The world is a gigantic landfill! Everyday tons of waste are generated from various households, hospitals, industries, construction and demolition sites and more. While today we have numerous ways to get rid of the accumulated waste, it still ends up affecting the safety and sustainability of the ecological system. Therefore, the best alternative is to reuse and recycle as much waste as possible. And offering an extra pair of hand in this are waste sorting and recycling robots.
Greyparrot, a start-up which uses computer vision for waste management, has been voted the winner of the Innovation Factory Grand Finale held as part of the year-round AI for Good Summit 2020. The Innovation Factory is AI for Good's platform to showcase startups which use artificial intelligence to tackle global challenges, providing them with feedback, mentorship and potential partnerships in social impact entrepreneurship. Greyparrot and three other start-ups received the highest scores for their innovative, scalable AI solutions for waste management, air quality, child malnutrition and agriculture. Meet the expert jury During the live Innovation Factory Grand Finale, these four startups recognized as Innovation Champions presented their solutions to a jury of experts and a public audience who then voted for a winner. Greyparrot seeks to resolve the waste crisis by using AI-based computer vision to provide actionable insights for the 530 billion-dollar global waste management industry.
The technology behind the First Industrial Revolution was water and steam power, which mechanized textile production. The innovation made factories commonplace, which brought more people to cities and caused social upheaval. In the second, electric power made mass production possible. The third was based on semiconductors, which facilitated the data processing that automated production and spawned the digital age. Now a fourth industrial revolution is taking shape. The technology behind it is the internet of things--networks of connected devices such as sensors, robots, and wearables.
NIR Calibration-Model Services Spectroscopy and Chemometrics News Weekly 43, 2020 NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Food Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK Get the "Spectroscopy and Chemometrics News Weekly" in real time on Twitter @ CalibModel and follow us. LINK "Aplicaciones de la Espectroscopia de Infrarrojo Cercano (NIR) para predecir el contenido y la actividad de agua del embutido tipo "Fuet "" LINK "Monitoring the Processing of Dry Fermented Sausages with a Portable NIRS Device" LINK "Modelling potentially toxic elements in forest soils with vis–NIR spectra and learning algorithms" LINK Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR) "Visible and near-infrared hyperspectral imaging techniques allow the reliable quantification of prognostic markers in lymphomas: a pilot study using the Ki67 proliferation index as an example." LINK "Key variables selection and models development based on near-infrared spectra for the multi-qualities in formula feedstuff for swine." LINK "Predicting Marian Plum Fruit Quality without Environmental Condition Impact by Handheld Visible–Near-Infrared Spectroscopy" LINK "Application of miniaturized near-infrared spectroscopy in pharmaceutical identification" LINK "Two standard-free approaches to correct for external influences on near-infrared spectra to make models widely applicable" LINK Hyperspectral Imaging (HSI) "Selecting Key Wavelengths of Hyperspectral imagine for Nondestructive Classification of Moldy Peanuts using Ensemble Classifier" LINK "A rapid and non-destructive detection of Escherichia coli on the surface of fresh-cut potato slices and application using hyperspectral imaging" LINK "Using Machine Learning for Estimating Rice Chlorophyll Content from In Situ Hyperspectral Data" RemoteSensing LINK Chemometrics and Machine Learning "Comparison of chemometrics and official AOCS methods for predicting the shelf life of edible oil" LINK "Study on a twodimensional correlation visiblenear infrared spectroscopy kinetic model for the moisture content of fresh walnuts stored at room temperature" LINK "Chemometric Strategies for Spectroscopy-Based Food Authentication" LINK "Development of a Near Infrared Spectroscopy Model for Prediction of Fibre Compounds in Alfalfa" LINK "Tracing the Geographical Origins of Dendrobe (Dendrobium spp.) by Near-Infrared Spectroscopy Sensor Combined with Porphyrin and Chemometrics" LINK Equipment for Spectroscopy "Evaluation of a micro-spectrometer for the real-time assessment of liver graft with mild-to-moderate macrosteatosis: A proof of concept study."
