Artificial Intelligence and Machine Learning have empowered our lives to a large extent. The number of advancements made in this space has revolutionized our society and continue making society a better place to live in. In terms of perception, both Artificial Intelligence and Machine Learning are often used in the same context which leads to confusion. AI is the concept in which machine makes smart decisions whereas Machine Learning is a sub-field of AI which makes decisions while learning patterns from the input data. In this blog, we would dissect each term and understand how Artificial Intelligence and Machine Learning are related to each other. What is Artificial Intelligence? The term Artificial Intelligence was recognized first in the year 1956 by John Mccarthy in an AI conference. In layman terms, Artificial Intelligence is about creating intelligent machines which could perform human-like actions. AI is not a modern-day phenomenon. In fact, it has been around since the advent of computers. The only thing that has changed is how we perceive AI and define its applications in the present world. The exponential growth of AI in the last decade or so has affected every sphere of our lives. Starting from a simple google search which gives the best results of a query to the creation of Siri or Alexa, one of the significant breakthroughs of the 21st century is Artificial Intelligence. The Four types of Artificial Intelligence are:- Reactive AI – This type of AI lacks historical data to perform actions, and completely reacts to a certain action taken at the moment. It works on the principle of Deep Reinforcement learning where a prize is awarded for any successful action and penalized vice versa. Google’s AlphaGo defeated experts in Go using this approach. Limited Memory – In the case of the limited memory, the past data is kept on adding to the memory. For example, in the case of selecting the best restaurant, the past locations would be taken into account and would be suggested accordingly. Theory of Mind – Such type of AI is yet to be built as it involves dealing with human emotions, and psychology. Face and gesture detection comes close but nothing advanced enough to understand human emotions. Self-Aware – This is the future advancement of AI which could configure self-representations. The machines could be conscious, and super-intelligent. Two of the most common usage of AI is in the field of Computer Vision, and Natural Language Processing. Computer Vision is the study of identifying objects such as Face Recognition, Real-time object detection, and so on. Detection of such movements could go a long way in analyzing the sentiments conveyed by a human being. Natural Language Processing, on the other hand, deals with textual data to extract insights or sentiments from it. From ChatBot Development to Speech Recognition like Amazon’s Alexa or Apple’s Siri all uses Natural Language to extract relevant meaning from the data. It is one of the widely popular fields of AI which has found its usefulness in every organization. One other application of AI which has gained popularity in recent times is the self-driving cars. It uses reinforcement learning technique to learn its best moves and identify the restrictions or blockage in front of the road. Many automobile companies are gradually adopting the concept of self-driving cars. What is Machine Learning? Machine Learning is a state-of-the-art subset of Artificial Intelligence which let machines learn from past data, and make accurate predictions. Machine Learning has been around for decades, and the first ML application that got popular was the Email Spam Filter Classification. The system is trained with a set of emails labeled as ‘spam’ and ‘not spam’ known as the training instance. Then a new set of unknown emails is fed to the trained system which then categorizes it as ‘spam’ or ‘not spam.’ All these predictions are made by a certain group of Regression, and Classification algorithms like – Linear Regression, Logistic Regression, Decision Tree, Random Forest, XGBoost, and so on. The usability of these algorithms varies based on the problem statement and the data set in operation. Along with these basic algorithms, a sub-field of Machine Learning which has gained immense popularity in recent times is Deep Learning. However, Deep Learning requires enormous computational power and works best with a massive amount of data. It uses neural networks whose architecture is similar to the human brain. Machine Learning could be subdivided into three categories – Supervised Learning – In supervised learning problems, both the input feature and the corresponding target variable is present in the