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Learn Face Recognition Step-by-Step using Local Binary Patterns (LBP) In this video you will learn, Face Recognition using Local Binary Patterns (LBP) . Video Timestamps: Introduction: 00:30 Face Recognition using LBP: 01:01 Conclusion: 08:22 Local Binary Pattern (LBP) Videos | Projects Click Here

JAYA Optimization Algorithm Step-by-Step with Numerical Example  JAYA Algorithm is very simple and new optimization algorithm used for solving constrained and unconstrained optimization problems. Jaya: An Advanced Optimization Algorithm. JAYA Algorithm is very simple and new optimization algorithm used for solving constrained and unconstrained optimization problems. It is Simple, Unique and Powerful Optimization Algorithm. JAYA algorithm is introduced by R.V. Rao in 2016. JAYA is a SANSKRIT word it means VICTORY. That's why this algorithm always tries to get closer to the source (i.e., reaching the BEST Solution) and at the same time tries to avoid the WORST Solution. All values at the end of iteration are maintained and these values become the INPUT to the next iteration. JAYA Algorithm is simpler than TLBO (Teaching Learning Based Optimization) algorithm. Author also compared this algorithm with latest approaches and found JAYA algorithm at RANK 01 for BEST and MEAN solution for

Face Recognition Using Local Binary Pattern (LBP) Multi-Block LBP is popular in texture recognition and is used for facial features extraction and detection has been used. The local binary operator is used for the calculation of binary patterns in digital images. The extracted features of the input images are displayed using the binary image. Binary images used two-pixel values and color black and white. The calculation of the local binary pattern is shown in Figure 3. A comparison of every neighboring pixel is done with the center pixel is done. If the neighbor pixel value is greater or equal (>=) to the center pixel value than we will assign 1 and if the neighbor pixel is smaller (<) than the central pixel than assign 0. Steps to calculate the binary patterns for face facial feature extraction and face detection are given below: Algorithm: Multi-Block LBP is used to encode the rectangular region’s intensity by using local binary patterns. Local Binary Pattern (LBP) looks at ni

Local Binary Patterns (LBP) Local Binary Pattern (LBP) Image Dataset Training for Face Recognition Local Binary Pattern (LBP) Feature Extraction Local Binary Pattern (LBP) Histogram Construction. Local Binary Pattern (LBP) Videos | Projects https://www.youtube.com/playlist?list=PLVLAu9B7VtkbmfbamE0kRutTMbPTlCYyM

Jaya Algorithm || JAYA OPTIMIZATION ALGORITHM   Jaya: An Advanced Optimization Algorithm. Video Timestamp's: Basic Introduction: 00:00 JAYA Algorithm Example: 00:55 JAYA Algorithm Application Areas: 05:07 JAYA Algorithm Mathematical Model: 05:25 JAYA Algorithm Pseudocode: 07:56 JAYA Algorithm is very simple and new optimization algorithm used for solving constrained and unconstrained optimization problems. It is Simple, Unique and Powerful Optimization Algorithm. JAYA algorithm is introduced by R.V. Rao in 2016. JAYA is a SANSKRIT word it means VICTORY. That's why this algorithm always tries to get closer to the source (i.e., reaching the BEST Solution) and at the same time tries to avoid the WORST Solution. All values at the end of iteration are maintained and these values become the INPUT to the next iteration. JAYA Algorithm is simpler than TLBO (Teaching Learning Based Optimization) algorithm. Author also compared this algorithm with latest approaches and found JAYA a

Constrained Engineering Optimization Problems In this video, we applied different Metaheuristic Optimization Algorithms on 3 different Constrained Engineering Design Optimization Problems E01, E02 and E03. E01: Welded beam design problem. E02: Speed Reducer design optimization problem. E03: Tension/Compression spring design optimization problem. All constrained engineering optimization problems have different Objective function, Decision variables and Constraints. We did not try to optimize SSA parameters, for each problem constraints are directly handled [it means IF Solution can not satisfy the constraints – we will consider it Infeasible Solution]. Three engineering problems are solved using Sparrow Search Algorithm (SSA). We also compared the results with respect to 3 Metaheuristic Algorithms: Particle Swarm Optimization Algorithm (PSO), Grey Wolf Optimization Algorithm (GWO) and Teaching Leaning Based Optimization Algorithm (TLBO). When we compared SSA with other algorithms, the p

