Edward Yapp Kien Yee

What helps PCB defect classification: crop scale, not generative augmentation

E. K. Y. Yapp

2026 IET International Conference on Engineering Technology and Applications (IET ICETA), 2026 (accepted)

Generative AI for defect generation in PCBs

E. K. Y. Yapp, H.-Y. Yeh

2026 IEEE International Conference on Consumer Electronics – Taiwan (ICCE–Taiwan), 923–924, 2026

Anomaly detection on MVTec AD using VQ-VAE-2

E. K. Y. Yapp, N. C. N. Doan

57th CIRP Conference on Manufacturing Systems (CMS), Procedia CIRP, 130:1809–1814, 2024

Towards cross-domain continual learning

M. de Carvalho, M. Pratama, J. Zhang, H. Chua, E. Yapp*

IEEE 40th International Conference on Data Engineering (ICDE), 1131–1142, 2024

A review of similarity-based few-shot learning methods for time series classification in manufacturing

S. Pan, H. Luo, M. C. H. Chua, K. Pugalenthi, E. K. Y. Yapp

6th International Conference on Industrial Artificial Intelligence (IAI), 1–6, 2024

Correlation analysis and predictive modeling for quality prediction in injection moulding process of learning factory

H. Ge, J. Zhuang, M. T. T. Nguyen, J. Fong, E. K. Y. Yapp, X. Li, N. Doan

13th Conference on Learning Factories (CLF 2023), 1–6, 2023

Autonomous deep quality monitoring in streaming environments

A. Ashfahani, M. Pratama, E. Lughofer, E. Y. K. Yee*

International Joint Conference on Neural Networks (IJCNN), 1–8, 2021

Quality monitoring for injection moulding process using a semi-supervised learning approach

D. N. C. Nam, T. V. Tung, E. Y. K. Yee*

IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society, 1–6, 2021

Data-driven quality estimation for production processes with lot-level quality control

N. J. Punnoose, P. Vadakkepat, A.-P. Loh, E. K. Y. Yap*

IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society, 1–6, 2021

Unsupervised probability matching for quality estimation with partial information in a multiple-instances, single-output scenario

K. J. Lee, E. K. Y. Yapp, X. Li

15th IEEE Conference on Industrial Electronics and Applications (ICIEA), 1432–1437, 2020

Soot particle size distributions in premixed stretch-stabilized flat ethylene–oxygen–argon flames

J. Camacho, A. V. Singh, W. Wang, R. Shan, E. K. Y. Yapp, D. Chen, M. Kraft, H. Wang

Proceedings of the Combustion Institute, 36:1001–1009, 2017

A fully coupled simulation of PAH and soot growth with a population balance model

D. Chen, Z. Zainuddin, E. Yapp*, J. Akroyd, S. Mosbach, M. Kraft

Proceedings of the Combustion Institute, 34:1827–1835, 2013

Modelling soot formation: model of particle formation

E. K. Y. Yapp, M. Kraft

In F. Battin-Leclerc, J. M. Simmie, E. Blurock (Eds.), Cleaner Combustion—Developing Detailed Chemical Kinetic Models (pp. 389–407). Springer, London, 2013

Multi-view dynastic classification and visual interpretation of blue-and-white porcelain

E. K. Y. Yapp, W. Zhuang, C. Chen, X. Wang, Y. Wen, H.-Y. Yeh

Social Sciences and Humanities Open, 13:102768, 2026

Night soil fertilizer use and parasitic infections in ancient China: a preliminary study of agricultural practices, economic factors and disease ecology in the Song dynasty

C. Chen, A. Y. X. Lee, E. K. Y. Yapp, H.-Y. Yeh

Parasitology, 1–9, 2026

An extensive experimental comparison of machine and deep learning methods for credit and bank fraud detection

E. K. Y. Yapp, H.-Y. Yeh

Finance Research Letters, 88:109190, 2026

What helps PCB defect classification: crop scale, not generative augmentation

E. K. Y. Yapp

2026 IET International Conference on Engineering Technology and Applications (IET ICETA), 2026 (accepted)

Generative AI for defect generation in PCBs

E. K. Y. Yapp, H.-Y. Yeh

2026 IEEE International Conference on Consumer Electronics – Taiwan (ICCE–Taiwan), 923–924, 2026

Cross-domain continual learning via CLAMP

W. Weng, M. Pratama, J. Zhang, C. Chen, E. Y. K. Yie*, R. Savitha

Information Sciences, 676:120813, 2024

Anomaly detection on MVTec AD using VQ-VAE-2

E. K. Y. Yapp, N. C. N. Doan

57th CIRP Conference on Manufacturing Systems (CMS), Procedia CIRP, 130:1809–1814, 2024

