Wei Pang

Research output

Research output per year 2002 2015 2016 2018 2019 2020 2021 2021

• 58 Article
• 44 Conference contribution
• 7 Paper
• 7 Conference article
• 8 More
  • 3 Chapter
  • 2 Working paper
  • 1 Book
  • 1 Meeting abstract
  • 1 Review article

Research output per year

Research output per year

Search results

• 2021

  A Genetic Algorithm with Tree-structured Mutation for Hyperparameter Optimisation of Graph Neural Networks

  Yuan, Y., Wang, W. & Pang, W., 6 Apr 2021, (Accepted/In press) CEC 2021: IEEE Congress on Evolutionary Computation. IEEE

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  File
  19 Downloads (Pure)

• A Multi-View Clustering Algorithm for Mixed Numeric and Categorical Data

  Ji, J., Li, R., Pang, W., He, F., Feng, G. & Zhao, X., 4 Feb 2021, In: IEEE Access. 9, p. 24913-24924 12 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Clustering algorithms 100%
  • Molecular Cluster 72%
  • Experiments 13%
  1 Citation (Scopus)

  34 Downloads (Pure)

• An Immune-Inspired Approach to Macro-Level Neural Ensemble Search

  Frachon, L., Pang, W. & Coghill, G. M., 9 Aug 2021, 2021 IEEE Congress on Evolutionary Computation (CEC). IEEE, p. 2491-2498 8 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  File

  • Immune system 100%
  • Macros 74%
  • Evolutionary algorithms 50%
  • Deep neural networks 26%
  • Network architecture 25%
  3 Downloads (Pure)

• Are Contrastive Explanations Useful?

  Forrest, J., Sripada, S., Pang, W. & Coghill, G., 2 Jul 2021, In: CEUR Workshop Proceedings. 2894, p. 9-16 8 p.

  Research output: Contribution to journal › Conference article › peer-review

  Open Access

  File

  • Controllers 100%
  1 Downloads (Pure)

• A Systematic Comparison Study on Hyperparameter Optimisation of Graph Neural Networks for Molecular Property Prediction

  Yuan, Y., Wang, W. & Pang, W., 26 Jun 2021, GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference. Association for Computing Machinery, p. 386–394 9 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  File

  • Hyperparameters 100%
  • Neural Networks 75%
  • Random Search 65%
  • Prediction 64%
  • Datasets 63%
  1 Downloads (Pure)

• Challenges and Future Directions for Accountable Machine Learning

  Zainyte, A. & Pang, W., 2 Jul 2021, In: CEUR Workshop Proceedings. 2894, p. 40-47 8 p.

  Research output: Contribution to journal › Conference article › peer-review

  Open Access

  File

  • Machine learning 100%
  • Finance 26%
  • Autonomous vehicles 24%
  • Learning systems 23%
  • Learning algorithms 18%
  6 Downloads (Pure)

• GUV-Net for high fidelity shoeprint generation

  Hassan, M., Wang, Y., Pang, W., Wang, D., Li, D., Zhou, Y. & Xu, D., 21 Oct 2021, (E-pub ahead of print) In: Complex and Intelligent Systems.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Deep learning 100%
  • Forensic science 88%
  • Image quality 51%
  • Decoding 47%

• Minimum distribution support vector clustering

  Wang, Y., Chen, J., Xie, X., Yang, S., Pang, W., Huang, L., Zhang, S. & Zhao, S., 8 Nov 2021, In: Entropy. 23, 11, 1473.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Support Vector 100%
  • Clustering 61%
  • Clustering Methods 29%
  • Margin 24%
  • margins 23%

• Multiscale Increment Entropy: An approach for quantifying the physiological complexity of biomedical time series

  Wang, X., Liu, X., Pang, W. & Jiang, A., 24 Nov 2021, (Accepted/In press) In: Information Sciences.

  Research output: Contribution to journal › Article › peer-review

  • Time series 100%
  • Entropy 97%
  • Electroencephalography 15%
  • Oxygen 13%
  • Aging of materials 13%

• On Evidence Capture for Accountable AI Systems

  Pang, W., Markovic, M., Naja, I., Fung, C. P. & Edwards, P., 2 Jul 2021, In: CEUR Workshop Proceedings. 2894, p. 33-39 7 p.