Metaheuristic Optimization Algorithms in Big Data, Web Mining and Text Clustering. Metaheuristic optimization algorithms are best swarm intelligence methods and widely used today in Big Data, Web Mining AND Text clustering. Using metaheuristic optimization algorithms we can solve complex Machine Learning problems.  Clustering: Clustering is a common text mining technique. We can used clustering technique for the representation of Dataset that contain similarities between objects. We can use clustering in Web mining, Image Processing, Sentiment Analysis, Data Clustering, Text document clustering, and Text classification. Clustering technique is classified into 3 classes: 1.) Overlapping 2.) Partitioning 3.) Hierarchical  In Partitioning Process we can use metaheuristic optimization approaches. Partitioning process is used for the transformation of any given problem into optimization problem. Partitioning process is based on either minimization or maximization. Partitioning methods are a

Bacterial Foraging Optimization Algorithm (BFOA)  Bacterial Foraging Optimization Algorithm is a recently developed nature-inspired optimization algorithm, which is based on the foraging behavior of Escherichia coli or E. coli bacteria. Bacterial Foraging Optimization Algorithm Advantages: 1.) Used to solve Engineering Problems. 2.) Used to Solve complex real world Optimization Problems. About Escherichia coli or E. coli bacteria. Escherichia coli or E. coli bacteria lives in our intestine and they are also found in the gut of some animals. Most of the Escherichia coli or E. coli bacteria are harmless. But some can cause Diarrhoea, if you eat contaminated food or drink fouled water. Escherichia coli or E. coli bacteria is mainly associated with Food positioning, Urinary Tract Infection (UTI) - approximate 75%-95% UTI are caused by Escherichia coli or E. coli bacteria. Escherichia coli or E. coli bacteria causes certain symptom's: Vomiting's, Confusion, Diarrhoea, Abdominal Cram

Video Timestamp's: Introduction: 00:00 Economic Load dispatch Cost Calculation: 00:37 Particle Swarm Optimization Parameter: 01:50 PSO Initialization: 02:19 PSO Main Loop: 03:25 Output : 06:06 Conclusion: 06:33 Economic Load Dispatch Problem (EDP) Economic Load Dispatch Problem using Lambda Iteration Method learn how we can solve Economic Load Dispatch Problem using Lambda Iteration Method  Step-by-Step with Numerical Example. This is a simple Economic load dispatch of Real power with Example.  Transmission losses are not considered.   Economic Dispatch Solution By Lambda-Iteration Method. Topics Covered in this video:  Power System Types​ Load Center, Power Plants.​ Economic Dispatch Problem?​ Economic Dispatch Problem: Equality and Inequality Constraints. ​ Economic Dispatch Problem Objective.​ Economic Dispatch Problem Step-by-Step Explanation with Numerical Example​

 Local Directional Pattern How to Calculate Local Directional Pattern (LDP) Code? With Example |Kirsch Compass Mask| ~xRay Pixy Click here - >  WATCH NOW What are Local Directional Patterns? LDP = Local Directional Pattern. Local Directional Pattern is a descriptor i.e., used for Face Recognition.  What is Descriptor?  Descriptors rely on Gradient-based or intensity variation approaches detect Local Features (e.g.,  Edges, Blobs and Regions).  BLOB = Binary Large Object (i.e., the region of the image). Descriptors such as HOG, SIFT, SURF (rely on local gradient computation).  Binary Descriptors such as BRISK, ORB or FREAK (rely on local intensity differences). Local Directional Pattern ( LDP) Assign code for each pixel in the image.  Local Directional Pattern ( LDP) encoded image is divided into regions. How LDP Calculate?  For Each pixel in the image LDP computes an 8-bit binary code. 8-bit binary pattern is calculated by involving the local regions of the image of size 3x3 with Kr

