School of Infocomm (SOI)

Smart Parking System for HDB Residences

Public carparks often experience inefficient use of designated parking spaces. Seasonal permit holders sometimes use short-term spots wrongly. This created frustration for short-term parkers especially in high-demand areas.

Streamlit is a platform developed in collaboration with the Housing and Development Board (HDB). It aims to address this issue by predicting parking needs and assigning lots effectively with the following key features: 

• User-friendly features: Residents can easily input their license plate, desired AI model, and parking duration.
• Prediction capabilities: Streamlit predicts the cost, determines short-term vs. seasonal parking needs, and assigns a designated lot number.
• Optimised allocation: Underlying algorithms are researched and fine-tuned for high accuracy, ensuring efficient use of designated spaces (e.g. lots 1-160: seasonal, 181-480: short-term, 481-500: motorcycles).
   

Team Members:
Claudio Angelico Jay Unating, Russell Chiu Dong Xuan, Villalba Benjamin Jeremiah Bacallo, Yoong Xihui

Supervisor:
Mr David Leong

Twibble

Singapore's demanding education system is linked to high-stress levels among students and teachers. Twibble, an AI-powered classroom game, tackles this challenge. It uses:

• Emotion & Facial Recognition: Identify student emotions and personalise the learning experience.
• Sentiment Analysis: Gauge student stress levels and adjust gameplay accordingly.
• Engaging Games: Interactive "Clicker Race!" and "Make that Face!" games to promote a fun and stress-free learning environment.

By incorporating these features, Twibble aims to create a more positive and supportive learning environment for both students and educators.
 

Team Members:
Low Tian Yee, Pritikaa Prabhu, Ng Hui Ting

Supervisor:
Mr Frankie Cha

The Drowning Detection

A joint study by Singapore General Hospital and the Singapore Civil Defence Force reveals that 70% of drownings occur without lifeguards present and 40% of such cases involve children under the age of 10. This project tackles this issue with an AI-powered solution:

• Real-time Drowning Detection: Multiple poolside cameras continuously monitor activity.
• Smart Alerts: AI analyses the footage and triggers audible alarms and a red detection box on lifeguard screens when drowning signs are detected.
• Mobile App for Supervision: Lifeguards equipped with a mobile app can monitor multiple pools and assess situations remotely.
• Unsupervised Pool Safety: In unsupervised areas, alarms and information are routed to designated personnel for swift response.
• Quick Intervention: This solution aims to equip lifeguards with AI-powered tools to effectively monitor pools and intervene quickly in potential drowning emergencies.
   

Team Members:
Chiong Jiaying Nicolette, Goh Sim Yng Nicole, Toh Yuan Wei, Dexter Chua Cheng Zuo

Supervisor:
Mr Peter Kenny

SignHouse

SignHouse empowers users to learn sign language and connect with the deaf community. Designed to raise awareness and break down communication barriers, SignHouse empowers users to:

• Learn Sign Language: SignHouse offers interactive lessons and packages to learn the alphabet and construct basic sentences in sign language.
• Break Down Barriers: A sign detection feature allows users to identify signs made by others, promoting understanding and communication.
• Raise Awareness: SignHouse aims to raise awareness within the deaf community, encouraging more people to learn sign language and facilitate inclusive interactions.
   

Team Members:
Muhammad Firdaus Bin Azman, Goutham Saravanan, Hsu Yati Ko, Lin Xiaoyu

Supervisor:
Mr Peter Kenn

R3Grow - Mobile app for sustainability

Climate change is a pressing concern in Singapore. Based on the data from National Climate Change Secretariat and the Climate Change Public Perception Survey 2019, over 90% of residents acknowledged its impact and nearly 80% expressed a willingness to act. The R3grow mobile app tackles this by gamifying sustainability:

• Engaging Incentives & Visuals: Users earn rewards for eco-friendly actions, keeping them motivated with visually appealing interfaces.
• Actionable Information: The app provides real-time updates on recycling locations and upcoming sustainability events, empowering informed decision-making.
• Empowering the 'Hero' Journey: R3grow positions users as sustainability heroes, fostering a sense of ownership and environmental responsibility.