  Research output: Contribution to journal › Conference article › peer-review

  Open Access

  File
  5 Downloads (Pure)

• The Accountability Fabric: A Suite of Semantic Tools For Managing AI System Accountability and Audit

  Markovic, M., Naja, I., Edwards, P. & Pang, W., 14 Oct 2021, In: CEUR Workshop Proceedings. 2980, 336.

  Research output: Contribution to journal › Conference article › peer-review

  Open Access

  • Audit 100%
  • Accountability 82%
  • Life Cycle 40%
  • Graph 36%
  • Managers 35%

• Towards Accountability Driven Development for Machine Learning Systems

  Fung, C. P., Pang, W., Naja, I., Markovic, M. & Edwards, P., 2 Jul 2021, In: CEUR Workshop Proceedings. 2894, p. 25-32 8 p.

  Research output: Contribution to journal › Conference article › peer-review

  Open Access

  File

  • Learning systems 100%
  • Machine learning 71%
  • Engineers 17%
  • Acceptance tests 15%
  • Testing 6%
  8 Downloads (Pure)

• Which hyperparameters to optimise? an investigation of evolutionary hyperparameter optimisation in graph neural network for molecular property prediction

  Yuan, Y., Wang, W. & Pang, W., 7 Jul 2021, GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, p. 1403–1404 2 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  File

  • Neural networks 100%
  • Evolutionary algorithms 75%
  • Experiments 29%
  1 Downloads (Pure)
• 2020

  A Multi-Modal Deep Learning approach to the Early Prediction of Mild Cognitive Impairment Conversion to Alzheimer’s Disease

  Rana, S. S., Ma, X., Pang, W. & Wolverson, E., 23 Oct 2020, (Accepted/In press) 2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT). IEEE, 9302550

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Deep Learning 100%
  • Cognitive Dysfunction 59%
  • Alzheimer Disease 56%
  • Neuroimaging 13%
  • Cognition 11%
  1 Citation (Scopus)

• An Efficient v-minimum Absolute Deviation Distribution Regression Machine

  Wang, Y., Wang, Y., Song, Y., Xie, X., Huang, L., Pang, W. & Coghill, G. M., 2020, In: IEEE Access. 8, p. 85533-85551 19 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Support vector regression 100%
  • Error 64%
  • Time 30%
  2 Citations (Scopus)

  7 Downloads (Pure)

• A Systematic Density-based Clustering Method Using Anchor Points

  Wang, Y., Wang, D., Pang, W., Miao, C., Tan, A-H. & Zhou, Y., 4 Aug 2020, In: Neurocomputing. 400, p. 352-370 19 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Anchors 100%
  • Cluster Analysis 88%
  • Datasets 26%
  • Facial Recognition 10%
  • Unsupervised learning 10%
  4 Citations (Scopus)

  5 Downloads (Pure)

• Cancer genotypes prediction and associations analysis from imaging phenotypes: a survey on radiogenomics

  Wang, Y., Wang, Y., Guo, C., Xie, X., Liang, S., Zhang, R., Pang, W. & Huang, L., Sep 2020, In: Biomarkers in Medicine. 14, 12, p. 1151-1164 14 p.

  Research output: Contribution to journal › Article › peer-review

  • Deep Learning 100%
  • Genotype 78%
  • Phenotype 65%
  • Neoplasms 35%
  • Application 34%
  1 Citation (Scopus)

• Clustering Mixed Numeric and Categorical Data with Cuckoo Search

  Ji, J., Pang, W., Li, Z., He, F., Feng, G. & Zhao, X., 2020, In: IEEE Access. 8, p. 30988-31003 16 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Mining 100%
  • Clustering algorithms 78%
  • Molecular Cluster 56%
  • Global optimization 45%
  • Data mining 36%
  5 Citations (Scopus)

  20 Downloads (Pure)

• Clustering Single-cell RNA-sequencing Data based on Matching Clusters Structures

  Wang, Y., Zhou, Y., Pang, W., Liang, Y. & Wang, S., 15 Feb 2020, In: Tehnički vjesnik – Technical Gazette. 27, 1, p. 89-95 7 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • RNA 100%
  • Labels 70%
  • K-means clustering 23%
  • Merging 20%
  5 Downloads (Pure)

• Deep Ensemble Learning for Human Action Recognition in Still Images

  Yu, X., Zhang, Z., Wu, L., Pang, W., Chen, H., Yu, Z. & Li, B., 31 Jan 2020, In: Complexity. 2020, 23 p., 9428612.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File
  10 Citations (Scopus)

  15 Downloads (Pure)

• Evolutionary Learning for Soft Margin Problems: A Case Study on Practical Problems with Kernels

