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Hidden Markov Model (HMM)

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Hidden Markov Model (HMM)  VIDEO LINK:  https://youtu.be/YIGCWNG8BIA A Hidden Markov Model (HMM) is a statistical model in which the system has hidden states that cannot be directly observed, but produce observable outputs. It is based on the Markov property, meaning the next state depends only on the current state. Video Chapters: HMM in Artificial Intelligence 00:00 Introduction 00:31 Statistical Model 00:54 HMM Examples 02:30 HMM 03:10 HMM Components 05:23 Viterbi Algorithm 06:23 HMM Applications 06:38 HMM Problems 07:28 HMM in Handwriting Recognition 11:20 Conclusion  HMM COMPONENTS A Hidden Markov Model (HMM) is a statistical model in which the system has hidden states that cannot be directly observed, but produce observable outputs. It is based on the Markov property, meaning the next state depends only on the current state. An HMM consists of states, observations, transition probabilities, emission probabilities, and initial probabilities. It is commonly used in a...

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 t he 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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