Wing Hing Paul Kwan

Scopus Publications

Preface
Communications in Computer and Information Science, 2026
On the quantitative analysis of assessment scores with implicit and explicit constraints
Sanjeeb Shrestha, Xiaoying Kong, Paul Kwan
Studies in Educational Evaluation, 2025
Knowledge Integrity in Large Language Models: A State-of-The-Art Review
Vadivel Abishethvarman, Fariza Sabrina, Paul Kwan
Information Switzerland, 2025
Large Language Models (LLMs) are emerging technologies and a growing research trend in Artificial General Intelligence (AGI), which envisions a future where machines can think and learn like humans across a wide range of tasks. Information generated by LLMs is essentially the prediction of next tokens in Natural Language Processing (NLP) tasks. However, the generated content is always subject to issues of truthfulness and hallucinations. The information and knowledge integrity of LLM-generated content therefore remains subjective. Exploring recent literature on the integrity of LLMs in a systematic manner is both timely and essential. Moreover, ensuring the reliability of LLMs in real-world applications is critical. Various approaches have been explored to promote information and knowledge integrity in LLMs, including adversarial training, data augmentation, and calibration methods. However, beyond these techniques, other strategies also contribute to maintaining knowledge integrity. This paper specifically focuses on three such approaches: knowledge distillation, semantic integrity, and provenance tracking, which play essential roles in ensuring that LLMs generate accurate, consistent, and trustworthy information. Knowledge distillation enhances model efficiency by transferring knowledge from larger models to smaller ones while preserving essential learning without compromising knowledge integrity. This reduces hallucinations. Semantic integrity safeguards consistency and strengthens the robustness of generated outputs. It is concurrently checking the meaningfulness of the outputs with the context. Provenance tracking improves transparency and trustworthiness through mechanisms such as data lineage and explainability, thereby ensuring the credibility of the LLM-generated responses. This review suggests that knowledge distillation, semantic integrity, and provenance tracking can enhance the reliability of LLM outputs, with prior studies reporting reductions in hallucination rates, improvements in robustness, and gains in factual consistency.
A Collaborative Model for Integrating Teacher and GenAI into Future Education
Tayab D. Memon, Paul Kwan
Techtrends, 2025
The integration of Generative AI (GenAI) technology in education is set to transform traditional teaching and learning models. The age of teacher-led instructional pedagogy, where teachers are the sole providers of knowledge and students being mere receivers, is long outdated. Teachers are confronted with the choice of embracing new Artificial Intelligence (AI) technologies or risking obsolescence, potentially falling behind a new generation of AI-skilled educators. To maximise the benefits, teachers and the new generation of AI tools must coexist in a collaborative relationship, underscoring a new model of pedagogy for the new age. This paper proposes a collaborative model where GenAI and teachers are envisioned to work together to optimise educational outcomes of current and future learners. Beginning with a review of recent trends of AI integration in education, we describe the proposed model that we envision will underscore the roles of teachers and GenAI technology in future teaching and learning scenarios. Innovatively, we categorise the degree of involvement—minor, medium, major—by teachers and GenAI in developing learners' cognitive, psychomotor, and affective skills, as informed by Bloom's taxonomy. This categorisation underpins how GenAI and human collaboration can effectively enhance various learner skills. Additionally, key issues including privacy, bias, emotional impacts, and job displacement are discussed, alongside opportunities for improving education quality and accessibility. We argue that aspects of our model have started to appear in educational scenarios in countries including Australia, USA, and China. We conclude by highlighting the implications and impacts of rapid and sustained advancements of AI will have on current and future educational policies and the frontiers for educational research.