  Wang, W., Pang, W., Bingham, P. A., Mania, M., Chen, T-Y. & Perry, J. J., 3 Sep 2020, 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, 9185574

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  File

  • Evolutionary Learning 100%
  • Kernel 61%
  • Margin 58%
  • Glass 40%
  • Polymers 39%
  20 Downloads (Pure)

• GANs-Based Data Augmentation for Citrus Disease Severity Detection Using Deep Learning

  Zeng, Q., Ma, X., Cheng, B., Zhou, E. & Pang, W., 2020, In: IEEE Access. 8, p. 172882-172891 10 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Deep learning 100%
  • Computational efficiency 12%
  3 Citations (Scopus)

  11 Downloads (Pure)

• Goods Consumed during Transit in Split Delivery Vehicle Routing Problems: Modeling and Solution

  Yang, W., Wang, D., Pang, W., Tan, A-H. & Zhou, Y., 2020, In: IEEE Access. 8, p. 110336-110350 15 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Vehicle routing 100%
  • Fuel 35%
  • Time 31%
  • Food 30%
  • Environment 24%
  1 Citation (Scopus)

  8 Downloads (Pure)

• Inferring Structure and Parameters of Dynamic System Models simultaneously using Swarm Intelligence Approaches

  Usman, M., Pang, W. & Coghill, G. M., Sep 2020, In: Memetic Computing. 12, 3, p. 267-282 16 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Swarm Intelligence 100%
  • Particle Swarm Optimization 86%
  • Swarm intelligence 71%
  • Dynamic Systems 59%
  • Particle swarm optimization (PSO) 50%
  8 Downloads (Pure)

• Mammographic image classification with deep fusion learning

  Yu, X., Pang, W., Xu, Q. & Liang, M., 1 Sep 2020, In: Scientific Reports. 10, 14361.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Deep Learning 100%
  • Boidae 26%
  • Neoplasms 23%
  • Politics 22%
  • Datasets 14%
  6 Citations (Scopus)

  11 Downloads (Pure)

• McDPC: multi-center density peak clustering

  Wang, Y., Wang, D., Zhang, X., Pang, W., Miao, C., Tan, A-H. & Zhou, Y., Sep 2020, In: Neural Computing and Applications. 32, 17, p. 13465-13478 14 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Clustering algorithms 100%
  • Image segmentation 26%
  • Experiments 9%
  11 Citations (Scopus)

  7 Downloads (Pure)

• Self-adaptive parameter and strategy based particle swarm optimization for large-scale feature selection problems with multiple classifiers

  Xue, Y., Tang, T., Pang, W. & Liu, A. X., Mar 2020, In: Applied Soft Computing. 88, 106031.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Particle swarm optimization (PSO) 100%
  • Feature extraction 92%
  • Classifiers 84%
  • Evolutionary algorithms 68%
  • Machine learning 21%
  32 Citations (Scopus)

• Spatial imputation for air pollutants data sets via low rank matrix completion algorithm

  Liu, X., Wang, X., Zou, L., Xia, J. & Pang, W., Jun 2020, In: Environment International. 139, 105713.

  Research output: Contribution to journal › Review article › peer-review

  Open Access

  File

  • Air Pollutants 100%
  • Datasets 60%
  • Spatial Analysis 16%
  • Air Pollution 14%
  • Correlation of Data 14%
  4 Citations (Scopus)

  19 Downloads (Pure)

• Transfer learning for endoscopy disease detection and segmentation with MASk-RCNN benchmark architecture

  Rezvy, S., Zebin, T., Braden, B., Pang, W., Taylor, S. & Gao, X. W., 23 Apr 2020, In: CEUR Workshop Proceedings. 2595, p. 68-72 5 p.

  Research output: Contribution to journal › Conference article › peer-review

  Open Access

  File

  • Endoscopy 100%
  • Transfer learning 96%
  • Semantics 55%
  • Masks 38%
  • Pipelines 30%
  3 Citations (Scopus)

  17 Downloads (Pure)
• 2019

  A Hexagonal Cell Automaton Model to Imitate Physarum Polycephalum Competitive Behaviour

  Awad, A., Pang, W., Lusseau, D. & Coghill, G. M., Jul 2019, ALIFE 2019: The 2019 Conference on Artificial Life. MIT Press, p. 203-210 8 p. (Artificial Life Conference Proceedings; no. 31).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  File