For my final capstone project in Flatiron School's Immersive Data Science Program, I decided to test my newfound skills and continue furthering my personal investigations into the relationships that exist between data, waste, and energy. Recently, I have been learning more about the various ways that Municipal Solid Waste (MSW) can be transformed into energy. The most promising and efficient technology that I have come across to date is Plasma Arc Gasification. In my research, I discovered that understanding specific composition details about the MSW to be used as feedstock is one of many critical steps in designing a plasma gasification facility. What I set out to do for my capstone project, was to see if I could find some MSW collection datasets and perform a Feedstock Analysis with the intent of calculating specific Waste Type Compositions, Energy Density (kWh/kg), and Total Energy (kWh) for each sample.
A Texas company is using edge computing and IoT sensors to help cities modernize crumbling water infrastructure and inaccurate water meters. The American Society of Civil Engineers has given the country's drinking water system a D- for the last 10 years. Many components of city water systems date back to the Civil War era. Olea Edge Analytics is using 21st century technology to spot needed repairs and make sure water bills are accurate. Dave Mackie, Olea Edge Analytics' CEO, said the company combines edge computing with artificial intelligence and machine learning to help cities make more informed decisions.
Water quality and logistics monitoring software Ketos has raised $15 million from a group of investors to take advantage of the growing demand for better water management tools and technologies. The potential for more stringent regulatory oversight of industrial water use and wastewater management from local, state and federal government coupled with increasing consumer and investor demands for better corporate environmental stewardship is driving an unprecedented adoption of technology and services aimed at increasing conservation and reducing waste across industries. Water monitoring can also provide relevant information to public officials about the potential for disease outbreaks and other health related issues in a population. Recently, monitoring wastewater streams have been used to detect outbreaks of the virus that causes COVID-19. The renewed attention on water is one reason why an investment arm of the banking giant Citi joined lead investor Motley Fool Ventures and Illuminated Funds Group to come as new investors into Ketos.
The E.Coli dataset is a very popular dataset to experiment on because it is a multi-classification that has several imbalances. The E.coli dataset is such a difficult dataset to find a solution for that I have not been able to find a lot written about it on the internet. Jason Brownlee, of masteringmachinelearning.com suggested deleting the rows deleting the rows of the highly imbalance classes, but in my opinion such a practice defeats the purpose of endeavouring to make predictions. After much exhaustive research I was able to come up with a solution where all eight classes in the dataset were identified and predicted on. The E.coli dataset is credited to Kenta Nakai and was developed into its current form by Paul Horton and Kenta Nakai in their 1996 paper titled "A Probabilistic Classification System For Predicting The Cellular Localization Sites Of Proteins."
A smart city is a municipality that uses information and communication technologies (ICT) to increase operational efficiency, share information with the public and improve both the quality of government services and citizen welfare. While the exact definition varies, the overarching mission of a smart city is to optimize city functions and drive economic growth while improving quality of life for its citizens using smart technology and data analysis. Value is given to the smart city based on what they choose to do with the technology, not just how much technology they may have. Several major characteristics are used to determine a city's smartness. A smart city's success depends on its ability to form a strong relationship between the government -- including its bureaucracy and regulations -- and the private sector.
Cleaning up the oceans is a huge undertaking, especially for a single nonprofit based out of the Netherlands, but having Microsoft on your side is a nice bonus. Boyan Slat launched The Ocean Cleanup nonprofit in 2013, with the goal of cleaning up the Great Pacific Garbage Patch. Since then, the project has also embraced the goal of preventing new waste from entering the ocean by cleaning up rivers the carry many of the pollutants. In 2018, The Ocean Cleanup was a participant in Microsoft's annual hackathon, where volunteers work together on moonshots to try to come up with innovative solutions. The resulting machine learning models have helped The Ocean Cleanup track plastic and other waste, and informed how and where the nonprofit deploys its giant autonomous plastic collectors.