AI Search Algorithms: BFS, DFS, and Hill Climbing
Breadth First Search (BFS) and Depth First Search (DFS) Algorithms
Breadth First Search (BFS) is a graph traversal algorithm that visits all the vertices level by level. It starts from a source vertex and explores all its neighboring vertices before moving to the next level. BFS uses a Queue (FIFO) data structure.
BFS Algorithm
- Start from the source vertex.
- Mark the source vertex as visited.
- Insert the source vertex into a queue.
- Repeat until the queue becomes empty:
- Remove the front vertex from the queue.
Neural Network Architectures and Learning Algorithms
Convolutional Neural Networks (CNN)
CNN is a deep learning model mainly used for processing image data. It automatically extracts features such as edges, textures, and shapes from images.
CNN Architecture
Input Image
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Convolution Layer
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Activation (ReLU)
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Pooling Layer
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Convolution + Pooling
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Fully Connected Layer
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Output Layer
Working of CNN
- Input Layer: Receives the image.
- Convolution Layer: Extracts features using filters.
- ReLU Layer: Introduces non-linearity.
- Pooling Layer: Reduces image dimensions.
- Fully
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1.Explain the Candidate-Elimination algorithm with a suitable example. Describe how the version space is maintained.
The Candidate-Elimination Algorithm is a concept learning method in Machine Learning that identifies all hypotheses consistent with the training data. Instead of finding just one hypothesis, it maintains a version space—the set of all hypotheses that correctly classify the observed examples.
Concept of Version Space:
The version space lies between two boundaries:
S (Specific boundary)
Read MoreFinite Element Method: Principles, Applications and Accuracy
Concept of FEM and Procedure
The Finite Element Method (FEM) is a numerical technique used to solve complex engineering problems by dividing a large system into smaller, simpler parts called finite elements.
Instead of solving the whole structure at once, FEM solves each small element and combines the results to get an approximate solution.
Procedure of FEM
- Discretization (Meshing): Divide the entire domain into small elements.
- Selection of Element Type: Choose suitable elements (1D, 2D, 3D).
- Derivation
Soft Computing Fundamentals: Neural Networks, Fuzzy Logic, and Optimization Techniques
Fundamentals of Soft Computing and Optimization
Definition of Multivariate Functions
A multivariate function is a function that has two or more independent variables. It assigns a single output value to each ordered set of input values.
Challenges in Non-Linear Optimization
- Multiple Local Optima: Non-linear functions can have many local minima and maxima, making it difficult to find the global optimum.
- Complex Behavior: The function may be curved, irregular, or non-convex, which makes analytical solutions
Rough Sets, Pareto Optimality, and Swarm Algorithms Explained
24(a) Meaning of Indiscernibility
Two objects are indiscernible if they have same values for a chosen set of attributes.
Forms equivalence classes (granules).
Basis for lower/upper approximations in Rough Sets.
24(b) Reduct & Core
Reduct
Minimum subset of attributes giving same classification power as full set.
Removing any attribute → information loss.
Core
Intersection of all reducts
Contains attributes must for classification.
Example (Medical dataset)
Attributes: Headache, Muscle Pain, Temperature