to tackle the epidemic caused by COVID-19. These may be utilized in four main fields such as prediction, diagnosis, drug design, and analyzing social implications for COVID-19 infected patients.

scholarly journals “Where’s the I-O?” Artificial Intelligence and Machine Learning in Talent Management Systems

2019 ◽  
Vol 5 (3) ◽  
Author(s):  
Manuel Gonzalez ◽  
John Capman ◽  
Frederick Oswald ◽  
Evan Theys ◽  
David Tomczak
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Adverse Reactions ◽  
Talent Management ◽  
Job Applicants ◽  
Selection Procedures ◽  
Organizational Contexts ◽  
Current State ◽  
Computer Scientists ◽  
Professional Disciplines

Artificial intelligence (AI) and machine learning (ML) have seen widespread adoption by organizations seeking to identify and hire high-quality job applicants. Yet the volume, variety, and velocity of professional involvement among I-O psychologists remains relatively limited when it comes to developing and evaluating AI/ML applications for talent assessment and selection. Furthermore, there is a paucity of empirical research that investigates the reliability, validity, and fairness of AI/ML tools in organizational contexts. To stimulate future involvement and research, we share our review and perspective on the current state of AI/ML in talent assessment as well as its benefits and potential pitfalls; and in addressing the issue of fairness, we present experimental evidence regarding the potential for AI/ML to evoke adverse reactions from job applicants during selection procedures. We close by emphasizing increased collaboration among I-O psychologists, computer scientists, legal scholars, and members of other professional disciplines in developing, implementing, and evaluating AI/ML applications in organizational contexts.

Applications of Artificial Intelligence in the Realm of Business Intelligence

2019 ◽  
pp. 1-38
Author(s):  
Prakhar Mehrotra
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Time Series ◽  
Natural Language Processing ◽  
Language Processing ◽  
Business Intelligence ◽  
Machine Learning Techniques ◽  
Current State ◽  
Learning Techniques ◽  
High Level

The objective of this chapter is to discuss the integration of advancements made in the field of artificial intelligence into the existing business intelligence tools. Specifically, it discusses how the business intelligence tool can integrate time series analysis, supervised and unsupervised machine learning techniques and natural language processing in it and unlock deeper insights, make predictions, and execute strategic business action from within the tool itself. This chapter also provides a high-level overview of current state of the art AI techniques and provides examples in the realm of business intelligence. The eventual goal of this chapter is to leave readers thinking about what the future of business intelligence would look like and how enterprise can benefit by integrating AI in it.

scholarly journals SIAPKAH PARA DOSEN AKUNTANSI DI PERGURUAN TINGGI VOKASI DALAM MENYAMBUT DATANGNYA REVOLUSI INDUSTRI 4.0 ?

2020 ◽  
Vol 18 (3) ◽  
pp. 465
Author(s):  
Diana Rino Putri ◽  
Nurafni Eltivia ◽  
Ari Kamayanti ◽  
Jaswadi Jaswadi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Distributed Processing ◽  
Current Level ◽  
Future Prospects ◽  
Vocational College ◽  
True Meaning ◽  
Learning Data

In developing countries such as Indonesia, a large number of academics are unfamiliar with the true meaning of terms such as Big Data, Exabyte, Petabyte, Brontobyte, Artificial Intelligence, Machine Learning, Data Mining, Data Warehousing, Distributed Processing, Grid Computing and Cloud Computing. In this paper, we report the results of a survey carried out to ascertain the current level of awareness regarding Big Data among academics in Vocational College. Respondents to a questionnaire formulated for this purpose. Results of the survey seem to indicate that there is a need for multi-faceted efforts aimed at creating awareness regarding Big Data, the related technologies, challenges and future prospects.

Stable Teamwork Marriages in Healthcare: Applying Machine Learning to Surgeon-Nurse-Patient Matching

2018 ◽  
Vol 62 (1) ◽  
pp. 1202-1206
Author(s):  
Lorenzo Barberis Canonico ◽  
Nathan J. McNeese ◽  
Marissa L. Shuffler
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Critical Care ◽  
Management Systems ◽  
Complex Environments ◽  
Patient Assignment ◽  
Effective Care ◽  
Matching Mechanism ◽  
Time And Energy ◽  
New Machine

Hospitals are plagued with a multitude of logistical challenges amplified by a time-sensitive and high intensity environment. These conditions have resulted in burnout among both doctors and nurses as they work tirelessly to provide critical care to patients in need. We propose a new machine-learning-powered matching mechanism that manages the surgeon-nurse-patient assignment process in an efficient way that saves time and energy for hospitals, enabling them to focus almost entirely on delivering effective care. Through this design, we show how incorporating artificial intelligence into management systems enables teams of all sizes to meaningfully coordinate in highly chaotic and complex environments.

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