scholarly journals Supervised Learning Methods and Applications in Medical Research

2022 ◽  
pp. 27-34
Author(s):  
Yung Ming ◽  
Lily Yuan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Feature Extraction ◽  
Job Performance ◽  
Computational Linguistics ◽  
New Technologies ◽  
Voice Recognition ◽  
Autonomous Driving ◽  
Automated Systems

Machine Learning (ML) and Artificial Intelligence (AI) methods are transforming many commercial and academic areas, including feature extraction, autonomous driving, computational linguistics, and voice recognition. These new technologies are now having a significant effect in radiography, forensics, and many other areas where the accessibility of automated systems may improve the precision and repeatability of essential job performance. In this systematic review, we begin by providing a short overview of the different methods that are currently being developed, with a particular emphasis on those utilized in biomedical studies.

scholarly journals Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review

2021 ◽  
Vol 18 (9) ◽  
pp. 4499
Author(s):  
Anil Babu Payedimarri ◽  
Diego Concina ◽  
Luigi Portinale ◽  
Massimo Canonico ◽  
Deborah Seys ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Public Transportation ◽  
Prediction Models ◽  
Positive Impact ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Commercial Activities

Artificial Intelligence (AI) and Machine Learning (ML) have expanded their utilization in different fields of medicine. During the SARS-CoV-2 outbreak, AI and ML were also applied for the evaluation and/or implementation of public health interventions aimed to flatten the epidemiological curve. This systematic review aims to evaluate the effectiveness of the use of AI and ML when applied to public health interventions to contain the spread of SARS-CoV-2. Our findings showed that quarantine should be the best strategy for containing COVID-19. Nationwide lockdown also showed positive impact, whereas social distancing should be considered to be effective only in combination with other interventions including the closure of schools and commercial activities and the limitation of public transportation. Our findings also showed that all the interventions should be initiated early in the pandemic and continued for a sustained period. Despite the study limitation, we concluded that AI and ML could be of help for policy makers to define the strategies for containing the COVID-19 pandemic.

scholarly journals IMPROVING INTERNET OF THINGS PARKING SYSTEMS

2021 ◽  
Vol 19 (3) ◽  
pp. 163
Author(s):  
Dušan Bogićević
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Information Needs ◽  
Vehicle Classification ◽  
The Internet ◽  
Parking Space ◽  
Automated Systems ◽  
The Internet Of Things

Edge data processing represents the new evolution of the Internet and Cloud computing. Its application to the Internet of Things (IoT) is a step towards faster processing of information from sensors for better performance. In automated systems, we have a large number of sensors, whose information needs to be processed in the shortest possible time and acted upon. The paper describes the possibility of applying Artificial Intelligence on Edge devices using the example of finding a parking space for a vehicle, and directing it based on the segment the vehicle belongs to. Algorithm of Machine Learning is used for vehicle classification, which is based on vehicle dimensions.

scholarly journals The Pros & Cons of Business Innovation at IT Organizations

2019 ◽  
Vol 7 (1) ◽  
pp. 82-85
Author(s):  
Geetha Swaminathan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Information Technology ◽  
Cloud Computing ◽  
New Technologies ◽  
Global Market ◽  
Innovative Product ◽  
Business Innovation ◽  
It Organization ◽  
Pros And Cons

In the 21st Century, the buzzword is often used in all fields is “Innovation". It is no wonder using Innovation in day to the conversation as well as striving for innovation execution at organisations in Information Technology (IT) sectors. When we need to talk about innovation in IT sectors in the fast-moving technology IT organisations, they are in a position in increasing its capability in its innovative product and services. There is a lot of benefits out of business innovations that are being reaped in IT companies; there are apparent disadvantages are also the outcome of them. It is quite common, despite all benefits and drawbacks, they are in apposition to survive in the global market. That becomes a great challenge to all IT organisations. In IT organisations which consist of departments such as Development, Testing, Consulting, Networking, Infrastructure, Process and having common platforms and legacy languages, Apart from that they are in the way of invading new technologies such as Digital, Mobile, IoT, Artificial Intelligence, Machine learning Cloud computing. In all the fields, as mentioned above and area, they need to do innovation to sustain their business. This paper will provide elaborate results on Pros and Cons of Business Innovation in IT Organization.

Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review

2021 ◽  
Vol 89 ◽  
pp. 177-198
Author(s):  
Quinlan D. Buchlak ◽  
Nazanin Esmaili ◽  
Jean-Christophe Leveque ◽  
Christine Bennett ◽  
Farrokh Farrokhi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Machine Learning Applications

FinTech Adoption in China

2021 ◽  
pp. 166-173
Author(s):  
Gagan Kukreja
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Financial Services ◽  
New Technologies ◽  
Large Population ◽  
Financial Inclusion ◽  
High Penetration ◽  
Digital Payments ◽  
Almost All

Almost all financial services (especially digital payments) in China are affected by new innovations and technologies. New technologies such as blockchain, artificial intelligence, machine learning, deep learning, and data analytics have immensely influenced all most all aspects of financial services such as deposits, transactions, billings, remittances, credits (B2B and P2P), underwriting, insurance, and so on. Fintech companies are enabling larger financial inclusion, changing in lifestyle and expenditure behavior, better and fast financial services, and lots more. This chapter covers the development, opportunities, and challenges of financial sectors because of new technologies in China. This chapter throws the light on opportunities that emerged because of the large population of 1.4 billion people, high penetration, and access to the latest and affordable technology, affordable cost of smartphones, and government policies and regulations. Lastly, this chapter portrays the untapped potentials of Fintech in China.

