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Self Paced

AI in Smart Cities and Infrastructure

Building the Cities of Tomorrow with AI: Smarter, Safer, and More Sustainable

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Enroll now for early access of e-LMS

MODE
Online/ e-LMS

TYPE
Self Paced

LEVEL
Moderate

DURATION
3 Weeks

About

Participants will learn how AI is applied in developing smart cities, from optimizing urban infrastructure and traffic management to enhancing public safety and sustainability efforts. The course focuses on real-time data analysis, predictive modeling, and IoT integration for smart city infrastructure.

Aim

This program explores how AI technologies are transforming urban planning, management, and infrastructure, focusing on intelligent systems for energy, transportation, security, and waste management. It equips learners with the knowledge to integrate AI into smart city frameworks.

Program Objectives

Understand AI’s role in transforming urban infrastructure and services.
Learn to implement AI systems for traffic, energy, and waste management.
Gain skills in AI-based public safety and urban sustainability.
Explore predictive analytics for smart city infrastructure optimization.
Develop practical experience through real-world case studies.

Program Structure

Program Structure: Smart Cities and AI

Module 1: Introduction to Smart Cities and AI

Chapter 1: Overview of Smart Cities: Concepts and Components
- Lesson 1.1: Definition and Key Characteristics of Smart Cities
- Lesson 1.2: Core Components: Infrastructure, Technology, and Services
- Lesson 1.3: The Role of Data in Smart Cities: Big Data, IoT, and AI
Chapter 2: Role of AI in Building and Managing Smart Cities
- Lesson 2.1: AI and its Role in Urban Management
- Lesson 2.2: AI-Enabled Urban Services: From Traffic to Healthcare
- Lesson 2.3: The Evolution of AI Applications in City Planning
Chapter 3: Case Studies: Smart Cities Worldwide
- Lesson 3.1: Singapore: A Global Leader in Smart City Innovation
- Lesson 3.2: Barcelona: A Blueprint for Smart Urban Living
- Lesson 3.3: Dubai: Pioneering Smart Governance and Technology-Driven Services

Module 2: AI for Traffic Management and Smart Mobility

Chapter 4: AI for Traffic Management and Transportation
- Lesson 4.1: AI for Real-Time Traffic Flow Analysis
- Lesson 4.2: Predictive Traffic Control Systems and Congestion Reduction
Chapter 5: Optimizing Public Transportation Networks with AI
- Lesson 5.1: AI for Route Optimization and Schedule Prediction
- Lesson 5.2: Enhancing Commuter Experience with AI Tools
Chapter 6: Autonomous Vehicles and Smart Mobility Solutions
- Lesson 6.1: Autonomous Vehicles: AI and the Future of Mobility
- Lesson 6.2: AI for Smart Mobility Ecosystems and Ride-Sharing Optimization

Module 3: AI in Energy Management and Sustainability

Chapter 7: AI for Energy Consumption Monitoring and Optimization
- Lesson 7.1: AI Tools for Real-Time Energy Consumption Tracking
- Lesson 7.2: Predictive Maintenance in Energy Systems with AI
Chapter 8: Smart Grids: AI for Demand Forecasting and Load Balancing
- Lesson 8.1: AI-Driven Smart Grids: An Overview
- Lesson 8.2: Energy Load Balancing with AI: Challenges and Solutions
Chapter 9: AI in Renewable Energy Integration
- Lesson 9.1: AI for Renewable Energy Forecasting and Grid Integration
- Lesson 9.2: Enhancing Energy Efficiency Through AI

Module 4: AI in Waste Management and Urban Sustainability

Chapter 10: Smart Waste Collection Systems Using AI
- Lesson 10.1: AI in Waste Collection Route Optimization
- Lesson 10.2: Reducing Waste with Data-Driven AI Solutions
Chapter 11: AI for Sorting and Recycling
- Lesson 11.1: Computer Vision in Recycling: Automating Sorting Processes
- Lesson 11.2: Robotics in Waste Management: AI Applications
Chapter 12: AI-Driven Sustainability Initiatives in Urban Environments
- Lesson 12.1: Leveraging AI for Urban Sustainability Metrics
- Lesson 12.2: AI for Resource Optimization and Circular Economy Initiatives

Module 5: Public Safety and Security in Smart Cities

Chapter 13: AI for Real-Time Surveillance and Monitoring
- Lesson 13.1: AI-Powered Surveillance: Technologies and Ethics
- Lesson 13.2: AI in Threat Detection and Urban Safety
Chapter 14: AI-Powered Emergency Response Systems
- Lesson 14.1: AI-Driven Emergency Dispatch and First Responder Systems
- Lesson 14.2: AI Applications in Predicting and Managing Urban Disasters
Chapter 15: Facial Recognition, Threat Detection, and Privacy Concerns
- Lesson 15.1: The Role of Facial Recognition in Smart City Security
- Lesson 15.2: Balancing Safety and Privacy: Ethical Implications