This user-friendly platform aims to make practising the 3Rs - Reduce, Reuse, Recycle - effortless, enabling Singaporeans to contribute to a greener future and support the nation's Green Plan 2030 goals.
 

Team Members: 
Lam Heng Yee, Ng Jia Wen, Lee Tong En Regan

Supervisor: 
Mary Yeo

Stack-AI-Timer: AI-Powered Cup Stacking Timer

This project elevates cup stacking games with AI-powered timing and monitoring key features:

• AI Motion Detection: AI algorithms analyse player movements, eliminating manual timing errors.
• Real-time Performance Display: Results instantly appear on a user-friendly dashboard.
• Enhanced Gaming Experience: Automating timing frees up organisers and allows players to focus on their game.
• Python & .NET Integration: Python handles AI processing, while .NET technologies power the visual dashboard.

This innovative approach showcases AI's potential to automate manual tasks in sports and other timed activities, offering valuable insights and tools for the gaming industry and beyond.
 

Team Members:
Yaswini D/o Elangkovan, Soo Jia En Charis, Gina Yong Sin Hwee, Faith Sim Min Hui

Supervisor:
Mr Jimmy Goh

Patient in Distress Alert System (PDAS)

This project, in collaboration with DataStax, tackles challenges in patient monitoring for healthcare providers. An AI model deployed within the application detects:

• Help Gestures: Three variations of a "help" gesture.
• Multilingual Speech Recognition: Variations of "HELP!" in English, Mandarin, and Malay.
• Inactivity Detection: Patients who remain static for extended periods.

The team also built an integrated website with administration features like:

• User Management: Logins for caregivers, patients, and administrators.
• Patient-Caregiver Matching: Efficiently connecting patients with caregivers.
• Alert Management: Capturing alerts in the database and notifying caregivers promptly.

This AI-powered platform aims for high accuracy in detecting patients' needs, allowing for customised alerts and improved care delivery.
 

Team Members:
Tang Cheng Yin, Lucas Tan Lok Yue, Gigi Wong Qi Hui

Supervisor:
Mr Ho Chee Wai

Safety First

Construction sites emphasise worker safety, particularly regarding Personal Protective Equipment (PPE) usage and hazard detection. This project tackles these concerns with an AI-powered ReactJS web application:

• Real-time Monitoring: Facial recognition identifies workers, while object detection spots PPE violations and hazards.
• Automated Alerts: Supervisors are notified via Telegram with pictures when safety breaches occur.
• Actionable Insights: OpenAI's generative AI model analyses data and provides recommendations for improved safety protocols.
• User-friendly Dashboard: Real-time information and safety data visualisations through clear graphs for informed decision-making.

This innovative solution empowers construction companies to proactively safeguard their workers and create a safer working environment.
 

Team Members:
Ngya Jing Wen Astro, Tran Tien Loc, Daren Lee Jie Wei, Chng Jia Cheng Christopher 

Supervisor:
Ms Grace Yap

Image Plagiarism Detector

Spotting plagiarism in image-based assignments is a growing challenge in AI. This project tackles the issue by developing an Image Plagiarism Detector that empowers educators to effectively detect plagiarism in image-based submissions:

• Image & Text Analysis: Leverages computer vision and natural language processing to analyse image similarities and text within submissions.
• Detailed Similarity Reports: Provides overall similarity scores, breakdowns of table similarity, and explanations for comparisons with existing databases.
• Model Answer Grading: Compares submissions to a designated model answer, aiding instructors in the grading process.
• Utilises Bert Siamese Network: Employs advanced AI models for marking submissions with accuracy.
   

Team Members:
Tan Yong Ler, Ashwin Edward Ananda Kumar, Wang Yanyu Kenny, Goh Shi Hui

Supervisor:
Mr Toh Kee Heng

Elderly Fall Detection and Response Management With AI

SteadyGo tackles falls, a major health risk for seniors living alone. By combining AI and user-friendly apps, SteadyGo empowers caregivers and protects seniors, enables faster fall response times and promotes timely medical attention with the following features:

• Real-time Fall Detection: AI analyses video data to detect falls, triggering immediate alerts.
• Telegram Notifications: Caregivers receive instant alerts via Telegram for swift response.
• Mobile & Web App Management: User-friendly apps allow caregivers to manage care recipient information and updates.
• Database Connectivity: Secure OTP verification ensures data protection.
   

