Learning driver-specific behavior for overtaking : a combined learning framework by Lu, Chao, Wang, Huaji, Lv, Chen, Gong, Jianwei, Xi, Junqiang, Cao, Dongpu

“…However, traditional offline learning methods lack the ability to adapt to individual driving behavior. …”
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Estimation of diaphragm wall deflections for deep braced excavation in anisotropic clays using ensemble learning by Zhang, Runhong, Wu, Chongzhi, Goh, Anthony Teck Chee, Böhlke, Thomas, Zhang, Wengang

“…Surrogate models were developed via ensemble learning methods (ELMs), including the eXtreme Gradient Boosting (XGBoost), and Random Forest Regression (RFR) to predict the maximum lateral wall deformation (δhmax). …”
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Using machine learning to generate novel hypotheses: increasing optimism about COVID-19 makes people less willing to justify unethical behaviors by Sheetal, Abhishek, Feng, Zhiyu, Savani, Krishna

“…The findings suggest that optimism can help reduce unethicality, and they document the utility of machine-learning methods for generating novel hypotheses.…”
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AI assisted indoor localization by Lee, Yih Jie

“…In this report, the obstacles that Wi-Fi fingerprinting and traditional machine learning methods face will be overcome by relying on deep learning approaches. …”
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Three-dimensional Softmax mechanism guided bidirectional GRU networks for hyperspectral remote sensing image classification by Wu, Guoqiang, Ning, Xin, Hou, Luyang, He, Feng, Zhang, Hengmin, Shankar, Achyut

“…The recent years have witnessed the potentials of deep learning methods have shown great promise in the hyperspectral image classification due to their ability to model complex structures and extract multiple features in an end-to-end fashion. …”
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Social media sentiment enhanced stock market prediction analysis by Tan, Adrian Yong Chang

“…The result implies that for classification, ensemble learning methods tend to perform better in terms of accuracy, while SVM tend to perform better in terms of F-Measure. …”
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Looking deep at people: towards understanding and generating humans in images with deep learning by de Bem, RA

“…</p> <p>This thesis pursues further advances towards understanding and generating people in visual data by the development of new discriminative and generative deep learning methods. The main contributions are: </p> <p>i) A deep learning framework for 2D human pose estimation, which allows for mean-field inference over part-based models; </p> <p>ii) A conditional deep generative model that achieves state-of-the-art results on generating images of humans conditioned on body posture; and </p> <p>iii) A structured semi-supervised deep generative model that jointly performs pose estimation and image generation, <em>understanding</em> and <em>generating</em> people in images in a single framework.…”

Unsupervised learning based performance analysis of n-support vector regression for speed prediction of a large road network by Asif, M. T., Oran, A., Fathi, E., Xu, M., Dhanya, M. M., Mitrovic, N., Jaillet, P., Dauwels, Justin, Goh, Chong Yang

“…Previous studies have shown that data driven machine learning methods like support vector regression (SVR) can effectively and accurately perform this task. …”
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Machine health monitoring using local feature-based gated recurrent unit networks by Zhao, Rui, Wang, Dongzhe, Yan, Ruqiang, Mao, Kezhi, Shen, Fei, Wang, Jinjiang

“…Inspired by the success of deep learning methods that redefine representation learning from raw data, we propose local feature-based gated recurrent unit (LFGRU) networks. …”
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Transfer learning application on snake classification by Tananda, Hans

“…This is possible by applying machine learning methods to classify the snake images and creating a simple mobile application for public use. …”
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Using AI for music source separation by Lee, Jasline Jie Yu

“…In recent years, supervised deep learning methods are known to be state-of-the-art source separation technology and can be categorised as Spectrogram-based and Waveform-based methods. …”
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Feature selection for demand forecasting incorporating external covariates by Mantri, Raghav

“…We utilise machine learning methods for this purpose and perform feature selection to use only relevant features. …”
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Crowd-based people detection using deep learning by Chen, Lei

“…This project first reviewed an extensive list of literature related to object detection based on handcraft and deep learning methods. Then, two state of art neural networks were introduced (EfficientDet and YOLOv5), and through further analysis, I analyzed the components and the thesis and the subsequent source codes, deduced the complete network structure, and explained the specific implementation process of the critical parts. …”
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Deep self-supervised representation learning for free-hand sketch by Xu, Peng, Song, Zeyu, Yin, Qiyue, Song, Yi-Zhe, Wang, Liang

“…We demonstrate the superiority of our sketch-specific designs through two sketch-related applications (retrieval and recognition) on a million-scale sketch dataset, and show that the proposed approach outperforms the state-of-the-art unsupervised representation learning methods, and significantly narrows the performance gap between with supervised representation learning.…”
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ELM embedded discriminative dictionary learning for image classification by Zeng, Yijie, Li, Yue, Chen, Jichao, Jia, Xiaofan, Huang, Guang-Bin

“…Results show that our approach achieves state-of-the-art performance compared to other dictionary learning methods.…”
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Deep learning for communication signal classification – part A by Wang, Chien Wei

“…Deep Learning methods have seen significant success in a variety of applications in recent years. …”
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Applications of deep learning to neurodevelopment in pediatric imaging: achievements and challenges by Hu, Mengjiao, Nardi, Cosimo, Zhang, Haihong, Ang, Kai Keng

“…We first introduce the commonly used deep learning methods and architectures in neuroimaging, such as convolutional neural networks, auto-encoders, and generative adversarial networks. …”
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Multi-agent deep reinforcement learning based distributed control architecture for interconnected multi-energy microgrid energy management and optimization by Zhang, Bin, Hu, Weihao, Ghias, Amer M. Y. M., Xu, Xiao, Chen, Zhe

“…Unlike existing single-agent deep reinforcement learning methods that rely on homogeneous MG settings, the proposed MADRL adopts a form of decentralized execution, in which agents operate independently to meet local customized energy demands while preserving privacy. …”
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Sensor fusion for object detection under adverse weather by Soh, Brandon Jian Zheng

“…In recent years, there has been a rise in the use of deep learning methods relying on LiDARs and Radars, given their long history of achieving state of art performance in different types of applications. …”
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An analytic end-to-end collaborative learning algorithm by Li, Sitan, Cheah, Chien Chern

“…However, most current deep learning methods are black-box approaches that are more focused on empirical studies. …”
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