Posts

New Post

Confusion Matrix with Real-Life Examples || Artificial Intelligence || ~...

Image
Learn about the Confusion Matrix with Real-Life Examples. A confusion matrix is a table that shows how well an AI model makes predictions. It compares the actual results with the predicted ones and tells which are right or wrong. It includes True Positive (TP), False Positive (FP), False Negative (FN), and True Negative (TN). Video Chapters: Confusion Matrix in Artificial Intelligence 00:00 Introduction 00:12 Confusion Matrix 03:48 Metrices Derived from Confusion Matrix 04:26 Confusion Matrix Example 1 05:44 Confusion Matrix Example 2 08:10 Confusion Matrix Real-Life Uses #artificialintelligence #machinelearning #confusionmatrix #algorithm #optimization #research #happylearning #algorithms #meta #optimizationtechniques #swarmintelligence #swarm #artificialintelligence #machinelearning

Virtualization in Cloud Computing

What is Virtualization? Virtual means not physically existing [made by software to appear]. Virtualization means the creation of a virtual version of something. What is Hypervisor? Hypervisor = Program for creating and running virtual machines. Hypervisor are used to control and monitor processes, memory, and other hardware resources.   How Virtualization works? Virtualization in cloud computing is a process in which users share data present in the cloud. Virtualization provides a virtual environment to users [virtual hardware, virtual software]. A hypervisor [virtual machine monitor – software that creates and maintains virtual machines] is connectivity between the server and the virtual environment. Hypervisor distributes the resources between different virtual environments. Popular hypervisors such as VMware’s vSphere, based on ESXi, and Microsoft’s Hyper-V. How Hypervisor works? A Hypervisor or VMM(virtual machine monitor) is a layer that exits between the operating system...

Line Drawing Algorithm

Image
 Line Drawing Algorithm We can specify points with an ordered pair of numbers ( x, y). Where, x = horizontal distance from the origin.      y = vertical distance from the origin.  Suppose 2 endpoints used to specify line at position (x1, y1) and (x2, y2). Line path between endpoints positions (x1, y1) and (x2, y2). The equation for straight line is given by: y = m . c + b Here, m = slope of the line            b  as y-intercept The first endpoint of the line as (x1, y1) The second endpoint of the line (x2, y2) We can calculate values for the slope m and y-intercept b with this equation: 𝑚=(𝑦2−𝑦1)/(𝑥2−𝑥1) b = y1 – m * x1. For any given x interval ∆x along a line. We can compute the corresponding y interval as ∆y as ∆y =m * ∆x  We can obtain x interval ∆x by ∆y.  ∆x = ∆y / m  DDA Algorithm Step 1. Input two Endpoints (x1, y1) and (x2, y2). Step 2: Calculate the difference between two endpoints....
More posts