Reimagining Flipped Learning via Bloom’s Taxonomy and Student–Teacher–GenAI Interactions
Paul Kwan, Rajan Kadel, Tayab D. Memon, Saad S. Hashmi
Education Sciences, 2025
This paper explores how generative artificial intelligence (GenAI) technologies, such as ChatGPT 4o and other AI-based conversational models, can be applied to flipped learning pedagogy to achieve enhanced learning outcomes for students. By applying Bloom’s taxonomy to intentionally align educational objectives to the key phases of flipped learning, our study proposes a model for assigning learning activities to pre-class, in-class, and post-class contexts that can be enhanced by the integration of GenAI. In the pre-class phase, GenAI tools can facilitate personalised content delivery, enabling students to grasp fundamental concepts at their own pace. During class, the interactions between students, teacher, and GenAI encourage collaborative learning and real-time feedback. Post-class activities utilise GenAI to reinforce knowledge, provide instant feedback, and support continuous learning through summarisation and content generation. Furthermore, our model articulates the synergies between the three key actors: interactions between students and teachers, learning support provided by GenAI to students, and use of GenAI by teachers to enhance their teaching strategies. These human–AI interactions fundamentally reshape the flipped learning experience, making it more adaptive, engaging, and supportive of the development of 21st-century skills such as critical thinking, collaboration, communication, and creativity.
An Adaptive Approach in Channel Quantization for Small Cells Based on Per-Receiver Antenna Quantization
Sanjeeb Shrestha, Xiaoying Kong, Paul Kwan, Xiaojing Huang
IEEE Access, 2025
The widespread deployment of small cells (SCs) plays a crucial role in enhancing system capacity, coverage, and quality of service (QoS) for smart applications. However, due to the dynamic nature of user demands and the limited resources available, SCs cannot support large quantization codebooks, which are typically more suitable for macro cells (MCs) in finite rate feedback (FRF)-based multiple input single output (MISO) systems. In this paper, we propose an adaptive quantization approach for SCs that adjusts the codebook size based on the number of receiver antennas. Additionally, we address the issue of code quantization error (CQE), which arises when two distinct channels are quantized using the same code, as well as the average system error (AvgSysErr), which can increase due to elevated CQE. Our analysis shows that for SCs to achieve convergence of AvgSysErr with FRF-based MISO systems, the probability of non-unique codes in the quantization codebook must be less than <inline-formula> <tex-math notation="LaTeX">$\\frac {1}{N}$ </tex-math></inline-formula>, where N is the number of antennas at the transmitter. Similarly, the lower bound for the non-unique code probability must be less than or equal to <inline-formula> <tex-math notation="LaTeX">$\\varepsilon $ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$\\varepsilon $ </tex-math></inline-formula> represents the difference between the non-unique code probabilities of <inline-formula> <tex-math notation="LaTeX">$\\frac {1}{N_{1}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\\frac {1}{N_{2}}$ </tex-math></inline-formula>, given <inline-formula> <tex-math notation="LaTeX">$\\frac {1}{N_{1}}\\gt \\frac {1}{N_{2}}$ </tex-math></inline-formula> (where <inline-formula> <tex-math notation="LaTeX">$N_{1}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$N_{2}$ </tex-math></inline-formula> denote the number of antennas at the transmitter).
Fusion of UAV-Acquired Visible Images and Multispectral Data by Applying Machine-Learning Methods in Crop Classification
Zuojun Zheng, Jianghao Yuan, Wei Yao, Paul Kwan, Hongxun Yao, et al.
Agronomy, 2024
The sustainable development of agriculture is closely related to the adoption of precision agriculture techniques, and accurate crop classification is a fundamental aspect of this approach. This study explores the application of machine learning techniques to crop classification by integrating RGB images and multispectral data acquired by UAVs. The study focused on five crops: rice, soybean, red bean, wheat, and corn. To improve classification accuracy, the researchers extracted three key feature sets: band values and vegetation indices, texture features extracted from a grey-scale co-occurrence matrix, and shape features. These features were combined with five machine learning models: random forest (RF), support vector machine (SVM), k-nearest neighbour (KNN) based, classification and regression tree (CART) and artificial neural network (ANN). The results show that the Random Forest model consistently outperforms the other models, with an overall accuracy (OA) of over 97% and a significantly higher Kappa coefficient. Fusion of RGB images and multispectral data improved the accuracy by 1–4% compared to using a single data source. Our feature importance analysis showed that band values and vegetation indices had the greatest impact on classification results. This study provides a comprehensive analysis from feature extraction to model evaluation, identifying the optimal combination of features to improve crop classification and providing valuable insights for advancing precision agriculture through data fusion and machine learning techniques.
Development of a Device and Algorithm Research for Akhal-Teke Activity Level Analysis
Xuan Chen, Fuzhong Li, Jinxing Li, Qijie Fan, Paul Kwan, et al.
