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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Minimum number of inertial measurement units needed to identify signiicant variations in walk patterns of overweight individuals walking on irregular surfaces by Sikandar, Tasriva, Rabbi, Mohammad Fazle, Kamarul Hawari, Ghazali, Altwijri, Omar, Almijalli, Mohammed, Ahamed, Nizam Uddin

“…We then used deep learning method to verify whether the IMU data recorded from the identified body locations could classify walk patterns across the surfaces. …”
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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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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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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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Statistical modeling of fully nonlinear hydrodynamic loads on offshore wind turbine monopile foundations using wave episodes and targeted CFD simulations through active sampling by Guth, Stephen, Katsidoniotaki, Eirini, Sapsis, Themistoklis P.

“…The novelty of our framework lies in its efficient construction of the surrogate model, utilizing the Gaussian process regression machine learning technique and a Bayesian active learning method to sequentially sample wave episodes that contribute to accurate predictions of extreme hydrodynamic forces. …”
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Lane detection algorithm for autonomous vehicle using machine learning by Wu, Guan Jie

“…Hence, there is a need to shift from traditional computer vision algorithms to deep learning method in feature extraction. Convolution neural network (CNN) has been the de facto feature extraction module in computer vision. …”
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The Effect Of Blended Learning And critical Thinking On Tertiary EFL Argumentative Writing In China by Wang, Yuling

“…With the rapid development of Information and Communications Technology (ICT), the blended learning method is widespread in English-as-a-Foreign-Language (EFL) instruction. …”
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Factor modeling for clustering high-dimensional time series by Zhang, Bo, Pan, Guangming, Yao, Qiwei, Zhou, Wang

“…We propose a new unsupervised learning method for clustering a large number of time series based on a latent factor structure. …”
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Computer Supported Cooperative Work and Social Computing : 15th CCF Conference, ChineseCSCW 2020, Shenzhen, China, November 7-9, 2020, Revised Selected Papers / by CCF Conference on Computer Supported Cooperative Work and Social Computing (15th : 2020 : Shenzhen, China), organiser 642953, Sun, Yuqing, editor 642954, Liu, Dongning, editor 642955, Liao, Hao, 1986-, editor 642956, Fan, Hongfei, editor 642957, Gao, Liping, editor 642958

Data-efficient modeling for power consumption estimation of quadrotor operations using ensemble learning by Dai, Wei, Zhang, Mingcheng, Low, Kin Huat

“…We employed an ensemble learning method, namely stacking, to develop a data-driven model using flight records of three different types of quadrotors. …”
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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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Refining learning models in grammatical inference by Wang, Xiangrui

“…We introduce the use of recurrent neural networks (RNNs) and present a pruning learning method to avoid the exponential space costs. …”
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Robot programming using augmented reality by Chong, Jonathan Wun Shiung

“…The Piecewise Linear Parameterization (PLP) algorithm and a curve learning method based on Bayesian neural networks and reparameterization are proposed. …”
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Transfer-recursive-ensemble learning for multi-day COVID-19 prediction in India using recurrent neural networks by Chakraborty, Debasrita, Goswami, Debayan, Ghosh, Susmita, Ghosh, Ashish, Chan, Jonathan H., Wang, Lipo

“…Each of the four models then gives 7-day ahead predictions using the recursive learning method for the Indian test data. The final prediction comes from an ensemble of the predictions of the different models. …”
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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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