Teaching artificial intelligence using a web-based game server

2013 ◽  
Author(s):  
Stefan Friese ◽  
Kristian Rother
Keyword(s):  
Artificial Intelligence ◽  
Web Based

Artificial intelligence-based automatic visual inspection system for built heritage

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lukman E. Mansuri ◽  
D.A. Patel
Keyword(s):  
Artificial Intelligence ◽  
Visual Inspection ◽  
Image Data ◽  
Inspection System ◽  
Data Set ◽  
Web Based ◽  
Content Type ◽  
Built Heritage ◽  
Automatic Visual Inspection ◽  
Inspection Systems

PurposeHeritage is the latent part of a sustainable built environment. Conservation and preservation of heritage is one of the United Nations' (UN) sustainable development goals. Many social and natural factors seriously threaten heritage structures by deteriorating and damaging the original. Therefore, regular visual inspection of heritage structures is necessary for their conservation and preservation. Conventional inspection practice relies on manual inspection, which takes more time and human resources. The inspection system seeks an innovative approach that should be cheaper, faster, safer and less prone to human error than manual inspection. Therefore, this study aims to develop an automatic system of visual inspection for the built heritage.Design/methodology/approachThe artificial intelligence-based automatic defect detection system is developed using the faster R-CNN (faster region-based convolutional neural network) model of object detection to build an automatic visual inspection system. From the English and Dutch cemeteries of Surat (India), images of heritage structures were captured by digital camera to prepare the image data set. This image data set was used for training, validation and testing to develop the automatic defect detection model. While validating this model, its optimum detection accuracy is recorded as 91.58% to detect three types of defects: “spalling,” “exposed bricks” and “cracks.”FindingsThis study develops the model of automatic web-based visual inspection systems for the heritage structures using the faster R-CNN. Then it demonstrates detection of defects of spalling, exposed bricks and cracks existing in the heritage structures. Comparison of conventional (manual) and developed automatic inspection systems reveals that the developed automatic system requires less time and staff. Therefore, the routine inspection can be faster, cheaper, safer and more accurate than the conventional inspection method.Practical implicationsThe study presented here can improve inspecting the built heritages by reducing inspection time and cost, eliminating chances of human errors and accidents and having accurate and consistent information. This study attempts to ensure the sustainability of the built heritage.Originality/valueFor ensuring the sustainability of built heritage, this study presents the artificial intelligence-based methodology for the development of an automatic visual inspection system. The automatic web-based visual inspection system for the built heritage has not been reported in previous studies so far.

scholarly journals Physicians’ requirements and expectations of future Artificial Intelligence applications for healthcare – a web-based survey in German university hospitals (Preprint)

2020 ◽  
Author(s):  
Oliver Maassen ◽  
Sebastian Fritsch ◽  
Julia Gantner ◽  
Saskia Deffge ◽  
Julian Kunze ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Practice ◽  
Patient Care ◽  
Biomedical Research ◽  
Positive Attitude ◽  
Patient Data ◽  
University Hospitals ◽  
Web Based ◽  
Wide Range ◽  
Medical Disciplines