  • Cellular automata 100%
  • Multiobjective optimization 28%
  • Brain 24%
  • Wireless sensor networks 23%
  • Mathematical models 20%
  10 Downloads (Pure)

• A Physarum-Inspired Competition Algorithm for Solving Discrete Multi-Objective Optimization Problems

  Awad, A., Usman, M., Lusseau, D., Coghill, G. M. & Pang, W., 13 Jul 2019, GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion. New York, USA: Association for Computing Machinery, p. 195-196 2 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Multiobjective optimization 100%
  • Cellular automata 16%
  • Evolutionary algorithms 13%
  1 Citation (Scopus)

• Case-based reasoning of a deep learning network for prediction of early stage of oesophageal cancer

  Gao, X., Braden, B., Zhang, L., Taylor, S., Pang, W. & Pettdis, M., 22 Nov 2019, (Accepted/In press) Proceedings of the 24th UK Symposium on Case-Based Reasoning.

  Research output: Chapter in Book/Report/Conference proceeding › Chapter

  • Case based reasoning 100%
  • Deep learning 75%
  • Testing 32%
  • Decision making 30%
  • Endoscopy 26%

• Data-driven two-layer visual dictionary structure learning

  Yu, X., Yu, Z., Wu, L., Pang, W. & Lin, C., 8 Mar 2019, In: Journal of Electronic Imaging. 28, 2, 023006.

  Research output: Contribution to journal › Article › peer-review

  • Glossaries 100%
  • Big data 15%
  • Deep learning 15%
  3 Citations (Scopus)

• DeepSwarm: Optimising Convolutional Neural Networks using Swarm Intelligence

  Byla, E. & Pang, W., 30 Aug 2019, (E-pub ahead of print) Advances in Computational Intelligence Systems. UKCI 2019. . Ju, Z., Yang, L., Yang, C., Gegov, A. & Zhou, D. (eds.). Springer, p. 119-130 12 p. (Advances in Intelligent Systems and Computing; vol. 1043).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Swarm intelligence 100%
  • Ant colony optimization 92%
  • Convolutional neural networks 76%
  • Reusability 46%
  • Deep learning 37%
  12 Citations (Scopus)

• Explosion gravitation field algorithm with dust sampling for unconstrained optimization

  Hu, X., Huang, L., Wang, Y. & Pang, W., Aug 2019, In: Applied Soft Computing. 81, 105500.

  Research output: Contribution to journal › Article › peer-review

  • Dust 100%
  • Explosions 92%
  • Gravitation 86%
  • Sampling 68%
  • Astronomy 10%

• FREDPC: A Feasible Residual Error-Based Density Peak Clustering Algorithm With the Fragment Merging Strategy

  Parmar, M. D., Pang, W., Hao, D., Jiang, J., Liupu, W., Wang, L. & Zhou, Y., 3 Jul 2019, In: IEEE Access. 7, p. 89789-89804 16 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Merging 100%
  • Clustering algorithms 92%
  • Specifications 13%
  • Experiments 7%
  15 Citations (Scopus)

  18 Downloads (Pure)

• Inferring Structure and Parameters of Dynamic Systems using Latin Hypercube Sampling Multi Dimensional Uniformity-Particle Swarm Optimization

  Usman, M., Awad, A., Pang, W. & Coghill, G. M., 13 Jul 2019, GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion. New York, USA: Association for Computing Machinery, p. 101-102 2 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Particle swarm optimization (PSO) 100%
  • Dynamical systems 97%
  • Sampling 83%
  • Time series 14%
  • Costs 6%
  1 Citation (Scopus)

• LatinPSO: An algorithm for simultaneously inferring structure and parameters of ordinary differential equations models

  Tian, X., Pang, W., Wang, Y., Guo, K. & Zhou, Y., Aug 2019, In: BioSystems. 182, p. 8-16 9 p.

