Class 9 Computer Science – Chapter 5: Applications of Computer Science

Board: Federal Board (FBISE)

Section I: Multiple-Choice Questions (MCQs)

i. The process of taking a known dataset as input to train an algorithm to create a model for prediction is known as:

a) Natural Language Processing
b) Expert System
c) Supervised Machine Learning
d) Unsupervised Machine Learning

Correct Answer: (c) Supervised Machine Learning

ii. The AI technology that enables computer systems to obtain meaningful information from digital images or videos is called:

a) Image Recognition
b) Speech Recognition
c) Scanner
d) Computer Vision

Correct Answer: (d) Computer Vision

iii. Instant answers to website visitors’ questions through text or voice interaction are provided by:

a) Robot
b) Chatbot
c) Cloud Computing
d) Expert System

Correct Answer: (b) Chatbot

iv. The cloud computing model in which resources are owned by and used inside a single organization is called:

a) Private Cloud
b) Public Cloud
c) Hybrid Cloud
d) Remote Cloud

Correct Answer: (a) Private Cloud

v. The AI tool used for correcting spelling and grammar mistakes is:

a) ChatGPT
b) Lovo AI
c) Grammarly
d) Virtual Assistant

Correct Answer: (c) Grammarly

vi. The virtual assistant that belongs to Apple Inc. is:

a) Siri
b) Alexa
c) Google Assistant
d) Cortana

Correct Answer: (a) Siri

vii. The AI tool that converts text to speech is:

a) ChatGPT
b) Grammarly
c) Alexa
d) Lovo AI

Correct Answer: (d) Lovo AI

viii. The technology used in self-driving cars is called:

a) Natural Language Processing
b) Chatbot
c) Computer Vision
d) Virtual Assistant

Correct Answer: (c) Computer Vision

ix. The technology concerned with training computers to understand spoken and written words and take action is known as:

a) Virtual Assistant
b) Natural Language Processing
c) Language Analysis
d) Virtual Training

Correct Answer: (b) Natural Language Processing

x. The technology concerned with performing specific tasks with little or no human intervention using computer-controlled machines is called:

a) Computer Vision
b) Computer Intelligence
c) Robotics
d) Virtual Technology

Correct Answer: (c) Robotics

Section II: Short Response Questions (SRQs)

1. Briefly describe the positive impact of AI in our daily lives.

Artificial Intelligence automates routine tasks and provides personal assistance through smart virtual assistants such as Siri and Alexa.

It enhances productivity through smart recommendation systems, real-time language translation, and intelligent navigation tools.

Additionally, AI improves safety and convenience in daily activities through automated fraud detection and smart home automation.

2. Differentiate between supervised and unsupervised machine learning.

Supervised machine learning trains algorithms using labelled datasets in which both input features and target outputs are provided.

Unsupervised machine learning analyses unlabelled data to independently identify patterns, structures, and clusters without guidance.

While supervised learning focuses on classification and regression tasks, unsupervised learning focuses on grouping features and detecting anomalies.

3. Describe how computer vision applications help in the automation of tasks.

Computer vision enables automated systems to process and interpret visual data from camera sensors in real time.

It supports autonomous vehicle navigation, automated visual quality inspection on manufacturing assembly lines, and facial recognition systems.

By replacing manual visual checks, computer vision increases processing speed, reduces human fatigue, and minimizes errors.

4. Compare the use of Natural Language Processing (NLP) with computer languages for interaction with computers.

NLP allows humans to communicate with computers using everyday human languages, such as English or Urdu, through voice or text.

In contrast, computer languages require strict syntax rules and exact command structures to execute program instructions.

Thus, NLP makes computing more accessible to non-technical users, whereas programming languages are primarily designed for software development.

5. What is a robot? Mention four areas where robots can replace humans that are not mentioned in this unit.

A robot is a programmable electromechanical machine capable of carrying out complex tasks automatically with precision and autonomy.

Four areas include:

1. Deep-sea underwater exploration.

2. Nuclear waste decontamination.

3. Space exploration missions.

4. Agricultural harvesting.

In these fields, robots can protect human lives by working in hazardous environments and performing high-precision operations.

6. Is the use of the Grammarly AI tool a perfect replacement for manual proofreading?

Grammarly AI effectively identifies grammatical errors, typing mistakes, and sentence structure issues using automated algorithms.

However, it cannot fully understand deep contextual meaning, emotional intent, cultural sensitivity, or creative literary tone in the same way as human proofreaders.

Therefore, while it works as an efficient preliminary editing assistant, it cannot completely replace human proofreading.

7. What harm can be caused to people due to wrong identification by an AI-based facial recognition system during criminal investigations?

Incorrect facial recognition matches can lead to wrongful arrests, unjust detentions, and psychological trauma for innocent individuals.

They can also cause serious reputational damage, financial stress due to legal battles, and violations of personal civil liberties.

Furthermore, repeated false identifications can reduce public trust in law enforcement agencies and automated judicial processes.

8. Describe the social implications that can arise due to the misuse of personal information stored in computer systems by people who use virtual assistants.

Unauthorized extraction of personal voice recordings and behavioural data from virtual assistants can lead to serious privacy violations and identity theft.

Malicious individuals can misuse location histories and private conversations to carry out targeted phishing scams, blackmail, or stalking.

Such misuse of personal data reduces individual privacy and weakens public trust in smart technologies.

Section III: Extended Response Questions (ERQs)

1. Explain how AI can be beneficial in education and business.

Artificial Intelligence serves as a transformative technology in both educational institutions and commercial enterprises.