Module 6: AI for Environmental Monitoring and Climate Action

Chapter 16: AI for Air Quality and Pollution Monitoring
- Lesson 16.1: Monitoring Air Quality with AI Sensors
- Lesson 16.2: Predictive Analytics for Pollution Control
Chapter 17: Climate Change Modeling and Mitigation with AI
- Lesson 17.1: AI for Climate Data Analysis and Forecasting
- Lesson 17.2: AI in Urban Climate Resilience Planning
Chapter 18: AI in Disaster Management
- Lesson 18.1: Predicting Natural Disasters with AI
- Lesson 18.2: AI Tools for Response and Recovery Planning

Module 7: AI in Public Health and Urban Wellbeing

Chapter 19: AI for Monitoring Public Health Trends
- Lesson 19.1: AI for Disease Detection and Epidemic Tracking
- Lesson 19.2: AI-Enhanced Urban Healthcare Infrastructure
Chapter 20: AI Applications for Mental Health and Wellbeing
- Lesson 20.1: AI-Driven Mental Health Monitoring Tools
- Lesson 20.2: Enhancing Urban Wellbeing with AI-Enabled Solutions

Module 8: AI for Water Management and Smart Infrastructure

Chapter 21: Optimizing Water Distribution Systems with AI
- Lesson 21.1: Real-Time Water Usage Monitoring with AI
- Lesson 21.2: AI in Predictive Maintenance for Water Networks
Chapter 22: AI for Leak Detection and Maintenance Planning
- Lesson 22.1: AI Solutions for Leak Detection in Smart Cities
- Lesson 22.2: AI for Efficient Water Resource Management

Module 9: Smart Buildings and AI-Driven Infrastructure Management

Chapter 23: AI and IoT Integration in Smart Cities
- Lesson 23.1: Role of IoT in Smart Cities: Sensors, Data, and AI
- Lesson 23.2: Integrating AI and IoT for Building Management Systems
Chapter 24: AI-IoT Solutions in Transportation, Security, and Energy Management
- Lesson 24.1: Use Cases of AI and IoT in Smart Transportation
- Lesson 24.2: AI for Smart Energy and Security Solutions in Urban Spaces

Module 10: Ethics, Privacy, and Security in AI-Driven Smart Cities

Chapter 25: Ethical Considerations for AI in Public Spaces
- Lesson 25.1: Responsible AI in Smart City Development
- Lesson 25.2: Case Studies in Ethical AI Implementations
Chapter 26: Ensuring Data Privacy in AI-Driven Smart Cities
- Lesson 26.1: AI and Data Privacy Regulations
- Lesson 26.2: Protecting Citizen Data in Smart Cities
Chapter 27: AI Governance and Regulation in Smart Cities
- Lesson 27.1: Regulatory Frameworks for AI in Urban Management
- Lesson 27.2: AI Governance Challenges in Smart Cities

Module 11: AI and Citizen Engagement in Smart Cities

Chapter 28: AI for Enhancing Civic Participation
- Lesson 28.1: AI Tools for Community Engagement and Urban Decision-Making
- Lesson 28.2: Case Studies of AI-Driven Civic Participation
Chapter 29: AI Tools for Urban Planning and Decision-Making
- Lesson 29.1: AI for Urban Planning: Tools and Techniques
- Lesson 29.2: Smart City Data Platforms for Transparency and Participation

Module 12: Final Project

Chapter 30: Final Project
- Lesson 30.1: Introduction to the Final Project: Problem-Solving in Smart Cities with AI
- Lesson 30.2: Example Projects: Traffic Optimization, Predictive Maintenance, or Public Health Systems
- Lesson 30.3: Project Development: From Ideation to Implementation

Participant’s Eligibility

Urban planners, data scientists, city management professionals, engineers, and AI specialists.

Program Outcomes

Expertise in designing and implementing AI systems for smart city projects.
Ability to optimize urban infrastructure through AI-powered technologies.
Knowledge of integrating AI with IoT and real-time data for smart city management.
Skills in applying AI to public safety, energy management, and sustainable urban development.

Fee Structure

Standard Fee: INR ~~4,998~~ USD 78

Discounted Fee: INR 2499 USD 39

We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!

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Key Takeaways

Program Assessment

Certification to this program will be based on the evaluation of following assignment (s)/ examinations:

Exam	Weightage
Mid Term Assignments	50 %
Project Report Submission (Includes Mandatory Paper Publication)	50 %

To study the printed/online course material, submit and clear, the mid term assignments, project work/research study (in completion of project work/research study, a final report must be submitted) and the online examination, you are allotted a 1-month period. You will be awarded a certificate, only after successful completion/ and clearance of all the aforesaid assignment(s) and examinations.

Program Deliverables

Access to e-LMS
Real Time Project for Dissertation
Project Guidance
Paper Publication Opportunity
Self Assessment
Final Examination
e-Certification
e-Marksheet

Future Career Prospects

Smart City Planner
AI Solutions Architect for Infrastructure
Urban Data Scientist
Smart Transportation Manager
Sustainability Consultant
AI Infrastructure Specialist

Job Opportunities

City governments integrating AI in smart city initiatives.
Urban planning and architecture firms implementing AI-based infrastructure.
Tech companies building AI-powered solutions for smart transportation and utilities.

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