Team Members:
Lim Yi Xian, Muhammad Nabeel Bin Noordeen Ahamed, Muhammad Ridhwanullah Bin Muhammad Ubaidurrahman

Supervisor:
Frankie Cha

Clean, Efficient, and Enjoyable - The Future of Hawker Centres

This project tackles low tray return rates, improves cleanliness, and empowers users with real-time information to create a smoother and more enjoyable hawker centre experience. It uses AI and apps to revolutionise hawker centres:

• AI-powered Seating & Cleanliness: An AI camera with a trained object classification model (using Microsoft Cognitive Services) provides real-time seating information and promotes tray return for cleaner dining areas.
• Mobile App Convenience: Personalised meal suggestions on the mobile app help users make quick decisions and streamline the dining experience.
• Reduced Waiting Times: Increased tray return rates facilitated by AI lead to faster table turnover and reduced waiting times.
• Telegram Bot Integration: Additional features or communication.
   

Team Members:
Tan Yu Xiang, Tan Wee Han, Phun Le Xin

Supervisor:
Frankie Cha

Enhancing Construction Site Safety through Computer Vision

This project uses AI-powered cameras and computer vision technology to improve safety and minimise accidents in construction sites with the following key features:

• Real-time PPE Detection: Cameras leverage computer vision to detect missing Personal Protective Equipment (PPE) like hard hats and vests.
• Quantitative Compliance Assessment: The system goes beyond detection, analysing data to measure the percentage of workers wearing PPE.
• Proactive Safety Management: Real-time insights empower construction companies to identify and address safety concerns promptly.
• Big Data Visualisation: Data is visualised to provide a clear understanding of safety compliance across construction sites.
   

Team Members:
Tay Huai Sheng, Daryl Choo Qi Peng, Cheang Weng Chi Gregory, Jocasta Soh Jing Wen

Supervisor:
Anthony Chong

Development of Image Recognition AI to Differentiate Recyclables in Industry

Partner Organisation: GlobalFoundries 

Many recyclable items are sent to landfill or incineration, because they are contaminated with non-recyclables (such as leftover food) which were improperly sorted and disposed of. Although most public recycling campaigns simplify waste categorisation into a few basic rules, it requires a deeper understanding of the types of materials and industrial treatment process to perform the task accurately. This is not common knowledge which should be expected of everyone. 

Key Features: 

• The project uses an artificial demonstration set that can correctly classify household trash against a variety of backgrounds and camera angles.

• It also has an expert system that may ask follow-up questions to verify the results and provide basic cleaning recommendations (such washing the item with soap) for recyclables.
   

Team Members: 
Koh Yik Heng Ryan, Wiz Lie Chu Lok, Syafid Danish Bin Mohamed Harifin, Mohammad Shahrin Idzuan Bin Iswandi

Supervisor:
Mr Ivan Wee

Booklink Transactions Reconciliator

Partner Organisation: Booklink 

Currently for every transaction performed at Booklink RP, the Office of Organisation and Service Excellence (OSE) will receive an email on the transaction details. OSE staff have to manually compile each email into an excel sheet monthly, and compare it to an Excel sheet given by Booklink. This entire process is very demanding and time-consuming. 

Recommended Process: 

This project uses Natural Language Processing (NLP) model to recognise the entities and filter the entities to be populated into the database.

The content in the database will then be populated into a blank Excel sheet to be automatically reconciled with the report given by Booklink, which in turn, eliminates the need for manual compilation.
 

Team Members: 
Ng Surwyn, Tan Bing Jun, Gerard Mah Jie Kang, Daniel Abishak

Supervisor:
Ms Sharmila Kanna

Park Visitors Analytics

Partner Organisation: National Parks Board (NParks) 

During the COVID-19 pandemic, people in Singapore were required to wear face masks when outdoors except during mealtimes and while exercising. However, not everyone adhered to the rules.

Moreover, especially in parks, there were also known problems such as littering of used plastic water bottles and empty canned drinks by visitors.

