scholarly journals A State-of-the-Art Survey on Artificial Intelligence to Fight COVID-19

2021 ◽  
Vol 10 (9) ◽  
pp. 1961
Author(s):  
Md. Mohaimenul Islam ◽  
Tahmina Nasrin Poly ◽  
Belal Alsinglawi ◽  
Ming Chin Lin ◽  
Min-Huei Hsu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Data Analysis ◽  
Digital Images ◽  
State Of The Art ◽  
Laboratory Data ◽  
Drug Repurposing ◽  
Current State ◽  
Immense Potential

Artificial intelligence (AI) has shown immense potential to fight COVID-19 in many ways. This paper focuses primarily on AI’s role in managing COVID-19 using digital images, clinical and laboratory data analysis, and a summary of the most recent articles published last year. We surveyed the use of AI for COVID-19 detection, screening, diagnosis, the progression of severity, mortality, drug repurposing, and other tasks. We started with the technical overview of all models used to fight the COVID-19 pandemic and ended with a brief statement of the current state-of-the-art, limitations, and challenges.

scholarly journals Repurposing as a strategy for the discovery of new anti-leishmanials: the-state-of-the-art

Parasitology ◽  
2017 ◽  
Vol 145 (2) ◽  
pp. 219-236 ◽  
Author(s):  
REBECCA L. CHARLTON ◽  
BARTIRA ROSSI-BERGMANN ◽  
PAUL W. DENNY ◽  
PATRICK G. STEEL
Keyword(s):  
State Of The Art ◽  
Drug Repurposing ◽  
Tropical Disease ◽  
Neglected Tropical Disease ◽  
Current State ◽  
Disability Adjusted Life ◽  
Life Years ◽  
Vector Borne ◽  
Faster Development ◽  
New Treatments

SUMMARYLeishmaniasis is a vector-borne neglected tropical disease caused by protozoan parasites of the genus Leishmania for which there is a paucity of effective viable non-toxic drugs. There are 1·3 million new cases each year causing considerable socio-economic hardship, best measured in 2·4 million disability adjusted life years, with greatest impact on the poorest communities, which means that desperately needed new antileishmanial treatments have to be both affordable and accessible. Established medicines with cheaper and faster development times may hold the cure for this neglected tropical disease. This concept of using old drugs for new diseases may not be novel but, with the ambitious target of controlling or eradicating tropical diseases by 2020, this strategy is still an important one. In this review, we will explore the current state-of-the-art of drug repurposing strategies in the search for new treatments for leishmaniasis.

scholarly journals Precision Agriculture Technology: A Literature Review

2020 ◽  
pp. 30-34
Author(s):  
C. A. Danbaki ◽  
N. C. Onyemachi ◽  
D. S. M. Gado ◽  
G. S. Mohammed ◽  
D. Agbenu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Precision Agriculture ◽  
State Of The Art ◽  
Irrigation Management ◽  
Conventional Farming ◽  
Livestock Farming ◽  
Current State ◽  
Art Methods ◽  
Increase In Productivity

This study is a survey on state-of-the-art methods based on artificial intelligence and image processing for precision agriculture on Crop Management, Pest and Disease Management, Soil and Irrigation Management, Livestock Farming and the challenges it presents. Precision agriculture (PA) described as applying current technologies into conventional farming methods. These methods have proved to be highly efficient, sustainable and profitable to the farmer hence boosting the economy. This study is a survey on the current state of the art methods applied to precision agriculture. The application of precision agriculture is expected to yield an increase in productivity which ultimately ends in profit to the farmer, to the society increase sustainability and also improve the economy.

scholarly journals Subpixel Localization of Isolated Edges and Streaks in Digital Images

2020 ◽  
Vol 6 (5) ◽  
pp. 33
Author(s):  
Devin T. Renshaw ◽  
John A. Christian
Keyword(s):  
Image Processing ◽  
Iterative Methods ◽  
Digital Images ◽  
State Of The Art ◽  
Measurement Data ◽  
Unified Framework ◽  
Signal To Noise ◽  
Sensing Systems ◽  
Current State ◽  
Common Signal

Many modern sensing systems rely on the accurate extraction of measurement data from digital images. The localization of edges and streaks in digital images is an important example of this type of measurement, with these techniques appearing in many image processing pipelines. Several approaches attempt to solve this problem at both the pixel level and subpixel level. While the subpixel methods are often necessary for applications requiring best-possible accuracy, they are often susceptible to noise, use iterative methods, or require pre-processing. This work investigates a unified framework for subpixel edge and streak localization using Zernike moments with ramp-based and wedge-based signal models. The method described here is found to outperform the current state-of-the-art for digital images with common signal-to-noise ratios. Performance is demonstrated on both synthetic and real images.

