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

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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
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introduction to Merkle-Hellman knapsacks Algorithm

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 Ralph Merkle and Martin Hellman Developed the first algorithm for Public Key for public-key Encryption, called the knapsacks Algorithm.

This algorithm is based on the Knapsack Problem.[this is actually a simple problem]

Given a pile of Items, each with different weights, is it possible to put some of them in a bag (i.e., knapsack) in such a way that the knapsack has a certain weight?

If M1, M2, M3,.., Mn are given values and S is the Sum, Find out bi so that:

           S = b1M1 + b2M2 + ... + bnMn

Each bi can be 0 or 1.  [1 indicates the item is in the knapsack and 0 indicates that it is not].

A Block of Plain text equal in length to the number of items in the pile would select the items in the knapsack. The ciphertext is the resulting sum. 

For example: if the knapsack is 1, 7, 8, 12, 14, 20 then the plain text and the resulting Ciphertext is shown as: 



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