Computer Vision Concepts: Image Processing, Transforms, and Models
Q1: Image Representation and Processing
In computer vision, image representation is the method of converting a real-world scene into a digital format that a computer can understand and process. A digital image is represented as a two-dimensional function f(x, y), where x and y denote spatial coordinates and f represents the intensity or channel values at that location. In grayscale images, each pixel stores a single intensity value; in color images, each pixel is represented using multiple channels
AI Threat Detection for Unknown and Large-Scale Attacks
Capabilities of AI for Unknown or Large-Scale Attacks
AI capabilities help networks detect attacks that evade traditional signature-based systems (unknown attacks) and handle the sheer volume of modern threats (scale) through the following mechanisms:
Behavioral Modeling
AI builds baseline (peacetime) models of every device, application, and service to understand normal behavior. This allows it to detect zero-day attacks (unknown threats) because they deviate from the established norm rather than relying
Read MoreFast R-CNN, GANs, Edge Detection and Core Image Processing Concepts
Fast R-CNN Multi-Stage Architecture and Benefits
Q. Explain the multi-stage architecture of Fast R-CNN and how it improves upon R-CNN.
Definition: Region-based Convolutional Neural Network
Fast R-CNN is an object-detection algorithm that improves R-CNN by using a single CNN and a multi-stage training architecture for faster and more accurate detection.
Multi-Stage Architecture of Fast R-CNN
Fast R-CNN works in the following stages:
Input Image
– The whole image is given as input once.
Shared Convolutional
Regression, Regularization and Time Series Concepts for ML
1. Univariate linear regression assumptions
- Linearity: The relationship between X and Y is linear.
- Independence: The residuals (errors) are independent of each other.
- Homoscedasticity: The variance of residuals is constant across all levels of X.
- Normality of errors: The residuals follow a normal distribution.
These assumptions are important because they ensure the reliability and accuracy of the linear regression model. If the relationship between X and Y is not truly linear, the model won’t capture
Read MoreAVL Tree and MinHeap Java Implementations with Complexity
AVL Tree Java Implementation
Balanced binary search tree (AVL) implementation in Java.
// AVLTree implementation
class AVLTree {
class Node {
int key, height;
Node left, right;
Node(int key) {
this.key = key;
this.height = 1;
}
}
Node root;
int height(Node n) {
return (n == null) ? 0 : n.height;
}
int balance(Node n) {
return (n == null) ? 0 : height(n.left) - height(n.right);
}
Node rotateRight(Computer Networks: Concepts, Topologies, Signals and Media
1. Introduction to Computer Networks (16 Marks)
Meaning of Computer Network
A computer network is a collection of two or more computers and devices connected together to share data, resources, and information using communication links.
Definition
A computer network is an arrangement of hardware and software that allows devices to communicate and exchange data.
Components of a Network
- Sender – Device that sends data
- Receiver – Device that receives data
- Transmission medium – Path for data (cable, air)