AdityaTheDev

BlurRemovalUsingAutoencoders

BlurRemoval-Using-an-Autoencoder Are you poor at taking photos Just like me? Here I have made a Deep learning model using Autoencoder architecture to remove unwanted blur from the image.

BrainTumorSegmentation-using-Transfer-Learning

Brain Tumor Segmentation using Transfer learning. Image segmentation is the task of clustering parts of an image together that belong to the same object class. This process is also called pixel-level classification. Brain Tumor Segmentation is a multi-class problem and this model classifies Gliomia tumor, Meningomia Tumor, Pituitary tumor and No tumor with an accuracy of 93%.

BreastCancerPrediction-Using-DeepNeuralNetwork

Breast Cancer prediction using Deep Neural Network. I got this dataset from UC Irvine medical website. This data set has 32 columns and 569 rows. This model tells you whether your Breast cancer is Malignant or Benign. I have used Keras to build the DeepNeuralNetwork. The neural network has 3 hidden layers which has 512 neurons each. I have used BatchNormalization to standardize the attributes. This model predicts with an accuracy of 93%

ConvolutionalNeuralNetwork-To-Classify-DogVsCat

Convolutional Neural Network to Classify Dogs and Cat. I built a ImageClassifier which classifies and tells you whether its a Dog image or a Cat image. I built a convolutional network which consists of Three Convolution layer and Three MaxPooling layer. Each Convolutional layer has filters, kernel size. Maxpooling layer has stride and pooling size. Then this Convolutional layer Connects to DeepNeuralNetwork. DNN has three hidden layer and output layer having Sigmoid Activation function. I trained this model for 31 epochs and achieved an accuracy of around 85%. I found this massive image dataset online which has 10,028 images(Ten Thousand and Twenty Eight). My model Predicted accurately during the testing phase. I even tested my model using my neighbor dog's pic and it predicted accurately.

covisense.github.io

FaceGenerationUsingVariationalAutoencoder

VARIATIONAL AUTOENCODERS are Generative model. A Generative Model is a powerful way of learning any kind of data distribution using unsupervised learning and it has achieved tremendous success over the past few years.

ImageDenoising-Using-Autoencoders

I built a Denoising Autoencoder to remove noise from the image. Image Denoising is the process of removing noise from the Images The noise present in the images may be caused by various intrinsic or extrinsic conditions which are practically hard to deal with. The problem of Image Denoising is a very fundamental challenge in the domain of Image processing and Computer vision. Therefore, it plays an important role in a wide variety of domains where getting the original image is really important for robust performance.

NudityDetection-Using-Deeplearning

Nudity/pornography detection using deeplearning. This model is trained using pretrained VGG-16. To know more about this check the readme file below

ReconstructionOfImage-Using-DeepAutoEnccoders

Autoencoder is a type of neural network where the output layer has the same dimensionality as the input layer. In simpler words, the number of output units in the output layer is equal to the number of input units in the input layer. An autoencoder replicates the data from the input to the output in an unsupervised manner and is therefore sometimes referred to as a replicator neural network. The autoencoders reconstruct each dimension of the input by passing it through the network. It may seem trivial to use a neural network for the purpose of replicating the input, but during the replication process, the size of the input is reduced into its smaller representation. The middle layers of the neural network have a fewer number of units as compared to that of input or output layers. Therefore, the middle layers hold the reduced representation of the input. The output is reconstructed from this reduced representation of the input.

TwitterSentimentAnalysis

Sentiment analysis (text mining and opinion mining) uses Natural Language Processing to determine whether data is positive, negative or neutral. Sentiment analysis is often performed on textual data to help businesses monitor brand and product sentiment in customer feedback, and understand customer needs.

AdityaTheDev/NudityDetection-Using-Deeplearning

Nudity/pornography detection using deeplearning. This model is trained using pretrained VGG-16. To know more about this check the readme file below

AdityaTheDev/FaceGenerationUsingVariationalAutoencoder

VARIATIONAL AUTOENCODERS are Generative model. A Generative Model is a powerful way of learning any kind of data distribution using unsupervised learning and it has achieved tremendous success over the past few years.

