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See all the wonderful things Fadratul Hafinaz Hassan has made

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[Sentiment Analysis / Natural Language Processing] ANALYZING THE USE OF EMOJIS IN TWEETS

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[Sentiment Analysis / Natural Language Processing] ANALYZING THE USE OF EMOJIS IN TWEETS

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Contact: Dr. Jasy Liew Suet Yan
Email: jasyliew@usm.my
Tel: 04-6534639
Description:
Emojis are pictographs encoded in Unicode commonly used in electronic messaging. This research project seeks to explore how emojis are used in microblogs. You will first collect a large sample of tweets from Twitter containing emojis. You will learn to develop research questions to understand how emojis are used to express emotions or convey other semantic meaning in tweets. This project requires the use of text analysis and natural language processing. You are required to write scripts to extract and analyze common linguistic cues associated with the emojis. The findings of this study will inform researchers and practitioners on how to leverage emojis to gain insights on user emotions in computer-mediated communication (CMC).

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[Sentiment Analysis / Natural Language Processing] DETECTING EXPERIENCER AND STIMULI IN EMOTION-BEARING TWEETS

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[Sentiment Analysis / Natural Language Processing] DETECTING EXPERIENCER AND STIMULI IN EMOTION-BEARING TWEETS

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Contact: Dr. Jasy Liew Suet Yan
Email: jasyliew@usm.my
Tel: 04-6534639
Description:
There is a theory explaining that emotions are expressed by an experiencer and caused by a stimulus. This research study will focus on the use of semantic role labeling to identify the experiencer and stimulus in a tweet. First, the study would involve studying and annotating a sample of emotion-bearing tweets to identify the emotion experiencer and stimuli. Then, you will investigate how the emotion experiencer and stimuli can be extracted from the tweets and evaluate the performance of your proposed method.

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Classification of Unbalanced Data using Optimised Machine Learning Algorithms

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Classification of Unbalanced Data using Optimised Machine Learning Algorithms

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Contact: Dr. Umi Kalsom Yusof
Email: umiyusof@usm.my
Tel: 04-6533036

Description:
Classification is a crucial task of knowledge discovery in databases and data mining. Classification modelling is to learn a function from training data, which makes as few errors as possible when being applied to data previously unseen (Sun et al., 2007). A range of classification modelling algorithms, such as decision tree, neural network, Bayesian network, nearest neighbour, support vector machines, and the newly reported associative classification, have been well developed and successfully applied to many application domains. With the emergence of new data types, many classification algorithms are facing challenges, even though they used to be successfully adapted in different fields. Class-unbalanced data are ubiquitous in various fields (Yin and Gai, 2015), such as biomedicine, cancer diagnosis using DNA microarray data, and image classification

In many machine learning (ML) applications, there is a significant difference between the prior probabilities of different classes, i.e., between the probabilities with which an example belongs to different classes of the classification problem. This situation is known as the unbalanced data or class imbalanced problem (Sun et al., 2009; López et al., 2013). Furthermore, it is worth to point out that the minority class is usually the one that has the highest interest from a learning point of view and it also implies a great cost when it is not well classified. The minority class usually represents the most important concept to be learned, and it is difficult to identify it since it might be associated with exceptional and significant cases (López et al., 2013), or because the data acquisition of these examples is costly (Weiss and Tian, 2008).

Since most of the standard learning algorithms consider a balanced training set, this may generate suboptimal classification models (i.e. a good coverage of the majority examples) whereas the minority ones are misclassified frequently. Therefore, those algorithms, which obtain a good behaviour in the framework of standard classification, do not necessarily achieve the best performance for unbalanced data sets (Fernández et al., 2010).

To this end, the main goal of this study is the development of a novel classification model that addresses unbalanced data which provide practical computational efficiency and high predictive accuracy. The proposed model is also expected to provide a feasible and meaning outcome in its related field.