Applied Sciences Switzerland, 2024
This study demonstrated that wearable devices can distinguish between different levels of horse activity, categorized into three types based on the horse’s gaits: low activity (standing), medium activity (walking), and high activity (trotting, cantering, and galloping). Current research in activity level classification predominantly relies on deep learning techniques, known for their effectiveness but also their demand for substantial data and computational resources. This study introduces a combined acceleration threshold behavior recognition method tailored for wearable hardware devices, enabling these devices to classify the activity levels of horses directly. The approach comprises three sequential phases: first, a combined acceleration interval counting method utilizing a non-linear segmentation strategy for preliminary classification; second, a statistical analysis of the variance among these segments, coupled with multi-level threshold processing; third, a method using variance-based proximity classification for recognition. The experimental results show that the initial stage achieved an accuracy of 87.55% using interval counting, the second stage reached 90.87% with variance analysis, and the third stage achieved 91.27% through variance-based proximity classification. When all three stages are combined, the classification accuracy improves to 92.74%. Extensive testing with the Xinjiang Wild Horse Group validated the feasibility of the proposed solution and demonstrated its practical applicability in real-world scenarios.
Intensifying learner engagement and focus by a block mode flipped learning pedagogy
Flipped Classrooms and Learning Perspectives Opportunities and Challenges, 2024
Analysis and Comparison of New-Born Calf Standing and Lying Time Based on Deep Learning
Wenju Zhang, Yaowu Wang, Leifeng Guo, Greg Falzon, Paul Kwan, et al.
Animals, 2024
Standing and lying are the fundamental behaviours of quadrupedal animals, and the ratio of their durations is a significant indicator of calf health. In this study, we proposed a computer vision method for non-invasively monitoring of calves’ behaviours. Cameras were deployed at four viewpoints to monitor six calves on six consecutive days. YOLOv8n was trained to detect standing and lying calves. Daily behavioural budget was then summarised and analysed based on automatic inference on untrained data. The results show a mean average precision of 0.995 and an average inference speed of 333 frames per second. The maximum error in the estimated daily standing and lying time for a total of 8 calf-days is less than 14 min. Calves with diarrhoea had about 2 h more daily lying time (p < 0.002), 2.65 more daily lying bouts (p < 0.049), and 4.3 min less daily lying bout duration (p = 0.5) compared to healthy calves. The proposed method can help in understanding calves’ health status based on automatically measured standing and lying time, thereby improving their welfare and management on the farm.
Confluence: A Robust Non-IoU Alternative to Non-Maxima Suppression in Object Detection
Andrew J. Shepley, Greg Falzon, Paul Kwan, Ljiljana Brankovic
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
An Empirical Study of Students’ Perception of and Key Factors Affecting Overall Satisfaction in an Intensive Block Mode and Flipped Classroom
Paul Kwan, Tayab D. Memon, Saad S. Hashmi, Flemming Rhode, Rajan Kadel
Education Sciences, 2022
Improving Wheat Yield Prediction Accuracy Using LSTM‐RF Framework Based on UAV Thermal Infrared and Multispectral Imagery
Yulin Shen, Benoît Mercatoris, Zhen Cao, Paul Kwan, Leifeng Guo, et al.
Agriculture Switzerland, 2022
A Deep Learning Model to Predict Student Learning Outcomes in LMS Using CNN and LSTM
Abdulaziz Salamah Aljaloud, Diaa Mohammed Uliyan, Adel Alkhalil, Magdy Abd Elrhman, Azizah Fhad Mohammed Alogali, et al.
IEEE Access, 2022
Studying learner’s perception of attaining graduate attributes in capstone project units using online flipped classroom
Tayab D. Memon, Monica Jurin, Paul Kwan, Tony Jan, Nandini Sidnal, et al.
Education Sciences, 2021
Automated muzzle detection and biometric identification via few-shot deep transfer learning of mixed breed cattle
Ali Shojaeipour, Greg Falzon, Paul Kwan, Nooshin Hadavi, Frances C. Cowley, et al.