AdityaTheDev/BrainTumorSegmentation-using-Transfer-Learning

Brain Tumor Segmentation using Transfer learning. Image segmentation is the task of clustering parts of an image together that belong to the same object class. This process is also called pixel-level classification. Brain Tumor Segmentation is a multi-class problem and this model classifies Gliomia tumor, Meningomia Tumor, Pituitary tumor and No tumor with an accuracy of 93%.

AdityaTheDev/ConvolutionNeuralNetwork-To-Predict-SkinCancer

Convolution Neural Network to predict Skin cancer. Skin cancer is considered as one of the most dangerous types of cancers and there is a drastic increase in the rate of deaths due to lack of knowledge on the symptoms and their prevention. Thus, early detection at premature stage is necessary so that one can prevent the spreading of cancer. Skin cancer is further divided into various types out of which the most hazardous ones are Melanoma, Basal cell carcinoma and Squamous cell carcinoma. This project is about detection of skin cancer using machine learning and image processing techniques. This model takes in image as input and tells you whether your skin cancer is Malignant or Benign. I got this dataset online. I trained this model for 25 epochs and achieved an accuracy of 89%. The Convolution Layer extracts the features of the images and is passed through a Deep Neural Network which uses Relu and sigmoid Activation functions to give us the final Output.

AdityaTheDev/Covid-19-Prediction-Using-CNN

Convolution Neural Network to predict Covid-19. This is a CNN model which predicts whether you have Healthy or you have Coronavirus or you have Pneumonia. I implemented CNN from Scratch and I implemented VGG-16 architecture. This model takes your CT scan report as input and will tell you the result. This Convolutional layer Connects to DeepNeuralNetwork. I found this image dataset(CT scan of patients) online and trained the model for 70 epochs using Softmax function in the output layer. If I had got a much more large image dataset(CT scan of patients) then I could have increased the accuracy a bit more. This model has the potential to become a breakthrough invention in the field of medical industry.

AdityaTheDev/ImageDenoising-Using-Autoencoders

I built a Denoising Autoencoder to remove noise from the image. Image Denoising is the process of removing noise from the Images The noise present in the images may be caused by various intrinsic or extrinsic conditions which are practically hard to deal with. The problem of Image Denoising is a very fundamental challenge in the domain of Image processing and Computer vision. Therefore, it plays an important role in a wide variety of domains where getting the original image is really important for robust performance.

AdityaTheDev/NaivesBayesClassifier-To-Classify-IrisFlower

Naives bayes classifier to classifier to classify Iris Flower. This is the most prominent Machine Learning dataset available online. There three Iris Flowers namely Iris Setosa, Iris Versicolor and Iris Virginica . The Iris Dataset contains four features (length and width of sepals and petals) of 50 samples of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). These measures were used to create a linear discriminant model to classify the species. The dataset is often used in data mining, classification and clustering examples and to test algorithms. Naives Bayes worked wonderfully and achieved an accuracy of 100%.

AdityaTheDev/ReconstructionOfImage-Using-DeepAutoEnccoders

Autoencoder is a type of neural network where the output layer has the same dimensionality as the input layer. In simpler words, the number of output units in the output layer is equal to the number of input units in the input layer. An autoencoder replicates the data from the input to the output in an unsupervised manner and is therefore sometimes referred to as a replicator neural network. The autoencoders reconstruct each dimension of the input by passing it through the network. It may seem trivial to use a neural network for the purpose of replicating the input, but during the replication process, the size of the input is reduced into its smaller representation. The middle layers of the neural network have a fewer number of units as compared to that of input or output layers. Therefore, the middle layers hold the reduced representation of the input. The output is reconstructed from this reduced representation of the input.

AdityaTheDev/BlurRemovalUsingAutoencoders

BlurRemoval-Using-an-Autoencoder Are you poor at taking photos Just like me? Here I have made a Deep learning model using Autoencoder architecture to remove unwanted blur from the image.

