NetworkLast Updated on May 3, 2021
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Image ProcessingLast Updated on May 3, 2021
What is image processing ?
The aim of pre-processing is an improvement of the image data that suppresses unwilling distortions or enhances some image features important for further processing, although geometric transformations of images (e.g. rotation, scaling, translation) are classified among pre-processing methods here since similar.
Preprocessing refers to all the transformations on the raw data before it is fed to the machine learning or deep learning algorithm. For instance, training a convolutional neural network on raw images will probably lead to bad classification performances.
convolutional neural network
A convolutional neural network (CNN) is a type of artificial neural network used in image recognition and processing that is specifically designed to process pixel data. ... A neural network is a system of hardware and/or software patterned after the operation of neurons in the human brain.
CNNs are used for image classification and recognition because of its high accuracy. The CNN follows a hierarchical model which works on building a network, like a funnel, and finally gives out a fully-connected layer where all the neurons are connected to each other and the output is processed.
Problem description: Case study , We have a dataset has 3 subfolders inside it are single prediction contains only 2 images to test the model and prediction so that we know our CNN model is working , test set with 2000 images (1000 of dogs and 1000 of cats) where we will evaluate our model , training set contains 8000 images 4000 of cats and 4000 of dogs as we are going to train our CNN model on these images of dogs and cats . so basically our CNN model is going to predict whether the image given is of a a cat or a dog. By generating random number on google then choosing the image . Eg: cat
Prediction for CAT
PREDICTION FOR DOG
Web Base Application Heart Failure Prediction SystemLast Updated on May 3, 2021
In this situation, approximately 17 million people kill globally per year in the whole world because of cardiovascular disease, and they mainly exhibit myocardial-exhibit myocardial infarction and heart failure. Heart failure (HF) occurs when the heart cannot pump enough blood to meet the needs of the body.
In this heart prediction problem statement, we are trying to predict whether the patient's heart muscle pumps blood properly or not using Logistic Regression. In this project, a dataset is downloaded from the UCI repository and this dataset is real. this dataset is collected from one of the most famous hospitals is in the United Kingdom (UK) in 2015 and there are 299 patient records and 12 features(attribute) and one label. Based on that 12 features, we will predict whether the patient's heart working properly or not.
In this problem statement, we analyze a dataset of 299 patients with heart failure collected in 2015. We apply several machine learning & classifiers to both predict the patient’s survival, and rank the features corresponding to the most important risk factors. We also perform an alternative feature ranking analysis by employing traditional biostatistics tests and compare these results with those provided by the machine learning algorithms. Since both feature ranking approaches clearly identify serum creatinine and ejection fraction as the two most relevant features, we then build the machine learning survival prediction models on these two factors alone.
For model building we use various library packages like Pandas, Scikit learns (sklearn), matplotlib, Seaborn, Tensorflow, Keras, etc., then we will use data description, Data description involves carrying out initial analysis on the data to understand more about the data, its source, volume, attributes, and relationships. Once these details are documented, any shortcomings if noted should be informed to relevant personnel. after that, we use the data cleaning method for cleaning the dataset to check if there are any missing values or not and we split the dataset into training & testing purposes with 70%, 30% criteria. Then the next step is Model Building, The process of model building is also known as training the model using data and features from our dataset. A combination of data (features) and Machine Learning algorithms together give us a model that tries to generalize on the training data and give necessary results in the form of insights and/or predictions. Generally, various algorithms are used to try out multiple modeling approaches on the same data to solve the same problem to get the best model that performs and gives outputs that are the closest to the business success criteria. Key things to keep track of here are the models created, model parameters being used, and their results. And the last step is to analyze the result in this step we check our model score or accuracy by using Confusion Matrix and Model Score. For this model, we got 80% accuracy. In the future, we try to improve that accuracy. For model deployment, we use the python flask and based on that we build the web-based application.
Python Snake GameLast Updated on May 3, 2021
This game reminds everyone their childhood memories.
In this snake game, the player has to move the snake to the fruit in order to eat it. The score will increase once the fruit is eaten. Also, the length of the snake will increase if the snake eats the fruit. The game will get over if the snake touches itself.
The turtle and random modules are used in this game project. So as to install these libraries, simply type “pip install turtle” and “pip install random” on the command prompt.
Turtle library allows us to create pictures, diagrams in a virtual form whereas random module gives the value between the given range of it.
There are 3 functions defined in this game which is “change”, “inside function”, and “move” function. In change function, the x-axis and y-axis are defined. In inside function, the logic of the game is written and in the move function, movement to the snake is given.
There are 4 keys mentioned in the code “right, left, up, down”.
If the player presses the right key, the snake will move to right direction, If the player presses the left key , the snake will move to left direction, If the player presses the up key , the snake will move to upward direction, If the player presses the down key , the snake will move to downward direction and if the snake touches itself the game will get over.
Natural Language ProcessingLast Updated on May 3, 2021
The problem statement is about allocation of projects using given dataset. We are provided with some requirements like project details (project name, project location and required project skills) and
candidate details (candidate id, location, candidate skills and description). From the given dataset, we have to filter the perfect candidate based on the requirements and their skills. Our work is to check whether the candidate is having required skills to do the project and also determine the evaluation status based on their location. If suppose the candidates is having required skills and match the location, the candidate is selected for that project, if does not match we reject the candidate for that project. In such case the rejected
candidates are checked with other projects. The foremost step is to clean up the data to highlight attributes.
Cleaning (or pre-processing) the data typically consists of a number of steps like remove punctuation, tokenization and remove stop words. I have taken a set of keywords which is most related to the skills that’s given in the project based on certain criteria .To describe the presence of keywords within the cleaned data we need to vectorize the data by Bag of Words. We are going to filter the candidate skills according to the current trends. Based on their number of skills known(languages) they will be prioritized. So, we want to use NLP Toolkit to arrange the candidates by their preferences. By doing this process in the given dataset, we can able to filter 50% of data. If the skills of the prioritized candidates match with same location of the project, the similarities will be calculated and the candidate is selected for that project else the candidate is rejected.
Human Computer Interaction Using Iris,Head And Eye DetectionLast Updated on May 3, 2021
HCI stands for the human computer interaction which means the interaction between the humans and the computer.
We need to improve it because then only it would improve the user interaction and usability. A richer design would encourage users and a poor design would keep the users at bay.
We also need to design for different categories of people having different age,color,gender etc. We need to make them accessible to older people.
It is our moral responsibility to make it accessible to disabled people.
So this project tracks our head ,eye and iris to detect the eye movement by using the viola Jones algorithm.But this algorithm does not work with our masks on as it calculated the facial features to calculate the distance.
It uses the eucledian distance to calculate the distance between the previous frame and the next frame and actually plots a graph.
It also uses the formula theta equals tan inverse of b/a to calculate the deviation.
Here we are using ANN algorithm because ANN can work with incomplete data. Here we are using constructive or generative neural networks which means it starts capturing our individual images at the beginning to create our individual patterns and track the eye.
Here we actually build the neural network and train it to predict
Finally we convert it to mouse direction and clicks and double clicks on icons and the virtual keyboard.
As a contributing or moral individuals it is our duty to make devices compatible with all age groups and differently abled persons.