AI Domains

AI can support healthcare through analysis and assistance, farming through crop or soil monitoring, and education through adaptive learning or feedback. Therefore all three listed areas are valid applications, making option D the complete answer. Selecting only one area would ignore the other legitimate uses described by the question.

A face-recognition system analyses visual features and patterns in an image or video and compares them with stored information to identify or verify a person. This data-based recognition is an application of Artificial Intelligence, often involving computer vision. A list, paper file, or simple lock cannot perform that analysis.

A voice assistant uses AI-related technologies to recognise speech, interpret language, determine the user’s intent, and produce an answer or action. A battery supplies energy and wires may provide connections, but neither understands spoken language. An ordinary fan has no such language-processing capability.

A recommendation system analyses information such as preferences, previous choices, searches, or behaviour to suggest content or products that may be relevant to a user. It supports personalisation, although its suggestions are not guaranteed to be perfect. Shutting down a computer, painting, and counting papers are unrelated functions.

Identifying an object in an image requires analysing visual information such as shapes, colours, edges, and patterns. This is visual recognition and is commonly associated with computer vision in AI. Numerical calculation processes numbers, while typing and printing are input or output activities rather than image-understanding abilities.

AI can analyse learning performance and suggest practice, explanations, or content suited to a student’s progress and needs. This supports personalised learning rather than treating every learner identically. Teaching everyone at one pace, ignoring needs, or blocking feedback removes the adaptation and support that make the educational application useful.

Cameras and sensors provide information about roads, signs, vehicles, pedestrians, and obstacles. AI can analyse that information to support perception, route planning, and driving decisions such as stopping or turning. Seat colour, tyre decoration, and window-glass weight are not the intelligent navigation tasks described in this example.

AI can analyse information about weather, soil, crop condition, and past yields to support decisions about sowing, irrigation, pest management, or crop care. Such advice should still be checked by farmers because conditions can change. The other options do not describe a farming-related AI application.

Pattern recognition is the process of finding repeated features, relationships, or regularities in data. AI can use such patterns to classify items, detect unusual cases, or make predictions. Merely storing data does not reveal its structure, and writing instructions or reducing energy use describes different activities rather than pattern recognition.

Prediction uses patterns learned from historical or current data to estimate a likely future event or value. For example, a model may estimate demand or rainfall from relevant past observations. Storage alone does not create a prediction, and resizing images or repeating fixed instructions does not infer a future possibility.

An AI translation system analyses words, sentence structure, and often context to produce an equivalent or meaningful expression in another language. It is not simply sorting words alphabetically. Converting handwriting to print is recognition or digitisation, and identifying objects in images is computer vision, not language translation.

The app in option A uses a person’s viewing history as data, identifies patterns in interests, and ranks or suggests content likely to be relevant. Because suggestions can differ among users, it is personalised. A fixed list is not personalised, and an alarm or printed timetable does not learn preferences or generate recommendations.

A voice assistant first processes spoken sound, converts it into words, interprets the meaning of the request, and then produces a suitable reply or action. The key AI ability described is language understanding, often supported by speech recognition and natural-language processing. Counting paper, painting, or opening locks is not shown by this example.

An AI system can combine weather, soil, crop, and historical data, identify patterns, and suggest choices such as a suitable crop, irrigation time, or sowing period. The farmer should still use local knowledge and judgement. Placing a chair, folding paper, or hanging a clock is a physical task, not data-based decision support.

A face-recognition system analyses visual input from a camera, extracts features such as shape and relative arrangement, and compares them with learned patterns to identify or verify a face. This is visual recognition, a computer-vision task. Sound amplification changes volume, while printing and decoration do not interpret image content.

In healthcare, AI can analyse medical images, laboratory results, symptoms, or other clinical data to identify patterns that may suggest disease. It supports trained professionals rather than replacing medical judgement, and results require careful validation. The other options are routine physical activities unrelated to disease detection.

A prediction model learns relationships or patterns from relevant examples and uses them to estimate an outcome for new input. For instance, past demand may help estimate future demand. The estimate can be uncertain and may fail when conditions change, but it is not based merely on luck, computer size, or random decisions without data.

A translation system processes words or speech in one language and expresses their meaning in another language. That is why option A is correct. Option B is speech recognition or transcription, where spoken language is changed into written text, usually in the same language. Image recognition and weighing are different tasks.

Classification means assigning items to categories according to their features or patterns. A spam filter examines characteristics such as words, sender details, or message patterns and places each email in a category such as spam or not spam. Option A therefore represents classification; deleting, sitting, or dismantling does not assign categories.

A self-driving vehicle can use cameras, sensors, and AI models to detect lanes, roads, vehicles, pedestrians, and obstacles. It then uses the detected information to support actions such as steering, slowing, or stopping. Option A is therefore a transport application. The other activities are ordinary manual actions and do not involve AI-based sensing or decision support.