Using AI / machine learning to solve real world problems by Lok, Ignatius Zhengrong

“…With the development of technology, it is common to see machine learning methods used and adopted to help solve real-world problems of both individuals and well as large corporations. …”
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Mobile robots autonomous exploration through deep reinforcement learning by Yin, Hanqiu

“…Existing deep learning-based methods suffer from problems such as low learning efficiency and might fall into local minima. …”
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Deep learning assisted optical wavefront shaping in disordered medium by Luo, Yunqi, Li, Huanhao, Zhang, Ruochong, Lai, Puxiang, Zheng, Yuanjin

“…Compared with previous methods which use iterative optimization, our method realizes a focused speckle pattern with the help of deep learning, which will definitely reduce complexity and time spent in optimization. …”
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Improved extreme learning machine for spectra classification of Covid-19 by Wang, Changyang

“…In this dissertation, we meticulously compare three sets of preprocessing methods and seven feature selection and extraction methods to determine the optimal data preprocessing and feature extraction techniques. …”
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Effectiveness of digital learning on students’ higher order thinking skills by Mat, Hamidah, Mustakim, Siti Salina, Razali, Fazilah, Ghazali, Norliza, Minghat, Asnul Dahar

“…In the realm of education, learning and instructional activities play a crucial role in cultivating lasting and meaningful comprehension among science students. …”
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Stomach tissue classification using autofluorescence spectroscopy and machine learning by Lim, Soo Y., Yoon, Hong M., Kook, Myeong-Cherl, Jang, Jin I., So, Peter T. C., Kang, Jeon W., Kim, Hyung M.

“…In recent years, alternative histochemical evaluation methods have been pursued to accelerate intraoperative diagnosis, often by bypassing the time-consuming step of dyeing. …”
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Machine Learning Prediction of Treatment Response to Inhaled Corticosteroids in Asthma by Ong, Mei-Sing, Sordillo, Joanne E., Dahlin, Amber, McGeachie, Michael, Tantisira, Kelan, Wang, Alberta L., Lasky-Su, Jessica, Brilliant, Murray, Kitchner, Terrie, Roden, Dan M., Weiss, Scott T., Wu, Ann Chen

“…Conclusions: An accurate risk prediction of ICS response can be obtained using machine learning methods, with the potential to inform personalized treatment decisions. …”
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Deep learning-based concrete image analysis and generalization capability by Qian, Hanjie

“…To achieve this, we propose analysis algorithms based on deep learning and design methods to enhance generalization capability and alleviate the reliance on labels. …”
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Causal Machine Learning to Discover Biochemical Determinants of Physical Fitness by Nawaz, Hesham

“…While contemporary deep learning models have demonstrated great success in pattern recognition and generation for various data modalities, their ability to decipher the causal mechanisms underlying these patterns is limited. …”
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“Who are you?” - Learning person specific classifiers from video by Sivic, J, Everingham, M, Zisserman, A

“…In this paper we build on that method, extending the coverage greatly by the detection and recognition of characters in profile views. …”

LKAW: a robust watermarking method based on large kernel convolution and adaptive weight assignment by Zhang, Xiaorui, Jiang, Rui, Sun, Wei, Song, Aiguo, Wei, Xindong, Meng, Ruohan

“…Robust watermarking requires finding invariant features under multiple attacks to ensure correct extraction. Deep learning has extremely powerful in extracting features, and watermarking algorithms based on deep learning have attracted widespread attention. …”
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Machine learning-based local collision avoidance for maritime navigation by Zou, Yixuan

“…This report investigates the application of Deep Reinforcement Learning (DRL) in collision avoidance in maritime navigation. …”
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Reinforcement learning based operational control of active distribution networks by Lou, Yutao

“…The dissertation explores voltage control in active distribution networks (ADNs) and proposes a reinforcement learning-based Voltage/Var control (VVC) strategy utilizing PV inverters to mitigate voltage fluctuation and reduce network energy loss in ADNs. …”
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Technology-enhanced learning (TEL) of finite state machines (FSMs) by Chua, Wen Huat

“…Traditional educational approaches to teaching FSMs typically involve theoretical lectures supplemented by physical modeling. However, these methods often lack the dynamic and interactive engagement necessary for optimal learning. …”
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"Workhorses": exploring how internal medicine residents learn efficiency by Ting, J

“…and “How do internal medicine residents learn to be efficient in clinical practice?”</p> <p><strong>Methods:</strong> A qualitative interpretivist approach was employed. …”

Public opinion trend analysis on nuclear energy via aspect-based sentiment analysis methods by Huang, Fan

“…With the help of the fine-grained Aspect-Based Sentiment Analysis (ABSA) method using Deep Learning models, the public opinions towards specific aspects within massive data could be understood and predicted more accurately. …”
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Assessing serial recall as a measure of artificial grammar learning by Jenkins, HE, de Graaf, Y, Smith, F, Riches, N, Wilson, B

“…Methods: We adapted the design of a previous Artificial Grammar Learning (AGL) study to use a visual serial recall task, as well as more traditional reflection-based grammaticality judgement and sequence completion tasks. …”

A nonparametric learning framework for nonlinear robust output regulation by Wang, Shimin, Guay, Martin, Chen, Zhiyong, Braatz, Richard D

“…Additionally, a nonparametric learning framework is proposed to eliminate the construction of an explicit regressor, as required in the adaptive method, which can potentially simplify the implementation and reduce the computational complexity of existing methods. …”
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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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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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