Virtual Dental ClinicLast Updated on May 3, 2021
Ongoing under the guidance of Dr. Sateesh Kumar Peddoju, Department of Computer Science & Engineering from November 2020 to present. In this project we are creating a platform using Nodejs where the patients can consult with dentists regarding their symptoms in a virtual environment made available via both a web-based application and mobile-based application compatible on android and ios devices. The patient will be able to easily connect to the dentists for timely collaboration and consultation according to their time and space feasibility. The patients can consult with a dentist of their choice via audio/video streaming and text-based messaging. The patients can receive diagnosis and prescription at a time and place more convenient to the patient. Patients will have to upload their current symptoms and the dentists, on the other hand, will analyze the patient’s reports and prior records to write and upload the prescriptions. The application will also maintain patients records for future reference in a secure database. We ensured the functional and non-functional requirements and design for such an application with emphasis on efficiency, reliability, and security of the services provided by the application and the data stored. The developed application will allow the patients with a quick, easy, and secure way of consulting with a dentist of their choice.
Evolution Of Sars-Cov-2 And The Antibody Immune Response In Humans During InfectionLast Updated on May 3, 2021
Aim: to understand the evolution of the SARS-CoV-2 viral population in hosts during illness, in relation with the infectivity of the virus and the establishment of the humoral (antibody-based) immune response. This knowledge is important to identify trends in disease progression and help improve the treatment and post-treatment follow-up given to patients. Detailed data on the evolution of the SARS-CoV-2 population in relation with disease progression, antiviral treatment and viral shedding sites will also be useful in evaluating links with disease severity and the potential failure of antiviral treatments.
The aim is to elucidate the kinetics of SARS-CoV-2 shedding in patients with Covid-19, depending on the severity of the disease, age and existing comorbidities (shedding in upper respiratory tracts – from the nose to the larynx – and lower respiratory tracts – from the windpipe to the alveoli, or at non-respiratory sites), and also to analyze the kinetics of the viral load at respiratory and non-respiratory sites in confirmed cases according to severity, age group and existing comorbidities. The project also intends to determine the correlation between the development of the viral load and infectivity, and to understand the kinetics of introducing a neutralizing humoral response (the body's production of antibodies to neutralize the virus).
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
Password CheckerLast Updated on May 3, 2021
This can be the most secure way for you to check if your password has ever been hacked. This is a password checker which checks whether this password has been used before or not. and if it has been used then the number of times it has been found. It makes it easy for you to understand that your password is strong enough to keep or is it too light. Its working is pretty simple, in my terminal i write the python file with my code checkmypass.py followed by the password to check if its ever been hacked , its gonna check as many passwords as we list in the terminal. I have used passwords API (pawned password) and SHAH1 (algorithm) to hash the given password into some complex output which is hard to hack also only the first five characters of hash version of password has been used for super privacy so that the real one is safe. The concept of k-anonymity is used it provides privacy protection by guaranteeing that each record relates to at least k individuals even if the released records are directly linked (or matched) to external information. I have added this on my Github repository.
THIS CAN BE REALLY EFFECTIVE FOR SOME PERSONEL USE.
AtmLast Updated on May 3, 2021
Me and my friends have done this project with the help of mentor assigned to us.The project is about the performance of ATM machine developed by Python.
For this project we imported sqlite3 and tkinter as tk. We used Tkinter for GUI applications.Tkinter provides a powerful object-oriented interface to the Tk GUI toolkit. We have created user defined functions such as creating_db, insert_money, insert_atm, check_100, check_200, check_500, check_2000,wd_money, update_bal and main_page. When we run the code the GUI application is created.In this application we can see a note as 'Welcome to ATM' and in the next lines we can see 100/-,200/-,500/- &2000/- notes. If we want to insert money we can click on the option called insert money, if we want to withdraw money we can click on withdraw at the same time if we want to check the availability of respective notes we can click on Check Availability beside the notes. After checking for the availability of notes the result will be displayed on the Python shell. This Python shell is also known as REPL (Read, Evaluate, Print, Loop), where it reads the command, evaluates the command, prints the result, and loop it back to read the command again.For every insert or withdraw update will be done. By using all this we can perform the operation that is required. All this transaction details will be stored in SQLite.
I hope this would be helpful for the public.