Agronomy, 2021
Automated location invariant animal detection in camera trap images using publicly available data sources
Andrew Shepley, Greg Falzon, Paul Meek, Paul Kwan
Ecology and Evolution, 2021
U-infuse: Democratization of customizable deep learning for object detection
Andrew Shepley, Greg Falzon, Christopher Lawson, Paul Meek, Paul Kwan
Sensors, 2021
Livestock classification and counting in quadcopter aerial images using Mask R-CNN
Beibei Xu, Wensheng Wang, Greg Falzon, Paul Kwan, Leifeng Guo, et al.
International Journal of Remote Sensing, 2020
Automated cattle counting using Mask R-CNN in quadcopter vision system
Beibei Xu, Wensheng Wang, Greg Falzon, Paul Kwan, Leifeng Guo, et al.
Computers and Electronics in Agriculture, 2020
Modelling the potential effects of climate factors on Dubas bug (Ommatissus lybicus) presence/absence and its infestation rate: A case study from Oman
Khalifa M Al Kindi, Paul Kwan, Nigel R Andrew, Mitchell Welch
Pest Management Science, 2019
Livestock vocalisation classification in farm soundscapes
James C. Bishop, Greg Falzon, Mark Trotter, Paul Kwan, Paul D. Meek
Computers and Electronics in Agriculture, 2019
Designing a multi-stage multivariate empirical mode decomposition coupled with ant colony optimization and random forest model to forecast monthly solar radiation
Ramendra Prasad, Mumtaz Ali, Paul Kwan, Huma Khan
Applied Energy, 2019
Factors that influence teachers’ decisions to use smartphone clicker apps to enhance teacher-student interactions in university classrooms in Saudi Arabia
Abdulaziz Aljaloud, William Billingsley, Paul Kwan
Learning Research and Practice, 2019
Saudi undergraduate students' perceptions of the use of smartphone clicker apps on learning performance
Abdualziz Salamah Aljaloud, Nicolas Gromik, Paul Kwan, William Billingsley
Australasian Journal of Educational Technology, 2019
A novel shape descriptor based on salient keypoints detection for binary image matching and retrieval
Houssem Chatbri, Keisuke Kameyama, Paul Kwan, Suzanne Little, Noel E. O’Connor
Multimedia Tools and Applications, 2018
Comparison of grazing behaviour of sheep on pasture with different sward surface heights using an inertial measurement unit sensor
Leifeng Guo, Mitchell Welch, Robin Dobos, Paul Kwan, Wensheng Wang
Computers and Electronics in Agriculture, 2018
Pricing mergers with differential synergies
Nipun Agarwal, Paul Kwan
Strategic Change, 2018
Behavioral merger and acquisition pricing: Application to Verizon mergers with AOL and Yahoo
Nipun Agarwal, Paul Kwan, David Paul
Strategic Change, 2018
Merger and acquisition pricing using agent based modelling
N. Agarwal, P. Kwan, David Paul
Economics Management and Financial Markets, 2018
Predicting the potential geographical distribution of parasitic natural enemies of the Dubas bug (Ommatissus lybicus de Bergevin) using geographic information systems
Khalifa M. Al‐Kindi, Ali K. Al‐Wahaibi, Paul Kwan, Nigel R. Andrew, Mitchell Welch, et al.
International Journal of Business Innovation and Research, 2018
Automatic MOOC video classification using transcript features and convolutional neural networks
Houssem Chatbri, Kevin McGuinness, Suzanne Little, Jiang Zhou, Keisuke Kameyama, et al.
Multiedtech 2017 Proceedings of the 2017 ACM Workshop on Multimedia Based Educational and Knowledge Technologies for Personalized and Social Online Training Co Located with mm 2017, 2017
Educational video classification by using a transcript to image transform and supervised learning
Houssem Chatbri, Marlon Oliveira, Kevin McGuinness, Suzanne Little, Keisuke Kameyama, et al.