Objective:

1.     To find the most effective machine learning model for unbalanced class problem

2.     To optimise the machine learning model to enhance the accuracy.

Note: There is a possibility of using the real industrial dataset

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[Artificial Intelligence / Optimization Methods] Improving the collaborative coalition formation (CCF) using modified Particle Swarm Optimization (PSO) with metaheuristic algorithms

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[Artificial Intelligence / Optimization Methods] Improving the collaborative coalition formation (CCF) using modified Particle Swarm Optimization (PSO) with metaheuristic algorithms

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Contact: Dr. Azleena Mohd Kassim
Email: azleena.mk@usm.my
Tel: 04-653 3645

Description:

A collaborative coalition or team can be formed based on social factors such as trust, personality, and skills. In order to find an optimal distribution of the team formed, algorithm such as genetic algorithm (GA) and particle swarm optimization (PSO) can be used. In this research, these algorithms (PSO and GA) will be experimented and hybridized to improve the accuracy of the solution.

Improving the optimization algorithm for CCF: In order to enhance the algorithm to be used in CCF, the objective function needs to be refined. This coalition formation handles multi-objective optimization as the objective can vary based on the type of request for the groups. The multi-objective function have to able to handle the dynamic requests of the factors to be used as the decision factors in the CCF. After the objective functions are developed, the optimization algorithms such as PSO and GA (and possibly others) will be explored and improved in order to increase the efficiency of the solution.

This topic is a sub-topic from my research but focused more on the optimization of the coalition formation. The thesis related to this research is available, so it can be used as the main reference.

Thank you

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[Image Processing and Computer Vision] Optimized Segmentation of Magnetic Resonance Angiogram (MRA) dataset using Improved Bilateral Filter

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[Image Processing and Computer Vision] Optimized Segmentation of Magnetic Resonance Angiogram (MRA) dataset using Improved Bilateral Filter

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Contact: Dr Nur Intan Raihana Ruhaiyem
Email: intanraihana@usm.my
Tel: 04-653 4387

Description:
The objectives of the research are:

1. To determine the significant features in edge-based algorithms for image segmentation of MRA  datasets.

2. To validate the segmentation result from adoption of high-pass domain kernel in classic 3D bilateral filter to validate the segmentation results.

3. To model a new scheme for small blood vessel segmentation based on high-pass domain kernel in 3D bilateral filter

4. To validate the new scheme (improved 3D bilateral filter) on large MRA datasets in terms of accuracy and computational time

This topic is an extended work from my previous student of CCS519 (done with the improved bilateral filter). The image data are all provided. If you want to see the report/dissertation on previous work, or keen to know further, feel free to contact me.

Thank you.

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[Image processing and Machine Learning]  Identifying Handwriting Features Useful for Automatic Screening of Autism Spectrum Disorder (ASD) among Children

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[Image processing and Machine Learning]  Identifying Handwriting Features Useful for Automatic Screening of Autism Spectrum Disorder (ASD) among Children

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Contact: Dr. Nur Intan Raihana Ruhaiyem
Email: intanraihana@usm.my
Tel: 04-653 4387

Description:
The main objective of the proposed research study is to investigate feature of handwriting impairments that can be leveraged to distinguish between normal and ASD children using an integrated image processing and machine learning framework.

This topic is an extended work from my previous students of Final Year Project (FYP) and CCS519.

I will assist you in collecting the dataset (children's handwritten) from NASOM Penang, as well as from normal children aged between 8 to 10 years old.

If you want to see the report/dissertation on previous work, or keen to know further, feel free to contact me.

Thank you.

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[Information Extraction/Text Mining/NLP] Structured Data Extraction from Unstructured Text for Scientific Article Domain using Relation Mining

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[Information Extraction/Text Mining/NLP] Structured Data Extraction from Unstructured Text for Scientific Article Domain using Relation Mining

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Contact: Dr. Gan Keng Hoon
Email: khgan@usm.my
Tel: 04-6534634

Description
The goal of this research is to improve the visibility of unstructured contents of Scientific article based on certain aspects objective, method, data, result etc. This can be done by extraction of specific data from the domain and represent it in structured form. Nevertheless, it is always challenging to extract the correct structured data pairs (or tuples) due to the nature of written text. This research will focus on NLP techniques to improve the process of relation mining in order to generate the structured data pairs (or tuples).

Input: Abstract or contents of article

Output: Structured data tuples, e.g. [method, knn], [algorithm, heuristics search] etc.

If you are interested, please drop me an email khgan@usm.my for more information.

