My project uses an artificial neural network to predict the probability of a medical device being compromised or hacked. This project would automate the risk assessment process that medical IT staff use to design a strategy for effectively addressing numerous system vulnerabilities. A need for an efficient and precise risk assessment tool has become more necessary and important with the recent increase in ransomware attacks against hospitals. Some of the biggest weak spots hospital systems have are found in their connected medical devices like EKGs. However, these are not as easy to analyze as software system vulnerabilities and thus take much more time. My project seeks to reduce the time and energy needed to tackle this weakness in hospital systems.
As the Philippines’ population grows, more solid waste is generated. Poor implementation of segregation policies and the lack of public trash cans have led to solid waste mismanagement in the country. Local landfills are almost at maximum capacity and residents turn to burning garbage as a solution. Unfortunately, majority of these wastes are either made out of plastic or paper which are found to be major pollutants. With this in mind, this project aims to create a budget-friendly trash can that can automatically segregate and shred wastes. This automated trash can and waste segregator is designed to sort the waste into three categories namely; metallic, organic, and plastic. Magnetic sensors are added to determine the type of material, thereby making waste segregation more effective. Additionally, ultrasonic sensors would be placed in all the garbage bins. When the garbage reaches the level of the sensor, then a microcontroller will signal the built-in shredder to operate.
Sophia Dominique Manalang
Philippine Science High School - CVC, NV, Philippines
Predict the Post-infection Complication of a SARS-CoV-2 Patient
In this current project, an AI-driven model would be built. This model would predict the post-infection complication that has the highest probability of occurrence after SARS-CoV-2. Data can also be analyzed and a correlation between a complication and a SARS-CoV-2 strain can be determined.
Waterloo Collegiate Institute, ON, Canada
GIS-enabled App for navigating the complex healthcare system
Recent findings that the COVID-19 case fatality rate (CFR) in the Asian American community is the highest of all ethnic groups in the U.S., and that COVID-19 CFR is not higher in Asians in other parts of the world, points toward the impact of health disparities. This has motivated a project at the University of Louisville to develop a GIS-enabled software to provide peer-to-peer support to help the first and second generation Asian American population navigating the complex healthcare system. The Asian Institute Crane House in Louisville, KY is the community partner for the project and Phase I of the project December 2021-September 2022. Community AI’s founder Shreas Kar was part of the research team in the prototyping stage of the mobile app development.
Shreyas Kar, duPont Manual High School
Combating Plastic Pollution
Estimates suggest that around 300 million tons of plastic is produced every year. Out of which, 8 million tons end up in the ocean, affecting at least 700 marine species. Plastic pollution is currently the most widespread problem affecting the marine environment. I have tried to produce a model that could be used as a measure to counter the growing plastic concentration in the world’s water bodies. The model would be a supervised-learning multi-tasking model trained to be able to pinpoint high-concentration oceanic zones of plastic debris as well as predict the odds of oceanic regions turning into high-contamination zones after a certain period of time. The model will also be trained to predict the geographical boundaries associated with those high-contamination zones for timely countermeasures. This project intends to utilize AI to help decontaminate the world's water resources in an even more efficient and accurate manner.
Luthra Higher Secondary School, J&K, India
CharityTrend is a website that is used to recommend charitable organizations based on news stories. Users paste an article link into CharityTrend‚ search bar, and an AI model will analyze the article‚ content for keywords that reveal the issues that are discussed. The model will then search for relevant charities. The user can also view trending news stories with charities that the model has picked. CharityTrend will also display additional information about each charity so that users can evaluate them further. Afterwards, users will be navigated to the donor page to make a secure donation.
Kenwood Academy, IL, USA
A-EYE for the Blind
Currently, there are about 300 million people globally with visual impairments. These people are unable to safely travel around on their own. For this reason, I decided to create A-EYE for the Blind: a product that provides blind people more freedom and safety while traveling. The hardware interface takes pictures of the user's surrounding, passes them through a 2D-image-to-depth-image machine learning model, and analyzes the depths of objects, providing users with audio feedback of obstructed areas. The images taken and other metadata are also uploaded to a database, and friends and family can check the images live on the website.