Towards cross-domain continual learning

M. de Carvalho, M. Pratama, J. Zhang, H. Chua, E. Yapp*

IEEE 40th International Conference on Data Engineering (ICDE), 1131–1142, 2024

A review of similarity-based few-shot learning methods for time series classification in manufacturing

S. Pan, H. Luo, M. C. H. Chua, K. Pugalenthi, E. K. Y. Yapp

6th International Conference on Industrial Artificial Intelligence (IAI), 1–6, 2024

A layer-wise neural network for multi-item single-output quality estimation

E. K. Y. Yapp, A. Gupta, X. Li

Journal of Intelligent Manufacturing, 34:3131–3141, 2023

Autonomous cross domain adaptation under extreme label scarcity

W. Weng, M. Pratama, C. Za'in, M. de Carvalho, R. Appan, A. Ashfahani, E. Y. K. Yee*

IEEE Transactions on Neural Networks and Learning Systems, 34:6839–6850, 2023

Correlation analysis and predictive modeling for quality prediction in injection moulding process of learning factory

H. Ge, J. Zhuang, M. T. T. Nguyen, J. Fong, E. K. Y. Yapp, X. Li, N. Doan

13th Conference on Learning Factories (CLF 2023), 1–6, 2023

ACDC: online unsupervised cross-domain adaptation

M. de Carvalho, M. Pratama, J. Zhang, E. Y. K. Yee*

Knowledge-Based Systems, 253:109486, 2022

Continual learning via inter-task synaptic mapping

F. Mao, W. Weng, M. Pratama, E. Y. K. Yee*

Knowledge-Based Systems, 222:106947, 2021

Online semisupervised learning approach for quality monitoring of complex manufacturing process

W. Weng, M. Pratama, A. Ashfahani, E. K. Y. Yapp

Complexity, 2021:3005276, 2021

Autonomous deep quality monitoring in streaming environments

A. Ashfahani, M. Pratama, E. Lughofer, E. Y. K. Yee*

International Joint Conference on Neural Networks (IJCNN), 1–8, 2021

Quality monitoring for injection moulding process using a semi-supervised learning approach

D. N. C. Nam, T. V. Tung, E. Y. K. Yee*

IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society, 1–6, 2021

Data-driven quality estimation for production processes with lot-level quality control

N. J. Punnoose, P. Vadakkepat, A.-P. Loh, E. K. Y. Yap*

IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society, 1–6, 2021

Comparison of base classifiers for multi-label learning

E. K. Y. Yapp, X. Li, W. F. Lu, P. S. Tan

Neurocomputing, 394:51–60, 2020

XGBoost improves classification of MGMT promoter methylation status in IDH1 wildtype glioblastoma

N. Q. K. Le, D. T. Do, F.-Y. Chiu, E. K. Y. Yapp, H.-Y. Yeh, C.-Y. Chen

Journal of Personalized Medicine, 10:128, 2020

DeepETC: a deep convolutional neural network architecture for investigating and classifying electron transport chain's complexes

N. Q. K. Le, Q.-T. Ho, E. K. Y. Yapp, Y.-Y. Ou, H.-Y. Yeh

Neurocomputing, 375:71–79, 2020

Incorporating convolutional neural networks and sequence graph transform for identifying multilabel protein Lysine PTM sites

J. N. Sua, S. Y. Lim, M. H. Yulius, X. Su, E. K. Y. Yapp, N. Q. K. Le, H.-Y. Yeh, M. C. H. Chua

Chemometrics and Intelligent Laboratory Systems, 206:104171, 2020

Unsupervised probability matching for quality estimation with partial information in a multiple-instances, single-output scenario

K. J. Lee, E. K. Y. Yapp, X. Li

15th IEEE Conference on Industrial Electronics and Applications (ICIEA), 1432–1437, 2020

Classifying promoters by interpreting the hidden information of DNA sequences via deep learning and combination of continuous FastText N-grams

N. Q. K. Le, E. K. Y. Yapp, N. Nagasundaram, H.-Y. Yeh

Frontiers in Bioengineering and Biotechnology, 7:305, 2019

Computational identification of vesicular transport proteins from sequences using deep gated recurrent units architecture

N. Q. K. Le, E. K. Y. Yapp, N. Nagasundaram, M. C. H. Chua, H.-Y. Yeh

Computational and Structural Biotechnology Journal, 17:1245–1254, 2019

Identification of clathrin proteins by incorporating hyperparameter optimization in deep learning and PSSM profiles