Proceedings of the 7th International Conference on Image Processing Theory Tools and Applications Ipta 2017, 2017
A Web-Based Infrastructure for the Assisted Annotation of Heritage Collections
Jacob Foley, Paul Kwan, Mitchell Welch
Journal on Computing and Cultural Heritage, 2017
Impact of environmental variables on Dubas bug infestation rate: A case study from the Sultanate of Oman
Khalifa M. Al-Kindi, Paul Kwan, Nigel Andrew, Mitchell Welch
Plos One, 2017
Modelling spatiotemporal patterns of dubas bug infestations on date palms in northern Oman: A geographical information system case study
Khalifa M. Al-Kindi, Paul Kwan, Nigel R. Andrew, Mitchell Welch
Crop Protection, 2017
Impacts of human-related practices on Ommatissus lybicus infestations of date palm in Oman
Khalifa M. Al-Kindi, Paul Kwan, Nigel R. Andrew, Mitchell Welch
Plos One, 2017
Remote sensing and spatial statistical techniques for modelling Ommatissus lybicus (Hemiptera: Tropiduchidae) habitat and population densities
Khalifa M. Al-Kindi, Paul Kwan, Nigel R. Andrew, Mitchell Welch
Peerj, 2017
A novel feature reduction framework for digital mammogram image classification
Hajar Alharbi, Gregory Falzon, Paul Kwan
Proceedings 3rd Iapr Asian Conference on Pattern Recognition Acpr 2015, 2016
Towards a segmentation and recognition-free approach for content-based document image retrieval of handwritten queries
Houssem Chatbri, Keisuke Kameyama, Paul Kwan
Proceedings 3rd Iapr Asian Conference on Pattern Recognition Acpr 2015, 2016
Indirect interaction: A computing lecture for five to seven year-olds
William Billingsley, Paul Kwan
DIS 2016 Companion Proceedings of the 2016 ACM Conference on Designing Interactive Systems Fuse, 2016
A comparative study using contours and skeletons as shape representations for binary image matching
Houssem Chatbri, Keisuke Kameyama, Paul Kwan
Pattern Recognition Letters, 2016
Improving the computational efficiency of an agent-based spatiotemporal model of livestock disease spread and control
R.A. Bradhurst, S.E. Roche, I.J. East, P. Kwan, M.G. Garner
Environmental Modelling and Software, 2016
Using a video-based critique process to support studio pedagogies in distance education – A tool and pilot study
Ascilite 2016 Conference Proceedings 33rd International Conference of Innovation Practice and Research in the Use of Educational Technologies in Tertiary Education Show Me the Learning, 2016
Genome-wide linkage disequilibrium and genetic diversity in five populations of Australian domestic sheep
Hawlader Abdullah Al-Mamun, Samuel A Clark, Paul Kwan, Cedric Gondro
Genetics Selection Evolution, 2015
Genome-wide association study of body weight in Australian Merino sheep reveals an orthologous region on OAR6 to human and bovine genomic regions affecting height and weight
Hawlader A. Al-Mamun, Paul Kwan, Samuel A. Clark, Mohammad H. Ferdosi, Ross Tellam, et al.
Genetics Selection Evolution, 2015
A hybrid modeling approach to simulating foot-and-mouth disease outbreaks in Australian livestock
Richard A. Bradhurst, Sharon E. Roche, Iain J. East, Paul Kwan, M. Graeme Garner
Frontiers in Environmental Science, 2015
Generalized gaussian reference curve measurement model for high-performance liquid chromatography with diode array detector separation and its solution by multi-target intermittent particle swarm optimization
Lizhi Cui, Zhihao Ling, Josiah Poon, Simon K. Poon, Hao Chen, et al.
Journal of Chemometrics, 2015
A knowledge-based Decision Support System for adaptive fingerprint identification that uses relevance feedback
Paul W. Kwan, Mitchell C. Welch, Jacob J. Foley
Knowledge Based Systems, 2015
Research trends in student response systems: A literature review
Abdulaziz Aljaloud, Nicolas Gromik, William Billingsley, Paul Kwan
International Journal of Learning Technology, 2015
Separation model of Generalized Reference Curve Measurement for HPLC-DAD and it solution by multi-target Bare Bones Particle Swarm Optimization
Lizhi Cui, Josiah Poon, Simon K. Poon, Paul Kwan, Junbin Gao, et al.
