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Video Link Multi-Block Local Binary Pattern || Calculate LBP Corner Pixel Values ||  https://youtu.be/o8qfJWQ_FG0  Local Binary Patterns (LBP) is a simple and efficient technique used in image processing to describe the texture or patterns within an image. LBP is widely used for applications like face recognition and texture classification since it is easy to compute and very effective at capturing the texture in photos.  Step How LBP WORKS:  For each pixel in the image, LBP looks at the pixel’s neighbors, typically the 8 pixels surrounding it in a 3x3 grid. LBP compares each of these neighboring pixels with the center pixel. If the neighboring pixel has a value greater than or equal to the center pixel, it's marked as 1; otherwise, it's marked as 0. This comparison forms a binary number for the pixel.  The binary number is then converted into a decimal value. This value represents the texture pattern at that pixel. By doing this for every pixel in the image, LBP creates a new
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Neuro Fuzzy System |Soft Computing| ~xRay Pixy

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  Neuro-fuzzy hybrid system tutorial |Soft Computing| Neuro-Fuzzy Hybrid System (NFHS) - Soft Computing (Neural Network) An introduction to the Neuro-Fuzzy System. In the field of artificial intelligence, neuro-fuzzy refers to combinations of artificial neural networks and fuzzy logic. Neuro-Fuzzy Hybrid System is a combination of Neural Network and Fuzzy Logic. Neuro-fuzzy hybridization results in a hybrid intelligent system that synergizes these two techniques by combining the human-like reasoning style of fuzzy systems with the learning and connectionist structure of neural networks. Neuro-fuzzy hybridization is widely termed as the fuzzy neural network (FNN) or neuro-fuzzy system (NFS) in the literature. Neuro-fuzzy system (the more popular term is used henceforth) incorporates the human-like reasoning style of fuzzy systems through the use of fuzzy sets and a linguistic model consisting of a set of IF-THEN fuzzy rules. The main strength of neuro-fuzzy systems is that they are uni
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Learn How to Apply Ant Colony Optimization to Traveling Salesman Problem...

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Ant Colony Optimization Metaheuristics for the Traveling Salesman Problem Numerical Example In this video you will learn, How to Solve Traveling Salesman Problem (TSP) using Ant Colony Optimization Algorithm (ACO). Ant System for the Traveling Salesman Problem Chapters: Introduction : 00:00 Traveling Salesman Problem (TSP): 00:33 Ant Colony Optimization Traveling Salesman Problem Example: 02:11 Distance Calculation between Cities: 04:30 Solution Construction for Artificial Ants: 06:45 Ants Tour Comparison for TSP: 14:52 Conclusion: 15:45 Part 1: Learn Ant Colony Optimization Algorithm Ant Colony Optimization Algorithm step-by-step with Example (ACO) ~xRay Pixy https://youtu.be/qYXeMFCf1Gk Ant System for the Traveling Salesman Problem. Ant Colony Optimization is a Nature Inspired Approach. Ant Colony Optimization (ACO) algorithm is basically inspired by the foraging behavior of ants searching for suitable paths between their colonies and food source. Ant Colony O
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ALGORITHM DESIGN TECHNIQUES

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WHAT IS ALGORITHM DESIGN? Algorithm Design is a specific method to create mathematical process in solving various classical problems, real world complex problems. Techniques for designing and implementing algorithm design are design patterns. ALGORITHM DESIGN TECHNIQUES We have 5 base techniques that can be used to design any algorithm.  Divide and Conquer Greedy Method Dynamic Programming Back Tracking Branch and Bound WHAT IS DIVIDE AND CONQUER TECHNIQUE?  In branch and bound technique, we break the main problem into several sub-problems. Sub-problems are similar to original problem but smaller in size. To solve given problem algorithm call themselves to deal with subproblems. Once subproblems are solved recursively then combine these solutions to create a final solution for the original problem. Merge Sort Algorithm follow Divide and Conquer method. Step 01: Divide the main problem into subproblems [n/2]. Step 02: Solve the subproblems recursively.  Step 03: Combine the solutions to
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Shuffled Frog Leaping Algorithm (SFLA) Step-by-Step with Example ~xRay Pixy

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Shuffled Frog Leaping Algorithm (SFLA)  Video Timestamps: Introduction: 00:00 Shuffled Frog Leaping Algorithm (SFLA) Steps: 00:46 Shuffled Frog Leaping Algorithm (SFLA) Example: 02:00 Conclusion: 06:30 There are over 6000 different species of frogs. Frogs are found all over the world expect Antarctica. Shuffled Frog Leaping Algorithm (SFLA) is an Nature Inspired Swarm Based Metaheuristic Optimization Algorithm. Shuffled Frog Leaping Algorithm (SFLA) is inspired by frogs behavior. Shuffled Frog Leaping Algorithm (S.F.L.A.) is proposed by Eusuff and Lansey in 2003. To determine the optimum size of New Pipes (in the development of Network of pipes). Shuffled Frog Leaping Algorithm is basically inspired by the frogs behavior in finding food in the wetlands. Shuffled Frog Leaping Algorithm is combination of PSO and Memetic Algorithm. In Shuffled Frog Leaping Algorithm (S.F.L.A.), Population is Groups of Frogs and e ach Frog: Solution / Answer for the given problem. Shuffled Frog Leapi
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