20BM224 – Bioinspired Soft Computing

20BM224 – Bioinspired Soft Computing

University

10 Qs

quiz-placeholder

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20BM224 – Bioinspired Soft Computing

20BM224 – Bioinspired Soft Computing

Assessment

Quiz

Biology

University

Practice Problem

Easy

Created by

Radhika Senthil

Used 2+ times

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10 questions

Show all answers

1.

MULTIPLE CHOICE QUESTION

1 min • 1 pt

Which of the following is a type of AI based on its capabilities?

Reactive

   Machines

Supervised

   Learning

Data Mining

Cloud Computing

2.

MULTIPLE CHOICE QUESTION

1 min • 1 pt

Which AI technique enables machines to learn from past experiences?

Blockchain

Machine 

   Learning

Cryptography

Big Data

3.

MULTIPLE SELECT QUESTION

1 min • 1 pt

What makes Generative AI more flexible than Traditional AI?

It can learn

     and create 

     new patterns

It does not

    require any  

   training data

It follows

    only pre-

   defined

   decision trees

It does not use

     neural networks

4.

MULTIPLE CHOICE QUESTION

1 min • 1 pt

How does Computational Intelligence differ from traditional AI?

Focuses on

    rule-based

    decision-

    making only

Includes

  adaptive

  learning and

  self-  

  improvement

Cannot

  handle

  uncertain or  

  imprecise

  data

Does not use

    machine

    learning

    techniques

5.

MULTIPLE CHOICE QUESTION

1 min • 1 pt

What part of the biological neuron receives signals from other neurons?

Axon

Dendrites

Synapse

Cell body

6.

MULTIPLE CHOICE QUESTION

1 min • 1 pt

In an artificial neuron, what is the role of the activation function?

To store data

    like memory

To determine

    whether a

    neuron  

    should fire

    based on

    input weights

To remove

     errors in a   

     neural network

To physically

    connect artificial

    neurons

7.

MULTIPLE CHOICE QUESTION

1 min • 1 pt

In an artificial neural network, what happens when the input weights and bias exceed a certain threshold?

In an artificial neural network, what happens when the input weights and bias exceed a certain threshold?

The neuron

    does nothing

The neuron

    activates and

    passes the

    signal forward

The network

    stops learning

The weights reset

    automatically

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