N. Q. K. Le, T.-T. Huynh, E. K. Y. Yapp, H.-Y. Yeh

Computer Methods and Programs in Biomedicine, 177:81–88, 2019

iEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou's 5-step rule and word embedding

N. Q. K. Le, E. K. Y. Yapp, Q.-T. Ho, N. Nagasundaram, Y.-Y. Ou, H.-Y. Yeh

Analytical Biochemistry, 571:53–61, 2019

iMotor-CNN: Identifying molecular functions of cytoskeleton motor proteins using 2D convolutional neural network via Chou's 5-step rule

N. Q. K. Le, E. K. Y. Yapp, Y.-Y. Ou, H.-Y. Yeh

Analytical Biochemistry, 575:17–26, 2019

ET-GRU: using multi-layer gated recurrent units to identify electron transport proteins

N. Q. K. Le, E. K. Y. Yapp, H.-Y. Yeh

BMC Bioinformatics, 20:377, 2019

A detailed particle model for polydisperse aggregate particles

C. S. Lindberg, M. Y. Manuputty, E. K. Y. Yapp, J. Akroyd, R. Xu, M. Kraft

Journal of Computational Physics, 397:108799, 2019

Application of computational biology and artificial intelligence technologies in cancer precision drug discovery

N. Nagarajan, E. K. Y. Yapp, N. Q. K. Le, B. Kamaraj, A. M. Al-Subaie, H.-Y. Yeh

BioMed Research International, 2019:8427042, 2019

In silico screening of sugar alcohol compounds to inhibit viral matrix protein VP40 of Ebola virus

N. Nagarajan, E. K. Y. Yapp, N. Q. K. Le, H.-Y. Yeh

Molecular Biology Reports, 46:3315–3324, 2019

The polarization of polycyclic aromatic hydrocarbons curved by pentagon incorporation: the role of the flexoelectric dipole

J. W. Martin, R. I. Slavchov, E. K. Y. Yapp, J. Akroyd, S. Mosbach, M. Kraft

Journal of Physical Chemistry C, 121:27154–27163, 2017

Modelling of soot formation in a diesel engine with the moment projection method

S. Wu, E. K. Y. Yapp, J. Akroyd, S. Mosbach, R. Xu, W. Yang, M. Kraft

Energy Procedia, 142:4092–4097, 2017

Extension of moment projection method to the fragmentation process

S. Wu, E. K. Y. Yapp, J. Akroyd, S. Mosbach, R. Xu, W. Yang, M. Kraft

Journal of Computational Physics, 335:516–534, 2017

A moment projection method for population balance dynamics with a shrinkage term

S. Wu, E. K. Y. Yapp, J. Akroyd, S. Mosbach, R. Xu, W. Yang, M. Kraft

Journal of Computational Physics, 330:960–980, 2017

Modelling PAH curvature in laminar premixed flames using a detailed population balance model

E. K. Y. Yapp, C. G. Wells, J. Akroyd, S. Mosbach, R. Xu, M. Kraft

Combustion and Flame, 176:172–180, 2017

Soot particle size distributions in premixed stretch-stabilized flat ethylene–oxygen–argon flames

J. Camacho, A. V. Singh, W. Wang, R. Shan, E. K. Y. Yapp, D. Chen, M. Kraft, H. Wang

Proceedings of the Combustion Institute, 36:1001–1009, 2017

Numerical simulation and parametric sensitivity study of optical band gap in a laminar co-flow ethylene diffusion flame

E. K. Y. Yapp, R. I. A. Patterson, J. Akroyd, S. Mosbach, E. M. Adkins, J. H. Miller, M. Kraft

Combustion and Flame, 167:320–334, 2016

Numerical simulation and parametric sensitivity study of particle size distributions in a burner-stabilised stagnation flame

E. K. Y. Yapp, D. Chen, J. Akroyd, S. Mosbach, M. Kraft, J. Camacho, H. Wang

Combustion and Flame, 162:2569–2581, 2015

A fully coupled simulation of PAH and soot growth with a population balance model

D. Chen, Z. Zainuddin, E. Yapp*, J. Akroyd, S. Mosbach, M. Kraft

Proceedings of the Combustion Institute, 34:1827–1835, 2013

Modelling soot formation: model of particle formation

E. K. Y. Yapp, M. Kraft

In F. Battin-Leclerc, J. M. Simmie, E. Blurock (Eds.), Cleaner Combustion—Developing Detailed Chemical Kinetic Models (pp. 389–407). Springer, London, 2013