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[/et_pb_text][et_pb_toggle title=”Improve sorting and segregation of waste using machine learning” _builder_version=”4.13.1″ _module_preset=”default” global_colors_info=”{}”]
Development of image recognition techniques to improve sorting and segregation process of solid waste management.
Solid Waste Management is a universal issue, and at the same time, it is the ‘need of the hour’ project. One of the major contributors to municipal waste is plastic waste and its generation and consumption have increased drastically over the past few years even without developing a strategy to manage the waste generated. UNEP reports suggest that so far, only 9% of all plastic waste produced after the 1950s was recycled, and the rest ended up either in landfills or in our environment [1]. The current recycling rate, improper management of the generated waste, and its accumulation in the environment pose a massive threat to the marine and land habitat. Studies indicate that even the remote areas of ocean and land ecosystems are affected by the scourge of plastic trash, chemicals and other pollutants. One such example is the Great Pacific Garbage Patch, a marine debris collection spot in the Pacific Ocean where several thousands of tonnes of ocean plastic are estimated to be floating on the surface [2]. The concern is not only about plastic waste: it is about all the trash generated: metallic, e-waste, organic, textiles etc. We need to adopt different strategies and recovery plans to manage these waste materials in order to reduce the impact they caused on our ecosystem.
The biggest challenge in recycling/re-using waste is sorting and segregating different types of waste since segregation of waste aids in targeted recycling or even decomposition. As an example, segregating a dry metal can from a metal can containing organic matter eases recycling. The necessary action for proper segregation of the waste on large scale is to identify various materials first. Once identified, neuromorphic tools could be used to sort things based on the identified parameters. However, while there exist several methods to identify different materials such as visual sensors, olfactory sensors as well as spectroscopic tools, there are very few or no attempts at using artificial intelligence to specifically identify materials from the waste, which could then be applied to ease the segregation process. We, therefore, propose to use visual image recognition to first identify objects, in their full form or by parts in order to be used later for segregation. For example, we envisage the identification of different materials such as plastics, metal and paper in a used milk carton, which could lead to proper recycling of plastics, paper and metal.
In this project, participants will be guided to perform the following steps:
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Collaborating researchers: Surya Abhishek Singaraju, Jennifer Joseph
https://www.nature.com/articles/s41598-018-22939-w.pdf
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We regularly hear that transport is a major cause of air pollution. The lockdowns have drastically limited population movements by car, train or airplane. The idea is to see if these limitations have had an impact on air quality in France and in which proportions.
Measure the impact of reduced mobility on air quality.
Generating an Analysis Report on the affects of lockdown on Air Pollution
Project 2
Every year, flu has a significant impact on hospitalizations. Flu mortality highlights the seriousness of the disease and the importance of vaccination for people at risk. Additionally, barrier measures are indispensable to limit the spread of the virus by direct contact.
Like Covid tracking tools, a flu tracker can make this information accessible.
The aim is to use the available open-source data on infections, deaths and vaccinations, in a way similar to what is done for Covid-19. This will inform people about public health and best practices in a simple way.
The project results will be made open-source. This project will also reveal the severity of influenza compared to Covid.
Week 1 | Week 2 | Week 3 | Week 4 |
– Find relevant data sources – Developing web scraping scripts, if needed |
– Exploratory Data Analysis(EDA) -Interactive plots with Real-time data -Interactive map vaccination tracker |
– Start building Streamlit WebApp (with Tableau plots, if needed) |
-Finish Integrating WebApp -Deploy the App in Cloud Application Platforms |
1. Scrape and parse open data
2. Exploratory data analysis to get main insights
3. Data modelling (for example model the risk of an event with nb of people and other variables)
4. Build a dashboard using data visualization tools using Streamlit and Tableau
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[/et_pb_text][et_pb_slider arrows_custom_color=”#000000″ dot_nav_custom_color=”#000000″ _builder_version=”4.13.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_slide heading=”Rebecca Alexander” button_link=”https://www.linkedin.com/in/rebecca-alexander/” url_new_window=”on” image=”https://cmsnew.omdena.com/wp-content/uploads/2021/07/Rebecca.jpg” _builder_version=”4.13.1″ _module_preset=”default” background_color=”#FFFFFF” background_enable_color=”on” background_layout=”light” link_option_url=”https://www.linkedin.com/in/rebecca-alexander/” link_option_url_new_window=”on” global_colors_info=”{}” sticky_transition=”on”]
Rebecca is a Physicist-turned-Data-Science professional, who enjoys coding to decode patterns in data. Prior to this, she completed her Ph.D. at the French Atomic Energy Commission (C.E.A., Saclay). She’s always interested in debates about causality v/s correlation and Data Science projects.
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Simply send us an email. You can reach use via [email protected], Tell us about your idea and how can help you. Looking forward to hearing from you!