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Intelligent Traffic Management Using || AI & Metaheuristics || ~xRay Pixy

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Hybrid Artificial Intelligence and Metaheuristics for Smart City TRafci Management Problem Video Chapters: 00:00 Introduction 00:40 Smart Cities 01:14 Traditional Methods for Traffic Management 02:12 Hybrid Approach AI and Metaheuristics 02:47 STEPS for Hybrid  Traffic Management System 08:40 Advantages of Smart Traffic Management System 09:33 Conclusion
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How do Face Recognition Algorithms Detect Human Faces

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First, learn about FACE DETECTION. Then face recognition would be an easy task for you. Because most of the computer algorithms work first on face detection after that they can recognize. First learn, how to detect humans or anything you want to detect (objects/ birds/other). When your software will be able to detect it. Then train it for recognition. I worked on Human face recognition. First, I created software for human face detection. Once your software can detect it. You can easily train it for recognition/identification.
Learn Image Processing in MATLAB
https://www.youtube.com/playlist?list=PLVLAu9B7VtkZAknbwBKJAI9Ny7c0M3nTT
Face Detection Systems are used for the identification of a person from digital images. The face detection system can extract the different facial features from a digital image. A set of facial features extracted from a digital image includes eyes, nose, mouth, and face region. Face recognition is a difficult task due to the different facial expressions. With the help of a face detection system presence of a human face can be detected easily. For the detection of different facial features, the Viola-Jones algorithm is used in the proposed work. To make the task more manageable, Viola-Jones requires full view frontal upright faces.
video is helpful for those who are working on Face Detection or Face Recognition Projects. - In this video, Different Facial Features like Eyes, Nose, and Mouth are Detected from the Digital Images. - The algorithm used for Facial Features detection is: Viola-Jones Algorithm. - Viola-Jones Algorithm is used for the Face Detection Projects for Face Detection. - Viola-Jones Algorithm works on different parameters and MATLAB Functions. - In this video, you can learn how to use the Viola-Jones Algorithm for the Detection of Different Facial Features from the Digital Images. Tools used: 1.) MATLAB SOFTWARE 2.) Digital Images for testing In this video, we detected different facial features such as Eyes, Nose, and Mouth. The Algorithm used for face detection is the Viola-Jones algorithm. Different models are available for face recognition. These models can detect the face, eyes, and nose. But the detection process is very slow. To solve this problem, the viola-jones algorithm is applied to detect the different facial features. Viola-jones algorithm is to face and provides accurate results from the input digital image. Software Required - MATLAB and Digital Images for Testing purposes. Facial FEATURES: In any recognition system, the selection of feature extraction plays a vital role. There are a lot of feature extraction techniques proposed efficiency of the proposed system is not acceptable due to the lack of appropriate feature selection. Working: A framework for the purposed face detection system is designed. First, upload the test image from the database for feature detection. Than the viola-jones algorithm is applied to the input image for the detection of different facial features.

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PARTICLE SWARM OPTIMIZATION ALGORITHM NUMERICAL EXAMPLE

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Cuckoo Search Algorithm for Optimization Problems

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PSO Python Code || Particle Swarm Optimization in Python || ~xRay Pixy

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Bat algorithm Explanation Step by Step with example

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Grey Wolf Optimization Algorithm

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how is the LBP |Local Binary Pattern| values calculated? Step-by-Step with Example

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Grey Wolf Optimization Algorithm Numerical Example

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 Grey Wolf Optimization Algorithm Numerical Example Grey Wolf Optimization Algorithm Steps 1.) Initialize Grey Wolf Population. 2.) Initialize a, A, and C. 3.) Calculate the fitness of each search agent. 4.) 𝑿_𝜶 = best search agent 5.) 𝑿_𝜷 = second-best search agent 6.) 𝑿_𝜹 = third best search agent. 7.) while (t<Max number of iteration) 8.) For each search agent       update the position of the current search agent by the above equations end for 9.) update a, A, and C 10.) Calculate the fitness of all search agents. 11.) update 𝑿_𝜶, 𝑿_𝜷, 𝑿_𝜹 12.) t = t+1 end while 13.) return 𝑿_𝜶 Grey Wolf Optimization Algorithm Numerical Example STEP 1.  Initialize the Grey wolf Population [Initial Position for each Search Agent] 𝒙_(𝒊  )  (i = 1,2,3,…n)     n = 6 // Number of Search Agents  [ -100, 100] // Range Initial Wolf Position      3.2228     4.1553    -3.8197     4.2330     1.3554    -4.1212 STEP 2. Calculate Fitness for Each Search Agent.    Objective Function: F6(x)  = su
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Whale Optimization Algorithm Code Implementation || WOA CODE || ~xRay Pixy

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