SomnathNikam's Stars
android/architecture-samples
A collection of samples to discuss and showcase different architectural tools and patterns for Android apps.
friuns2/BlackFriday-GPTs-Prompts
List of free GPTs that doesn't require plus subscription
skydoves/PowerSpinner
🌀 A lightweight dropdown popup spinner, fully customizable with an arrow and animations for Android.
HashenUdara/edoc-doctor-appointment-system
This PHP-based open source project is a web application for booking medical appointments. Patients can use the platform to easily schedule appointments with their doctors, saving time and effort. The project's source code is open for anyone to use, modify, and distribute according to their needs.
gnbaron/signature-recognition
Verify the authenticity of handwritten signatures through digital image processing and neural networks. ✍️
ayushreal/Signature-recognition
Signature recognition is a behavioural biometric. It can be operated in two different ways: Static: In this mode, users write their signature on paper, digitize it through an optical scanner or a camera, and the biometric system recognizes the signature analyzing its shape. This group is also known as “off-line”. Dynamic: In this mode, users write their signature in a digitizing tablet, which acquires the signature in real time. Another possibility is the acquisition by means of stylus-operated PDAs. Some systems also operate on smart-phones or tablets with a capacitive screen, where users can sign using a finger or an appropriate pen. Dynamic recognition is also known as “on-line”. Dynamic information usually consists of the following information:
beyhangl/Signature_Recognition_DeepLearning
Signature recognition with Keras,Deep learning
UXScripts/Signature-Inspector
Offline Signature Recognition and Verification - with python and opencv
hassan-arif/lung-cancer-prediction
ML-based model with 99.67% accuracy. Predicts lung cancer risk using age, gender, lifestyle, and medical history. Flask web app included.
Aayushi-2808/Cervical_cancer_detection_using_ML
# Cervical_cancer_detection_using_ML # Introduction According to World Health Organisation (WHO), when detected at an early stage, cervical cancer is one of the most curable cancers. Hence, the main motive behind this project is to detect the cancer in its early stages so that it can be treated and managed in the patients effectively. # Flow of project is as explained below: This project is divided into 5 parts: 1. Data Cleaning 2. Exploratory Data Analysis 3. Baseline model: Logistic Regression 4. Ensemble Models: Bagging with Decision Trees, Random forest and Boosting 5. Model Comparison and results # Refer below for References: Link to basic information regarding cervical cancer : https://www.cdc.gov/cancer/cervical/basic_info/index.htm The dataset for tackling the problem is supplied by the UCI repository for Machine Learning. Link to Dataset : https://archive.ics.uci.edu/ml/datasets/Cervical+cancer+%28Risk+Factors%29 The dataset contains a list of risk factors that lead up to the Biopsy examination. The generation of the predictor variable is taken care of in part 2 (Exploratory data analysis) of this report. We will try to predict the 'biopsy' variable from the dataset using Logistic Regression, Random Forest, Bagging with Decision Trees and Boosting with XGBoost Classifier. # Results: Based on our Base model and The Ensemble Models we used, we observed - 1. After the entire process of training, hyperparameter tuning and tackling class imbalance was complete , we obtained the results as depicted through the graphics. 2. We observe that Bagging and Random Forest gives the highest accuracy and precision of 97.09 and 80% resp. 3. Plotting the Confusion matrix showed us that Random Forest using upsampling and class weights gives us 2 false positives and 3 false negatives with auc of 0.87 # Why random forest is the best model?? 1. So as we see, while comparing all of our models,RF has maximum f1_score and accuracy along with Bagging i.e. 76.2 n 97.09% resp. 2. And it also produces the same amount of false negatives with a recall of 72.73% just like all the other models. 3. But we still consider RF better coz of its added advantage that, the decision trees are decorrelated as compared to bagging leading to lesser variance and greater ability to generalize. # Conclusion: On observing the feature importance of the best model i.e random forest, we can see that the most important features are Schiller, Hinselmann, HPV, Citology, etc. This also makes sense because Schiller and Hinselmann are actually the tests used to detect cervical cancer. # Problems Faced: A major problem encountered while training the model was that it had too little data to train. On collaborating with all the hospitals in India, we can have enough data points to train a model with a higher recall, thus making the model better. # Scope of Improvement As next steps I would want to do exactly that, to deploy the model and refine it. We may also modify the number of the predictor variables, as it may well turn out that there are other predictors which may not be present in our current dataset. This can only be found by practical implementation of our predictions.