Coppell High School, TX, USA
Incentiva is a minimalistic task manager that is packed with features to help significantly improve the productivity of the user. Incentiva aims to keep users productive and going in their busy day-to-day lives, ‚incentivizing‚ users to keep their good habits and to drop their bad ones. Advanced AI analyzes the user‚ habits to show them detailed reports on what they should be doing more/less of. Incentiva is different from other task managers in that it actually ‚manages‚ your tasks. It motivates users to keep going, and includes productivity features such as a Pomodoro Clock to ensure their work is getting done in a timely manner.
Hamilton Southestern High School - IN, USA
From clueless to the perfect trip
We created a site that allows young people to plan out entire trips with just a few clicks. We believe that quick and easy traveling can help solve racism by opening up the worldview of our generation. Young people don't like complicated planning. So, our application allows you to discover a new travel destination by inputting a few adjectives and plans out your entire trip, including meals, with the most famous landmarks and fastest routes. Additionally, all hotel and flight information is provided and can be customized so that everything you need for your trip can be purchased with a single button.
Lake Forest Academy - IL, USA
Global tree cover loss rose from an average of 17.1 million hectares a year in the 2000s to 23.1 million in the 2010s. The loss of forests has several detrimental effects on the environment such as the acceleration of global warming, the increase of biodiversity loss, the disruption of rainfall patterns, and many more. This project focussed on three solutions to combat deforestation: reforestation, eco-forestry, and green agriculture. All three solutions will use a combination of neural networks, image processing, satellite imagery, and classification.
California High School, CA, USA
Your Vaccine Buddy
This project uses binary classification and machine learning to predict how likely it is for someone to experience side effects after recieving a Coronavirus vaccine. This will be implemented with an app. When someone downloads the app and receives their vaccine, they will fill out a form on their app. They will have to provide information about themselves, such as health conditions they have. The app will also contain data relating to symptoms people experienced in the past after getting these vaccines, and all of their information. The app will perform binary classification on this data. It will use a separate model for each symptom, and will try to find a pattern that separates people who experienced a certain side effect from those who didn’t. This includes many common symptoms. After the person inputs their information into the app, it will tell them if they are more likely to experience certain symptoms or not. Once it gives you a list of symptoms the app can tell you what you should do so that the symptoms don’t last for as long. The app will use the symptoms to make a prediction on how dangerous getting the vaccine will be.
Aarna Puvvala and Anika Sivarasa
Academy for Science and Design , NH, USA
With institutions open across the country during this pandemic, we are increasing the risk of outbreaks. On-location activity is particularly important for traders who need robust, real-time communication and sales teams that are subject to specific compliance monitoring. In the event of an outbreak, an institution may not be able to accurately identify every employee/student at risk and in turn, can be forced to shut down completely. There is a huge opportunity cost due to a loss of learning potential/revenue from shutdowns. OPEX is a startup focusing on providing solutions for contact tracing. Our long-term vision is to provide asset tracking solutions maximizing the efficiency of manufacturing infrastructure.
A Solar tracker was created in order to focus solar panels perpendicular to incoming sunlight. This position of the solar panel is optimal for photovoltaic energy generation. The efficiency of the Solar Tracking system vs normal use of solar panels was compared using data collected by an Arduino Power Logger.
By Varun Chandrashekhar
Amber Alert Vehicle Detection
In the United States, and especially in urban areas, child abduction is a serious issue. When this happens, an amber alert is sent but this is largely ineffective and drains police resources. This project automates the vehicle detection process by constructing an AI in traffic cameras to detect and identify similar vehicles to that of the child abductor. This is done through frame differencing to find the vehicles in the video and then a convolutional neural network to compare features of the vehicles. As a result, criminals in urban areas can be found more easily, quickly, and safely. This will help discourage criminal activity and create a safer environment for children.
Mission Hills High School, CA, USA
Michael E. DeBakey High School for Health Professions, TX, USA
Product Manufacturing and Quality Control
In product manufacturing, quality control is imperative, because if the quality of a product and the standards in which it is created were to decrease or cease to exist, the company manufacturing the products may start profiting less. Try as we may, humans are prone to mistakes, faults, and accidents. In business and production, this costs money, and a more efficient way of manufacturing can be achieved using machines. But even with the improved accuracy of production brought about by the machines, major and minor defects can still be found in factory-made products. Since mistakes in manufacturing are prone to happening, the best thing to do is plan for it.
