MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Artificial neural networks (ANNs) consist of input, hidden, and output layers with connected neurons (nodes) to simulate the human brain. The existing nodes process and transmit input signals to the next nodes [1]. ANNs are nonlinear models to classify complicated relationships.

Detailed explanation-2: -Artificial neural networks (ANNs) are biologically inspired computer programs designed to simulate the way in which the human brain processes information. ANNs gather their knowledge by detecting the patterns and relationships in data and learn (or are trained) through experience, not from programming.

Detailed explanation-3: -The most obvious similarity between a neural network and the brain is the presence of neurons as the most basic unit of the nervous system. But the manner in which neurons take input in both cases is different.

Detailed explanation-4: -An artificial neural network (ANN) is similar, but a computing network in science that resembles the properties of the human brain. ANN can model the original neurons of the human brain, so its processing parts are called “artificial neurons.”

Detailed explanation-5: -They pass data in one direction, through different input nodes, until it comes to the output node. This kind of ANN computational model is utilized in technologies such as natural language processing (NLP) and computer vision.