Real-Time Face Mask Detector With Python,

Last Updated on May 3, 2021

About

1) Project Idea:- Real-Time Face Detector with python.

During pandemic COVID-19, WHO has made wearing masks compulsory to protect against this deadly virus.

In this project, we will build a model to detect whether a person is wearing a mask in real-time.

We will do this using the concepts of computer vision using the OpenCV library and Keras

Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled.

a)Category: Deep Learning, Machine Learning, Computer Vision

b)Programming Language: Python

c)Tools & Libraries: OpenCV

IDE: Google Colab

Front End: NA

Back End: Python

Prerequisites: Python, Deep Learning, Machine Learning

Intended Audience: Education, Developers, Data Scientists, AI professionals

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Enterprise Ai

Last Updated on May 3, 2021

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Enterprise AI is about enhancing the customer satisfaction index and to ensure customer stickiness to your organization. By infusing emerging technologies like artificial intelligence to engage and retain the set of customers. Using AI algorithm, we should address the use case. The business operation

processes like determining the customer sentiments from various different media like - Social media, Audio Calls, Video Calls, Images, Emails & Chats, interact with customers to provide quick and effortless

solutions, analyze and learn from buying behaviour to generate next best offer, ascertain customer retention and ensure lesser churn, derive AI-based Customer Segmentation, manage customer

touchpoints, evaluate customer feedback and engage with the customers. We provide a membership card to all the customers who purchase stocks in the store. By scanning the QR code the customer can fill the

feedback. Through the user can easily complete the feedback (Bad, Good, Very good) after purchasing. We are providing three categories (Bronze, Gold and Platinum) for our customers to categorize their

purchasing list to calculate the purchasing efficiency based on their quality ,they purchase. The customer who gives feedback as very good, they come under platinum category, best offers are provided to

them (free purchase for Rs.1000). Notifications will be sent to customers through the messages about the new products available along with its price. Best offers are also provided on festival occasions. We classify the feedback using classification algorithms like random forest to get the positive and negative feedbacks.

Negative feedback will be collected and rectified soon. Through this approach, the shopkeeper is able to get clear feedback about his shop easily.



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Natural Language Processing

Last Updated on May 3, 2021

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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.

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Government Fund Tracking System Using Blockchain

Last Updated on May 3, 2021

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The main idea behind the project is to track the funds hierarchically i.e from central government to the common man including in this chain. We have considered four hierarchical components which are: Central government, state government, Contractor, resource provider/dealer. In the beginning, the budgets which would get finalized in the house will be uploaded according to their respective category. After funds allocation state government will instigate the required projects by documenting them and will send the document to the central government. Now the Central government will verify the project details and if satisfied, they will grant the project funds to the state government else they can reject the project. After receiving funds from the central government, the state government will open the tenders for the contractor and by proper bidding system the contractor will be chosen for the specific project. As bidding and tender allocation will be carried out by an automation bidding system with no human intervention involved, it would reduce corruption. Government committee will check the amount of work done synchronously and will mark every progress by submitting a brief report to the hierarchical officer, who will add it to the blockchain. In this report the progress can be portrayed in the form of images, videos, written plan of the building or structure, etc. To get the payment the contractor will have to submit a form of his total spendings with proper distribution over the duration. This form details will then be checked by the respective authority of the state government and then will initiate the payment to the contractor. In this way doing work over a period gets paid, this process will repeat until a particular work is being done completely.

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Human Computer Interaction Using Iris,Head And Eye Detection

Last Updated on May 3, 2021

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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.

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Hospital Readmission

Last Updated on May 3, 2021

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Hospital readmissions of diabetic patients are expensive as hospitals face penalties if their readmission rate is higher than expected and reflects the inadequacies in health care system. For these reasons, it is important for the hospitals to improve focus on reducing readmission rates.

We have to Identify the key factors that will influence readmission for diabetes and to predict the probability of patient readmission.

A leading hospital in the US is suddenly seeing increase in the patient readmission in less than 30 days. This is serious concern for the hospital as it may indicate insufficient treatment or diagnosis when the patient was admitted first and later released under clean bill of health. Hence it is in Hospital’s interest to support their diagnosis by a better predictive model which we are going to build.

Here the objective is: Classify the patients treated by this hospital into two primary categories:

·      Readmitted within 30 days

·      Not readmitted

The dataset chosen is that available on the UCI website which contains the patient data for the past 10 years for 130 hospitals. The code has been written in Python using different libraries like scikit-learn, seaborn, matplotlib etc. Different machine learning techniques for classification and regression like Logistic regression, Random forest etc .have been used to achieve the objective.

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