AI Driven Optimization of Public Transportation Schedules
In Boston, the budget of the MBTA transportation system continues to increase, yet the majority of citizens are unsatisfied with its service. In recent decades, inequitable transportation has received increased attention on its impacts on economic disparity and families, costing industries millions. This project aims to make bus transportation more efficient, reliable while reducing its costs on the MBTA’s limited resources. Using a polynomial regression model to predict the number of people who arrive at the bus stop every day with a number of different variables including time of day, area, and day of the week, this accurate prediction enables much greater efficiency in creating bus schedule timings. An algorithm used on this regression curve, a function of the number of people arrived at the stop will optimize departures, evening them based on how many people are at the station. This prototype could then also provide very useful data its predictive power, to also be able to numerically predict the number of people currently at a bus stop.
Food waste and food insecurity are two big issues in the world. 40% of the food bought by Americans are wasted, amounting to $165 billion annually. Whereas 49 million Americans struggle to put food on the table. Food waste is the third largest producer of harmful greenhouse gas. Foodle is an IoT and AI-based integrated system to reduce food waste, food insecurity and save the environment.
By Shraman Kar
New York Stop The Spread (NYSS)
Everyone around the globe is all too familiar with the term ‘coronavirus’. As this disease plagued our lives for the worst and took away too many others. At the end of the tunnel we saw light, the Approved usages of vaccines that have been clinically proven to combat covid. However, due to mass misinformation circling the internet, it’s not surprising that many are hesitant to receive the vaccine. Not all is lost, with the assistance of a machine learning app. Through the use of neural networks and multivariable linear regression, it will analyze the user’s age, health conditions, etc. to recommend the user correlating articles with reliable sources from doctors and researchers. It can show the individual nearby vaccine locations and what vaccine(s) they offer. This app will serves as a guide or rather a helping hand to educate someone regarding the importance of getting vaccinated and make booking appointment(s) less of a hassle. At the end of the day the purpose of CAYF is to nudge everyone to receive both doses of the vaccine to achieve herd immunity so no one has to live in fear of a threat that cannot be seen by the human eye.
Lizzie Chen Natalie Guo
High school for Dual Language and Asian Studies, NY, USA
Machine Learning and IoT enabled machine to maintain a healthy garden while conserving water. It predicts and checks the nutrients and water in the soil and dispense them automatically when needed.
By Shraman Kar
A Global Landslide Analytics System
Landslides cost billions in damage annually in the U.S. & have affected 4.8 million people over the past 2 decades. Existing landslide warning systems are ineffective in accuracy, latency, & scalability. There is a lack of publicly accessible data of landslide features on a global scale. The researchers compiled a global landslide dataset consisting of ~18,000 landslide incidences and 100 related climate and terrain features. Machine learning models obtained 92.5% accuracy & 94.1% detection rates when forecasting whether or not landslides would occur 5 days into the future. A data-driven approach to landslide susceptibility map generation was derived.
Plano East Senior High School - TX, USA
Predicting Small Business Loans & Job Growth
The goal of my project is to predict the amount of jobs created and retained from a loan to a business, as well as to predict the dollar amount of a loan to approve. The main benefits of this project is that it allows banks to choose what businesses to provide loans to that best help the job market and how much of a loan to provide the business with. Right now especially, a lot of people have lost their jobs due to the pandemic, and many business are low on money to provide for infrastructure, employees, and the products and servies they sell. I implemented this project through both linear regression and an artificial neural network. As inputs, this neural network takes in the borrower's city, state, and zipcode, bank name and state, NAICS, length of term (months), number of business employees, is a new or existing business, is urban or rural, if real estate was used, and if a recession happened. As outputs, the linear regression and artificial neural network result with the number of jobs created, the number of jobs retained, and the gross amount of loan approved by the bank and the SBA.