Education:

• Adaptive Learning Platforms: AI algorithms analyse individual student progress and provide customized study materials according to their learning speed.

• Automated Grading: Intelligent software grades objective assessments and essay submissions, giving teachers more time for direct student guidance.

• 24/7 Tutoring Assistants: AI chatbots provide round-the-clock academic explanations and homework support to students around the world.

Business:

• Operational Automation: AI streamlines supply chain management, customer relationship management (CRM), and automated inventory tracking.

• Predictive Analytics: Companies use predictive AI models to forecast sales trends, monitor changes in consumer demand, and allocate resources efficiently.

• Customer Service: AI-powered conversational bots handle large numbers of customer queries simultaneously, reducing the workload and costs of support departments.

2. Explain how AI-based technology can improve healthcare and help farmers increase crop production.

AI integration provides useful solutions to important real-world sectors.

Healthcare Improvement:

• Diagnostic Imaging: AI-based computer vision algorithms examine X-rays, MRIs, and CT scans to detect tumours, fractures, and early-stage diseases with high precision.

• Drug Discovery: Machine learning models analyse molecular structures rapidly, reducing the time and cost required to discover medicines.

• Remote Monitoring: Wearable smart devices continuously monitor patients’ vital signs and alert doctors before medical emergencies occur.

Agricultural Production:

• Precision Farming: Drones equipped with computer vision scan fields to monitor crop health, identify soil nutrient deficiencies, and assess water requirements.

• Pest and Disease Detection: AI mobile applications examine images of leaves to identify crop infestations early, allowing targeted pesticide application.

• Yield Forecasting: Predictive models analyse weather forecasts, soil conditions, and historical data to optimize planting schedules and harvest yields.

3. Describe the benefits of cloud computing. Differentiate between public and private clouds.

Cloud computing provides on-demand computing services, including servers, storage, databases, networking, and software, over the internet.

Key Benefits of Cloud Computing:

• Cost Efficiency: It eliminates the need for large initial investments in physical servers, maintenance teams, and data centres.

• Scalability: Organizations can increase or decrease IT resources according to user demand.

• Accessibility and Reliability: It provides access to shared files from different locations and supports automated data backups for disaster recovery.

Comparison: Public Cloud vs. Private Cloud

4. Compare traditional algorithms with AI-based algorithms that use machine learning. Explain why AI-based algorithms sometimes produce incorrect results.

Comparison:

• Traditional Algorithms: Follow explicit, step-by-step rules programmed directly by human engineers. They process inputs according to fixed instructions and produce predictable outputs without adapting to unseen data patterns.

• AI-Based Algorithms (Machine Learning): Learn patterns directly from training data to develop predictive models. They can adapt and improve their performance as more data becomes available.

Why AI-Based Algorithms Sometimes Produce Incorrect Results:

• Garbage In, Garbage Out: AI models depend heavily on data quality. If training datasets contain historical biases, missing information, or incorrect labels, the model may produce flawed results.

• Black Box Problem: Complex neural networks can be difficult to interpret, making it challenging for engineers to understand why an incorrect decision was generated.

• Hallucinations and Overfitting: AI models can learn noise from training data instead of useful patterns or generate false responses (hallucinations). This can create risks in critical fields such as automated surgery and aviation.

5. Describe the ethical issues in using the following AI tools.

a) ChatGPT

ChatGPT can create ethical concerns related to plagiarism, the spread of misinformation, bias in generated text, unauthorized use of copyrighted online material during model training, and academic dishonesty among students.

b) Lovo AI

Lovo AI can create voice-cloning risks, including unauthorized deepfake voice generation for financial fraud, impersonation of public figures without consent, and possible job displacement for human voice actors.

c) Virtual Assistants

Virtual assistants may collect continuous audio recordings, creating privacy concerns, unintended data sharing with third-party advertisers, and the possibility of unauthorized surveillance inside private homes.

6. Describe the social implications that AI algorithms may create in society.

The widespread use of AI technology can have several important social impacts:

• Job Displacement and Automation: Rapid automation of routine manual and intellectual tasks can threaten traditional employment in manufacturing, customer service, and transportation.

• Algorithmic Bias and Discrimination: AI systems used in hiring, credit scoring, and law enforcement can reinforce social inequalities if trained on biased datasets.

• Deepfakes and Misinformation: AI-generated fake videos and audio can undermine digital trust, threaten electoral security, and damage personal reputations.

• Digital Divide: Individuals and societies without access to advanced AI infrastructure may face increasing economic and educational inequalities.

7. What are expert systems? Describe the steps required in the development of expert systems.

An Expert System is an AI computer application that imitates the decision-making ability of a human expert in a specialized field, such as medical diagnosis or geological exploration.

It typically consists of a knowledge base, an inference engine, and a user interface.

Steps Required in the Development of Expert Systems:

1. Problem Identification: Define the specific field and problem that requires expert-level reasoning.

2. Knowledge Acquisition: Collect expert knowledge, operational rules, and practical experience from human experts through interviews and observation.

3. Knowledge Representation: Convert human expert knowledge into computer-readable rules, such as IF-THEN statements, and store them in the Knowledge Base.

4. Inference Engine and System Design: Develop the Inference Engine, which applies rules to user queries, along with the User Interface.

5. Testing and Validation: Test the system using real-world cases and compare its recommendations with those of human experts. Refine the rules until the system achieves satisfactory accuracy.

Notes by Sir Dilawar

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