dataset. Unsupervised Learning – The dataset is not labeled in an unsupervised learning problem i.e., only the input features are present, but not the target variable. The algorithms need to find out the separate clusters in the dataset based on certain patterns. Reinforcement Learning – In this type of problems, the learner is rewarded with a prize for every correct move, and penalized for every incorrect move. The application of Machine Learning is diversified in various domains like Banking, Healthcare, Retail, etc. One of the use cases in the banking industry is predicting the probability of credit loan default by a borrower given its past transactions, credit history, debt ratio, annual income, and so on. In Healthcare, Machine Learning is often been used to predict patient’s stay in the hospital, the likelihood of occurrence of a disease, identifying abnormal patterns in the cell, etc. Many software companies have incorporated Machine Learning in their workflow to steadfast the process of testing. Various manual, repetitive tasks are being replaced by machine learning models. Comparison Between AI and Machine Learning Machine Learning is the subset of Artificial Intelligence which has taken the advancement in AI to a whole new level. The thought behind letting the computer learn from themselves and voluminous data that are getting generated from various sources in the present world has led to the emergence of Machine Learning. In Machine Learning, the concept of neural networks plays a significant role in allowing the system to learn from themselves as well as maintaining its speed, and accuracy. The group of neural nets lets a model rectifying its prior decision and make a more accurate prediction next time. Artificial Intelligence is about acquiring knowledge and applying them to ensure success instead of accuracy. It makes the computer intelligent to make smart decisions on its own akin to the decisions made by a human being. The more complex the problem is, the better it is for AI to solve the complexity. On the other hand, Machine Learning is mostly about acquiring knowledge and maintaining better accuracy instead of success. The primary aim is to learn from the data to automate specific tasks. The possibilities around Machine Learning and Neural Networks are endless. A set of sentiments could be understood from raw text. A machine learning application could also listen to music, and even play a piece of appropriate music based on a person’s mood. NLP, a field of AI which has made some ground-breaking innovations in recent years uses Machine Learning to understand the nuances in natural language and learn to respond accordingly. Different sectors like banking, healthcare, manufacturing, etc., are reaping the benefits of Artificial Intelligence, particularly Machine Learning. Several tedious tasks are getting automated through ML which saves both time and money. Machine Learning has been sold these days consistently by marketers even before it has reached its full potential. AI could be seen as something of the old by the marketers who believe Machine Learning is the Holy Grail in the field of analytics. The future is not far when we would see human-like AI. The rapid advancement in technology has taken us closer than ever before to inevitability. The recent progress in the working AI is much down to how Machine Learning operates. Both Artificial Intelligence and Machine Learning has its own business applications and its usage is completely dependent on the requirements of an organization. AI is an age-old concept with Machine Learning picking up the pace in recent times. Companies like TCS, Infosys are yet to unleash the full potential of Machine Learning and trying to incorporate ML in their applications to keep pace with the rapidly growing Analytics space. Conclusion The hype around Artificial Intelligence and Machine Learning are such that various companies and even individuals want to master the skills without even knowing the difference between the two. Often both the terms are misused in the same context. To master Machine Learning, one needs to have a natural intuition about the data, ask the right questions, and find out the correct algorithms to use to build a model. It often doesn’t requiem how computational capacity. On the other hand, AI is about building intelligent systems which require advanced tools and techniques and often used in big companies like Google, Facebook, etc. There is a whole host of resources to master Machine Learning and AI. The Data Science blogs of Dimensionless is a good place to start with. Also, There are Online Data Science Courses which cover the various nitty gritty of Machine Learning.