osofela/barber-system-final-project
BarberQ is a web application that I built in my last year in college. The idea came from a personal friend of mine who is a men's barber. She was thinking of starting up her own business and wanted to provide her clients with a way of booking appointments online. She wanted to make the business unique by providing clients with a more personal and richer experience. To make it unique she came up with the idea of separate boots for haircuts and also providing the client a music choice and beverage choice when making an appointment. So each client would feel like it's a private haircut and this will create trust between the client and barber. I mentioned also that there could be employee system in the web app where she can login and control her business, employees, etc. I used Laravel for the backend and AngularJS for the frontend. The first step of the web application was setting up the backend and building a REST API. During this, I was building requirements and figuring out the structure of the database. The backend is very important because it is holding all the data and we want the data to be structured correctly. Once the backend was done I started working on the frontend by creating a Barbers page where the admin barber can login and create new barbers, update barbers, etc. The next step was creating the appointments page where any barber can login and look at their appointments for the day, week, month. Appointments at the start were displayed in a table but after having a talk I changed it to display them on a calendar which would be easier for the users. Thanks to using Laravel it already provides authentication out of the box so I already had the register and login pages complete. For making an appointment this would be very much the same for the client and the barber. This page is a modal form where the client or barber can select the barber for the appointment, the client for the appointment, music choice, beverage choice, date, etc. The only difference is that the client when filling in the form won't have an option to select a client they will only be able to select the barber of their choice. After the appointment form was done I then spent time on the design and see what was more comfortable for the user. Lastly, I decided to integrate an analytics software called Mixpanel. I thought this could really improve the business and help answer some important questions in the business. I really enjoyed building this site and interacting with her to find out exactly what she wanted and how she wanted it to look. Building this app got me hooked on web development and development in general.
simonorozcoarias/ML_DL_microArrays
Here, we describe the comparison of the most used algorithms in classical ML and DL to classify carcinogenic tumors described on 11_tumor data base, obtaining accuracies between 76.97% and 100% for tumor identification. Our results bring up a more efficient an accurate classification method based on gene expression (microarray data) and ML/DL algorithms, which facilitates the prediction of the tumor type from a multiple cancer types scenario
android/betocq
BeToCQ (Better Together Connectivity Quality) test suite
yagmurerdogan/Navigation-Component
🧜🏼♀️ Simple project examples to understand Navigation Component
abhinavsri360/Edukalp
An AR/VR integrated android application for school students to learn and grasp concepts easily and quickly. :school:
Iamchibu/AI-ChatBot-Powered-Decision-Support-Systems-for-Sustainable-Argiculture
AI-Powered Decision Support Systems for Sustainable Argiculture using AI-Chatbot Solution in Python Programming Language.
marofES/Home-Automation-with-Arduino-in-Proteus-with-LCD-Display
How to Make Home Automation with Arduino in Proteus with Lcd Display Android Control Home Automation - Proteus simulation. Bluetooth Based Home Automation System Simulation on Proteus using Arduino Code Smart Home Automation Project using IoT | Final Year Project Ideas Wireless Home Automation using Arduino- Proteus Simulation IMPLEMENTATION OF HOME AUTOMATION USING ARDUINO IN PROTEUS SOFTWARE
pg815/Kidney_Cancer_Prediction_Using_Machine_Learning
Python code for Kidney Cancer Prediction Using ML Algorithms
ChihebDabebi/Elife
eLife: A smart city and residence app transforming urban living. Integrates with city services for real-time info on traffic, public transport, energy. Transforms homes into smart homes, enabling appliance control, energy monitoring, and security management.
KAdamczykk/ML-Synthea-lung-cancer-prediction
repo for ML project, using data from harward dataverse, about results of patients tested for lung cancer
sanjaygarag8648/CancerDiseasePredictionUsingML
The project “Cancer Disease Prediction” is used for the all the users to know about their health condition and find which type of Cancer Disease. The application “Cancer Disease Prediction” is the web application is developed using the Python, Django, Machine Learning models
DunZoulDev/SUKJAI-MobileApp-Android-Studio
Mobile application for merit activities in new normal life (AR, VR)
Narode-exe/Unity-Haptics-For-AR-VR-Application-Android
AR/VR learning project.
saroooo/X_Club
An android application, in which the users can search easily for a suitable activities, events, trips and nearest club for them . Using Android Studio, Java.
Sohelshaikh290/thyroid-cancer_prediction
using ml
SomnathNikam/NavigationComponentflow
SomnathNikam/NumberFactsApp
An Android App that provides the facts of any number or also generate the Random Facts about the numbers. It uses an api to do these all works
SomnathNikam/StylishChipBottomNavigationBar
Implementation of Easy Stylish Chip Bottom Navigation Bar Through Third Party Dependency
ThakurSaAbhay/EdVerse
This is an android application made on android studio giving the UI of an education app which is integrated with firebase for authentication and storage. This application is basically an edtech application which uses AR/VR for more interactive classes for user. It also has interactive animations in it's UI which makes it aesthetically better.
ThakurSaAbhay/Prakriti