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[Information Extraction/Machine Learning/NLP] Learning to Generate Structured Abstract from Scientific Article 

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[Information Extraction/Machine Learning/NLP] Learning to Generate Structured Abstract from Scientific Article 

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Contact: Dr. Gan Keng Hoon
Email: khgan@usm.my
Tel: 04-6534634

Description
Abstract plays an important role for article as it serves as a preview of the article. Different from the usual abstract, a structured abstract clearly indicate the needed sections like Purpose, Design/methodology/approach, Findings, Originality/value etc. Being able to generate this type abstract automatically could potential help author to get an initial draft of the abstract, or publisher to promote the article more effectively via the text snippet. This research will focus on the combination of rule-based and machine learning method to select suitable sentences for structured abstract generation.

Input: Contents of article

Output: Structured Abstract

If you are interested, please arrange a session with me khgan@usm.my for more information.

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[Artificial Intelligence/Robotics] Optimised Mobile Robot's Path Planning based on  Meta-heuristic Algorithm

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[Artificial Intelligence/Robotics] Optimised Mobile Robot's Path Planning based on  Meta-heuristic Algorithm

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Contact: Dr. Mohd Nadhir Ab Wahab
Email: mohdnadhir@usm.my
Tel: 04-653 2320

Description:
A movement from initial location to final location, then perform obstacle avoidance and do necessary reaction towards environment changes are simple tasks for us but not for a autonomous mobile robot. A mobile robot uses sensors equipped to perceive the environment (up to some degree of uncertainty) and to build or update its environment map.

Path planning is used to solve problems in different fields, from simple spatial route planning to selection of an appropriate action sequence that is required to reach a certain goal. Since the environment is not always known in advance, this type of planning is often limited to the environments designed in advance and environments that we can describe accurately enough before the planning process. Path planning can be used in fully known or partly known environments, as well as in entirely unknown environments where sensed information defines the desired robot motion.

Requirements:
Student must be able to do basic programming either C++/Python/MATLab. The mobile robot can be run through simulation or physical robot.

Reference:
1. Zafar, M. N., & Mohanta, J. C. (2018). Methodology for Path Planning and Optimization of Mobile Robots: A Review. Procedia Computer Science. https://doi.org/10.1016/j.procs.2018.07.018

2. Zhang, H. Y., Lin, W. M., & Chen, A. X. (2018). Path planning for the mobile robot: A review. Symmetry. https://doi.org/10.3390/sym10100450

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Exploring User’s Perspective on Decision-Making of Information Spreading during Disasters in Malaysia

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Exploring User’s Perspective on Decision-Making of Information Spreading during Disasters in Malaysia

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Contact : Dr. Nor Athiyah Abdullah
Email : athiyah@usm.my

Summary:
The social media and new communication technologies available has become an important and dependable disaster communication tool particularly for communication and information sharing purpose. Better understanding of why people choose to spread information with these platform is helpful to improve the usefulness of social media as an important communication tool. Before effective communication can take place, there is a need to understand how people communicate using available technologies such as social media. Therefore, the principal aim of this research is to gain insight and understand how Malaysian citizen utilize social media as disaster communication tool. By understanding both side’s communication behaviour, effective communication approach can be proposed to benefit both parties; the citizen and the government.

Additional note: Feel free to drop me an email if you have any questions regarding the topic or user-related studies :)

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Contact: Dr. Teh Je Sen
Email: jesen_teh@usm.my

Background:
Block ciphers are used to secure private data to ensure that no malicious parties can illegally access them. The security of block ciphers are evaluated using various cryptanalytic attacks such as differential and linear cryptanalysis, which targets different aspects of the cipher. Differential cryptanalysis is a method that observes the propagation of a pair of input messages through a cipher and identifies patterns that can be used to recover the secret key.

Problem Statement:
There are many lightweight ciphers being proposed in recent years, however, not all of them have been extensively cryptanalysed. Cryptanalysis work needs to be done to ensure that these ciphers are suitable for real-life applications.

Research Goal:
The goal of this research is to modify an existing differential attack algorithm to target a specific lightweight block cipher. Then, experiments need to be conducted to find the best attack possible, which is a measure of the security of the targeted cipher.

Requirements:
Student must be good at mathematics and basic programming (C++) to undertake this research project.

Additional Note:
Multiple students can undertake this project because there are a variety of lightweight block ciphers that need to be analysed. The student is required to select, study, and attack a specific lightweight block cipher.

