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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Investigation on effective solutions against insider attacks by Ang, Jun Hao

“…This report investigates the effectiveness of dimensionality reduction techniques in reducing this high demand needed by the machine learning methods used for insider threat detection. The dimensionality reduction techniques discussed in this report are feature selection methods i.e. …”
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Exploring cultural competence through the 'humans of Malaysia project by Marof, Aini Azeqa

“…This experiential learning method provided students with a platform to apply essential social psychology concepts such as intergroup relations, contact theory, attribution, and attitude change in authentic, real-world environments. …”
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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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Individual Mobility Prediction in Mass Transit Systems Using Smart Card Data: An Interpretable Activity-Based Hidden Markov Approach by Mo, Baichuan, Zhao, Zhan, Koutsopoulos, Haris N, Zhao, Jinhua

“…Therefore, the activity-based prediction framework offers a way to preserve the predictive power of advanced machine learning methods while enhancing our ability to generate insightful behavioral explanations, which is useful for user-centric policy design and intelligent transportation applications such as personalized traveler information.…”
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Accelerating Urban Building Energy Modeling by Le Hong, Zoe, Wolk, Samuel

“…Identifying machine learning methods as a viable approach, we implement convolutional neural networks (CNNs) which embed timeseries from hourly weather data and building schedules; the embeddings are then combined with static building characteristics and projected to monthly heating and cooling loads. …”
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Invited perspectives : how machine learning will change flood risk and impact assessment by Wagenaar, Dennis, Curran, Alex, Balbi, Mariano, Bhardwaj, Alok, Soden, Robert, Hartato, Emir, Mestav Sarica, Gizem, Ruangpan, Laddaporn, Molinario, Giuseppe, Lallemant, David

“…Flood risk and impact assessments are also being influenced by this trend, particularly in areas such as the development of mitigation measures, emergency response preparation and flood recovery planning. Machine learning methods have the potential to improve accuracy as well as reduce calculating time and model development cost. …”
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Camera domain transfer for video-based person re-identification by Ding, Bangjie

“…Besides, feature learning based on deep learning methods is prone to overfitting on the relatively small scale video dataset. …”
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Optimising public transit using big data and machine learning by Lee, Kelvin

“…Despite decades of research on optimisation of public transit, recent advances in big data collection and machine learning methods have created new possibilities for further optimisation. …”
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Outlier detection by Li, Shukai

“…Subsequently, a set of largely violated labeling vectors are combined via multiple kernel learning methods to robustly detect the outliers. To further enhance the efficacy of our outlier detector, we also explore the use of the Maximum Volume Criterion to measure the quality of separation between the outliers and the normal patterns. …”
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Memory and fluctuations in chemical dynamics by Farahvash, Ardavan

“…I discuss how the strategic application of machine learning methods can drastically reduce the number of electronic structure calculations needed to produce a complete exciton trajectory. …”
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Semantic segmentation with less annotation efforts by Zhang, Tianyi

“…To alleviate the content misalignment problem, two approaches are proposed in this thesis to regularize adversarial learning methods: the first is to embed the global structure knowledge into the feature-level adversarial learning step. …”
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