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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...
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Virtualization in Cloud Computing

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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...
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Line Drawing Algorithm

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 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....
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