BACKGROUND The increasing development of artificial intelligence (AI) systems in medicine driven by researchers and entrepreneurs goes along with enormous expectations for medical care advancement. AI might change the clinical practice of physicians from almost all medical disciplines and in most areas of healthcare. While expectations for AI in medicine are high, practical implementations of AI for clinical practice are still scarce in Germany. Moreover, physicians’ requirements and expectations of AI in medicine and their opinion on the usage of anonymized patient data for clinical and biomedical research has not been investigated widely in German university hospitals. OBJECTIVE Evaluate physicians’ requirements and expectations of AI in medicine and their opinion on the secondary usage of patient data for (bio)medical research e.g. for the development of machine learning (ML) algorithms in university hospitals in Germany. METHODS A web-based survey was conducted addressing physicians of all medical disciplines in 8 German university hospitals. Answers were given on Likert scales and general demographic responses. Physicians were asked to participate locally via email in the respective hospitals. RESULTS 121 (39.9%) female and 173 (57.1%) male physicians (N=303) from a wide range of medical disciplines and work experience levels completed the online survey. The majority of respondents either had a positive (130/303, 42.9%) or a very positive attitude (82/303, 27.1%) towards AI in medicine. A vast majority of physicians expected the future of medicine to be a mix of human and artificial intelligence (273/303, 90.1%) but also requested a scientific evaluation before the routine implementation of AI-based systems (276/303, 91.1%). Physicians were most optimistic that AI applications would identify drug interactions (280/303, 92.4%) to improve patient care substantially but were quite reserved regarding AI-supported diagnosis of psychiatric diseases (62/303, 20.5%). 82.5% of respondents (250/303) agreed that there should be open access to anonymized patient databases for medical and biomedical research. CONCLUSIONS Physicians in stationary patient care in German university hospitals show a generally positive attitude towards using most AI applications in medicine. Along with this optimism, there come several expectations and hopes that AI will assist physicians in clinical decision making. Especially in fields of medicine where huge amounts of data are processed (e.g., imaging procedures in radiology and pathology) or data is collected continuously (e.g. cardiology and intensive care medicine), physicians’ expectations to substantially improve future patient care are high. However, for the practical usage of AI in healthcare regulatory and organizational challenges still have to be mastered.

Ideas on the Future of Intelligent Web-Based E-Learning

2018 ◽  
pp. 2274-2287
Author(s):  
Utku Kose
Keyword(s):  
Artificial Intelligence ◽  
Teaching And Learning ◽  
Current Status ◽  
Learning Approaches ◽  
Learning Tools ◽  
Web Based ◽  
The Future ◽  
Teachers And Students ◽  
Important Approach ◽  
E Learning

With the outstanding improvements in technology, the number of e-learning applications has increased greatly. This increment is associated with awareness levels of educational institutions on the related improvements and the power of communication and computer technologies to ensure effective and efficient teaching and learning experiences for teachers and students. Consequently, there is a technological flow that changes the standards of e-learning processes and provides better ways to obtain desired educational objectives. When we consider today's widely used technological factors, Web-based e-learning approaches have a special role in directing the educational standards. Improvements among m-learning applications and the popularity of the Artificial Intelligence usage for educational works have given great momentum to this orientation. In this sense, this chapter provides some ideas on the future of intelligent Web-based e-learning applications by thinking on the current status of the literature. As it is known, current trends in developing Artificial Intelligence-supported e-learning tools continue to shape the future of e-learning. Therefore, it is an important approach to focus on the future. The author thinks that the chapter will be a brief but effective enough reference for similar works, which focus on the future of Artificial Intelligence-supported distance education and e-learning.

Educational Responsibility in the Deepfake Era

2021 ◽  
pp. 1-23
Author(s):  
Rebecca J. Blankenship
Keyword(s):  
Artificial Intelligence ◽  
Instructional Design ◽  
Student Learning ◽  
Learning Outcomes ◽  
Student Learning Outcomes ◽  
Web Based ◽  
Student Work ◽  
Integrated Classrooms ◽  
The Right ◽  
Design Challenges

Choosing the right technologies to match student learning outcomes in today's technology-integrated classrooms presents educators with multiple instructional design challenges including selecting appropriate technologies to match desired student learning outcomes. As students continue to have broad access to information from a variety of web-based platforms, teachers are increasingly tasked with ensuring the information used to complete key assignments is authentic and from a verifiable resource. As such, the era of deep fakes in images, audio, videos, and digital texts is more prevalent than ever as numerous programs using artificial intelligence (AI) can significantly alter original content to fundamentally change the intent of original content. A discussion of educational and pedagogic responsibility in the era of deep fakes will serve as the primer for reform of the TPACK construct with recommendations for remediating student work in which deep fake resources were utilized.

