Results 61 to 70 of about 1,710,361 (207)

Use of automated machine learning for an outbreak risk prediction tool

Informatics in Medicine Unlocked, 2022
The frequency of serious epidemics has continued to increase in the last decade. The ability to predict the risk of outbreaks can improve prevention and control.
Tianyu Zhang, Fethi Rabhi, Ali Behnaz, Xin Chen, Hye-young Paik, Lina Yao, Chandini Raina MacIntyre   +6 more
doaj   +1 more source

Analysis of value added services on GDP Growth Rate using Data Mining Techniques [PDF]

Database Systems Journal, 2018
The growth of Information Technology has spawned large amount of databases and huge data in numerous areas. The research in databases and information technology has given rise to an approach to store and manipulate this data for further decision making ...
Stefan PREDA
doaj   +2 more sources

Driver Drowsiness Detection using Evolutionary Machine Learning: A Survey [PDF]

BIO Web of Conferences
One of the factors that kills hundreds of people every year is driving accidents caused by drowsy drivers. There are different methods to prevent this type of accidents. Recently Machine Learning (ML) and Deep Learning (DL) have emerged as very effective
Yasir Jumhaa Maha, Majeed Osama, Taima Alaa   +2 more
doaj   +1 more source

ML-MEDIC: A Preliminary Study of an Interactive Visual Analysis Tool Facilitating Clinical Applications of Machine Learning for Precision Medicine

Applied Sciences, 2020
Accessible interactive tools that integrate machine learning methods with clinical research and reduce the programming experience required are needed to move science forward.
Laura Stevens, David Kao, Jennifer Hall, Carsten Görg, Kaitlyn Abdo, Erik Linstead   +5 more
doaj   +1 more source

Next-Generation Machine Learning in Healthcare Fraud Detection: Current Trends, Challenges, and Future Research Directions

Information
The growing complexity and size of healthcare systems have rendered fraud detection increasingly challenging; however, the current literature lacks a holistic view of the latest machine learning (ML) techniques with practical implementation concerns. The
Kamran Razzaq, Mahmood Shah
doaj   +1 more source

Machine learning (ML) in science and STEM education: a systematic review

Frontiers in Education
Especially in Machine Learning (ML), the quick development of Artificial Intelligence (AI) has heightened interest in its ability to aid science and STEM education. Still, ML has frequently been used as a technical tool for assessment and analysis inside
Malek Jdaitawi, Malek Jdaitawi, Ashraf Fouad Kan'an, Reda Al-Mawadieh, Burhan Hamadneh, Hala Alsabatin, Seif Alkafaween, Omar Alomar   +7 more
doaj   +1 more source

Leveraging machine learning and blockchain in E-commerce and beyond: benefits, models, and application

Discover Artificial Intelligence, 2023
Blockchain technology (BT) allows market participants to keep track of digital transactions without central recordkeeping. The features of blockchain, including decentralization, persistency, and attack resistance, allow data security and privacy ...
Hrag Jebamikyous, Menglu Li, Yoga Suhas, Rasha Kashef   +3 more
doaj   +1 more source

EXPLORING THE ROLE OF MACHINE LEARNING IN ADVANCING THE SUSTAINABLE DEVELOPMENT AGENDA [PDF]

Proceedings on Engineering Sciences
A subset of artificial intelligence (AI), machine learning (ML) has the potential to transform several industries by increasing productivity and encouraging sustainability completely.
Diksha Dalal , Ankit Kumar , Sandeep Kumar   +2 more
doaj   +1 more source

FDA-Approved Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices: An Updated Landscape

Electronics
As artificial intelligence (AI) has been highly advancing in the last decade, machine learning (ML)-enabled medical devices are increasingly used in healthcare.
Geeta Joshi, Aditi Jain, Shalini Reddy Araveeti, Sabin Adhikari, Harshit Garg, Mukund Bhandari   +5 more
semanticscholar   +1 more source

Machine Learning (ML) for Signal Detection

, 2021
NPS NRP Executive Summary Research has shown that machine learning holds promise as a technique to improve the identification and classification of signals of interest. This study proposes the use of machine learning and generative adversarial networks (GANs) to classify received signals based on their down-converted (but not demodulated) in-phase and ...
Kragh, Frank E., Miller, Donna L.
openaire   +2 more sources