Bluespace Ecommerce Website Dbms Project

Last Updated on May 3, 2021

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A ecommerce website named bluespace using MongoDB as database, where person can put the details of their products which they want to sell(detail such as name ,image, description) and can buy the product from other people who put their product to sell.Node.js , Express.js(framework) and MongoDB (database) are used to build the website. Website consist of front page where user can provide its authentication(login ,sign in ) ,then dashboard where every product is available for sell along with a button to upload new product for sell by the user who logged in. Then on the show page where name ,image and description of the product will be visible and a buy button which on pressing will present a form to fill the details of the person ,location etc. to sell product.


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Human Computer Interaction Using Iris,Head And Eye Detection

Last Updated on May 3, 2021

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HCI focus on interfaces between people and computers and how to design, evaluate, and implement interactive computer systems that satisfy the user. The human–computer interface can be described as the point of communication between the human user and the computer. The flow of information between the human and computer is defined as the loop of interaction. It deals with the design, execution and assessment of computer systems and related phenomenon that are for human use. HCI process is completed by applying a digital signal processing system which takes the analog input from the user by using dedicated hardware (Web Camera) with software.

Eye tracking is the process of measuring either the point of gaze(where one is looking)or the motion of the eye relative to the head. An eye tracker is a device for measuring eye positions and eye movement. Eye trackers are used in research on the visual system, in psychology, in marketing, as an input device for human computer interaction, and in product design. There are a number of methods for measuring the eye movement. The most popular variant uses video images from which the eye positions are extracted. Eye movement are made using direct observations. It is observed that reading does not involve a smooth sweeping of the eyes along the text, as previously assumed, but a series of short stops(called fixations).All the records show conclusively that the character of the eye movement is either completely independent of or only very slightly dependent on the material of the picture and how it is made. The cyclical pattern in the examination of the picture is dependent not only on what is shown on the picture, but also on the problem facing the observer and the information that ones hopes to get from the picture. Eye movement reflects the human thought process; so the observers thought may be followed to some extent from records of eye movement. It is easy to determine from these records from which the elements attract the observers eye in what order, and how often.

We build a neural network here and there are two types of network:Feed-forward networkFeed back network


Using video-oculography, horizontal and vertical eye movements tend to be easy to characterize, because they can be directly deduced from the position of the pupil. Torsional movements, which are rotational movements about the line of sight, are rather more difficult to measure; they cannot be directly deduced from the pupil, since the pupil is normally almost round and thus rotationally invariant. One effective way to measure torsion is to add artificial markers (physical markers, corneal tattoos, scleral markings, etc.) to the eye and then track these markers. However, the invasive nature of this approach tends to rule it out for many applications. Non-invasive methods instead attempt to measure the rotation of the iris by tracking the movement of visible iris structures.

Methodology

To measure a torsional movement of the iris, the image of the iris is typically transformed into polar co-ordinates about the center of the pupil; in this co-ordinate system, a rotation of the iris is visible as a simple translation of the polar image along the angle axis. Then, this translation is measured in one of three ways: visually, by using cross-correlation or template matching, or by tracking the movement of iris features. Methods based on visual inspection provide reliable estimates of the amount of torsion, but they are labour intensive and slow, especially when high accuracy is required. It can also be difficult to do visual matching when one of the pictures has an image of an eye in an eccentric gaze position.

If instead one uses a method based on cross-correlation or template matching, then the method will have difficulty coping with imperfect pupil tracking, eccentric gaze positions, changes in pupil size, and non-uniform lighting. There have been some attempts to deal with these difficulties but even after the corrections have been applied, there is no guarantee that accurate tracking can be maintained. Indeed, each of the corrections can bias the results.

The remaining approach, tracking features in the iris image, can also be problematic. Features can be marked manually, but this process is time intensive, operator dependent, and can be difficult when the image contrast is low. Alternatively, one can use small local features like edges and corners. However, such features can disappear or shift when the lighting and shadowing on the iris changes, for example, during an eye movement or a change in ambient lighting. This means that it is necessary to compensate for the lighting in the image before calculating the amount of movement of each local feature.