DataPro

2021 ◽  
pp. 98-108
Author(s):  
Vala Ali Rohani ◽  
Sedigheh Moghavvemi ◽  
Tiago Pinho ◽  
Paulo Caldas
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Unemployment Rate ◽  
Recommender System ◽  
Social Problems ◽  
National Level ◽  
Point Of View ◽  
Web Based ◽  
Four Dimensions ◽  
Online Portal

Due to the COVID‐19 pandemic, most countries are exposed to unprecedented social problems in the current global situation. According to the official reports, it caused a dramatic increase of 44% in graduates' unemployment rate in Portugal. Moreover, from the human resource point of view, the whole of Europe is expected to face a shortage of 925,000 data professionals by 2025. Given the existing situations, the DataPro aims to propose a national-level reskilling solution in big data to mitigate both social problems of unemployability and the shortage of data professionals in Portugal. DataPro project consists of four dimensions, including an online portal for the hiring companies and unemployed graduates, along with a web-based analytics talent upskilling (ATU) platform empowered by an artificial intelligence recommender system to match the reskilled data professionals and the hiring companies.

scholarly journals The Value of Artificial Intelligence in Laboratory Medicine

2020 ◽  
Author(s):  
Ketan Paranjape ◽  
Michiel Schinkel ◽  
Richard D Hammer ◽  
Bo Schouten ◽  
R S Nannan Panday ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Laboratory Medicine ◽  
Medical Community ◽  
Barriers To Adoption ◽  
Web Based ◽  
Medical Practitioners ◽  
Privacy Concerns ◽  
Key Stakeholders ◽  
Clinical Benefits ◽  
High Investment

Abstract Objectives As laboratory medicine continues to undergo digitalization and automation, clinical laboratorians will likely be confronted with the challenges associated with artificial intelligence (AI). Understanding what AI is good for, how to evaluate it, what are its limitations, and how it can be implemented are not well understood. With a survey, we aimed to evaluate the thoughts of stakeholders in laboratory medicine on the value of AI in the diagnostics space and identify anticipated challenges and solutions to introducing AI. Methods We conducted a web-based survey on the use of AI with participants from Roche’s Strategic Advisory Network that included key stakeholders in laboratory medicine. Results In total, 128 of 302 stakeholders responded to the survey. Most of the participants were medical practitioners (26%) or laboratory managers (22%). AI is currently used in the organizations of 15.6%, while 66.4% felt they might use it in the future. Most had an unsure attitude on what they would need to adopt AI in the diagnostics space. High investment costs, lack of proven clinical benefits, number of decision makers, and privacy concerns were identified as barriers to adoption. Education in the value of AI, streamlined implementation and integration into existing workflows, and research to prove clinical utility were identified as solutions needed to mainstream AI in laboratory medicine. Conclusions This survey demonstrates that specific knowledge of AI in the medical community is poor and that AI education is much needed. One strategy could be to implement new AI tools alongside existing tools.

CytoNet, A Versatile Web-Based System for Accessing Advisory Cytology Services

2018 ◽  
Vol 7 (3) ◽  
pp. 37-56 ◽  
Author(s):  
Rallou Perroti ◽  
Abraham Pouliakis ◽  
Niki Margari ◽  
Eleni Panopoulou ◽  
Efrossyni Karakitsou ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Needle Aspiration ◽  
Daily Routine ◽  
Thyroid Ultrasonography ◽  
Specialized Knowledge ◽  
Aspiration Cytology ◽  
Web Based ◽  
Vector Quantizer ◽  
Needle Aspiration Cytology ◽  
Web Based System

This article describes how the use of artificial intelligence applications as a consultation tool on a cytological laboratory's daily routine has been suggested for several decades. In addition to the use of high-resolution thyroid ultrasonography and fine-needle aspiration cytology, a further reduction of the number of unnecessary thyroidectomies can be achieved through the access to such techniques. Despite the evident advantages, artificial intelligence applications hardly ever find their way to end-users due to the specialized knowledge necessary for designing and using them, as well as the users' unfamiliarity with the required technology. The authors aimed to design an easy-to-use online platform (CytoNet) that gives access to a learning vector quantizer neural network (LVQ NN) that discriminates benign from malignant thyroid lesions to users (medical doctors) with no specialized technical background on artificial intelligence.

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