Essential Features and Critical Issues With Educational Chatbots

2021 ◽  
pp. 246-262
Author(s):  
Jeremy Riel
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Formal Learning ◽  
Automated Systems ◽  
Conversational Agents ◽  
Teacher Instruction ◽  
Practical Applications ◽  
Significant Potential ◽  
Critical Issues ◽  
Future Work

Conversational agents, also known as chatbots, are automated systems for engaging in two-way dialogue with human users. These systems have existed in one form or another for at least 60 years but have recently demonstrated significant potential with advances in machine learning and artificial intelligence technologies. The use of conversational agents or chatbots for education can potentially reduce costs and supplement teacher instruction in transformative ways for formal learning. This chapter examines the design and status of chatbots and conversational agents for educational purposes. Common design functions and goals of educational chatbots are described, along with current practical applications of chatbots for educational purposes. Finally, this chapter considers issues about pedagogical commitments, ethics, and equity to suggest future work in the field.

scholarly journals A technologist view of the next decade evolution of the librarian work environment

2018 ◽  
Vol 186 ◽  
pp. 09004
Author(s):  
André Schaaff ◽  
Marc Wenger
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Work Environment ◽  
New Technologies ◽  
State Of The Art ◽  
Working Environment ◽  
Online Resources ◽  
Mining Machine ◽  
Daily Work

The work environment has deeply evolved in recent decades with the generalisation of IT in terms of hardware, online resources and software. Librarians do not escape this movement and their working environment is becoming essentially digital (databases, online publications, Wikis, specialised software, etc.). With the Big Data era, new tools will be available, implementing artificial intelligence, text mining, machine learning, etc. Most of these technologies already exist but they will become widespread and strongly impact our ways of working. The development of social networks that are "business" oriented will also have an increasing influence. In this context, it is interesting to reflect on how the work environment of librarians will evolve. Maintaining interest in the daily work is fundamental and over-automation is not desirable. It is imperative to keep the human-driven factor. We draw on state of the art new technologies which impact their work, and initiate a discussion about how to integrate them while preserving their expertise.

scholarly journals Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review

2020 ◽  
Vol 130 ◽  
pp. 109899 ◽  
Author(s):  
Ioannis Antonopoulos ◽  
Valentin Robu ◽  
Benoit Couraud ◽  
Desen Kirli ◽  
Sonam Norbu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Energy Demand ◽  
Learning Approaches ◽  
Demand Side ◽  
Demand Side Response

Artificial intelligence to diagnose ischemic stroke and identify large vessel occlusions: a systematic review

2019 ◽  
Vol 12 (2) ◽  
pp. 156-164 ◽  
Author(s):  
Nick M Murray ◽  
Mathias Unberath ◽  
Gregory D Hager ◽  
Ferdinand K Hui
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Ischemic Stroke ◽  
Acute Stroke ◽  
Feature Detection ◽  
Performance Metrics ◽  
Large Vessel ◽  
Image Feature ◽  
Stroke Treatment

Background and purposeAcute stroke caused by large vessel occlusions (LVOs) requires emergent detection and treatment by endovascular thrombectomy. However, radiologic LVO detection and treatment is subject to variable delays and human expertise, resulting in morbidity. Imaging software using artificial intelligence (AI) and machine learning (ML), a branch of AI, may improve rapid frontline detection of LVO strokes. This report is a systematic review of AI in acute LVO stroke identification and triage, and characterizes LVO detection software.MethodsA systematic review of acute stroke diagnostic-focused AI studies from January 2014 to February 2019 in PubMed, Medline, and Embase using terms: ‘artificial intelligence’ or ‘machine learning or deep learning’ and ‘ischemic stroke’ or ‘large vessel occlusion’ was performed.ResultsVariations of AI, including ML methods of random forest learning (RFL) and convolutional neural networks (CNNs), are used to detect LVO strokes. Twenty studies were identified that use ML. Alberta Stroke Program Early CT Score (ASPECTS) commonly used RFL, while LVO detection typically used CNNs. Image feature detection had greater sensitivity with CNN than with RFL, 85% versus 68%. However, AI algorithm performance metrics use different standards, precluding ideal objective comparison. Four current software platforms incorporate ML: Brainomix (greatest validation of AI for ASPECTS, uses CNNs to automatically detect LVOs), General Electric, iSchemaView (largest number of perfusion study validations for thrombectomy), and Viz.ai (uses CNNs to automatically detect LVOs, then automatically activates emergency stroke treatment systems).ConclusionsAI may improve LVO stroke detection and rapid triage necessary for expedited treatment. Standardization of performance assessment is needed in future studies.

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