UGC NET Paper 2 Computer Science – Unit 10: Artificial Intelligence (AI) PYQ (June 2024 Re-Exam)
| List I | List II |
|---|---|
| (A) The activation | (I) is called the delta function rule. |
| (B) The learning method of perceptron | (II) is one of the key components of the perceptron, as in the most common neural network architecture. |
| (C) Areas of application of artificial neural network include | (III) is always boolean, like a switch. |
| (D) The output of the perceptron | (IV) system identification and control. |
- (A)-(II), (B)-(IV), (C)-(III), (D)-(I)
- (A)-(IV), (B)-(III), (C)-(II), (D)-(I)
- (A)-(II), (B)-(I), (C)-(IV), (D)-(III)
- (A)-(III), (B)-(IV), (C)-(II), (D)-(I)
(A) Selection
(B) Initialization
(C) Crossover
(D) Mutation
(E) Evaluation
Choose the correct answer from the options given below:
- (A), (B), (C), (D), (E)
- (E), (A), (B), (D), (C)
- (B), (E), (A), (C), (D)
- (A), (C), (B), (D), (E)
(A) Weight initialization
(B) Feed forward
(C) Back Propagation
(D) Loss Calculation
(E) Weight Update
Choose the correct answer from the options given below:
- (A), (B), (D), (C), (E)
- (D), (B), (A), (C), (E)
- (A), (C), (D), (B), (E)
- (E), (C), (B), (D), (A)
| List I | List II |
|---|---|
| (A) Natural language processing | (I) A method of training an algorithm by rewarding desired behaviour and/or punishing undesired behaviour. |
| (B) Reinforcement learning | (II) System designed to emulate the decision-making abilities of a human expert. |
| (C) Support vector machine | (III) A branch of AI focused on understanding and generating human language. |
| (D) Expert system | (IV) A machine learning technique that finds the hyperplane that best separates different classes in a feature space. |
- (A)-(I), (B)-(II), (C)-(IV), (D)-(III)
- (A)-(III), (B)-(II), (C)-(I), (D)-(IV)
- (A)-(III), (B)-(I), (C)-(IV), (D)-(II)
- (A)-(II), (B)-(IV), (C)-(III), (D)-(I)
Directions (Q91–95): Artificial Neural Networks (ANNs) are computational models inspired by the human brain's neural networks. They consist of interconnected nodes, or neurons, organized into layers: an input layer, one or more hidden layers and an output layer. Each connection between neurons has a weight that adjusts as learning progresses, allowing the network to adapt and improve its performance. ANNs are particularly effective in recognizing patterns, making them valuable for tasks such as image and speech recognition, natural language processing and predictive analytics. Learning in ANNs typically involves training algorithms like back propagation, which minimize the error by adjusting the weights. As a subset of machine learning, ANNs have revolutionized the field of Artificial Intelligence by providing solutions to complex problems that traditional algorithms struggle with.
- To reduce error
- To secure the network
- To control the speed of data
- To add different layers
- To store data
- To adjust and improve network performance
- To control the speed
- To secure the network
(A) Natural Language Processing
(B) Image Processing
(C) Pattern Recognition
(D) Speech Recognition
Choose the correct answer from the options given below:
- (A) and (B) Only
- (B) and (C) Only
- (A), (B) and (C) Only
- (A), (B), (C) and (D)
- Quantum mechanics
- The human brain's neural network
- Computer hardware architecture
- Genetic algorithms
- Input layer
- Hidden layer
- Output layer
- Physical layer