Proceedings 2014 IEEE International Conference on Bioinformatics and Biomedicine IEEE Bibm 2014, 2014
Social recommender system for predicting the needs of students/instructors: Review and proposed framework
Hadeel Alharbi, Ashoka Jayawardena, Paul Kwan
Proceedings 2014 International Conference on Future Internet of Things and Cloud Ficloud 2014, 2014
An application-independent and segmentation-free approach for spotting queries in document images
Houssem Chatbri, Paul Kwan, Keisuke Kameyama
Proceedings International Conference on Pattern Recognition, 2014
An improved independent component analysis model for 3D chromatogram separation and its solution by multi-areas genetic algorithm
Lizhi Cui, Josiah Poon, Simon K Poon, Hao Chen, Junbin Gao, et al.
BMC Bioinformatics, 2014
A modular approach for query spotting in document images and its optimization using genetic algorithms
Houssem Chatbri, Paul Kwan, Keisuke Kameyama
Proceedings of the 2014 IEEE Congress on Evolutionary Computation CEC 2014, 2014
A parallel model of independent component analysis constrained by a 5-parameter reference curve and its solution by multi-target particle swarm optimization
Lizhi Cui, Zhihao Ling, Josiah Poon, Simon K. Poon, Hao Chen, et al.
Analytical Methods, 2014
Feature Extraction in Content-Based Image Retrieval
Jacob John Foley, Paul Kwan
Encyclopedia of Information Science and Technology Third Edition Volume 1 10, 2014
Applying Graphics Processing Unit Technologies to Agent-Based Simulation
Mitchell Welch, Paul Kwan
Encyclopedia of Information Science and Technology Third Edition Volume 1 10, 2014
Extended space decision tree
Md. Nasim Adnan, Md. Zahidul Islam, Paul W. H. Kwan
Communications in Computer and Information Science, 2014
Applying GIS and high performance agent-based simulation for managing an Old World Screwworm fly invasion of Australia
M.C. Welch, P.W. Kwan, A.S.M. Sajeev
Acta Tropica, 2014
Sketch-based image retrieval by size-adaptive and noise-robust feature description
Houssem Chatbri, Keisuke Kameyama, Paul Kwan
2013 International Conference on Digital Image Computing Techniques and Applications Dicta 2013, 2013
Parallel model of independent component analysis constrained by reference curves for HPLC-DAD and its solution by multi-areas genetic algorithm
Lizhi Cui, Josiah Poon, Simon K. Poon, Kei Fan, Hao Chen, et al.
Proceedings 2013 IEEE International Conference on Bioinformatics and Biomedicine IEEE Bibm 2013, 2013
Fuzzy image segmentation for mass detection in digital mammography: Recent advances and techniques
Hajar Mohammedsaleh H. Alharbi, Paul Kwan, Ashoka Jayawardena, A. S. M. Sajeev
Image Processing Concepts Methodologies Tools and Applications, 2013
Parallel evolutionary computation in R
Cedric Gondro, Paul Kwan
Bioinformatics Concepts Methodologies Tools and Applications, 2013
Active contour texture segmentation in modulus wavelet feature spaces
Ashoka Jayawardena, Paul Kwan
Lecture Notes in Electrical Engineering, 2013
Finite impulse response double density filter banks and framelets
ASHOKA JAYAWARDENA, PAUL KWAN
International Journal of Wavelets Multiresolution and Information Processing, 2013
A high performance, agent-based simulation of old world screwworm fly lifecycle and dispersal using a graphics processing unit (GPU) platform
Proceedings 20th International Congress on Modelling and Simulation Modsim 2013, 2013
Modelling the spread of livestock disease on a national scale: The case for a hybrid approach
Proceedings 20th International Congress on Modelling and Simulation Modsim 2013, 2013