References:
https://doi.org/10.1109/TC.2017.2699190

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Contact: Dr. Teh Je Sen
Email: jesen_teh@usm.my

Background:
Substitution boxes (s-box) play a key role in symmetric-key encryption algorithms. They map a fixed-length binary input to a fixed-length binary output in a deterministic, yet nonlinear manner. Most s-boxes are static, meaning the same s-box is used for a particular encryption algorithm. Dynamic s-boxes, on the other hand, constantly change depending on the secret key or some other shared parameter.

Problem Statement:
An algorithm is required to generate dynamic s-boxes prior to the encryption process. Chaotic maps have been commonly used to implement these algorithms. However due to the nature of chaotic maps, these algorithms are usually inefficient.

Research Goal:
The goal of this research project is to develop an efficient or lightweight algorithm to generate s-boxes based on chaotic maps. To facilitate this process, a simple 1D chaotic map will be employed. Instead of floating point representation, fixed point representation will be used to improve upon efficiency. The proposed s-box generation algorithm will be compared against other chaos-based dynamic s-box algorithms in terms of security and performance.

Requirements:
Student must be good at mathematics and basic programming (C++, Matlab, etc.) to undertake this research project.

References:
https://doi.org/10.1155/2017/9040518
https://doi.org/10.1155/2017/6969312
https://doi.org/10.1109/GIIS.2009.5307035
https://doi.org/10.1080/24751839.2018.1434723

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nce System (ANFIS) to Predict Readiness to Change Bad Habits Among /Heart Diseases/Obese Patients 
Contact: Dr. Pantea Keikhosrokiani
Email: pantea@usm.my 

Description:
A new predictive model will be proposed to predict readiness to change bad habits among obese patients. Adaptive Neuro-Fuzzy Inference System (ANFIS) needs to be optimized and applied for the new model. A survey will be conducted online to provide dataset. MATLAB needs to be used for implementation. 
[Information Security/Cryptography] Efficient Chaos-based Algorithm for Dynamic Permutation Boxes
[Information Security/Cryptography] Efficient Chaos-

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[Information Security/Cryptography] A Chaos-based Dynamic LFSR for Cryptographic Applications

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[Information Security/Cryptography] A Chaos-based Dynamic LFSR for Cryptographic Applications

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Contact: Dr. Teh Je Sen
Email: jesen_teh@usm.my

Background:
Linear feedback shift registers (LFSR) are commonly used to design pseudorandom number generators (PRNG) and stream ciphers in cryptography. The operation of a LFSR is simple, leading to efficient implementations.

Problem Statement:
Due to the linearity of their feedback polynomial, LFSRs are susceptible to certain forms of cryptanalysis such as correlation attacks. Using a chaotic map to dictate the feedback polynomial can lead to secure implementations. However, chaotic maps are usually inefficient.

Research Goal:
The goal of this research project is to develop an efficient or lightweight dynamic LFSR based on chaotic maps. To facilitate this process, a simple 1D chaotic map will be employed to select bits for the feedback polynomial. Instead of floating point representation, fixed point representation will be used to improve upon efficiency. The dynamic LFSR can then be used in a PRNG or stream cipher and be compared against other chaos-based implementations in terms of security and performance.

Requirements:
Student must be good at mathematics and basic programming (C++, Matlab, etc.) to undertake this research project.

References:
https://doi.org/10.1109/ICT4M.2014.7020598

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Prediction of Readiness to Change Bad Habits Among /Heart Diseases/Obese Patients using Optimized   Neuro-Fuzzy System 

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Prediction of Readiness to Change Bad Habits Among /Heart Diseases/Obese Patients using Optimized   Neuro-Fuzzy System 

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Contact: Dr. Pantea Keikhosrokiani
Email: pantea@usm.my

Description:

A new predictive model will be proposed to predict readiness to change bad habits among obese patients. Adaptive Neuro-Fuzzy Inference System (ANFIS) needs to be optimized and applied for the new model. A survey will be conducted online to provide dataset. MATLAB needs to be used for implementation.

