Tag: Detect

How artificial intelligence can help detect and fight wildfires in Canada

Greta
How artificial intelligence can help detect and fight wildfires in Canada
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Melanie Morin, a prevention agent with the Society of Safety of Forests from Hearth, walks by way of an space of burned forest near Lebel-sur-Quevillon, Que., on July 5, 2023.Adrian Wyld/The Canadian Press

Immediately after a devastating begin to Canada’s wildfire time still left history-breaking wreckage in its wake, scientists are hunting to synthetic intelligence to participate in an critical potential purpose in detecting and fighting blazes.

Swaths of fires igniting from British Columbia to Nova Scotia quickly displaced 1000’s of Canadians countrywide, spewed smog as significantly as Europe and burned through 3.3 million hectares of forest – equal to fifty percent the sizing of New Brunswick. All this inspite of the federal government shelling out somewhere around $1-billion a calendar year to mitigate the blazes.

Industry experts around the globe have warned in current a long time that the warming outcomes of local weather alter will guide to ever more powerful wildfire seasons and extended periods of smoke exposure in Canada. In February, 2022, the United Nations predicted fires will improve more harmful by the yr and named wildfire threat reduction “more essential than ever.”

These world wide alarm bells are top authorities to forecast that Canada will have to increasingly faucet into AI systems in the form of drones, sensors and significant-tech satellites to continue to keep up with the fires.

“Change is coming in phrases of weather and know-how,” mentioned Joshua Johnson, forest hearth analysis scientist with All-natural Means Canada (NRC). “I believe which is scary at situations, but it’s forcing us to get inventive.”

Canada sees 100,000 square kilometres burned this history-breaking wildfire year

Viewpoint: Canada’s wildfire method requires to change from reactive to proactive

The latest AI revolution, pushed by the arrival of effective details-processing machines these as

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Scientists use artificial intelligence to detect opportunity unsafe places in towns

Greta
Scientists use artificial intelligence to detect opportunity unsafe places in towns
Researchers from the GIST use artificial intelligence to identify potential unsafe locations in cities
Identifying place-distinct characteristics is an vital factor of social synthetic intelligence. Nonetheless, types that are often educated on subjective perceptions and however illustrations or photos are unreliable in predicting crime. Now, scientists from GIST in Korea choose matters to the upcoming degree by teaching a neural community with a geotagged dataset of noted deviant incidents and sequential photographs of deviant locations to properly figure out unsafe locations by linking the deviant conduct to the visual features of a metropolis. Credit score: Gwangju Institute of Science and Know-how

Determining possible hotspots of criminal offense in a city is an crucial concern for urban security progress and can enable the authorities acquire necessary steps to make the city safer for its inhabitants. The performance of these types of preventive measures depends on the accuracy of the predictions, which are more and more getting designed by synthetic intelligence (AI)-based mostly models. Most current versions use subjective perceptions of secure destinations, socioeconomic status, and continue to photos of crime scenes, and only a several violent crimes are categorized as input details. As a final result, there is generally a discrepancy among their predictions and fact.

In a new review posted in AAAI Conference on Artificial Intelligence, researchers from the Gwangju Institute of Science and Technologies (GIST) in South Korea proposed a diverse tactic centered on a significant-scale dataset and the thought of “deviance,” which involved not only violent crimes but also civil issues regarding behaviors violating social norms, which is also called “deviant conduct.”

Appropriately, they developed a convolutional neural community product, aptly referred to as “DevianceNet,” and experienced it working with a geotagged dataset of deviant incident reports with corresponding sequential illustrations or photos of the incident places obtained applying Google avenue watch. “Our perform is the first review that investigates the

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Artificial Intelligence Can Assess Eye Scans To Detect Patients at Substantial Possibility of Heart Assault

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Artificial Intelligence Can Assess Eye Scans To Detect Patients at Substantial Possibility of Heart Assault
Eye As Window Into Heart Disease

A graphical illustration of the notion of applying a scan of the eye to get a window into coronary heart wellness. Credit score: University of Leeds

  • AI technique is “trained” to read common retinal scans for symptoms of coronary heart sickness
  • The method — which has 70% to 80% Scan of the Eye

    A scan of the eye. Credit: UK Biobank

Professor Alex Frangi, who holds the Diamond Jubilee Chair in Computational Medicine at the University of Leeds and is a Turing Fellow at the Alan Turing Institute, supervised the research. He said: “Cardiovascular diseases, …

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Artificial Intelligence Could Help Detect Onset of Cardiovascular Disease

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Artificial Intelligence Could Help Detect Onset of Cardiovascular Disease

Cardiovascular diseases are diagnosed using an array of laboratory tests and imaging studies. The primary part of diagnosis is medical and family histories of the patient, risk factors, physical examination, and coordination of these findings with the results from the tests and procedures. For the first time, researchers at the University of Utah (U of U) Health have demonstrated that artificial intelligence (AI) could help predict the onset and course of cardiovascular disease.

Their findings are published in the journal PLOS Digital Health in a paper titled, “An explainable artificial intelligence approach for predicting cardiovascular outcomes using electronic health records.” The researchers collaborated with physicians from Intermountain Primary Children’s Hospital.

“Understanding the conditionally-dependent clinical variables that drive cardiovascular health outcomes is a major challenge for precision medicine,” the researchers wrote. “Here, we deploy a recently developed massively scalable comorbidity discovery method called Poisson binomial based comorbidity discovery (PBC), to analyze electronic health records (EHRs) from the University of Utah and Primary Children’s Hospital (over 1.6 million patients and 77 million visits) for comorbid diagnoses, procedures, and medications.”

“We can turn to AI to help refine the risk for virtually every medical diagnosis,” said Martin Tristani-Firouzi, MD, the study’s corresponding author and a pediatric cardiologist at U of U Health and Intermountain Primary Children’s Hospital, and scientist at the Nora Eccles Harrison Cardiovascular Research and Training Institute. “The risk of cancer, the risk of thyroid surgery, the risk of diabetes—any medical term you can imagine.”

The researchers from U of U Health and Intermountain Primary Children’s Hospital used machine learning software to sort through more than 1.6 million EHRs after names and other identifying information were deleted.

These electronic records helped the researchers identify the comorbidities most likely to aggravate a particular medical condition such as cardiovascular disease. They

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