Read the below passage and answer the questions. Artificial Neural Networks…
2024
Read the below passage and answer the questions.
Artificial Neural Networks (ANNs) are computational models inspired by the human brain's neural networks. They consist of inter-connected 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 backpropagation, 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.
Which of the following is/are the application area(s) of ANN?
(A) Natural Language Processing
(B) Image Processing
(C) Pattern Recognition
(D) Speech Recognition
Choose the correct answer from the options given below:
- A.
(A) and (B) Only
- B.
(B) and (C) Only
- C.
(A), (B) and (C) Only
- D.
(A), (B), (C) and (D)
Attempted by 84 students.
Show answer & explanation
Correct answer: D
Answer: All four listed areas — Natural Language Processing, Image Processing, Pattern Recognition, and Speech Recognition — are application areas of artificial neural networks.
Key ideas: Neural networks excel at learning patterns from data, so they are applicable across multiple domains.
Natural Language Processing — models such as RNNs and transformers are used for language modeling, translation, and sentiment analysis.
Image Processing — convolutional neural networks (CNNs) are widely used for tasks like image classification, object detection, and segmentation.
Pattern Recognition — ANNs are general-purpose pattern learners used for classification, clustering, and anomaly detection across many data types.
Speech Recognition — neural architectures like RNNs, CNNs, and transformers power speech-to-text, speaker identification, and acoustic modeling.
Therefore, the correct answer includes Natural Language Processing, Image Processing, Pattern Recognition, and Speech Recognition.