Spray Characteristics Of The Slurry Fuels (Automatic Extraction Of Ligament Length,Diameter And Droplet Diameter)(September 2020- November 2020) (Python,Ml)Last Updated on May 3, 2021
-Numerically solving complex mathematical relations to determine droplet diameter, ligament length and diameter is very difficult and time consuming, thus the aim of the project is to automize this task.
-Supervised learning methodology applied here and the developed technique is purely based on image processing, thus applicable for all kinds of fuels.
-Irrespective of the nature of the fuel, be it used in combustion chamber of jet engines or be it used in commercial electrical power plant, this method will be applicable.
-Method only focus on the geometric aspects of the disintegration of the fuel spray. Thus with just image processing, computation of combustion chamber size can be calculated.
-Thousands of images of atomisation of fuel spray can be easily processed for their characteristic dimensions and easiest method of determining combustion chamber size etc.
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HacktubeLast Updated on May 3, 2021
A Chrome extension that fights online harassment by filtering out comments with strong language.
YouTube is a place for millions of people to share their voices and engage with their communities. Unfortunately, the YouTube comments section is notorious for enabling anonymous users to post hateful and derogatory messages with the click of a button. These messages are purely meant to cause anger and depression without ever providing any constructive criticism. For YouTubers, this means seeing the degrading and mentally-harmful comments on their content, and for the YouTube community, this means reading negative and offensive comments on their favorite videos. As young adults who consume this online content, we feel as though it is necessary to have a tool that combats these comments to make YouTube a safer place.
What it does
HackTube automatically analyzes every YouTube video you watch, targeting comments which are degrading and offensive. It is constantly checking the page for hateful comments, so if the user loads more comments, the extension will pick those up. It then blocks comments which it deems damaging to the user, listing the total number of blocked comments at the top of the page. This process is all based on user preference, since the user chooses which types of comments (sexist, racist, homophobic, etc) they do not want to see. It is important to note that the user can disable the effects of the extension at any time. HackTube is not meant to censor constructive criticism; rather, it combats comments which are purely malicious in intent.
How we built it
Challenges we ran into
Accomplishments that we're proud of
We are proud of making a functional product that can not only fight online harassment and cyberbullying but also appeal to a wide variety of people.
What we learned
We learned how to dynamically alter the source code of a webpage through a Chrome extension. We also learned just how many YouTube comments are full of hate and malicious intent.
What's next for HackTube
Right now, for demo purposes, HackTube merely changes the hateful comments into a red warning statement. In the future, HackTube will have an option to fully take out the malicious comment, so users’ YouTube comments feed will be free of any trace of hateful comments. Users won’t have to worry about how many comments were flagged and what they contained. Additionally, we will have a way for users to input their own words that offend them and take the comments that contain those words out of the section.
Heart Attack PredictionLast Updated on May 3, 2021
I did this project in the first semester of my MTech studies at Ahmedabad University. This project is all about predicting the heart attack based on different parameters such as cholesterol, bp, exercise, age, sex, chest pain type, slope, etc. The dataset size was 27 kb. It had 13 columns and 303 rows, I got this dataset from Kaggle. First I did data cleaning in which I removed outliers, null values, duplicate values. After that, I did some data visualization to get insight from the data. During the data visualization, some insights I got from the data were people mostly aged above 40 are suffering/ suffered from a heart attack once in their life, heart rate and chest pain are highly correlated with a heart attack, stress and cholesterol are also one of the main factors of a heart attack, we can see that the patient suffering from heart disease have high cholesterol as compared to the patient not suffering from heart disease. In this project, I have used different machine learning algorithms to predict the Heart attack. I used Logistic regression in which I got 85% accuracy, and decision tree I got 72% accuracy. In the end, there is a decision tree that shows the parameters affecting in order of correlation.
Determination Of A Person’S HealthLast Updated on May 3, 2021
Determination of person’s health
The project was built with the intend of helping the society. It has been calculated that approx. 1.9 billion people die due to health-related problems every year. This rate is very high, and the disease is easily preventable
The project has been made with the help of Data Analysis and Machine Learning using Python with a GUI output page. In this project, the machine will analyse the already present data first and then conclude upon a person’s health on his/her given factors.
In this project, gender and either height or weight will be given to the machine. If the height is given then the weight will be predicted and vice-versa. Through these predictions the machine will tell us about the health of a person.
The main goal is to help the society for its betterment as far as health is concerned.
