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Confusion Matrix with Real-Life Examples || Artificial Intelligence || ~...

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Learn about the Confusion Matrix with Real-Life Examples. A confusion matrix is a table that shows how well an AI model makes predictions. It compares the actual results with the predicted ones and tells which are right or wrong. It includes True Positive (TP), False Positive (FP), False Negative (FN), and True Negative (TN). Video Chapters: Confusion Matrix in Artificial Intelligence 00:00 Introduction 00:12 Confusion Matrix 03:48 Metrices Derived from Confusion Matrix 04:26 Confusion Matrix Example 1 05:44 Confusion Matrix Example 2 08:10 Confusion Matrix Real-Life Uses #artificialintelligence #machinelearning #confusionmatrix #algorithm #optimization #research #happylearning #algorithms #meta #optimizationtechniques #swarmintelligence #swarm #artificialintelligence #machinelearning
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Software Engineering

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 Software Engineering 

Software Engineering: Software Engineering (S.E.) is a profession dedicated to the designs, implementation, and modification of software. Applications of Software Engineering are: 
  1. Re-engineering of software
  2. Software Testing
  3. Software Maintainance 
  4. Software Analysis
  5. Software Design
  6. Software Implementation
The objective of software engineering is to produce good quality software, on time and within budget. To obtain this objective it is very important to focus on Software Quality and Software Development Process.
Software Characteristics are: 
  1. Reusability of the components.
  2. Softwares are not manufactured as hardware. 
  3. In the Software development process, there is no wear-out phase.
  4. Software is fixable.
Software Life Cycle Models: Software life cycle means the time period when a software product is conceived and when the software product is no longer available for use. The software Life cycle includes different phases:
  1. Requirement Phase
  2. Design Phase
  3. Implementation Phase
  4. Testing Phase
  5. Installation Phase
  6. Operation and Maintenance Phase
  7. Retirement Phase 
Software Requirement - > Software Design - > Software Implementation - > Software Testing - > Software Installation - > Software Maintenance - > Software Retirement. 

Software life cycle models in software engineering are often known as Software Development Life cycle (S.D.L.C). Different software life cycle models are:
  1. The Waterfall Model
  2. Increment Process Models
  3. Evolutionary Process Model
  4. Prototyping Model
  5. Spiral Model
  6. COCOMO Model - Constructive Cost Estimation Model
COCOMO Model: COCOMO (Constructive Cost Estimation Model) is a heuristic estimation technique proposed by Boehm. According to Bohem, there are 3 categories for software development based on development complexity: 
  1. Organic - Application. 
  2. Semidetached - Utility.
  3. Embedded - System Programs.
Software cost estimation should be done through 3 stages: Basic COCOMO, Intermediate COCOMO, and Complete COCOMO. COCOMO models are useful for project Time and Cost estimation. 







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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 (Particle Swarm Optimization) Example Step-by-Step

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 Particle swarm optimization (PSO) What is meant by PSO? PSO is a computational method that Optimizes a problem. It is a Population-based stochastic search algorithm. PSO is inspired by the Social Behavior of Birds flocking. n Particle Swarm Optimization the solution of the problem is represented using Particles. [Flocking birds are replaced with particles for algorithm simplicity]. Objective Function is used for the performance evaluation for each particle / agent in the current population. PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). Local and global optimal solutions are used to update particle position in each iteration. How PSO will optimize? By Improving a Candidate Solution. How PSO Solve Problems? PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). The population of Candidate Solutions (i.e., Particles). What is Search Space in PSO? It is the range in which the algorithm computes th...
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how is the LBP |Local Binary Pattern| values calculated? Step-by-Step with Example

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

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Grey Wolf Optimization Algorithm

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Grey Wolf Optimization Algorithm Numerical Example

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Bat algorithm Explanation Step by Step with example

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Whale Optimization Algorithm Code Implementation || WOA CODE || ~xRay Pixy

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