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Contact: Dr. Teh Je Sen
Email: jesen_teh@usm.my

Background:
Permutation boxes (p-box) play a key role in symmetric-key encryption algorithms. They are involved in diffusing any changes to the inputs in a linear manner, to ensure that slight changes introduced by the key or nonlinear operations will be spread out throughout the final output. Most p-boxes are static, meaning the same p-box is used for a particular encryption algorithm. Dynamic p-boxes, on the other hand, constantly change depending on the secret key or some other shared parameter.

Problem Statement:
An algorithm is required to generate dynamic p-boxes prior to the encryption process. Chaotic maps have been commonly used to implement these algorithms. However due to the nature of chaotic maps, these algorithms are usually inefficient.

Research Goal:
The goal of this research project is to develop an efficient or lightweight algorithm to generate p-boxes based on chaotic maps. To facilitate this process, a simple 1D chaotic map will be employed. Instead of floating point representation, fixed point representation will be used to improve upon efficiency. The proposed p-box generation algorithm will be compared against other chaos-based dynamic p-box algorithms in terms of security and performance.

Requirements:
Student must be good at mathematics and basic programming (C++, Matlab, etc.) to undertake this research project.

References:
http://dx.doi.org/10.1155/2014/795292
https://doi.org/10.1109/ISIE.2008.4676931

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TEXT SUMMARIZATION BY DEEP LEARNING TECHNIQUES

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TEXT SUMMARIZATION BY DEEP LEARNING TECHNIQUES

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Contact: Dr. Chew XinYing

Email: xinying@usm.my

Tel: 04-6532668

Descriptions:

Text summarization is considered as an operator-based transformation process by which knowledge representation structures, as generated by natural language text understanding system, are mapped into conceptually more abstract, condensed knowledge structures forming a text summary at the representational level. Searching information by reading through tons of articles is very time consuming and not efficient. Text summarization techniques are very useful where it helps to summarize long articles into short paragraphs which contain the essence of the article. This research project helps to review / compare the famous existing text summarization DL techniques and finally propose the best techniques based on different scenarios.

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Deep Learning in Automatic License Plate Recognition

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Deep Learning in Automatic License Plate Recognition

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Dr Sukumar Letchmunan
sukumar@usm.my

Automatic License Plate Recognition(ALPR) is used in many domains. In recents years, the importance of ALPR have increased. Using deep learning could be best option to improve the accuracy and speed of solving the ALPR.
In this research student should explore and propose the best method using deep learning techinique.

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Object detection and localization using deep learning

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Object detection and localization using deep learning

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Prof. Dr Putra Sumari
putras@usm.my

Object localization is one of the important methods in object detection in which it is to move a rounding-box within and image look and detect for objects. Deep learning is one prominent area today that can perform better localization.  You are required to investigate and fine tune the deep learning based object detection algorithms in the literature, specifically the localization method of it. Once algorithm is identified, an experiment need to done to measure the performance against few similar methods

Compulsory
1) Attend my hands on short course title: Deep learning: Convolutional neural network using Python(Keras & Tensorflow)
2) One hour weekly meeting with me.

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Prof Dr. Putra Sumari
putras@usm.my

The use of deep learning in various of medical image diagnosis.

1)      Brest cancer

2)      Lung cancer

3)      Diabetic retinopathy

4)      Glaucoma detection

There are plenty algorithms for a above applications. You are required to apply deep learning approach onto one of these applications of your choice. You are required to apply the algorithm with performance measurement.

Compulsory
1) Attend my hands on short course title: Deep learning: Convolutional neural network using Python(Keras & Tensorflow)
2) One hour weekly meeting with me.

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Single shot detector (SSD) in object detection. 

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Single shot detector (SSD) in object detection. 

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Prof. Dr Putra Sumari
putras@usm.my

SSD has a good accuracy of object detection technique in an image. SSD uses the concept of applying a convolutional neural network (CNN) to the complete image.

The research is to do investigation of SSD and later applying it into today object detection application. Various object detection domain of your choice such as clothing, leaf disease, fruit recognition, vehicles detection and etc. Few experiments need to be done for comparison performance against various applications.

Compulsory
1) Attend my hands on short course title: Deep learning: Convolutional neural network using Python(Keras & Tensorflow)
2) One hour weekly meeting with me.