Main Goal: 

This project aims to enhance the experience of park visitors by detecting whether they are wearing a mask and identifying the location of litter.
 

Team Members: 
Ainsley Chang, Sng Zhi Hao, Tang Sze Chuan Marcus, Leong Jun Wei Dexter

Supervisor:
Mr Seow Khee Wei

Pest Detection Application

Partner Organisation: Huawei International Pte Ltd 

There were growing concerns of infestation as some eateries stopped their pest control services during the COVID-19 pandemic as part of their cost-saving strategy. With less human activity, there would be an increase in pest infestations, which resulted in raising the spectre of another outbreak as Singapore battled against the coronavirus. 

Advantages of Huawei Atlas Robot Car:

• This project incorporates Artificial Intelligence and Optical Recognition (OR) Tools in the application development process to comb areas for signs of pests and to perform pest classification.

• This helps in solving commonly faced urban safety issues and creates a much safer environment for everyone to live in.
   

Team Members: 
Elijah Tan Jia Wei, Ong Yee Fei, Lau Shao Xian

Supervisor:
Mr Jimmy Goh

Logistics Delivery Robot

Partner Organisation: NCS Pte. Ltd

Deliveries have become part and parcel of life for many people. To ensure that the experience is kept to highest standard in terms of convenience and hygiene, contactless mode of delivery has overtaken the traditional one, which involves human interaction. 

Key Features: 

• This project looks at the issues of convenience and hygiene, as well as reduces costly manpower by deploying automated delivery robots.

• Building upon Cloud-Based Technology and coupled with automated programmable delivery robots, the presence of a human being from a goods delivery process is eliminated.
  
  

Team Members: 
Lee Joon Hean Mason, Tan Li Shing, Barry, Soo Shan Yong, Adam Goh Zheng Shan, Mirza Bin Mohamed Aljaru

Supervisors:
Ms Grace Yap and Mr Tan Cheng Kok

RP Chatbot Tutor

Students are not able to contact the lecturers outside school especially on weekends, and when they have questions on their revision or tasks, they are not able to find the answers online as only the lecturers have the answers to their questions.

RP Chatbot Tutor improves user experience in the following ways:

• It allows a student to ask questions and get a reply immediately if the answer is found in the database.

• If the answer is not available in the database, the student can then add in the question into database for the lecturers to answer.

• The project also allows lecturers to manage the questions and answers.

• There is also a dashboard function to allow lecturers to track students’ performance.
  
  

Team Members: 
Lim Kai Peng, Speed, Chu Yanni Sennett Charis, Muhammad Raimi B Hasri, Wee Jun Jie Desmond

Supervisor:
Mr Patrick Wang

RP Chatbot Tutor

RP Chatbot Tutor is a chatbot that is user-friendly and efficient. It answers students’ questions, gives quizzes, and collects questions/feedback from students.

The chatbot came about because lecturers often have to spend a lot of time answering common questions from students. As a result, individual problems from students are not properly addressed. 

Key Features: 

• The chatbot is developed to understand natural language.

• It processes vernaculars or spoken speech the same way humans do. This significantly enhances the user experience.

• Data is collected from the chatbot and displayed on the backend website.

• Only lecturers and staff members have access to the site whereby all modules are managed.
  
  

Team Members: 
Tan Zhi Yin, Ahmad Husaini B Hamsani

Supervisor:
Mr Patrick Wang

Customer Service Chatbot

Improve Customer Response Time

Main Aim of the Project:

• Our project aims to build a Customer Service Chatbot for NEA that connects to the public. 

• It provides virtual service with 24/7 on demand support to enable immediate responses to customers’ enquiry on operational procedures, payment of fines, illegal dumping, process to handle letter of reminder/advice, work instructions for Singapore Food Agency (SFA), etc. 

• The chatbot relies on Natural Language Processing (NLP) trained using Artificial Intelligence (AI) to understand and correctly interpret customers’ requests.

Final Solution:

• The chatbot system is developed with Google-owned DialogFlow that uses human-computer interaction technologies based on natural language conversations. 

• Further exploration and development are also accomplished using Rasa conversational AI platform. 