Parallel evolutionary computation in R
Cedric Gondro, Paul Kwan
Multidisciplinary Computational Intelligence Techniques Applications in Business Engineering and Medicine, 2012
A comparative study of fuzzy thresholding techniques for mass detection in digital mammography
Hajar Alharbi, Paul Kwan, A. S. M. Sajeev
ACM International Conference Proceeding Series, 2012
Fuzzy image segmentation for mass detection in digital mammography: Recent advances and techniques
Hajar Mohammedsaleh H. Alharbi, Paul Kwan, Ashoka Jayawardena, A. S. M. Sajeev
Multidisciplinary Computational Intelligence Techniques Applications in Business Engineering and Medicine, 2012
Improving the efficiency of large-scale agent-based models using compression techniques
Mitchell Welch, Paul Kwan, A.S.M. Sajeev, Graeme Garner
Multidisciplinary Computational Intelligence Techniques Applications in Business Engineering and Medicine, 2012
A co-evolving memetic wrapper for prediction of patient outcomes in TCM informatics
Dion Detterer, Paul Kwan, Cedric Gondro
Frontiers of Computer Science in China, 2012
Classification of crops and weeds from digital images: A support vector machine approach
Faisal Ahmed, Hawlader Abdullah Al-Mamun, A.S.M. Hossain Bari, Emam Hossain, Paul Kwan
Crop Protection, 2012
Generalized Evidential Processing in Multiple Simultaneous Threat Detection in UNIX
Zafar Sultan, Paul Kwan
Enhancing Enterprise and Service Oriented Architectures with Advanced Web Portal Technologies, 2012
COW: A co-evolving memetic wrapper for herb-herb interaction analysis in TCM informatics
Dion Detterer, Paul Kwan
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2012
A study on local binary pattern for automated weed classification using template matching and support vector machine
Faisal Ahmed, A.S.M. Hossain Bari, ASM Shihavuddin, Hawlader Abdullah Al-Mamun, Paul Kwan
12th IEEE International Symposium on Computational Intelligence and Informatics Cinti 2011 Proceedings, 2011
Analysis of herbal formulation in TCM: Infertility as a case study
S. K. Poon, Kei Fan, J. Poon, C. Loy, K. Chan, et al.
2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops Bibmw 2011, 2011
Content-based image retrieval of cultural heritage symbols by interaction of visual perspectives
PAUL W. KWAN, KEISUKE KAMEYAMA, JUNBIN GAO, KAZUO TORAICHI
International Journal of Pattern Recognition and Artificial Intelligence, 2011
A novel approach in discovering significant interactions from TCM patient prescription data
Simon K. Poon, Josiah Poon, Martin McGrane, Xuezhong Zhou, Paul Kwan, et al.
International Journal of Data Mining and Bioinformatics, 2011
On engineering challenges of applying relevance feedback to fingerprint identification systems
Mitchell Welch, Paul W. Kwan, A. S. M. Sajeev
2010 International Conference on Computational Intelligence and Software Engineering Cise 2010, 2010
Analysis of synergistic and antagonistic effects of TCM: Cases on diabetes and insomnia
Martin McGrane, Simon Poon, Josiah Poon, Kelvin Chan, Clement Loy, et al.
2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops Bibmw 2010, 2010
Studying herb-herb interaction for insomnia through the theory of complementarities
Josiah Poon, Simon Poon, Dawei Yin, Kelvin Chan, Clement Loy, et al.
2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops Bibmw 2010, 2010
Novel two-stage analytic approach in extraction of strong herb-herb interactions in TCM clinical treatment of insomnia
Xuezhong Zhou, Josiah Poon, Paul Kwan, Runshun Zhang, Yinhui Wang, et al.
