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AI & more.
A glimpse of the key projects related Genetic Algorithm, Computer Vision & Machine Learning
Computer Vision: Object Recognition
Statement and Objective
A project that aims at creating a generalized object recognition scanner that could recognize anything that it may get to scan using a smartphone. It will synthesize the scanned objects and present them using labels on a digital display screen. Along with camera recognition, this application will also indicate the direction and distance of the object from a person using it.
Benefits
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Accessibility: Empowering users with visual impairments or limited knowledge to identify everyday objects.
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Educational Tool: Serves as a learning aid for children and adults to explore and understand the world around them.
An Image representing the way the object recognition model understands objects on a laptop desk and chair
Data for training
More than 2000 Images of each item around us like a laptop, charger, table, book, lamp, etc.(data is available with me since I have been performing image processing ) with different gradients, colors, under different reflections, and light exposure.
For this purpose, data would be stored in the tuples in the form of files based on the range that has been mentioned above already.
IOT Modelling:
The Arduino model that I have used is a specific image recognition scanner that would recognize a specific set of objects that I would make it train with. The scanner would act as a tester that would recognize the object and would display the label of the object on the screen as illustrated in the example below. This would be able to predict new images based on the number of images of different types of objects that we would feed into it.
Natural Language Processing & Machine Learning: Predictive Analytics in Real Estate
project abstract.
This study aims to explore the use of the "Housing & Ocean Proximity" dataset to predict key real estate market indicators. It focuses on forecasting housing prices, crime rates, and identifying areas for potential urban revamping. By integrating machine learning algorithms with comprehensive housing data, including neighborhood demographics and economic conditions, the study aims to offer actionable insights for investors, urban planners, and policy makers, contributing to a more responsive and informed approach in the dynamic real estate sector.
domain: NLP & Machine Learnning
type: course project
duration: 5 months
team: 1 person ( individual project )
tools: Weka, LightSide
language: Python