The traffic handling schemes that are in use today are fixed time allocated traffic signal which do not change on incoming traffic or fail to provide time allocation scheme over changing traffic. A solution is required to the traditional traffic signal problem. Thus there is need to make smart traffic control system which can identify types of vehicles in a video frame belonging to categories of car, truck, bikes and buses along with number of vehicles present to control traffic by adjusting traffic signal timing for each individual lane and send this data to its connected signals and alert them of incoming traffic to calculate respective time allocation for each individual lane by using deep learning algorithms and object detection. In this work vehicles are categorized into different class such as car, truck, bike, and bus based on our own dataset which contains labeled image dataset. This classification and object detection model can be used for traffic detection, vehicle detection and other respective fields of vehicle detection.

Explore how to practice real world Data Science by collecting data, curating it and apply advanced Deep Learning techniques to create high quality models which can be deployed in production. Use Keras and Pytorch libraries in python for applying advanced techniques like data augmentation, drop out, batch normalization and transfer learning

A deep learning–based computer vision training pipeline for car damage detection using a Co-DETR learner enhanced with CBAM Attention, Hybrid Loss, and Albumentations. Trains on Colab to identify and localize car body defects such as scratches, dents, and rust. Includes end-to-end model training and quantitative evaluation.

Abstract With the advancement of Deep Neural Networks (DNN), the accuracy of sound classification such as Urban Sound Classification, Environmental Sound Classification etc., has been significantly improved. In this project, we propose a model that uses Convolutional Neural Networks (CNN) to identify sound based on the spectrograms for different sound samples collected. The model can be used for detection of deforestation, detection of shooting in urban areas and detection of strange noises at odd hours in streets such as Air Conditioner, Car Horn, Children Playing, Dog bark, Drilling, Engine Idling, Gun Shot, Jackhammer, Siren, Street Music etc., Challenges Environmental sound work has two major obstacles, namely the lack of audio data labelled. Previous work focused on audio from carefully produced films or TV tracks from particular environments such as elevators or office spaces and commercial or proprietary datasets. Lack of fundamental vocabulary in Environmental Sounds work. This means that the classification of sounds in to the semantic groups may vary from study to study, making it difficult to compare results so the goal of this notebook is to address the two challenges mentioned above. Dataset The dataset is called UrbanSound8K and contains 8732 labelled sound excerpts (<=4s) of urban sounds from 10 classes: - The dataset contains 8732 sound excerpts (<=4s) of urban sounds from 10 classes, namely: Air Conditioner Car Horn Children Playing Dog bark Drilling Engine Idling Gun Shot Jackhammer Siren Street Music The attributes of data are as follows: ID Unique ID of sound excerpt Class type of sound Problem statement It will show how to apply Deep Learning techniques to environmental recognition sounds, focusing specifically on recognizing unique Environmental sounds. If we give an audio sample of a few seconds duration in a computer-readable format (such as a.wav file), we want to be able to determine whether it contains one of the target Environmental sounds with a corresponding classification accuracy score. Note: Loading audio files and pre-processing takes some times to complete with large dataset. To avoid reload every time reset the kernel or resume works on next day, all loaded audio data will be serialized into a object file. so next round only need to load the seriazed object file. Optional GPU configuration initialization

An end-to-end Self-driving car using CNN to map pixels from front-camera to steering angles on a simulator. This deep learning approach required minimum training data & the system learned to steer, with or without lane markings, on both local roads & highways, even with unclear visual guidance in various weather conditions. The vehicle could identify traffic signs & avoid collisions. Implemented NVIDIA's End-to-End Deep Learning Model for Self-Driving Car.

Image recognition, in the context of machine vision, is the ability of software to identify objects, places, people, writing and actions in images. Computers can use machine vision technologies in combination with a camera and artificial intelligence software to achieve image recognition. Image recognition is used to perform a large number of machine-based visual tasks, such as labeling the content of images with meta-tags, performing image content search and guiding autonomous robots, self-driving cars and accident avoidance systems. While human and animal brains recognize objects with ease, computers have difficulty with the task. Software for image recognition requires deep machine learning. Performance is best on convolutional neural net processors as the specific task otherwise requires massive amounts of power for its compute-intensive nature. Image recognition algorithms can function by use of comparative 3D models, appearances from different angles using edge detection or by components. Image recognition algorithms are often trained on millions of pre-labeled pictures with guided computer learning. Current and future applications of image recognition include smart photo libraries, targeted advertising, the interactivity of media, accessibility for the visually impaired and enhanced research capabilities. Google, Facebook, Microsoft, Apple and Pinterest are among the many companies that are investing significant resources and research into image recognition and related applications. Privacy concerns over image recognition and similar technologies are controversial as these companies can pull a large volume of data from user photos uploaded to their social media platforms.

This project focuses on developing an object detection system using the YOLOv5 deep learning framework. The primary goal is to create an efficient and accurate model that can identify cars in real-world images. The system involves collecting and labeling image datasets, training the YOLOv5 model, and integrating the model with a user interface

As automobile accidents rise, demand for high-performance collision detection systems rises. We offer a car crash detection system that employs dashboard camera video. Different data. The proposed vehicle collision detection system leverages an ensemble of information sources (e.g., dashboard cameras) to identify collisions.Multi-modal deep learning (i.e., both videos). These different viewpoints complement one other and improve detection since one view may contain knowledge the other doesn't. The proposed vehicle collision detection system will beat existing technologies, according to experiments. 80% of we trained and tested our model using the repository. Through testing and tuning, the model could identify accidents quickly and with 90% accuracy. The prediction method underperformed for numerous reasons. Dataset limitations. This method might have a substantial socioeconomic and environmental impact and foster innovation.

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The project aims to automate a car in a simulator using OpenCV and Deep Learning. A model processes input from the car's camera to identify lane lines and steer the car accordingly. The model is trained with CNN and OpenCV is used to preprocess image data. Keras and TensorFlow.

A deep learning model built with YOLOv8 to accurately identify and localize various types of car damage. Leveraging transfer learning and a comprehensive dataset, the model provides efficient damage assessment for the insurance industry. Deployed as a user-friendly web application using Streamlit.