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2010
Generalized evidential processing in multiple simultaneous threat detection in UNIX
Zafar Sultan, Paul Kwan
International Journal of Web Portals, 2010
Sparse kernel learning with LASSO and Bayesian inference algorithm
Junbin Gao, Paul W. Kwan, Daming Shi
Neural Networks, 2010
A learning framework for adaptive fingerprint identification using relevance feedback
PAUL W. KWAN, JUNBIN GAO, YI GUO, KEISUKE KAMEYAMA
International Journal of Pattern Recognition and Artificial Intelligence, 2010
Learning gradients with Gaussian processes
Xinwei Jiang, Junbin Gao, Tianjiang Wang, Paul W. Kwan
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2010
Regularized Kernel Local Linear Embedding on dimensionality reduction for non-vectorial data
Yi Guo, Junbin Gao, Paul W. Kwan
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2009
Comprehensive analysis for the local fisher discriminant analysis
JUNBIN GAO, PAUL W. H. KWAN, XIAODI HUANG
International Journal of Pattern Recognition and Artificial Intelligence, 2009
A user-centered framework for adaptive fingerprint identification
Paul W. H. Kwan, Junbin Gao, Graham Leedham
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2009
Robust multivariate L1 principal component analysis and dimensionality reduction
Junbin Gao, Paul W. Kwan, Yi Guo
Neurocomputing, 2009
L-1 LASSO modeling and its Bayesian inference
Junbin Gao, Michael Antolovich, Paul W. Kwan
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2008
Twin kernel embedding
Y. Guo, J. Gao, P.W. Kwan
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008
Twin Kernel Embedding with back constraints
Yi Guo, Paul W. Kwan, Junbin Gao
Proceedings IEEE International Conference on Data Mining Icdm, 2007
Learning out-of sample mapping in non-vectorial data reduction using constrained twin kernel embedding
Yi Guo, Junbin Gao, Paul W. Kwan
Proceedings of the Sixth International Conference on Machine Learning and Cybernetics Icmlc 2007, 2007
Learning framework for examiner-centric fingerprint classification using spectral features
Paul W. H. Kwan, Yi Guo, Junbin Gao
Proceedings of SPIE the International Society for Optical Engineering, 2007
Robust L1 PCA and application in image denoising
Junbin Gao, Paul W. H. Kwan, Yi Guo
Proceedings of SPIE the International Society for Optical Engineering, 2007
Visualization of protein structure relationships using Twin Kernel Embedding
Yi Guo, Junbin Gao, Paul W. Kwan, Kevin X. Hou
2007 1st International Conference on Bioinformatics and Biomedical Engineering Icbbe, 2007
Twin kernel embedding with relaxed constraints on dimensionality reduction for structured data
Yi Guo, Junbin Gao, Paul W. Kwan
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2007
Learning optimal Kernel from distance metric in Twin Kernel Embedding for dimensionality reduction and visualization of fingerprints
Yi Guo, Paul W. Kwan, Junbin Gao
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2007
A multi-step strategy for approximate similarity search in image databases
Conferences in Research and Practice in Information Technology Series, 2006
Visualization of non-vectorial data using twin kernel embedding
Yi Guo, Junbin Gao, Paul Kwan
Integrating AI and Data Mining 1st International Workshop Proceedings Aidm 2006, 2006
Content-based retrieval of kamon images by image smoothing and relaxation
Proceedings of the IASTED International Conference on Modelling Identification and Control Mic, 2005
A Contour Tracing Algorithm that Avoids Duplicate Tracing Common Boundaries between Regions
Journal of the Institute of Image Electronics Engineers of Japan, 2004
A New Method on Assigning Function Types to Line Segments for Function Approximation-based Image Coding
IEEE Pacific RIM Conference on Communications Computers and Signal Processing Proceedings, 2003
A Method on Tracking Common Boundaries of Color Regions in Function Approximation-based Image Coding
IEEE Pacific RIM Conference on Communications Computers and Signal Processing Proceedings, 2003
On a relaxation-labeling algorithm for real-time contour-based image similarity retrieval
Paul W.H. Kwan, Keisuke Kameyama, Kazuo Toraichi
Image and Vision Computing, 2003
Approximate query processing for a content-based image retrieval method
Paul W. H. Kwan, Kazuo Toraichi, Hiroyuki Kitagawa, Keisuke Kameyama
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2003
On a Fluency Image Coding System for Beef Marbling Evaluation
Kazuo Toraichi, Paul Wing Hing Kwan, Kazuki Katagishi, Tetsuo Sugiyama, Koichi Wada, et al.
Pattern Recognition Letters, 2002
A publishing system based on fluency coding method
IEEE International Conference on Image Processing, 2002
A method on tracking unit pixel width line segments for function approximation-based image coding
Fumio Kawazoe, Kazuo Toraichi, Paul W.H. Kwan, Koichi Wada
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2002
TAST - Trademark Application Assistant
IEEE International Conference on Image Processing, 2002
Connecting image similarity retrieval with consistent labeling problem by introducing a Match-all label
IEEE International Conference on Fuzzy Systems, 2001
Trademark retrieval by relaxation matching on fluency function approximated image contours
IEEE Pacific RIM Conference on Communications Computers and Signal Processing Proceedings, 2001
On an image contour compression method using Compactly Supported Sampling Functions
IEEE Pacific RIM Conference on Communications Computers and Signal Processing Proceedings, 2001

Wing Hing Paul Kwan

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