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

What is Cathode Ray Tube ?

 Display Devices:  Display devices are also known as Output devices. A commonly used output device in a graphics system is Video Monitor. The operations of most video monitors is based on the Cathode Ray Tube Design.  Cathode Ray Tube [CRT] CRT: Simplest version of CRT consists of a gas-filled glass tube in which two metal plates that is Cathode and Anode have placed. When a large voltage is placed across the electrodes, the Neutral Gas inside the Gas Tube Ionize into conducting plasma, and the current will flow as electrons travel from Cathode to the other side. CRT is a type of Display Device.  CRT are special electronic vacuum tubes that use a focused electron beam to Display Images.  Where Cathode Ray Tubes are Used? Television. Computers. Oscilloscopes. Radar Display. In video games Equipment. What is inside Cathode Ray Tubes? A CRT has a negatively charged terminal (i.e., Heated Filament).  The filament is contained inside a vacuum with a glass tube....

How to Detect Masked Face from Digital Images using Viola Jones Algorithm.

  How to Detect Masked Face from Digital Images using Viola Jones Algorithm.  Source Code : I = imread('4.jpg'); faceDetector = vision.CascadeObjectDetector; bboxes = step(faceDetector, I); IFaces = insertObjectAnnotation(I, 'rectangle', bboxes, 'Face'); figure imshow(IFaces), title('Detected faces'); Output: Video Link:  https://www.youtube.com/watch?v=bgnb8kLhoWs #faceDetection #imageProcessing #matlab
More posts