• The customer service chatbot is deployed to the cloud through Amazon Web Service (AWS), and it can be communicated via Telegram, a cloud based instant messaging service app on mobile phones and laptops.

Technologies: 

• Amazon Web Services (AWS)
• DialogFlow
• Rasa
• Telegram
   

Team Members: 
Wong Zen Yang, Muhammad B Alfyan Sapwan, Chan Jia Hui, Chia Yun Lydia.

Supervisor:
Mr David Leong

Plant Disease Detection

Modern economies are facing issues regarding food security. Traditional plant-based farms spanning across large areas pose challenges for timely crop monitoring.

With urbanisation increases, farmers are also turning to urban farming. Modern urban farming techniques include vertical farming and rooftop farming. Timely and efficient monitoring is important to ensure crop productivity. 

Plant Drone Flying – An Efficient Solution:

Develop a drone system to detect and scan the health of plants.

• It can fly at a height and scan the leaves of the plants.

• Utilising principles of Deep Learning, it can display the plant's health and send farmers alerts when there are problems with the plants' health. 

Technologies: 

• Drone Flying

• Web Development

• Deep Learning
  
  

Team Members:
Balbin Kristin Clarise Mandap, Reuben Loo Jie Long, Patrick Heng Jun Xiang, Ngieng Min Yi

Supervisor:
Mr Gary Chan

Computer Vision

Real-Time Response 

In sports events, referees are needed to determine the happenings during the event and then perform specific actions based on the key events that they have identified. However, a referee might misjudge some events, hence we want to ensure that all key events are automatically and correctly identified. 

System Requirements:  

• A detection system is required to detect the key events. We are training the models to detect specific objects based on four different algorithms.

• The trained models are used to detect the objects using Tensorflow object detection API.

Custom Trained Detection System Solution: 

A Custom-Trained Detection System will be built to detect 4 different objects in a soccer match. (Penalty Kick, Free-Kick, Throw-In, Corner Kick) using the 4 different algorithms (Single Shot Detectors, YOLO v3, Faster R-CNN and Mask-RCNN). 

With the detection model set in place, it will automatically detect the key events which help to reduce any human errors, increase convenience and accuracy. The output can either be image, video or webcam. 

Technologies: 

• TensorFlow

• NumPy

• Algorithms

• Anaconda virtual environment

• Python
   

Team Members:
Royden See An Jun, Chew Tze Nam, Zhang HongYing, Du Mengxue;

Supervisor:
Mr Zack Toh

Thyroid Nodule Classification

Thyroid cancer is a malignant tumour that occurs in the thyroid gland and is the most common malignant tumour in the endocrine system.

Ultrasound images of thyroid papillary carcinoma are mostly represented by two-dimensional greyscale pixels in lower resolution. It is difficult to distinguish and diagnose due to the complicated internal tissue structure and lack of obvious cancer features.

System Requirements:

• Design and implement a custom Convolutional Neural Network (CNN).

• Perform training and evaluation of well-known models through various hyperparameters tuning techniques.

• Predict and classify thyroid tumour.

AI-based System Solution:

It provides first-level classification and identification of thyroid nodules will help to speed up the analysis, assisting clinicians in determining and locating malignant tumours. 

Technologies: 

• Keras

• Tensorflow

• Python

• Anaconda virtual environment

• Python
  
  

Team Members:
Ng Jia Wei, Tino Chia Jun Rong, Md Haikal Iskandar Bin Osman, Md Haiqal Bin Mohamed Rafiee;

Supervisor:
Mr Seow Khee Wei

Thyroid Nodule Classification

As thyroid cancer is very common worldwide, doctors have to deal with many ultrasound thyroid images. Doctors might not be 100% accurate in their predictions. Usually, the results take a while to be processed too. 

System Requirements:

Design, train and evaluate a Convolutional Neural Network (CNN) and compare its performance against well-known CNN architectures like Inception v3 and VGG16. 

CNN-based Solution:  

With the help of CNN, it will help the doctors to predict the type of thyroid cancer found on the patients effectively. 

Technologies:

• PyCharm 

• Anaconda
   

Team Members:
Phua Guan Wu, Ng Kai Xuan, Han Jian Le, Darren Wong;

Supervisor:
Mr Seow Khee Wei

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