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Localization and classification of structural damage using deep learning single-channel signal-based measurement

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Novel 1D CNN named BuildingNet learns features & classifies real-time damage in various scenarios. Vibration-based DL real-time methodology analyzes damage with high precision and fast computational time. Single-channel vibration-based detector evaluates structural safety via an economical and practical SHM system. Model's efficiency & robustness indicated by use of 20% random Gaussian noise & validated on case study. Diligent damage identification is a core thrust of structural health monitoring (SHM).


A review and case study of Artificial intelligence and Machine learning methods used for ground condition prediction ahead of tunnel boring Machines

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Several machine learning methods can be used to predict ground conditions ahead of TBMs with high accuracy. Ensemble methods have better ground condition prediction accuracy than other machine learning models evaluated. The classification system used in characterizing the ground condition affects the performance of the machine models. The prediction performance of the machine models is different in soils and rocks of different lithologies. There have been significant advances in the use of both unsupervised and supervised machine learning (ML) methods to predict the ground condition or rock mass class ahead of tunnel boring machines (TBMs).


The effect of machine learning explanations on user trust for automated diagnosis of COVID-19

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Machine learning explanations for CT images have high precision but low recall as compared to human. Clinicians understand machine learning explanations for diagnosis when they match human judgement. Low precision of machine learning explanation lowers reliance on AI model for COVID-19 diagnosis and decision making. High precision of machine learning explanations enhances trust on AI model for COVID-19 diagnosis and decision making. Recent years have seen deep neural networks (DNN) gain widespread acceptance for a range of computer vision tasks that include medical imaging.


How To Be A Great Data Tutor

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Being a data tutor can be a lucrative side-gig. Being a data tutor can also be a fulfilling full-time role. What I like most about working as a data tutor, as I occasionally do, is that it is an opportunity to help others do well in the field. Being a data tutor is not for everyone. This article will help you sort out whether being a data tutor might be a good option for you. As a data tutor, you control who you will work with. Data tutors are also in charge of their own schedules. If you know where to go to find students, you can work as much or as little as you like.



Overcoming large 3D microscopy imaging data with AI

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Medical imaging generates an enormous amount of data that is impossible to manually analyze. With advances in artificial intelligence (AI) researchers are now looking at how this technology can be used to help manage and simplify the analysis of large 3D microscopy image datasets. In this SelectScience article, we speak with Jianxu Chen, head of the new Analysis of Microscopic BIOMedical Images (AMBIOM*) group at ISAS. Chen discusses how his group is developing scalable, AI-based image analysis algorithms to help support disease studies. Chen also explores some current trends in laboratories adopting AI and machine learning.


AI Models Accurately Predict Clinical Risks in Multiple Hospitals Using Live Data

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The researchers began by training prediction models for the three use cases using retrospective data from each hospital. They trained each retrospective model using a calibration tool common for all hospitals and use cases. Then, they built and trained new models for use in live clinical workflows and calibrated those models with each hospital's specific data. These new models were then deployed in the hospitals and used in regular clinical practice. Their performance was compared with the models using only retrospective data for each hospital, and the researchers also conducted a cross-hospital evaluation by generating predictions for one hospital using a model trained on another hospital's data.


Essential Questions for Assessing Artificial Intelligence Vendors in Radiology

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What are the key questions radiologists should ask when assessing artificial intelligence (AI) vendors? While the list can be long, one important question is ascertaining the volume and nature of the data used to develop and train a given AI algorithm, according to Sonia Gupta, MD, an abdominal radiologist, and chief medical officer at Change Healthcare. In a recent video interview, Dr. Gupta said knowing the volume of cases that went into the training of an AI model is an important consideration as is the diversity of that data in terms of factors such as age, gender, health issues and comorbidities to name a few. "All of those factors will influence the model training and ultimately the performance of that AI algorithm," noted Dr. Gupta, who lectured about AI at the recent Society for Imaging Informatics in Medicine (SIIM) conference. "I encourage radiologists looking at potential AI vendors to dig into that information right off the bat."


Best dating sites and apps for people over 40 -- and which ones to avoid

Mashable

Dating when you're 40 or older can be intimidating -- unlike when you're in your 20s or 30s, you can't assume everyone your age is single and looking. If you've found yourself "on the market" again, it's important to remember that half of U.S. marriages do end in divorce, so the dating pool isn't as small as you might think. Meeting people organically out in public still happens, but sometimes it's easier and less intimidating to meet people where they are. There's a comfort in knowing that the people you find on dating apps are single (hopefully) and looking for a romantic relationship, so at least you're both on the same page. The first step is just acknowledging that you're ready.


Partner Content

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You might not notice it, but you've likely adopted artificial intelligence into your daily life. It can be as simple as personalizing your news feeds, searching for products on shopping sites or voice-to-text conversion on smartphones. It can also be applied to more sophisticated tasks like predicting court outcomes in cases involving employment law or used for robotic welding applications. The transformative power of AI is also an economic growth driver, which is why the Canadian government has given the green light to advancing the country's AI strategy. According to a recent announcement from Minister of Innovation, Science and Industry François-Philippe Champagne, more than $443 million in Budget 2021 is designated for the second phase of the pan-Canadian Artificial Intelligence Strategy.