In our application of the Maximally Stable Volumes detector, we choose the third dimension to be time, not space, which means that we can identify two-dimensional features that persist in time. The resulting features are maximally stable in space (2-D) and time (1-D), which means that they are 3-D intensity troughs with steep edges. However, the method of Maximally Stable Volumes is rather memory intensive, meaning that it can only be used for a small number of frames (in our case, 130 frames) at a time. Thus, we divide up the original movie into shorter overlapping movie segments for the purpose of finding features. We use an overlap of four frames, since the features become unreliable at the ends of each sub-movie. We set the parameters of the Maximally Stable Volumes detector such that we find almost all possible features. Of these features, we only use those that are near to the detected pupil center (up to 6 mm away) and small (smaller than roughly 1% of the iris region). We remove features that are large in angular extent (the pupil and the edges of the eyelids), as well as features that are further from the pupil than the edges of the eyelids (eyelashes).

We have used to track the eye movement and convert to mouse direction using eucledian distance which would greatly help the disabled people.I have also implemented a virtual keyboard.



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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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Long Term Tool

Last Updated on May 3, 2021

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My previous project was shear project project that is Long term tool .This tool is used by wind farm owners who want to know in which location it is going to give best profits.

Suppose A wants to start a wind farm business A is having money but he is not aware of wind speeds at particular location ,so he took help from B (The wind pioneers) wind pioneers uses sensor for every wind station to find the wind speed and wind direction. Here wind pioneers role is to record the data which contain wind speeds and wind directions for every hour.

wind pioneers measuring wind speeds at various heights of sensor like ws_120m,ws_100m. For each minute we have some observations ,for every hour the number of observations will increases ,so it is very large data to deal. so we cannot do manual calculations for analyzing this big data. So here we come up with one tool that is long term tool.

I worked on this project along with team this tool provide you interactive software for performing all the analysis like plots, correlation values, scatter plots for finding relationship between two variables. You can just simply download the files that you are working for. It will going to give you everything in detail.

Here we are taking Reference data as NASA data of past 30 years which contains wind speed and wind direction In order to predict the wind speeds of particular location for next 30 years by making use of linear regression model .

Here we are predicting wind speeds of next 30 years for particular location by taking reference data as NASA data.

We are performing linear model for various time periods 1hr,6hr,1 day,3day,7day,10 day,1 month. Again sometimes your weather file and climate file may be differ with time In order to compensate time period we are using time shifting for reference file.



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Credit Card Detection

Last Updated on May 3, 2021

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models trained to label anonymized credit card transactions as fraudulent or genuine. Dataset from Kaggle. In this project I build machine learning models to identify fraud in credit card transactions. I also make several data visualizations to reveal patterns and structure in the data.

The dataset, hosted on Kaggle, includes credit card transactions made by cardholders. The data contains 7983 transactions that occurred over of which 17 (0.21%) are fraudulent. Each transaction has 30 features, all of which are numerical. The features V1, V2, ..., V28 are the result of a PCA transformation. To protect confidentiality, background information on these features is not available. The Time feature contains the time elapsed since the first transaction, and the Amount feature contains the transaction amount. The response variable, Class, is 1 in the case of fraud, and 0 otherwise. Project Introduction

The approaches for the project are :

Randomly split the dataset into train, validation, and test set.
Do  feature engineering.
Predict and evaluate with validation set.
Train on  train set then predict and evaluate with validation set.
Try other different models.
Compare the difference between the predictions and choose the best model.
Predict on test set to report final result.

Data Description

I was able to accurately identify fraudulent transactions using a LogisticRegression model. Features V1, V2, ... V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. Feature 'Class' is the target variable with value 1 in case of fraud and 0 otherwise.To improve a particular model, I optimized hyperparameters via a grid search with 3-fold cross-validation

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Hyderabad House Price Predictor

Last Updated on May 3, 2021

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Hyderabad House Price Predictor


ML model which predicts the price of a house based on features like total Sq. ft area,total number of bedrooms,balconies etc.

The front-end of this model is made by boot-strap and Flask,where as the backend is a Machine learning model which is trained on the housing-price dataset and the algorithm used is Random-Forest

the model is hosted at------> https://homepricepredictor.herokuapp.com/



General Overview of the Project 


Starting of with the home page which is designed using bootstrap classes,here we in this template the general overview of the project is mentioned,along with that the parameters which are required for predicting the price of the house are also mentioned here,here's a glimpse of it