Sheekar Banerjee's Projects
Config files for my GitHub profile.
This is an Android mobile application prototype where user can upload a Tomographic Brain X-ray image into the system and get an AI based prediction out of it. The prediction mainly relates to the stage detection of Alzheimer's disease such as: Demented, Mild Demented, Very Mild Demented and Non Demented.
This is the Asia's First Bluetooth Submarine adorned with Arduino Microcontroller, Servo and DC motors. The source code is given in the container with proper explanation.
This is the first ever full fledged Autonomous Rail Crossing System in Bangladesh. The Arduino source codes are given in the container.
This embedded system based IoT project focuses over Barometric Pressure and Weight Sensors like BMP280 Pressure, Digital Electronic Weighing (Scale 1 kg) and GY-87 10 DOF MPU6050 HMC5883L BMP180 Sensor with the interfacing codes and mechanisms.
This project focuses over the interaction of multiple wired and wireless Biometric Sensors like AD8232 ECG Measurement Module, BH1750FVI Digital Light Intensity Sensor, TTL (GT-511C3) Fingerprint Scanner, HB100 Microwave Sensor 10.525 GHz Doppler RADAR Motion Detector and Heart Rate Pulse Sensor
This is a Brain Tumor Detection System where multiple types of Deep Learning Neural Networks like CNN and CNN VGG16 have been used to tune, train and test for achieving highest possibility of accuracies.
This is a Breast Cancer Detection project with unsupervised learning algorithmic approaches alongside Naive Bayes Classifier Algorithm, Logistic Regression and GaussianNB.
Rigorous implementations and iterative result analysis of different cutting-edge modified versions of EfficientNet architectures namely EfficientNet-V1 (b0-b7) and EfficientNet-V2 (b0-b3) with ultrasound image, named as CEIMVEN.
This is one kind of retrieval-based chatbot which uses predefined input patterns and responses. Here, I have used Deep Learning techniques with Keras, NLTK and Numpy Libraries.
This embedded system based IoT project focuses over Color, RGB and Gyroscopic Sensors like APDS-9930 RGB and Gesture, GY-9960LLC APDS-9960 RGB, RGB Full Color LED SMD Module and TCS230 TCS3200 Color Sensor with the interfacing codes and mechanisms.
Drowsiness detection is a safety technology that can prevent accidents that are caused by drivers who fell asleep while driving. The objective of this software is to build a drowsiness detection system that will detect that a personβs eyes are closed for a few seconds. This system will alert the driver when drowsiness is detected.