Presentation

Student Team Members

Name	Study Program	Study Year
Sanskar SHRESTHA	Computer Science	4
KADESSOVA Ayazhan	Computer Science	3
UCHE Destiny Nnanna	Computer Science	3
Mohammad Riasad HUQ	Computer Science	2
ONG Jun Kye Eric	Computer Science	3

InfoBite: Enhancing Food Awareness and
Nutritional Monitoring for Vulnerable Individuals

Climate change poses significant challenges to vulnerable individuals, including those with diabetes, pregnant women, people with chronic illnesses, and cardiovascular risk factors. These individuals face increased risks associated with the foods they consume. Accessing comprehensive information about the potential health effects of certain foods is complex and often controversial. Additionally, vulnerable individuals have unique dietary needs, restrictions, and preferences, making it challenging to find suitable foods.

Thus, we are developing an AI-powered solution that leverages technology to empower vulnerable individuals by providing them with comprehensive information about their food intake and alerting them to potential risks based on their specific vulnerabilities. This solution aligns with the theme of "AI-empowered Solutions for Climate Change Challenges" and contributes to the United Nations' Sustainable Development Goals.

Presentation

Student Team Members

Name	Study Program	Study Year
LIU Xianlong Lambert	Computer Science	2
CHAN Lap Yan Lennon	Artificial Intelligence Science and Technology	1
Thapakorn PIPATPAJONG	Computational Data Science	2
WEI Chi Kong Thomas	Earth and Environmental Sciences	2

Prediction of Natural Catastrophe using Artificial Intelligence

Natural catastrophes can cause loss of life and damage property. With the increasing severity of climate change, the frequency of disasters has significantly risen.

Consequently, having accurate predictions of these disasters would be immensely beneficial for addressing the problems of lack of preparation for people living there when they are facing the catastrophe, especially in less developed countries.

In our project, we predict the occurrence of a weather-related catastrophe using weather data as input and catastrophe data as labels, including heat waves, and typhoons. After feeding the data to our model, the project can forecast some catastrophes in the near future based on the weather data and able to inform people to be prepared in advance.

Presentation

Student Team Members

Name	Study Program	Study Year
GUO Aobo	Electrical Engineering	2
CHU Weiqi	Civil Engineering	3
Sanseong KWEON	Nuclear & Risk Engineering	2
Jason ALEXANDER	Civil Engineering	3
QUE Yue	Data Science	3

AI-Assisted Smart Space Cooling Approach with Ultra-High Cooling Power

The increasing worldwide demand for cooling and electricity exacerbates the problem of global warming. Recent studies indicate that the increasing global adoption of air conditioners will triple the energy demand for space cooling without any action to address energy efficiency by 2050. Therefore, developing cooling technologies that operate with lower energy consumption is the research we investigate. Inspired by how camels adapt in the desert, we design a camel-fur-inspired sorbent that can periodically absorb the moisture from the air at night and release its stored water during the daytime for building cooling. The novel sorbent would circulate in response to the environment and exhibit outstanding cooling performance, which is almost 4 times the classical radiative-cooling approach. Concurrently, we propose an Artificial Intelligence (AI) integrated with ultra-high cooling power sorbent to achieve smart cooling in buildings. Highly effective cooling control can be managed by analysing user behaviour through AI.

Presentation

Student Team Members

Name	Study Program	Study Year
PAL Ileana	Mechanical Engineering	3
DUAN Mingjie Josie	Land Surveying and Geo-informatics	3
WANG Zian Peter	Electronic and Information Engineering	3
GAMAGE Sashenka	Electronic and Information Engineering	2
WANG Youkang Albert	Computer Science	2
ZHANG Tianyi Tonax	Computer Science	3

EcoFlight: Enhancing Aircraft Ground Operations for Reduced Carbon Emissions

We will develop an innovative AI solution to reduce carbon emissions from aircraft ground operations. Aviation accounts for a large share of transport sector pollution, and engines emit substantial greenhouse gases during taxi, takeoff, and landing. Using sensors to capture real-time engine usage and flight schedules, our system trains a predictive model on how operational factors like pushback timing, taxi routes, and takeoff weight affect emissions output. This model then identifies strategic recommendations tailored for each airport and airline to minimize pollution through optimized engine start windows, efficient runway sequencing, and congestion-alleviating gate assignments that shorten taxi distances. Our automated solution assigns boarding gates and designs taxi routes leveraging its emission predictions. By facilitating intelligent, emissions-conscious ground movement planning through our data-driven approach, we aim to help the aviation industry significantly lower its environmental impact through airport resource utilization.

Presentation

Student Team Members

Name	Study Program	Study Year
CHEN Wai Yan Grace	School of Engineering, Artificial Intelligent	1
CHI Ting Hsuan	Computer Science and Electronic Engineering	3
TAM Siu Ho	Electronic Engineering, minor in Information Technology	2
TANG Justin Kit Hang	World Bachelor in Business	2
TANG Lok Hang	School of Engineering	1
WONG Wing Him	Electronic Engineering	3

AI-based Photogrammetry for 3D Reconstruction and
Monitoring of Underwater Coral Growth

AI-based photogrammetry is a potent tool for researching and overseeing the development of underwater coral reefs impacted by climate change. These reefs house diverse marine life like seahorses, turtles, and seagrasses, while also aiding in climate change mitigation by absorbing CO2. Through photogrammetry, scientists can create precise 3D coral models by merging 2D images. To streamline this process, our project introduces an inventive AI-driven photogrammetry method for quick and dependable underwater coral 3D reconstruction and monitoring. This innovation will empower scientists to study coral reefs more effectively, offering critical insights for ecosystem understanding. By employing AI algorithms, we accelerate reconstruction and enhance model accuracy. Ultimately, this initiative will contribute to coral reef preservation, safeguarding the environment for future generations.

Presentation

Student Team Members

Name	Study Program	Study Year
Jose Alberto Espino PITTI	Computer Engineering	3
En Yu YAP	Chemistry	3
Patt PHURTIVILAI	Computer Science, minor in statistics	2
Abraham CHANDAFA	Computer Science, minor in Electrical and Electronics Engineering	2
Cheuk Lam TSANG	Chemistry	2
Hanyi XIONG	Applied AI	1

FarmGPT: Securing Food Production in the face of
Climate Change with Approachable AI

Climate change has a significant impact on global food production and farmers, jeopardising their livelihoods with unpredictable weather, temperature variations, poor soils, pest infestations, and droughts.

To address these issues, we propose FarmGPT, an AI-based solution for farmers. Our project involves fine-tuning a chatbot with two types of information. Firstly, we utilize farming-related data, such as AFCD reports, to ensure the model provides accurate information. Secondly, we input farm-specific information obtained through sensors and cameras to generate tailored recommendations that minimize the impact of these challenges.

FarmGPT has a twofold impact.
Firstly, it empowers farmers by providing dynamic AI analysis of their farmland, bridging the technology gap through conversational and digestible information.
Secondly, it enables precision agriculture, allowing farmers to scale their operations while adapting to specific land needs. This approach reduces the reliance on destructive practices like tilling and nitrogen fertilizers, which contribute to climate change. By embracing precision agriculture, farmers can better adapt to the changing environment caused by climate change and mitigate its effects.