Car Damage Detection Model is a full-stack AI system with React-Tailwind frontend, Flask backend, and a five-stage deep learning pipeline. It detects car damage, classifies its type, identifies affected parts, and assesses severity, generating precise Excel/CSV reports with time and accuracy tracking.

Applying deep learning to classify road traffic signs that are captured using the camera mounted in front of the car. We create a model that reliably classifies traffic signs, learning to identify the most appropriate features for this problem by itself. Model works with 98% accuracy

This project focuses on detecting three-wheel cars using deep learning and computer vision techniques. Leveraging the powerful RetinaNet architecture with a ResNet-50 backbone, the model is trained to accurately identify three-wheel vehicles in images and videos.

This project is a deep learning-powered Streamlit application that can automatically detect car damage and identify its location — whether it’s on the front or rear side, and whether it’s crushed, broken, or normal. It uses a fine-tuned ResNet-50 model trained on a labeled dataset of car images with various types of damages.

A deep learning-based Streamlit web app for detecting and classifying car damage from images using a fine-tuned ResNet50 model. The app supports multi-class classification to identify various damage types such as broken, crushed, and normal, along with location-based categories like front, rear damage.

This project implements a powerful real-time car and license plate detection system using deep learning and computer vision techniques. The system is capable of identifying vehicles and extracting license plates from video feeds, achieving high accuracy with both trained and untrained AI models.

🚀 Real-Time Object Detection using YOLOv8 Developed a deep learning-based object detection system using the YOLOv8 model to detect multiple objects in real-time from images and live video streams (webcam/CCTV). The model identifies and tracks objects like people, cars, animals, etc., with high accuracy and speed.

This project implements a Convolutional Neural Network (CNN) to classify images from the CIFAR-10 dataset. It focuses on multi-class image classification, where the model learns to identify objects such as airplanes, cars, and animals, providing hands-on experience with deep learning and computer vision techniques.

Data preprocessing and analysis were conducted to identify factors influencing car sale prices, utilizing Seaborn for insights. A TensorFlow/Keras model with Dense layers was built for price prediction, optimized for accuracy. Its effectiveness was confirmed through R2 score evaluation, demonstrating deep learning's predictive capabilities.

Logo identification is more challenging than object or logo recognition owing to the lack of original data. Businesses often use logos to identify themselves and their goods. Shapes, colours, text, and textures are often used. Logo recognition is important for several applications, including online brand management, copyright infringement, context-specific advertising placement, and vehicle recognition. Even while companies do not need to alter their logos often, the context in which they appear varies for each product of the same company. Changing backdrops, perspective distortions, warping, occlusions, colours, and sizes are some of the challenges with precise logo recognition. The growing number of goods (brands) with customised logos further complicates logo recognition. Logo recognition requires significant processing power to enable multi-class classification. The uses of vehicle logo recognition are many. The control unit system at military camps, government buildings, at crossroads and traffic signals, at checkpoints across the city and surrounding regions, and far beyond the nation's territory, are sensitive places. Humans can easily identify and distinguish logos in everyday settings. However, in densely populated cities with high vehicle and human densities, data breaches are common. To recognise and identify car logos, automated methods are needed. This project's main aim is to utilise deep learning models to ensure logo recognition without needing a lot of computing resources

Logo identification is more challenging than object or logo recognition owing to the lack of original data. Businesses often use logos to identify themselves and their goods. Shapes, colours, text, and textures are often used. Logo recognition is important for several applications, including online brand management, copyright infringement, context-specific advertising placement, and vehicle recognition. Even while companies do not need to alter their logos often, the context in which they appear varies for each product of the same company. Changing backdrops, perspective distortions, warping, occlusions, colours, and sizes are some of the challenges with precise logo recognition. The growing number of goods (brands) with customised logos further complicates logo recognition. Logo recognition requires significant processing power to enable multi-class classification. The uses of vehicle logo recognition are many. The control unit system at military camps, government buildings, at crossroads and traffic signals, at checkpoints across the city and surrounding regions, and far beyond the nation's territory, are sensitive places. Humans can easily identify and distinguish logos in everyday settings. However, in densely populated cities with high vehicle and human densities, data breaches are common. To recognise and identify car logos, automated methods are needed. This project's main aim is to utilise deep learning models to ensure logo recognition without needing a lot of computing resources