Under the current difficult circumstances of coronavirus (COVID-19), our first thought is the safety of all and the safely of all the front-liners. OperAI is working, with strategic partners, on the development of applications and IoT devices to help in surveillance and slow the spread of the virus, in both the short and in the long-terme within the sustainability perspective. 

« Slowing the spread of the infection is nearly as important as stopping it. »
27 March, 2020 - New York Times 
Predicting likely hotspots earlier can help to take preventive measures to reduce the number of infections and stop the virus from spreading. 
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Predictive models have shown that by acting just a week earlier, 65% of infections would have been avoided. If the same protective measures had been applied three weeks earlier that number would have reached 95% of reduced infections.
  
Covid-19 likely hotspots based on confirmed cases as of June 5, 2020. These predicted hotspots are currently the main areas with the most covid-19 cases in the USA. 
OperAI uses privacy-preserving approaches along with modeling techniques to help curb the spread of covid-19, in addition to physical distancing. These are some illustrations of the application that we have been developing, some of the app scripts are made available at OperAI GitHub. These scripts can be used with granular data, such as a city or a county data with the aim for a near real-time tracing of the virus to stop its spread and of infecting people. The app traces the virus but not people. 
Pathogen Tracer is a Multi-Device Application  

The "Pathogen Tracer" App aims to help with tracing and predicting/forecasting hotspots of covid-19. The app is taking its final shape and its first version will be launched soon. With partners we are exploring other alternative protective measures to contribute to stop the spread of the virus during the reopening period and after.
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Number of daily conformed new cases
​April, 25, 2020
This graph shows that the virus spread is "retreating" as new cases are declining, Caution is required to avoid surges. 
​April 17, 2020
The curves and the maps of Covid-19 are used to trace its spread and develop models. The models, in turn, are being used to predict how the disease spreads and help to take appropriate measures.   
OperAI works with partners in academia and industry to co-design and develop math-based solutions to speed up and streamline operational processes at the edges of the Cloud. OperAI develop IoT devices with embedded machine learning and artificial intelligence applications for real time analytics.
​Tracing the spread can be based on privacy-preserving approaches. One of such approaches has been proposed, recently, by experts on privacy, in Europe. They called it "Decentralized Privacy-Preserving Proximity Tracing (DP-PPT) to help curb the spread with confidentiality. The approach we have been using to trace and model the spread is also a privacy-preserving approach where no personal information is used in the prediction. 
The Internet of Things can also help to curb current outbreaks. Early studies show that IoTs can be used to trace the origin of an outbreak, such as the case of dengue virus in Singapore during 2013 and 2014. To manage some aspects of the case of current COVID-19 outbreaks, IOTs, such as drones are being used for public surveillance to ensure quarantine implementan. IoT devices generate valuable data that may exhibit patterns, which in turn can be used to predict likely outbreaks, if any, and take necessary measure on real-time. Data that has been previously dismissed as chaotic has been found to exhibit hidden patterns, such as the 2002–2003 respiratory syndrome (SARS). In the case of 2013-2014 dengue virus, the cell phone data was found to be valuable in monitoring mobility patterns in near real-time. ​
Book on Edge Analytics by OperAI
“" "Both edge analytics and fog analytics involve pushing intelligence and processing capabilities down closer to where the data originates."
​Automation World (2018)​
Codes and examples of the edge analytics book above are available at the software development platform GitHub. These codes and examples include deep learning and reinforcement learning cases.
Image App 
Signal App
Text App 
Some of OperAI edge applications in the pipeline 
The need to carry out real-time analytics and inference processes at the edges is driven by the unprecedented rapid proliferation of IoT devices along with the increase of machine-to-machine communication (M2M). 
OperAI is developing with partners other alternatives to help with covid-19 containment, during the deconfinement and after. The overall aim is to contribute to sustain the economy and society within the sustainability perspective.
  • OperAI helps to carry out real-time analytics and streamline operational activities by leveraging Big Bata, Machine Learning and Artificial Intelligence, using different platforms. 
  • ​OperAI focuses on operational data at the edges or networked IoT devices, the fog, to process and analyse data streams on real-time, whether as a signal, image or textual data.     
  • OperAI works with partners in the industry, research and academia in the realm of industry 4.0 for more accurate, efficient, inclusive and environmentally friendly operational processes at the edges, with people in the loop.
  • OperAI has expertise on preparing and developing Big Data/ML/AI tools that helped in speeding up discovery and delivery processes, with results considered as breakthroughs. These tools are used in research and development (R&D), including international research organisations and development agencies as well as industries. 
Industry 4.0 refers to the current fourth industrial revolution where new technologies are all merging together, linking the real world to the digital world, involving big data, automation, ML and AI. It is named as the fourth generation in subsequent to the third industrial revolution on electronics and information technology to automate production. Industry 4.0 includes the Industrial Internet of things (IIoT), edge-to-cloud analytics, including fog analytics and cognitive analytics. The IIoT devices are poised, today, to increase production efficiency by optimizing and streamlining operations in the industrial equipment and machinery space. While the second industrial revolution refers to the use of electric power in the production system, the first refers to the mechanization of production using water and steam power. 
OperAI co-develops tools and AI applications involving cognitive analytics with people in the loop.
80% 
of employees are keen to learn artificial intelligence skills and
are comfortable with AI in the workplace
​(TechRepublic 2019).
​
80%
of Business and Tech Leaders mentioned that
AI creates jobs and increases productivity,
(BusinessWire, 2015).

Services & Products
Reviews & Comments
Team Members

​Operational AI to speed up processes of
production & protection at the edges,
​with people in the loop.
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OperAI co-develops also tools within the sustainability perspective 
​by including e-waste in the process of making new products with its 
recycling partners.
IoT deployment can help to curb global warming by cutting carbon emissions by 15%.
​OperAI has been selected among the first projects of the first NextAI Montréal program, in 2018 as an edge AI-based analytics platform. It has been then launched officially with the support of the Government of Québec early 2019.
OperAI has also joined the Evolution of Networked Services through a Corridor in Québec and Ontario for Research and Innovation program, known as ENCQOR, early 2020 and Réseau Montréal  recently.
OperAI has also partners working on the development of IoTs in Québec. 
It is expanding in its partnership to include new partners to speed operational process and curb the spread of covid-19.  
​OperAI will be looking for members to join OperAI team. Looking for new members with math and/or engineering background and experience in programming and prototyping. ​
OperAI will be looking for developers to help with  designing and developing applications at the edges.  The tasks include:         
         - Writing and compiling codes in R, Spark and C++.
         - Debugging codes by testing and fixing errors, if any.
         - Simulations and testing programs in real situations before their launch.
         - Deployment and launch of IoT applications, including mobile apps.


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