Breast Cancer Analysis And Prediction Using MlLast Updated on May 3, 2021
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
- ID number
- 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
Social Distance Monitoring System(Python, Deep Learning And Opencv)(Research Paper)Last Updated on May 3, 2021
Social distancing is one of the community mitigation measures that may be recommended during Covid-19 pandemics. Social distancing can reduce virus transmission by increasing physical distance or reducing frequency of congregation in socially dense community settings, such as ATM,Airport Or market place .
Covid-19 pandemics have demonstrated that we cannot expect to contain geographically the next influenza pandemic in the location it emerges, nor can we expect to prevent international spread of infection for more than a short period. Vaccines are not expected to be available during the early stage of the next pandemic (1), a Therefore, we came up with this system to limit the spread of COVID via ensuring social distancing among people. It will use cctv camera feed to identify social distancing violations
We are first going to apply object detection using a YOLOv3 model trained on a coco dataset that has 80 classes. YOLO uses darknet frameworks to process incoming feed frame by frame. It returns the detections with their IDs, centroids, corner coordinates and the confidences in the form of multidimensional ndarrays. We receive that information and remove the IDs that are not a “person”. We will draw bounding boxes to highlight the detections in frames. Then we use centroids to calculate the euclidean distance between people in pixels. Then we will check if the distance between two centroids is less than the configured value then the system will throw an alert with a beeping sound and will turn the bounding boxes of violators to red.
Research paper link: https://ieeexplore.ieee.org/document/9410745
Automated Generation Of Videos From News StoriesLast Updated on May 3, 2021
Recent advancements in internet, media capturing, and mobile technologies have let fast growing News industries to produce and publish News stories rapidly. In recent days News industry is trying lot to make their news stories attractive and more engaging to their readers. Youngsters these days often do not have much time to go through an entire news article to understand the content yet they want to know all the important elements the article. Recent surveys suggest that Millennials and other similar age group of people prefer news stories as videos over news as text. However manual generation of videos for each news article is considered costly and laborious. Hence there is a requirement for news video generation system that can create interesting, engaging, concise and high-quality news videos from text news stories with little or no human intervention.
This research will develop an end-to-end automated solution for generating videos from news articles. The system will have different NLP based components for automated news content analysis. Detection of key phrases from the news article will be done using NLP based or Deep learning solutions. Named entities in a news story such as person, time, place, brand etc can be automatically detected using NER for highlighting them in videos. Detection of emotions in news text or phrases for automated suggestion of background music or emojis for video production. In addition, famous tweets related to the news covered by the article can be detected and included in the final video. Also images and videos related to news content should be automatically discovered by crawling from internet and can be instantly used as background scenery in the video. This effort will also consider the analysis of the aforementioned steps in a faster manner for real-time video production.
House Price PredictorLast Updated on May 3, 2021
Here we have taken the information of a valid housing data set consisting of information of 500+ Houses. By taking all attributes as factors we will predict the price of the house. We are going to take advantage of all of the feature variables available to use and use it to analyze and predict house prices. Here we have to predict the price of the house on the basis of the following attributes:
~lot size – Square feet of the house I need. (Numerical)
~Bedroom- How many bedrooms I need? (Numerical)
~bathroom – How many bathrooms I need? (Numerical)
~stories-How many stories building I need? (Numerical)
~driveway –Whether I need a driveway or not? (Binary)1 for yes and 0 for no.
~recreational room-Whether I need a rec room or not? (Binary)1 for yes and 0 for no.
~Gas hot water - Whether I need Gas Hot water or not? (Binary)1 for yes and 0 for no.
~full base- Whether I need a full base or not? (Binary)1 for yes and 0 for no.
~Air condition- Whether I need Air condition or not? (Binary)1 for yes and 0 for no.
By entering all these inputs of the attributes, and by using multivariate regression we will predict the house at price in $.
We have split the dataset into two parts training and testing set. Then by training the dataset we will use multivariate regression and predict the house of the price in the testing data set.
Here we have also compared actual and predicted price using Machine Learning
Convolutional Neural Network Application To Classify Identical Twins(December 2019-March 2020) (Python,Ml)Last Updated on May 3, 2021
-Transfer learning is employed to classify the images of identical twins.
-Transfer learning basically means using the knowledge acquired during a previous problem solving for finding a solution to a new problem in hand.
-Weights and biases values are used with some fine tuning.
-Pre-trained Convolutional Neural Network models which are developed for image recognition tasks on ImageNet data set, provided by Keras API is used as feature extractor preprocessor.
-All these models are proven to be very efficient in image recognition task.They have showcased very high accuracy on ImageNet dataset.The labels they have given for images were highly accurate with very less error percentage.
-Only convolutional base layers are used here,that is fully connected layers are not included here.
-Fully connected layer is not included and a new fully connected layer is addee at the end for the required categorisation of the data.
-During dataset building-collected images of the identical twins.Two categoried were defined in this way.
-VGG19 is used as a standalone program to extract features from the dataset.
-Feature vectors for training dataset is obtained and mean feature vector for both categories were calculated.
-Testing is done by comparing the feature vectors of testing data with mean feature vectors of each category using cosine similarity.
-Obtained a fair accuracy while testing.