  Research output: Contribution to journal › Article › peer-review

  • HIV 100%
  6 Citations (Scopus)

• Swarm Inspired Approaches for K-prototypes clustering

  Albalawi, H., Pang, W. & Coghill, G. M., 30 Aug 2019, (E-pub ahead of print) Advances in Computational Intelligence Systems. UKCI 2019. . Ju, Z., Yang, L., Yang, C., Gegov, A. & Zhou, D. (eds.). Springer, p. 201-209 9 p. ( Advances in Intelligent Systems and Computing; vol. 1043).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Clustering algorithms 100%
  • Machine learning 81%
  • Data mining 45%

• TCPModel: A Short-Term Traffic Congestion Prediction Model Based on Deep Learning

  Xu, X., Gao, X., Xu, Z., Zhao, X., Pang, W. & Zhou, H., 2019, Artificial Intelligence: ICAI 2019. Singapore: Springer, p. 66-79 14 p. (Communications in Computer and Information Science; vol. 1001).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Traffic congestion 100%
  • Deep learning 79%
  • Spatial distribution 10%
  2 Citations (Scopus)

• Towards machine learning approaches for predicting the self-healing efficiency of materials

  Wang, W., Moreau, N. G., Yuan, Y., Race, P. R. & Pang, W., Oct 2019, In: Computational Materials Science. 168, p. 180-187 8 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Self-healing materials 100%
  • Machine learning 58%
  • Learning systems 41%
  • Hybrid materials 28%
  • Repair 16%
  3 Citations (Scopus)

  6 Downloads (Pure)

• Towards Real-Time Detection of Squamous PreCancers from Oesophageal Endoscopic Videos

  Gao, X., Braden, B., Taylor, S. & Pang, W., 7 Oct 2019, (Accepted/In press) 2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  File

  • Epithelial Cells 100%
  • Masks 45%
  • Graphics processing unit 14%
  • Deep learning 14%
  • Pixels 12%
  38 Downloads (Pure)

• 异构信息网络中基于元结构的协同过滤算法

  Wang, X., Pang, W. & Wang, Z., Jun 2019, In: Computer Science. 46, 6A, p. 397-401 5 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Collaborative filtering 100%
  • Semantics 15%
  • Experiments 7%
  17 Downloads (Pure)
• 2018

  A Conceptual Framework of Starlings Swarm Intelligence Intrusion Prevention for Software Defined Networks

  Karatu, M. T., Pang, W. & Coghill, G. M., 30 Jul 2018, In: CEUR Workshop Proceedings. 2151, S6.

  Research output: Contribution to journal › Conference article › peer-review

  Open Access

• A General Framework for Accelerating Swarm Intelligence Algorithms on FPGAs, GPUs and Multi-core CPUs

  Li, D., Huang, L., Wang, K., Pang, W., Zhou, Y. & Zhang, R., 20 Nov 2018, In: IEEE Access. 6, p. 72327-72344 18 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Swarm intelligence 100%
  • Graphics processing unit 77%
  • Program processors 70%
  • Field programmable gate arrays (FPGA) 68%
  • Computer hardware 32%
  6 Citations (Scopus)

  45 Downloads (Pure)

• An Evolutionary Computation Based Feature Selection Method for Intrusion Detection

  Xue, Y., Jia, W., Zhao, X. & Pang, W., 9 Oct 2018, In: Security and Communication Networks. 2018, 2492956.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Intrusion detection 100%
  • Evolutionary algorithms 89%
  • Feature extraction 75%
  • Wireless sensor networks 61%
  • Network security 21%
  22 Citations (Scopus)

  7 Downloads (Pure)

• An improved EMD-based dissimilarity Metric for Unsupervised Linear Subspace Learning

  Yu, X., Yu, Z., Pang, W., Li, M. & Wu, L., 18 Feb 2018, In: Complexity. 2018, 8917393.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Earth (planet) 100%
  • Feature extraction 45%
  • Experiments 21%
  4 Citations (Scopus)

  5 Downloads (Pure)

• A Self-adaptive Artificial Bee Colony Algorithm with Symmetry Initialization

  Xue, Y., Jiang, J., Ma, T., Liu, J. & Pang, W., 2018, In: Journal of Internet Technology. 19, 5, p. 1347-1362 16 p.

  Research output: Contribution to journal › Article › peer-review

  • Evolutionary algorithms 100%
  3 Citations (Scopus)

• A Self-adaptive Fireworks Algorithm for Classification Problems

  Xue, Y., Zhao, B., Ma, T. & Pang, W., 25 Jul 2018, In: IEEE Access. 6, p. 44406-44416 11 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Adaptive algorithms 100%
  • Swarm intelligence 14%
  • Linear equations 13%
  • Global optimization 12%
  • Evolutionary algorithms 11%
  14 Citations (Scopus)

• CLEMI-imputation evaluation

  Chapman, A., Pang, W. & Coghill, G., 23 Aug 2018, 2018 IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE, 6 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Machine learning 100%
  • Testing 70%
  1 Citation (Scopus)