Plant Species' Health Detection

Last Updated on May 3, 2021

About

A Drone that scans Agriculture and examines the classification of flowers, fruits, vegetables, and diseases using Deep learning. 

How to get rid of those diseases and what precautions we can perform on that crop to make it better.

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Resistar

Last Updated on May 3, 2021

About

Augmented Reality Circuit Visualizer and Solver.

Categories Won

TartanHacks Grand Prize

Facebook Company Prize

Attempted Prize Categories

Duolingo’s Social Impact Prize (Educational)

GoDaddy’s Social Impact Prize (“Best app that improves STEM education”)

Inspiration

Long hours spent on ECE problem sets and frustration visualizing convoluted circuits caused these four CMU undergrads to create a circuit visualization system that would also help them solve circuits. A member of the team is currently in the intro ECE course: "Well it's not bad, I guess." - Team Member

What it does

ResistAR is an Augmented Reality Circuit Visualizer and Solver. A user can place down circuit elements in parallel and series configurations and ResistAR will solve the current through and voltage across each element of the circuit. It gives the user an easy way to see (sharp) the circuit.

How we built it

We first began with 3D printed chassis for the VuMark targets. These targets are identified and parsed by the program and cross checked against our cloud database on Vuforia. We then created 3D, textured, models in Blender that will hover over the VuMark targets. We then wrote the code in Unity that will calculate voltage and current values using concepts from vector calculus and matrix algebra.

Challenges we ran into

The math was very difficult and attempting to rush a 3D printed design was also difficult but there was a rush because 3D printing would be a very time consuming process. Thus we also had to create a lot of our latter designs around the already 3D printed parts. VuMarks were also difficult to create. VuMarks must be very easily distinguishable from each other and non-symmetric along any axis, and therefore took a while to get finely tuned and calibrated. Finally the math was a very difficult thing to visualize. We had to go from 3D space to 2D space and there were some difficulties with projections. The coders did end up writing relatively bug-free code, but not before a long, arduous thinking process.

Accomplishments that we're proud of

The two logic/algorithm gods that we had on our team solved an extremely complex math problem very quickly. Also our 3D printed parts are actually fire though. Just saying.

What we learned

Two 5 hour energies in 72 hours is actually not as bad an idea as some might think. Math is hard.

What's next for ResistAR

Norton and Thevenin Equivalents. Yikes.

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Breast Cancer Analysis And Prediction Using Ml

Last Updated on May 3, 2021

About

Project EDA-

Done by using module called Pandas Profiling


Data Set Information:

Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. n the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23-34].

This database is also available through the UW CS ftp server: ftp ftp.cs.wisc.edu cd math-prog/cpo-dataset/machine-learn/WDBC/

Also can be found on UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29


Attribute Information:

  1. ID number
  2. Diagnosis (M = malignant, B = benign) 3-32)

Ten real-valued features are computed for each cell nucleus:

a) radius (mean of distances from center to points on the perimeter) b) texture (standard deviation of gray-scale values) c) perimeter d) area e) smoothness (local variation in radius lengths) f) compactness (perimeter^2 / area - 1.0) g) concavity (severity of concave portions of the contour) h) concave points (number of concave portions of the contour) i) symmetry j) fractal dimension ("coastline approximation" - 1)

The mean, standard error and "worst" or largest (mean of the three largest values) of these features were computed for each image, resulting in 30 features. For instance, field 3 is Mean Radius, field 13 is Radius SE, field 23 is Worst Radius.

All feature values are recoded with four significant digits.

Missing attribute values: none

Class distribution: 357 benign, 212 malignant

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Voice Of The Day

Last Updated on May 3, 2021

About

Inspiration

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.

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Online Depression Detection

Last Updated on May 3, 2021

About

Purpose: Social networks have been developed as a great point for its users to communicate with their interested friends and share their opinions, photos, and videos refecting their moods, feelings and sentiments. This creates an opportunity to analyze social network data for user’s feelings and sentiments to investigate their moods and attitudes when they are communicating via these online tools.

Methods: Although diagnosis of depression using social networks data has picked an established position globally, there are several dimensions that are yet to be detected. In this study, we aim to perform depression analysis on Facebook data collected from an online public source. To investigate the efect of depression detection, we propose machine learning technique as an efcient and scalable method.

Results: We report an implementation of the proposed method. We have evaluated the efciency of our proposed method using a set of various psycholinguistic features. We show that our proposed method can signifcantly improve the accuracy and classifcation error rate. In addition, the result shows that in diferent experiments Decision Tree (DT) gives the highest accuracy than other ML approaches to fnd the depression.

Conclusions: Machine learning techniques identify high quality solutions of mental health problems among Facebook users.

Keywords: Social network, Emotions, Depression, Sentiment analysis

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Iris Flower Prediction

Last Updated on May 3, 2021

About

Understanding the scenario

Let’s assume that a hobby botanist is interested in distinguishing the species of some iris flowers that she has found. She has collected some measurements associated with each iris, which are:

  • the length and width of the petals
  • the length and width of the sepals, all measured in centimetres.

She also has the measurements of some irises that have been previously identified by an expert botanist as belonging to the species setosa, versicolor, or virginica. For these measurements, she can be certain of which species each iris belongs to. We will consider that these are the only species our botanist will encounter.

The goal is to create a machine learning model that can learn from the measurements of these irises whose species are already known, so that we can predict the species for the new irises that she has found.

Modules imported

  • SkLearn is a pack of Python modules built for data science applications (which includes machine learning). Here, we’ll be using three particular modules:
  • load_iris: The classic dataset for the iris classification problem. (NumPy array)
  • train_test_split: method for splitting our dataset.
  • KNeighborsClassifier: method for classifying using the K-Nearest Neighbor approach.
  • NumPy is a Python library that makes it easier to work with N-dimensional arrays and has a large collection of mathematical functions at its disposal. It’s’ base data type is the “numpy.ndarray”.

Building our model

As we have measurements for which we know the correct species of iris, this is a supervised learning problem. We want to predict one of several options (the species of iris), making it an example of a classification problem. The possible outputs (different species of irises) are called classes. Every iris in the dataset belongs to one of three classes considered in the model, so this problem is a three-class classification problem. The desired output for a single data point (an iris) is the species of the flower considering it’s features. For a particular data point, the class / species it belongs to is called its label.

As already stated, we will use the Iris Dataset already included in scikit-learn.

Now, let’s print some interesting data about our dataset:

ACCURACY we get an accuracy of 93%

OUTPUT IN THIS CASE    as we have 2 samples [[3,5,4,2], [2,3,5,4]]

so the iris type predicted by our model based on the given features are

predictions:  ['versicolor', 'virginica']

for more details this is my Github repository

ml-2/iris_flower.ipynb at main · THC1111/ml-2 (github.com)




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