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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The analysis of teaching quality evaluation for the college sports dance by Convolutional Neural Network model and Deep Learning by Guo, Shuqing, Yang, Xiaoming, Farizan, Noor Hamzani, Samsudin, Shamsulariffin

“…This study aims to comprehensively analyze and evaluate the quality of college physical dance education using Convolutional Neural Network (CNN) models and deep learning methods. The study introduces a teaching quality evaluation (TQE) model based on one-dimensional CNN, addressing issues such as subjectivity and inconsistent evaluation criteria in traditional assessment methods. …”
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Predicting drivers’ route trajectories in last-mile delivery using a pair-wise attention-based pointer neural network by Mo, Baichuan, Wang, Qingyi, Guo, Xiaotong, Winkenbach, Matthias, Zhao, Jinhua

“…Results from an extensive case study on real operational data from Amazon’s last-mile delivery operations in the US show that our proposed method can significantly outperform traditional optimization-based approaches and other machine learning methods (such as the Long Short-Term Memory encoder–decoder and the original pointer network) in finding stop sequences that are closer to high-quality routes executed by experienced drivers in the field. …”
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Computational Approaches for Understanding and Redesigning Enzyme Catalysis by Karvelis, Elijah

“…The approach combined statistical mechanical path sampling algorithms and machine learning methods to identify the structural characteristics of enzyme-substrate complexes primed for successful conversion of substrate to product, which were then energetically stabilized by mutating KARI. …”
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Transforming kernel-based learners to incorporate domain knowledge from climate science by Bouabid, S

“…<p>In the face of persistent modelling and observational challenges in climate science, which hinder our understanding of the climate system, statistical machine learning has emerged as a potential ally in recent years. Modern machine learning methods promise to leverage the vast volumes of data from climate model simulations, satellite imagery, or in-situ measurements to advance our understanding of the climate system and, thereby, our ability to anticipate the adverse consequences of climate change. …”

Transferring a deep learning model from healthy subjects to stroke patients in a motor imagery brain-computer interface by Nagarajan, Aarthy, Robinson, Neethu, Ang, Kai Keng, Chua, Karen Sui Geok, Chew, Effie, Guan, Cuntai

“…Motor imagery (MI) brain-computer interfaces (BCIs) based on electroencephalogram (EEG) have been developed primarily for stroke rehabilitation, however, due to limited stroke data, current deep learning methods for cross-subject classification rely on healthy data. …”
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Self-supervised Self2Self denoising strategy for OCT speckle reduction with a single noisy image by Ge, Chenkun, Yu, Xiaojun, Yuan, Miao, Fan, Zeming, Chen, Jinna, Shum, Perry Ping, Liu, Linbo

“…Results compared with those of the existing methods demonstrate that S2Snet not only outperforms those existing self-supervised deep learning methods but also achieves better performances than those non-deep learning ones in different cases. …”
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