Report from the 2nd Int. Workshop on Education through Advanced Software Engineering and Artificial Intelligence (EASEAI '20)

2021 ◽  
Vol 46 (2) ◽  
pp. 28-29
Author(s):  
Benoît Vanderose ◽  
Julie Henry ◽  
Benoît Frénay ◽  
Xavier Devroey
Keyword(s):  
Artificial Intelligence ◽  
Software Engineering ◽  
Engineering Education ◽  
State Of The Art ◽  
Future Directions ◽  
The Past ◽  
Software Engineering Education ◽  
Current State ◽  
Amount Of Knowledge ◽  
Software Engineering Tools

In the past years, with the development and widespread of digi- tal technologies, everyday life has been profoundly transformed. The general public, as well as specialized audiences, have to face an ever-increasing amount of knowledge and learn new abilities. The EASEAI workshop series addresses that challenge by look- ing at software engineering, education, and arti cial intelligence research elds to explore how they can be combined. Speci cally, this workshop brings together researchers, teachers, and practi- tioners who use advanced software engineering tools and arti cial intelligence techniques in the education eld and through a trans- generational and transdisciplinary range of students to discuss the current state of the art and practices, and establish new future directions. More information at https://easeai.github.io.

scholarly journals State-of-the-Art of Artificial Intelligence

2021 ◽  
Author(s):  
Ramjee Prasad ◽  
Purva Choudhary
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
State Of The Art ◽  
Turing Test ◽  
Rapid Progress ◽  
Current State ◽  
Recent Developments ◽  
Historical Developments ◽  
Insight Into ◽  
Made In

Artificial Intelligence (AI) as a technology has existed for less than a century. In spite of this, it has managed to achieve great strides. The rapid progress made in this field has aroused the curiosity of many technologists around the globe and many companies across various domains are curious to explore its potential. For a field that has achieved so much in such a short duration, it is imperative that people who aim to work in Artificial Intelligence, study its origins, recent developments, and future possibilities of expansion to gain a better insight into the field. This paper encapsulates the notable progress made in Artificial Intelligence starting from its conceptualization to its current state and future possibilities, in various fields. It covers concepts like a Turing machine, Turing test, historical developments in Artificial Intelligence, expert systems, big data, robotics, current developments in Artificial Intelligence across various fields, and future possibilities of exploration.

Easing and promoting the application of ML and AI in earth system sciences - introducing the KI:STE platform

2021 ◽  
Author(s):  
Thomas Seidler ◽  
Norbert Schultz ◽  
Dr. Markus Quade ◽  
Christian Autermann ◽  
Dr. Benedikt Gräler ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Analysis ◽  
State Of The Art ◽  
System Modeling ◽  
Earth System ◽  
Earth System Modeling ◽  
System Science ◽  
Software Frameworks ◽  
Learning Platform

<p>Earth system modeling is virtually impossible without dedicated data analysis. Typically, data are big and due to the complexity of the system, adequate tools for the analysis lie in the domain of machine learning or artificial intelligence. However, earth system specialists have other expertise than developing and deploying state-of-the art programming code which is needed to efficiently use modern software frameworks and computing resources. In addition, Cloud and HPC infrastructure are frequently needed to run analyses with data beyond Tera- or even Petascale volume, and corresponding requirements on available RAM, GPU and CPU sizes. </p><p>Inside the KI:STE project (www.kiste-project.de), we extend the concepts of an existing project, the Mantik-platform (www.mantik.ai), such that handling of data and algorithms is facilitated for earth system analyses while abstracting technical challenges such as scheduling and monitoring of training jobs and platform specific configurations away from the user.</p><p>The principles for design are collaboration and reproducibility of algorithms from the first data load to the deployment of a model to a cluster infrastructure. In addition to the executive part where code is developed and deployed, the KI:STE project develops a learning platform where dedicated topics in relation to earth system science are systematically and pedagogically presented.</p><p>In this presentation, we show the architecture and interfaces of the KI:STE platform together with a simple example.</p>

Conceptual Model of a Virtual Laboratory for Data Analysis and Artificial Intelligence Methods