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A PROPOSED VSI KERNEL-DISTANCE-BASED CONTROL CHART BY MACHINE LEARNING TECHNIQUES

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A PROPOSED VSI KERNEL-DISTANCE-BASED CONTROL CHART BY MACHINE LEARNING TECHNIQUES

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Contact: Dr. Chew XinYing

Email: xinying@usm.my

Tel: 04-6532668

Descriptions:

Control charting technique is a useful technique in Statistical Process Control (SPC) for improving productivity, reducing defects and providing diagnostic information. It is important for intelligent manufacturing, which is a part of the Industrial Revolution 4.0. SPC techniques have been used to monitoring the quality of various manufacturing processes. The traditional control chart helps industrial practitioners to measure the performance of the production process. However, traditional control chart lies with its insensitivity toward the detection of small shifts in the process mean. This may cause some of the invisible or indirect production cost be spent. Thus, this research project proposed a new adaptive control chart which vary the sampling interval (VSI) of the traditional control chart by integrating machine learning technique (e.g. Support Vector Machine, SVM).

Requirements:

It will be good if student have mathematics/statistics background.

References:

https://avestia.com/MCM2018_Proceedings/files/paper/ICMIE/ICMIE_129.pdf

https://onlinelibrary.wiley.com/doi/full/10.1002/qre.2536

https://www.sciencedirect.com/science/article/abs/pii/S0169743918304726

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TEXT SUMMARIZATION BY MACHINE LEARNING TECHNIQUES

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TEXT SUMMARIZATION BY MACHINE LEARNING TECHNIQUES

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Contact: Dr. Chew XinYing

Email: xinying@usm.my

Tel: 04-6532668

Descriptions:

Text summarization is considered as an operator-based transformation process by which knowledge representation structures, as generated by natural language text understanding system, are mapped into conceptually more abstract, condensed knowledge structures forming a text summary at the representational level. Searching information by reading through tons of articles is very time consuming and not efficient. Text summarization techniques are very useful where it helps to summarize long articles into short paragraphs which contain the essence of the article. This research project helps to review / compare the famous existing text summarization ML techniques and finally propose the best techniques based on different scenarios.

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[PROJECT 4]: Avoiding Crime Scene using Pedestrian Simulator: Show Me the Safest Route. 

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[PROJECT 4]: Avoiding Crime Scene using Pedestrian Simulator: Show Me the Safest Route. 

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Contact: Dr. Fadra Hassan
Email: fadratul@usm.my
WhatsApp: +60 17 461 7103

To develop 2D pedestrian simulator in public space using Cellular Automata (CA) -- one of the rule-based modeling technique in Agent-Based Modeling.
The simulator will be used to automatically find the safest path in public area that is with a lower crime rate.

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[PROJECT3]: Autonomous Wheelchair during Emergency Evacuation: Assisting Walking Disabilities People to Evacuate.

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[PROJECT3]: Autonomous Wheelchair during Emergency Evacuation: Assisting Walking Disabilities People to Evacuate.

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Contact: Dr. Fadra Hassan
Email: fadratul@usm.my
WhatsApp: +60 17 461 7103

It is reported that the current registered student with a wheelchair in the Universiti Sains Malaysia’s main campus are more than 50 students. Although the number of students using the wheelchair is small compared to the whole registered student without disabilities, the need to provide better environment to this group of students should not be neglected.

It is expected this project will generate two main outputs,

1. Framework for an autonomous wheelchair with an evacuation module.

2. A prototype of autonomous wheelchair with the designed framework from output (1).

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[PROJECT2]: Uniform-Building-by-Law Auto-Approval for Commercial Building: Speed up the Plan Approval Process.

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[PROJECT2]: Uniform-Building-by-Law Auto-Approval for Commercial Building: Speed up the Plan Approval Process.

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Contact: Dr. Fadra Hassan
Email: fadratul@usm.my
WhatsApp: +60 17 461 7103

This project is part of a research collaboration project with School of Housing, Building & Planning at Universiti Sains Malaysia,  Penang, Malaysia.

General objective: To develop an auto-checker for the Uniform-Building-by-Law (UBBL) upon plan approval.

The candidate will work closely with the architects from the School of Housing, Building & Planning at Universiti Sains Malaysia. All the UBBL data will be provided. On site training will be given on the building floor plan approval process.

Objective: 1. To investigate the effectiveness implementing an automatic checker for building plan approval. 2. To develop a system for automatic plan approval based on the UBBL data.