Water & Waste Management


IBM built a handheld counterfeit goods detector

Engadget

Just a month after IBM announced it's leveraging the blockchain to guarantee the provenance of diamonds, the company has revealed new AI-based technology that aims to tackle the issue of counterfeiting -- a problem that costs $1.2 trillion globally. IBM Crypto Anchor Verifier brings together AI and optical imaging to help prove the identity and authenticity of frequently forged goods such as fine wine, diamonds and medicine, as well as analyze water quality and detect bacteria, such as E.coli. And the technology is small enough to use with a cell phone camera. It works by identifying the optical patterns that are unique to every single object or substance, and draws on AI models that use machine learning trained to recognize these optical signature. These patterns can distinguish an organic ear of corn from a genetically modified one, or a $1,000 bottle of red wine from a much cheaper variety, for example.


AI Identifies Patients at Highest Risk of Cholera Infection

#artificialintelligence

Image has been cropped and resized. Scientists have developed machine-learning algorithms that can identify patterns in the bacteria of a patient's gut to determine whether the patient is likely to get infected if exposed to cholera. The researchers believe such artificial intelligence (AI) could be critical in areas of high cholera risk, since it can analyze trillions of bacteria, much more than can be done by humans. The study also demonstrates the power of machine learning to uncover medical insights that would otherwise remain obscure. READ: AI's Ethical Concerns Go Beyond Data Security and Quality The research is a collaboration between Duke University, Massachusetts General Hospital, and the International Centre for Diarrheal Disease Research, in Bangladesh.


Unleashing Bots potential – Chatbot News Daily

#artificialintelligence

The study intended to understand spatial distribution patterns of odour complaints as well as to investigate socioeconomic* characteristics of the company's operational area. Wastewater treatment works and their impact on environment, particularly on air quality, have been reported since air pollution started to become a serious threat in populated areas such as urban agglomerations. Odour nuisance from wastewater treatment installations can also be linked to concerns on air quality especially since studies on odour effects on human health started to show its impacts on local communities. Symptoms associated to air pollution such as headache, nausea, hoarseness, cough, congestions, shortness of breath, eye, nose, throat irritation have been widely reported by local authorities showing the importance of control and reduction of air pollution.


A new machine learning tool could flag dangerous bacteria before they cause an outbreak

#artificialintelligence

A new machine learning tool that can detect whether emerging strains of the bacterium, Salmonella are more likely to cause dangerous bloodstream infections rather than food poisoning has been developed. The tool, created by a scientist at the Wellcome Sanger Institute and her collaborators at the University of Otago, New Zealand and the Helmholtz Institute for RNA-based Infection Research, a site of the Helmholtz Centre for Infection Research, Germany, greatly speeds up the process for identifying the genetic changes underlying new invasive types of Salmonella that are of public health concern. Reported today (8 May) in PLOS Genetics, the machine learning tool could be useful for flagging dangerous bacteria before they cause an outbreak, from hospital wards to a global scale. As the cost of genomic sequencing falls, scientists around the world are using genetics to better understand the bacteria causing infections, how diseases spread, how bacteria gain resistance to drugs, and which strains of bacteria may cause outbreaks. However, current methods to identify the genetic adaptations in emerging strains of bacteria behind an outbreak are time-consuming and often involve manually comparing the new strain to an older reference collection.


Machine learning flags emerging pathogens: A new machine learning tool could flag dangerous bacteria before they cause an outbreak, from hospital wards to a global scale

#artificialintelligence

Reported today (8 May) in PLOS Genetics, the machine learning tool could be useful for flagging dangerous bacteria before they cause an outbreak, from hospital wards to a global scale. As the cost of genomic sequencing falls, scientists around the world are using genetics to better understand the bacteria causing infections, how diseases spread, how bacteria gain resistance to drugs, and which strains of bacteria may cause outbreaks. However, current methods to identify the genetic adaptations in emerging strains of bacteria behind an outbreak are time-consuming and often involve manually comparing the new strain to an older reference collection. The group of bacteria known as Salmonella includes many different types that vary in the severity of the disease they cause. Some types cause food poisoning, known as gastrointestinal Salmonella, whereas others cause severe disease by spreading beyond the gut, for example Salmonella Typhi which causes typhoid fever.


