scholarly journals Artificial Intelligence and Optimisation Techniques for Risk Reduction in Civil Engineering

2022 ◽  
pp. 55-72
Author(s):  
William K. P. Sayers
Keyword(s):  
Artificial Intelligence ◽  
Risk Reduction ◽  
Real World ◽  
Civil Engineering ◽  
Broad Array ◽  
Major Shift ◽  
Future Potential ◽  
State Of Research ◽  
Engineering Projects ◽  
Optimisation Techniques

Artificial intelligence techniques are at the centre of a major shift in business today. They have a very broad array of applications within businesses, including that of optimisation for risk reduction in civil engineering projects. This is an active area of research, which has started to see real-world applications over the last few decades. It is still hindered by the extreme complexity of civil engineering problems and the computing power necessary to tackle these, but the economic and other benefits of these emerging technologies are too important to ignore. With that in mind, this chapter reviews the current state of research and real-world practice of optimisation techniques and artificial intelligence in risk reduction in this field. It also examines related promising techniques and their future potential.

A4. UNRWA, IMPACT OF THE FIRST PHASE OF THE SECURITY BARRIER ON THE QALQILYA, TULKARM, AND JENIN DISTRICTS, GAZA CITY, JULY 2003 (EXCERPTS).

2003 ◽  
Vol 33 (1) ◽  
pp. 153-160
Keyword(s):  
Civil Engineering ◽  
West Bank ◽  
The West ◽  
Full Report ◽  
The World ◽  
Aid Coordination ◽  
President Bush ◽  
Engineering Projects ◽  
The U.S

The separation wall, one of the largest civil engineering projects in Israel's history, has been criticized even by the U.S. administration, with Condoleezza Rice stating at the end of June 2003 that it ““arouses our [U.S.] deep concern”” and President Bush on 25 July calling it ““a problem”” and noting that ““it is very difficult to develop confidence between the Palestinians and Israel with a wall snaking through the West Bank.”” A number of reports have already been issued concerning the wall, including reports by B'Tselem (available at www.btselem.org), the UN Office for the Coordination of Humanitarian Affairs (available at www.palestinianaid.info), and the World Bank's Local Aid Coordination Committee (LACC; also available at www.palestinianaid.info). UNRWA's report focuses on the segment of the wall already completed and is based on field visits to the areas affected by the barriers, with a special emphasis on localities with registered refugees. Notes have been omitted due to space constraints. The full report is available online at www.un.org/unrwa.

scholarly journals Stress testing reveals gaps in clinic readiness of image-based diagnostic artificial intelligence models

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Albert T. Young ◽  
Kristen Fernandez ◽  
Jacob Pfau ◽  
Rasika Reddy ◽  
Nhat Anh Cao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Stress Testing ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Skin Lesions ◽  
Stress Tests ◽  
Performance Metric ◽  
Intelligence Models ◽  
Artificial Intelligence Models

AbstractArtificial intelligence models match or exceed dermatologists in melanoma image classification. Less is known about their robustness against real-world variations, and clinicians may incorrectly assume that a model with an acceptable area under the receiver operating characteristic curve or related performance metric is ready for clinical use. Here, we systematically assessed the performance of dermatologist-level convolutional neural networks (CNNs) on real-world non-curated images by applying computational “stress tests”. Our goal was to create a proxy environment in which to comprehensively test the generalizability of off-the-shelf CNNs developed without training or evaluation protocols specific to individual clinics. We found inconsistent predictions on images captured repeatedly in the same setting or subjected to simple transformations (e.g., rotation). Such transformations resulted in false positive or negative predictions for 6.5–22% of skin lesions across test datasets. Our findings indicate that models meeting conventionally reported metrics need further validation with computational stress tests to assess clinic readiness.

scholarly journals Human-Centered AI to Support an Adaptive Management of Human-Machine Transitions with Vehicle Automation

Information ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 13
Author(s):  
Thierry Bellet ◽  
Aurélie Banet ◽  
Marie Petiot ◽  
Bertrand Richard ◽  
Joshua Quick
Keyword(s):  
Artificial Intelligence ◽  
Adaptive Management ◽  
Driving Simulator ◽  
End Users ◽  
Point Of View ◽  
Human Centered Design ◽  
Perceived Utility ◽  
New Type ◽  
Future Potential ◽  
Vehicle Automation

This article is about the Human-Centered Design (HCD), development and evaluation of an Artificial Intelligence (AI) algorithm aiming to support an adaptive management of Human-Machine Transition (HMT) between car drivers and vehicle automation. The general principle of this algorithm is to monitor (1) the drivers’ behaviors and (2) the situational criticality to manage in real time the Human-Machine Interactions (HMI). This Human-Centered AI (HCAI) approach was designed from real drivers’ needs, difficulties and errors observed at the wheel of an instrumented car. Then, the HCAI algorithm was integrated into demonstrators of Advanced Driving Aid Systems (ADAS) implemented on a driving simulator (dedicated to highway driving or to urban intersection crossing). Finally, user tests were carried out to support their evaluation from the end-users point of view. Thirty participants were invited to practically experience these ADAS supported by the HCAI algorithm. To increase the scope of this evaluation, driving simulator experiments were implemented among three groups of 10 participants, corresponding to three highly contrasted profiles of end-users, having respectively a positive, neutral or reluctant attitude towards vehicle automation. After having introduced the research context and presented the HCAI algorithm designed to contextually manage HMT with vehicle automation, the main results collected among these three profiles of future potential end users are presented. In brief, main findings confirm the efficiency and the effectiveness of the HCAI algorithm, its benefits regarding drivers’ satisfaction, and the high levels of acceptance, perceived utility, usability and attractiveness of this new type of “adaptive vehicle automation”.

scholarly journals A Survey on Bias and Fairness in Machine Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Ninareh Mehrabi ◽  
Fred Morstatter ◽  
Nripsuta Saxena ◽  
Kristina Lerman ◽  
Aram Galstyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Future Directions ◽  
Discriminatory Behavior ◽  
Real World Applications ◽  
Near Future ◽  
Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

scholarly journals Real-world analysis of artificial intelligence in musculoskeletal trauma

2021 ◽  
Vol 22 ◽  
pp. 101573
Author(s):  
Pranav Ajmera ◽  
Amit Kharat ◽  
Rajesh Botchu ◽  
Harun Gupta ◽  
Viraj Kulkarni
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Musculoskeletal Trauma

scholarly journals Deep learning-enabled medical computer vision

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Andre Esteva ◽  
Katherine Chou ◽  
Serena Yeung ◽  
Nikhil Naik ◽  
Ali Madani ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Medical Imaging ◽  
Real World ◽  
Recent Progress ◽  
Medical Applications ◽  
Modern Computer ◽  
Medical Computer

AbstractA decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit—including cardiology, pathology, dermatology, ophthalmology–and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.

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