Wayzata High School, MN, USA
AI for the Prevention of Misdiagnosis of Mental Illness
Mental health (emotional, psychological and social well-being) affects how we think, feel, act and therefore, our response to stress and the behaviour of people surrounding us. It decides whether we make healthy choices or not. However, various factors such as cultural biases, uneducated assumptions and very similar symptoms of more than one type of mental illness can lead to misdiagnosis of diseases. Studies show 2 out of 5 patients with bipolar disorder are always misdiagnosed with major depressive orders, at first. The mean period of delay in bipolar diagnosis ranges from around 6 to 8 years. This project aims to utilize artificial intelligence to minimize (if not completely eradicate) this problem of misdiagnosis. It plans to make computers learn the symptoms and characteristics of each illness, their similarities with other illnesses, the limitations of current diagnostic tests and many other important factors to give a proper diagnostic report of a certain patient. It aims to consider all sorts of medical data available to provide the report, starting from the digitalized data of the blood samples of the patients to results of various conventional diagnostic tests undertaken by the same set of people.
Mastermind School, Dhaka, Bangladesh
The Human Immunity System is a complex network of cells and proteins that defends the body against infection. Abnormalities of the immune system can lead to allergic diseases, immunodeficiencies, and autoimmune disorders. Many people around the world suffer or die due to the effect of diseases without having knowledge about their immune system. Every disease has its own unique T-cell signature. This program is aimed to match human T cells information with disease T cells data. It is designed using (RNN) Recurrent Neural Networks algorithm with multiple steps and layers.
Overall the results help doctors and medical diagnostic centers to serve their patients with proactive health suggestions to boost their immunity and help from diseases.
Machine Learning Algorithm (Convolutional Neural Networks) to Detect Icebergs from Satellite Images to Help Reduce Global Warming, Oil Spill and Improve Safety of Arctic navigation
By Shraman Kar
Using AI Against Invasive Plants
Invasive plants can negatively impact the environment, the economy of a region, and human health. They are fire hazards, bring other plants to extinction, damage power plants, decrease land values, and more. The project will use AI and ML to identify where invasive plants may reside, identify which plants they are, and give them a risk assessment. The AI will take input from sensors and cameras to identify whether an invasive plant is in that area. Then, the AI uses image recognition to find the plants. Based on the data and new data observed, the AI gives a risk assessment from 1 - 5 based on the risk to the environment. If the risk is high, the program will alert proper authorities.
Using Machine Learning to Predict the Relative Risk of Driving Using Weather Conditions and Driver Statistics
By: Amy Chen and Kaavya Thirumurugan
AI for the Prevention of Misdiagnosis of Mental Illness
In this world there are spots frequented by human activity being littered. This harms marine and wildlife besides degrading the environment as they consist of plastic and other toxic substances. This is caused by deliberate dumping, uninformed and uncaring people who do not realize the harm done by their actions.This is an issue that can be solved by the proposed Trash Tracker. Trash Tracker is a solution which reads satellite imagery data, identifies where these dumps of trash exist, and alerts Environment Union teams to their location, who can then process the trash, segregate and recycle it. It improves the machine learning algorithms by looking at heat maps, since trash alters the heat pattern, and areas frequented by human traffic (e.g. beaches, nature trails). This algorithm can also help in a city environment using the same approach.Trash Tracker can help in identifying the placement of the trash cans, frequency of cleaning, and right sizing the trash can and how many trash cans need to be placed, in order to handle the volume of trash.
Oakridge International School - Bangalore, KA, India
Shopping and hyper-consumerism have been the root cause of growing environmental problems through the past century. More often than not, consumers unknowingly purchase items or goods whose constituents have a toxic, long-lasting impact on the environment. While consumers today have a fair idea of the various environmental problems and their associated causes, their actual behaviour is often far from sustainable. This is largely due to a lack of awareness of the environmental consequences and impact of their purchases. "Sustainable Shopper" is a web-app that uses artificial intelligence to drive positive behaviour change in consumers and encourage them to make a sustainable choice. Through realtime image processing, it scans products for their ingredients, and based on that, determines a sustainability score.
Intelligently identify the combustibility of a trash and segregate them from non-combustible ones.
By: Divija Nath and Senria Nath
Just the other day I read a headline, farmer couple commits suicide after killing minor‚ and this made my heart skip a beat. And I thought to address the problems of yield losses due to diseases with what I love, tech. Plant AI is a web application that helps crop growers to easily diagnose diseases in plants from plant images, using Machine Learning possible all on the web. This relies on a model I built to identify plant diseases from images, that runs on-device to prematurely identify plant diseases and also suggest farmers actionable steps to solve it.
Project Showcase: If you have built any AI driven or any technology driven project that made impact to the community or environment please tell us about it and we can showcase your project. Please click here to submit your project to us.