AdityaTheDev/covisense.github.io

AdityaTheDev/TwitterSentimentAnalysis

Sentiment analysis (text mining and opinion mining) uses Natural Language Processing to determine whether data is positive, negative or neutral. Sentiment analysis is often performed on textual data to help businesses monitor brand and product sentiment in customer feedback, and understand customer needs.

AdityaTheDev/CalculatorApp

This is a simple calculator app created by using Android Studio. This app can perform all simple operations. Used Java language to develop this app. I created this app to practice my Android Studio.

AdityaTheDev/CalculatorUsingNodejs

This is a basic calculation website made using Nodejs and ExpressJs. Deployed the website on heroku. Please visit the Master branch for the code https://calculatorusingnodejs.herokuapp.com/

AdityaTheDev/CalculatorWebApp

Created a Calculator website using HTML, CSS and JavaScript. This is a fully functional calculator with fantastic CSS UI. It also has slide in animation. The logic of calculator was built using JavaScript,

AdityaTheDev/CarFrontEnd

AdityaTheDev/commclassroomOP

AdityaTheDev/ConfessionForms

Confession Forms where people can write confessions. My First Web Development project using HTML, CSS and integrated an API to receive the user inputs to my mail.

AdityaTheDev/DakshRecruitmentWebsite

https://adityathedev.github.io/DakshRecruitmentWebsite/

AdityaTheDev/FetalHealthClassification-Using-SupportVectorMachine

Reduction of child mortality is reflected in several of the United Nations' Sustainable Development Goals and is a key indicator of human progress. The UN expects that by 2030, countries end preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce under‑5 mortality to at least as low as 25 per 1,000 live births. Parallel to notion of child mortality is of course maternal mortality, which accounts for 295 000 deaths during and following pregnancy and childbirth (as of 2017). The vast majority of these deaths (94%) occurred in low-resource settings, and most could have been prevented. In light of what was mentioned above, Cardiotocograms (CTGs) are a simple and cost accessible option to assess fetal health, allowing healthcare professionals to take action in order to prevent child and maternal mortality. The equipment itself works by sending ultrasound pulses and reading its response, thus shedding light on fetal heart rate (FHR), fetal movements, uterine contractions and more.

AdityaTheDev/FlappyBirdGame

Created this FlappyBird game using HTML, CSS and JavaScript. Wrote functionality in JavaScript. Play this classic game and enjoy

AdityaTheDev/Git-Github-Practice

AdityaTheDev/GlaucomaDetection-

AdityaTheDev/InstagramFakeAccount-Prediction

Social media fake accounts and spam accounts have become a huge problem these days. Some had spammed me twice on Instagram. Here I have used various Machine learning techniques to spot the fake/spam accounts

AdityaTheDev/LoginPageUsingNodeJS

This is my first backend project tried creating a login page using Nodejs. Used HTML to create a pages index, login and register. Used Array DataStructure to store the information of the users. Created Express Server for server side app. Used the following NPM libraries express, bcrypt, passport, express-session, express-flash and method-override

AdityaTheDev/ML-CaPsule

Full Machine learning Guide basic to advance

AdityaTheDev/SentimentAnalysisOf1.6MillionTweets

This is the assignment given by NITK professor

AdityaTheDev/SentimentAnalysisTwitter-Reddit

AdityaTheDev/SmithNet

RNN network for anomaly detection

AdityaTheDev/SnakeGame

This is a classic game SNAKE GAME. This is the game first mobile game which was in NOKIA 3030. Developed this game using HTML, CSS and JavaScript. I have created different modules for different functionalities. I have got hands on experience of Software Development by doing this project.

AdityaTheDev/StockMarketLiveUpdates

Off late, I stumbled upon the fee technical stock market terms and I was fascinated to get to know abt it more. And I thought of creating a monitoring system which can fetch live updates of stocks. So I created this sheet by fetching the data from Google finance. This sheet contains Nifty 50, NSE 500 and Midcap 250 stocks. This would be beneficial for the Intraday traders and value investors