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shubham.software

For this project, I registered a domain through name.com and set up a Linux Ubuntu instance on Amazon EC2 with Apache, MySQL, and PHP. The domain was pointed to EC2 VM. I created an "about me" homepage with HTML5 and a video, figure, background image, and navigation. I secured the site with HTTPS using a manual certification from Cloudflare.

skin-disease-detection-team-technophile

We are team technophiles and participated in 48hrs hackathon organized by Nirma University in collabration with Binghamton University. Our Problem Definition : To develop a solution, the first step is to understand the problem. The problem here is to develop an Application Programming Interface which can be easily integrated with Android and IOS to detect the skin disease without any physical interaction with a Dermatologist. The detected skin disease should be sent through whatsapp to a particular patient and doctor. Our college name: Pandit Deendayal Energy University Team Members: Rushabh Thakkar, Divy Patel, Denish Kalariya, Yug Thakkar, and Shubham Vyas. Project Details: We made an application which classifies the skin diseases into these given types healthy, lupus, ringworm and scalp_infections How did we make? The data given was analysed first. We came to conclusion that the data given was not enough so we searched for new datasets. We got these datasets: https://ieee-dataport.org/documents/image-dataset-various-skin-conditions-and-rashes https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T We segregated the datasets of harvard. Combined all the datasets and trained the tensorflow image classification model multiple times. Accuracy was not satisfying. Augmented the data to unbaised the model and the dataset would be balanced. Data Augmentation was done on the data given . We generated 800 images per disease. Again we had trained the model. Accuracy was good. Exported the .tflite and label.txt file. We imported the files into android studio We have used three python codes: data_removal.py This code is used to remove data randomly from the folder if there are more number of images than required. We just need to change total_files_req variable in the code to number of files required after deletion. data_augmentation.py This code is used to augment the data randomly from the folder if there are less number of images than required. We just need to change total_files_req variable in the code to number of files required after augmentation. We change various parameters of images like clearity, rotation, brightness, etc. image_classification_code.py This is the main code in which we have trained the model and exported it to run on the app Models we tried: efficientnet-lite0(USED in our project) efficientnet-lite1 efficientnet-lite2 efficientnet-lite3 efficientnet-lite4 API: TensorFlowLite Used Android studio for App development . Used Language = java We sync all the grade files. Changed the model files and update it with the new model Working model file name is model.tflite Tflite classifier working java files are CameraActivity.java CamerConnectionFragment.java ClasssifierActivity.java LegacyCameraConnectionFreagment.java Dataset: Uploaded on Github WORKING MODEL LINK: https://drive.google.com/file/d/1BnqfFInFkJJDkYDlmdj9VB601f7PjTdj/view?usp=sharing

skin-diseases-detection-hackbash

Dermatological Issues/disorders are most commonly spread worldwide. This can be caused by various fungal, bacterial, or skin allergies. Effective use of Emerging technologies like AI/ML can recognize such diseases. Computer Vision is one such platform that made the possibility of detecting the cause accurately through Images.The problem here is to develop an Application Programming Interface which can be easily integrated with Android app to detect the skin disease without any physical interaction with a Dermatologist.

social-determinants-health-analysis

sparkfoundation-internship

basic banking app using php

stockmarket-prediction

Stock Price Prediction Using Python & Machine Learning (LSTM).

stockmarketprediction_streamlitapp

Stock Price Prediction Using Python & Machine Learning (LSTM) deployment on Streamlit.

syntaxsquad

tictactoe

This is a TicTacToe game developed by java Applet.

waterwise

website

A flat simple design using CSS, HTML, and JS.

wifi-clientbasic

Using ESP32 as a middle box, I have setup the ESP32 to worked as both a WiFi client and a WiFi access point

wifiaccesspoint

Using ESP32, I setup a web server & access point which will switch LEDs on/off.