UNIVERSITI TUNKU ABDUL RAHMAN
Postgraduate Final Year Project Titles
Last update : 25/2/2024
| A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z || No | Supervisor | Co-Supervisor | Title | Skills/Tools | Industry Linked? | Status | Brief Description | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Aun Yichiet | Png Wen Hao | Carbon footprinting on Ethereum Blockchain |
|
to be extendeded into... | project details ⊳ To profile consumer products carbon footprint based on consumer consumption behaviors |
|||||
| 2 | Chan Lee Kwun | Not Available | Exploring social media big data for consumer behaviour analysis by using business intelligence tools |
|
No | project details ⊳ The objective of this project is to explore how social media big data can facilitate consumer behavior analysis. Based on the the sources and characteristics of social media big data, consumer behavior analysis will be explored in order to gain a better understand of the consumer needs, attitudes and purchase intentions, and identify market trends and competitive dynamics. |
|||||
| 3 | Goh Chuan Meng | Not Available | Development of Deep Learning algorithms for cell death prediction in in-Vivo imaging |
|
Preferably yes | project details ⊳ This research aims to create advanced computational tools that can analyze in-vivo medical images to predict and identify instances of cell death. Cell death is a crucial aspect in various biomedical and clinical research areas, as it can provide insights into disease progression, treatment effectiveness, and tissue health. This research involves the design, training, and validation of deep learning models, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs), to process complex in-vivo imaging data. The ultimate goal of this research is to develop accurate and robust algorithms that can not only detect cell death events but also predict them in real-time or over specific timeframes. |
|||||
| 4 | Goh Hock Guan | Not Available | Internet of Things for Green House Agriculture Monitoring |
|
Yes | project details ⊳ This project aims to design and develop a green house agriculture monitoring system using the Internet of Things technology. When a plant is migrated to a green house or indoor, the environment is changed and many parameters need to be monitored carefully so that necessary action can be taken immediately to prevent more damages on the plant. |
|||||
| 5 | Khor Siak Wang | Not Available | Object Detection and Recognition |
|
Preferably yes | project details ⊳ To develop a system that could help detect and recognize the presence of an object. It's particularly useful in detecting some unusual activities, e.g. abandoned objects, intruders, vandalism, snatch thieves etc. |
|||||
| 6 | Kiran Adnan | Not Available | Enhancing Cybersecurity through Advanced Threat Detection and Response Techniques |
|
Yes | project details ⊳ This project aims to identify and assess advanced threat detection and response techniques. Also, it aims to explore the integration of new techniques with existing cybersecurity frameworks. And, to identify the challenges and limitations associated with the implementation of advanced threat detection and response techniques. |
|||||
| 7 | Lim Seng Poh | Not Available | Performance Analysis of Differential Evolution on Benchmark Functions |
|
No | project details ⊳ Differential Evolution (DE) is one of the popular evolutionary algorithms. Some of the previous works did not consider identifying suitable generation schemes and control parameters for producing trial vectors. Consequently, a longer duration is needed to produce good results. With the increasing application of DE in solving optimisation problems, the performance of DE becomes an interesting topic to explore. Therefore, it is essential to study the performance of different DE models before applying them in specific case studies. |
|||||
| 8 | Muhammad Syaiful Amri bin Suhaimi | Not Available | Camera-based eye gaze and head movement tracking for online learning |
|
No | project details ⊳ The main objectives is to track the eye pupil or gaze using computer vision/image processing during an online class or a meeting. There are several challenges for the project:
|
|||||
| 9 | Png Wen Hao | Not Available | Network compression in generative models for light-weight style-transfer applications |
|
No | project details ⊳ Deep generative models require extensive computational cost to deliver high-quality computer-aided graphic design. However, multi-layer features redundancy in deep network framework leads to slow synthesis (optimization) process. In this project, network compression technique shall be applied to reduce the redundancy for lightweight and fast-optimizing style transfer applications. |
|||||
| 10 | Disentangle deep features in generative convolution network for interpretable neural-based synthesis applications |
|
project details ⊳ Deep convolution networks deliver high-level of embedded vision applications. The embedded vision is deeply entangled due to the convolutional property of network. Such entanglement leads to inseparable features in most generative models, thus reduces the controllability of generation process. In this project, deep learning and image processing techniques shall be applied to disentangle the deep feature, thereby deliver a style transfer application with high-degree of control to the user. |
||||||||
| 11 | Ramesh Kumar Ayyasamy | Not Available | Developing a sentiment analysis model using generative pretrained transformer for education domain |
|
No | project details ⊳ This project aims to develop a sentiment analysis model using the generative AI techniques to evaluate ChatGPT-related tweets in education contexts.The categorization and analysis of thematic content within tweets provide valuable context for understanding the specific areas of education where ChatGPT is generating interest and discussion. |
|||||
| © | 2024 | Syed Muhammad | DIS | FICT | UTAR | ||||||