Short Notes

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Short Answer Type Questions

1. How is data important in AI-based medical imaging systems?

  • AI in medical imaging (like X-rays, CT scans, and MRIs) depends on large amounts of data to learn and make accurate predictions.

  • The more high-quality data available, the better the AI can recognize patterns of diseases (e.g., tumors, fractures).

  • Data helps AI models train, validate, and improve accuracy of diagnosis.

  • Without sufficient data, the AI system may give wrong or incomplete results.

Example: An AI tool trained on thousands of lung X-ray images can detect pneumonia more accurately.

2. How do some APIs help collect relevant data? Explain.

  • APIs (Application Programming Interfaces) allow different software systems to communicate and share data.

  • They help in automatic data collection from multiple sources such as websites, apps, or medical devices.

  • APIs ensure that data is gathered in a structured format, making it easy to analyze.

Example: A healthcare app can use an API to collect patient heart rate and step count data from a smartwatch.

3. How can data be collected through crowdsourcing? Explain.

  • Crowdsourcing means collecting information from a large group of people, usually via the internet.

  • Organizations invite volunteers or users to contribute data, which is then used for AI training.

  • It helps gather diverse, real-world data quickly and at a lower cost.

Example: During a disease outbreak, people can report symptoms through a mobile app, and this crowdsourced data helps AI track the spread of the disease.



Long Answer Type Questions


1. What do you understand by data? Explain its various types.

  • Data refers to raw facts, figures, or information that can be processed by computers to gain insights or make decisions.

Types of data:

  1. Text data 

  2. Image dataUnstructured Data 

  3. Audio Data 

  4. Video Data 

2. What are the various methods of collecting relevant data in AI project development?
AI projects use different data collection methods such as:

  1. Surveys & Questionnaires – Collecting responses directly from people.

  2. Sensors & IoT Devices – Gathering data from machines, wearables, or cameras.

  3. APIs (Application Programming Interfaces) – Pulling data from apps, websites, or external databases.

  4. Web Scraping – Extracting information from websites automatically.

  5. Crowdsourcing – Collecting information from a large group of people through online platforms.

  6. Existing Databases & Open Datasets – Using government, research, or public datasets, kaggle.

3. What are the various applications of data in AI? Explain.
Data is applied in AI for multiple purposes:

  • Training AI Models: Data is used to teach AI systems to recognize patterns (e.g., training a chatbot on past conversations).

  • Prediction & Forecasting: AI uses data to predict stock prices, weather, or disease outbreaks.

  • Decision-making: Data-driven AI assists in making accurate business, medical, or financial decisions.

  • Automation: AI uses data to perform repetitive tasks automatically, such as fraud detection.

  • Personalization: Data allows AI to recommend products, movies, or music tailored to user preferences.

Example: Netflix uses user data to recommend shows, while healthcare AI uses medical data to detect diseases early.