2021 ◽  
pp. 37-42
Author(s):  
M. Bocharov ◽  
N. Voznesenskaya
Keyword(s):  
Artificial Intelligence ◽  
Data Analysis ◽  
Conceptual Model ◽  
Teaching Methods ◽  
Virtual Laboratory ◽  
Technological Processes ◽  
Current State ◽  
Artificial Intelligence Methods ◽  
Level Of Training ◽  
Available Information

Artificial intelligence technologies are increasingly penetrating the processes of the socio-economic sphere, having a significant impact on the organization of information, social, technological processes based on the analysis of available information. This article reviews the availability of training at various levels of education, content and teaching methods in the field of artificial intelligence. The approaches to organizing practical activities in the process of mastering artificial intelligence technologies based on the model of a virtual laboratory for data analysis and artificial intelligence methods have been determined. The developed model of the virtual laboratory provides for the ability to track the current state of the level of training in the field of data analysis and the use of artificial intelligence methods in practice.

Considering Students' Emotions in Computer-Mediated Learning Environments

2006 ◽  
pp. 122-138 ◽  
Author(s):  
Patrícia A. Jaques ◽  
Rosa M. Viccari
Keyword(s):  
Artificial Intelligence ◽  
Human Computer Interaction ◽  
Learning Environments ◽  
State Of The Art ◽  
Learning Systems ◽  
New Work ◽  
Mediated Learning ◽  
Current State ◽  
Computer Mediated ◽  
E Learning

This text aims to present the current state of the art of the e-learning systems that consider the student’s affect. It presents the perspectives adopted by researchers for the solution of problems (for example, which kind of tools we might use to recognize users’ emotions) and also some better-known works in order to exemplify. It also describes the necessary background to understand these studies, including some concepts in the fields of Artificial Intelligence, Computers in Education, and Human-Computer Interaction, and a brief introduction on the main theories about emotion. The authors conclude the chapter by presenting challenges and the main difficulties of research in affectivity in e-learning systems and ideas on some new work on the matter.

scholarly journals Open Data to Support CANCER Science—A Bioinformatics Perspective on Glioma Research

Onco ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 219-229
Author(s):  
Fleur Jeanquartier ◽  
Claire Jean-Quartier ◽  
Sarah Stryeck ◽  
Andreas Holzinger
Keyword(s):  
Artificial Intelligence ◽  
Cancer Research ◽  
Targeted Therapies ◽  
State Of The Art ◽  
Open Data ◽  
Signal Transduction Pathways ◽  
Cell Level ◽  
Classification Analysis ◽  
Current State ◽  
Novel Biomarkers

Supporting data sharing is paramount to making progress in cancer research. This includes the search for more precise targeted therapies and the search for novel biomarkers, through cluster and classification analysis, and extends to learning details in signal transduction pathways or intra- and intercellular interactions in cancer, through network analysis and network simulation. Our work aims to support and promote the use of publicly available resources in cancer research and demonstrates artificial intelligence (AI) methods to find answers to detailed questions. For example, how targeted therapies can be developed based on precision medicine or how to investigate cell-level phenomena with the help of bioinformatical methods. In our paper, we illustrate the current state of the art with examples from glioma research, in particular, how open data can be used for cancer research in general, and point out several resources and tools that are readily available. Presently, cancer researchers are often not aware of these important resources.

scholarly journals A survey of 25 years of evaluation

2019 ◽  
Vol 25 (06) ◽  
pp. 753-767
Author(s):  
Kenneth Ward Church ◽  
Joel Hestness
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Graduate Student ◽  
State Of The Art ◽  
Reliability And Validity ◽  
Turing Test ◽  
First Principle ◽  
Current State ◽  
Language And Vision ◽  
Better Than

AbstractEvaluation was not a thing when the first author was a graduate student in the late 1970s. There was an Artificial Intelligence (AI) boom then, but that boom was quickly followed by a bust and a long AI Winter. Charles Wayne restarted funding in the mid-1980s by emphasizing evaluation. No other sort of program could have been funded at the time, at least in America. His program was so successful that these days, shared tasks and leaderboards have become common place in speech and language (and Vision and Machine Learning). It is hard to remember that evaluation was a tough sell 25 years ago. That said, we may be a bit too satisfied with current state of the art. This paper will survey considerations from other fields such as reliability and validity from psychology and generalization from systems. There has been a trend for publications to report better and better numbers, but what do these numbers mean? Sometimes the numbers are too good to be true, and sometimes the truth is better than the numbers. It is one thing for an evaluation to fail to find a difference between man and machine, and quite another thing to pass the Turing Test. As Feynman said, “the first principle is that you must not fool yourself–and you are the easiest person to fool.”

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