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Related Experiment Video

Updated: Sep 6, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

The Smart in Smart Cities: A Framework for Image Classification Using Deep Learning.

Rabiah Al-Qudah¹, Yaser Khamayseh^2,3, Monther Aldwairi^2,3

¹Department of Computer Science and Software Engineering, Concordia University, Montreal, QC H3G 1M8, Canada.

Sensors (Basel, Switzerland)

|June 24, 2022

Summary

This summary is machine-generated.

Smart city zoning designs and technology models address global challenges like pandemics and climate change. A deep learning image processing model automates decision-making for non-technical users.

Keywords:

automation deep learning images smart city transfer learning zoning

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Area of Science:

Urban Planning
Computer Science
Artificial Intelligence

Background:

Increasing global challenges (pandemics, climate change, resource scarcity) necessitate innovative urban solutions.
Smart city initiatives leverage technology to address complex urban issues and improve resilience.
Existing smart city solutions face limitations due to cost, security, and usability for non-technical personnel.

Purpose of the Study:

To propose a zoning design and technology framework for smart cities to mitigate current global challenges.
To develop a generalized, deep learning-based image processing model for automated data analysis in smart cities.
To enable automated decision-making for non-technical users in smart city contexts.

Main Methods:

Development of a smart city zoning design framework and a supporting technology-driven model.
Implementation of a smart image handling system for non-technical users.
Design and implementation of a generalized deep learning image processing model with self-tuning capabilities.

Main Results:

A proposed framework for smart city design integrating zoning and technology components.
A functional smart image handling system demonstrating the framework's utility.
A deep learning model capable of automated image analysis and insight generation without human intervention.

Conclusions:

The proposed smart city framework and technology model offer a viable approach to addressing contemporary urban challenges.
The generalized deep learning image processing model significantly enhances accessibility and automation for smart city applications.
Automated decision-making through advanced AI models can overcome barriers to smart city adoption.