In this video, you will learn how we can solve Economic Load Dispatch Problem using Lambda Iteration Method  Step-by-Step with Numerical Example. This is a simple Economic load dispatch of Real power with Example.  Transmission losses are not considered.   Economic Dispatch Solution By Lambda-Iteration Method. Topics Covered in this video:  Power System Types​ Load Center, Power Plants.​ Economic Dispatch Problem?​ Economic Dispatch Problem: Equality and Inequality Constraints. ​ Economic Dispatch Problem Objective.​ Economic Dispatch Problem Step-by-Step Explanation with Numerical Example​ Particle Swarm Optimization Algorithm for Solving Economic Load Dispatch Problem

Butterfly Optimization Algorithm (B.O.A) Butterfly optimization algorithm : a novel approach for global optimization It is a novel optimization technique that mimics the food foraging behavior of butterflies. Keywords Butterfly optimization algorithm ·Global optimization ·Nature inspired ·Metaheuristic ·Benchmark testfunctions ·Engineering design problem Butterfly are Flying Insects. About Butterfly Butterfly Features: Small Head, 2 compound eyes. Butterfly basically feed on Nectar from flowers. Adult Butterfly consume only liquid [nectar from flowers]. They use their Antenna to sense air from wind and fragrance. Butterfly can fly only when their temperature is 27℃ or 81℉.  Largest butterfly in the world: Queen Alexandra Birdwing. Butterflies also derive nourishment from rotting fruits, dung, decaying flesh, dissolved minerals in the dirt/ sand. Butterfly Lifecycle An Adult Butterfly lay eggs on the food plant. From Eggs to Larva [Larva consume plant leaves]. When metamorphosis complete.

Moth Flame Optimization Algorithm (MFOA) is inspired by moth's behavior in nature. Moth Flame Optimization Algorithm is a nature-inspired population-based algorithm used to solve real-life optimization problems. Video timestamps Introduction: 00:00 About Moths: 00:30 Moth Flame Optimization Algorithm (MFOA): 01:20 MFOA Mathematical Model: 02:50 Moth Flame Optimization Algorithm Steps: 04:19 Moth Flame Optimization Algorithm Assumptions: 05:15 Moth Flame Optimization Algorithm Example: 06:43 Conclusion: 08:28 Topics Covered in this video What is a moth-flame optimization algorithm? The mathematical model for moth flame optimization algorithm. How moth-flame optimization algorithm works? Certain features about moth-flame optimization algorithm. Certain Assumptions about moth-flame optimization algorithm. Moth-flame optimization algorithm Application areas. How to solve moth-flame optimization algorithm? Moth Flame Optimization Algorithm Applications Forecast the electricity consumpt

Knapsack Problem using Greedy Method Algorithm Design Techniques Divide and Conquer Greedy Method Dynamic Programming Back Tracing Branch and Bound Divide and Conquer:  Many algorithms are recursive in structure. To solve any problem, they call themselves recursively again and again [one or more times]. Three steps are followed by divide and conquer algorithms. 1.) Divide the problem into the number of sub-problems. 2.) Conquer the sub-problems by solving them recursively. 3.) Combine the solution to the sub-problems into the solution for the original problem. The greedy method  is the Straight design technique. It can be applied to a wide variety of problems. Obtain a subset that satisfies the same constraints.  Feasible Solution: If any subset satisfies these constraints.  Our GOAL: Find a feasible solution that either Maximize or Minimize the given Objective Function. A feasible solution that does this is known as OPTIMAL SOLUTION.   A feasible Solution is  any subset that satisfie

 Optimization Engineering - Metaheuristic Optimization Algorithms Optimization plays a very important role in science and engineering. Optimization aim is to find out the minimum or maximum value using any objective function or cost function. In optimization different Metaheuristic Algorithms are used to solve complex problems in various fields such as Engineering Problems, Medical Problems, Computer Problems, and different real-life problems that can not be solved using classical methods. Metaheuristic optimization algorithms are classified into two main categories as Single-based optimization algorithms and Population-based optimization algorithms.  Single-based Meta-heuristic algorithms are also known as Trajectory Algorithms. Single-based metaheuristic algorithms provide the single solution in every iteration. Single-based Metaheuristic algorithm examples: Tabu Search, Guided Local Search, Iterated Local Search, Stochastic Local Search, Iterated Local Search, Variable neighborhoo