Machine Learning Flags Emerging Pathogens

#artificialintelligence

A new machine learning tool that can detect whether emerging strains of the bacterium, Salmonella are more likely to cause dangerous bloodstream infections rather than food poisoning has been developed. The tool, created by a scientist at the Wellcome Sanger Institute and her collaborators at the University of Otago, New Zealand and the Helmholtz Institute for RNA-based Infection Research, a site of the Helmholtz Centre for Infection Research, Germany, greatly speeds up the process for identifying the genetic changes underlying new invasive types of Salmonella that are of public health concern. Reported today (8 May) in PLOS Genetics, the machine learning tool could be useful for flagging dangerous bacteria before they cause an outbreak, from hospital wards to a global scale. As the cost of genomic sequencing falls, scientists around the world are using genetics to better understand the bacteria causing infections, how diseases spread, how bacteria gain resistance to drugs, and which strains of bacteria may cause outbreaks. However, current methods to identify the genetic adaptations in emerging strains of bacteria behind an outbreak are time-consuming and often involve manually comparing the new strain to an older reference collection.


Machine learning flags emerging pathogens

#artificialintelligence

A new machine learning tool that can detect whether emerging strains of the bacterium, Salmonella are more likely to cause dangerous bloodstream infections rather than food poisoning has been developed. The tool, created by a scientist at the Wellcome Sanger Institute and her collaborators at the University of Otago, New Zealand and the Helmholtz Institute for RNA-based Infection Research, a site of the Helmholtz Centre for Infection Research, Germany, greatly speeds up the process for identifying the genetic changes underlying new invasive types of Salmonella that are of public health concern. Reported today (8 May) in PLOS Genetics, the machine learning tool could be useful for flagging dangerous bacteria before they cause an outbreak, from hospital wards to a global scale. As the cost of genomic sequencing falls, scientists around the world are using genetics to better understand the bacteria causing infections, how diseases spread, how bacteria gain resistance to drugs, and which strains of bacteria may cause outbreaks. However, current methods to identify the genetic adaptations in emerging strains of bacteria behind an outbreak are time-consuming and often involve manually comparing the new strain to an older reference collection.


Machine learning regression on hyperspectral data to estimate multiple water parameters

arXiv.org Machine Learning

In this paper, we present a regression framework involving several machine learning models to estimate water parameters based on hyperspectral data. Measurements from a multi-sensor field campaign, conducted on the River Elbe, Germany, represent the benchmark dataset. It contains hyperspectral data and the five water parameters chlorophyll a, green algae, diatoms, CDOM and turbidity. We apply a PCA for the high-dimensional data as a possible preprocessing step. Then, we evaluate the performance of the regression framework with and without this preprocessing step. The regression results of the framework clearly reveal the potential of estimating water parameters based on hyperspectral data with machine learning. The proposed framework provides the basis for further investigations, such as adapting the framework to estimate water parameters of different inland waters.


Salt River Project Looks to ASU Robots to Maintain Canals

U.S. News

KJZZ-FM reports Mike Ploughe, a senior scientist with the Salt River Project water quality and waste management services, says robots will make the operation work a lot more efficiently, as opposed to using resources to dispatch individuals, their trucks and equipment, to a remote location.


AI-Driven Test System Detects Bacteria In Water

#artificialintelligence

"Clean water and health care and school and food and tin roofs and cement floors, all of these things should constitute a set of basics that people must have as birthrights."1 Obtaining clean water is a critical problem for much of the world's population. Testing and confirming a clean water source typically requires expensive test equipment and manual analysis of the results. For regions in the world in which access to clean water is a continuing problem, simpler test methods could dramatically help prevent disease and save lives. To apply artificial intelligence (AI) techniques to evaluating the purity of water sources, Peter Ma, an Intel Software Innovator, developed an effective system for identifying bacteria using pattern recognition and machine learning.