The data set used is from UCI repository. It includes four attributes-
The machine will be trained in these aspects to determine a person’s health or weight and the category it will lie in.
The categories are-
1. 0 – Underweight
2. 1 – Normal weight
3. 2 – Healthy
4. 3 – Over weight
5. 4 – Obesity
The methods followed in chronological form are-
1. Loading dataset (using pandas library)
2. Dataset cleaning (using pandas and numpy libraries)
3. Dataset pre-processing
4. Data visualization (using seaborn, matplotlib and matplotlib.pyplot libraries)
4.1 Univariate analysis
4.2 Bivariate analysis
5. Correlation matrix
The machine learning algorithms applied were-
1. Linear Regression
2. Logistic Regression
3. KNN Classifier
4. Decision Tree Classifier
5. Random Forest Classifier
Random Forest Classifier gave highest accuracy of about 95% while logistic regression gave the leas with about 76%.
The user in the GUI page will be asked:
1. Full name
3. Whether they know their height or weight
4. Their height or weight
Voice Of The DayLast Updated on May 3, 2021
The format used to work well on the radio, so we wanted to recreate those memories on Alexa.
What it does
Players can listen to up to three voice clips of well-known people and/or celebrities talking every day, as well as view a blurred image of the celebrity. After each clip, Alexa will ask you who you think is talking, and you must try to answer correctly. This will earn you a score for the monthly and all-time leader boards. The player can ask Alexa for hints, or to skip that voice clip to move onto the next one. Users scores are awarded depending on how many incorrect answers they gave for that voice and whether they used a hint or not. Users can also ask to hear yesterday’s answers, in case they couldn’t get the answers on that day.
How I built it
To create the structure of the skill, we used the Alexa Skills Kit CLI.
We used Amazons S3 storage for all our in-game assets such as the audio clips and images.
We used the Alexa Presentation Language to create the visual interface for the skill.
We used the Amazon GameOn SDK to create monthly and all-time leader boards for all users to enter without any sign up.
Every day, free users will be given the ‘easy’ clip to answer. The set of clips each day will be selected dependant on the day of the year. Users who have purchased premium gain access to the ‘medium’ and ‘hard’ clips every day, as well as being able to ask for hints for the voices, skip a voice if they are stuck, and enter scores onto the leader boards.
Accomplishments that I’m proud of
As well as creating a high-quality voice-only experience, we developed the skill to be very APL focused, which we are very proud of. The visual assets we used for the project were very high quality and we were able to dynamically display the appropriate data for each screen within the skill. The content loaded depends on who is talking, as well as the difficulty of the voice that the user is answering. APL also allowed us to blur and unblur the celebrity images, as opposed to having separate blurred and unblurred images for each person.
We were also very pleased with how we implemented the GameOn SDK SDK into the skill. When the user submits a score, they have a random avatar created for them, and their score will be submitted under this avatar. This avoids any sign up to use the leader boards, allowing all users to use it easily.
GameOn SDK also allows us to create monthly and all-time competitions/leader boards which all users are automatically entered.
What I learned
I have learnt how to develop APL as well as better practices for structuring it more efficiently. For example, there are many APL views in the project, all of which are almost identical, what I have learnt that would be more effective in future projects would be to condense these down into one primary view that I would use for each screen and just use the appropriate data.
I have also been able to hone prompts to the user for upsells and showing the leader boards. Testing has shown that constant prompts on each play for these things can become tedious to the user, and so we have reduced the frequency of these for a much better user experience.
Real Estate Price PredictionLast Updated on May 3, 2021
People looking to buy a new home tend to be more conservative with their budgets and market strategies. The existing system involves calculation of house prices without the necessary prediction about future market trends and price increase. The goal of this project is to predict the efficient house pricing for real estate customers with respect to their budgets and priorities. By analyzing previous market trends and price ranges, and also upcoming developments future prices will be predicted. The functioning of this project involves a website which accepts customer’s specifications and then combines the application of multiple linear regression algorithm of data mining. This application will help customers to invest in an estate without approaching an agent. It also decreases the risk involved in the transaction.
Housing prices are an important reflection of the economy, and housing price ranges are of great interest for both buyers and sellers. In this project. house prices will be predicted given explanatory variables that cover many aspects of residential houses. Thus, there is a need to predict the efficient house pricing for real estate customers with respect to their budgets and priorities. This project uses random forest algorithm to predict prices by analyzing current house prices, thereby forecasting the future prices according to the user’s requirements. The goal of this project is to create a regression model that are able to accurately estimate the price of the house given the features.