Reallocation of time between device-measured movement behaviours and risk of incident cardiovascular disease by Walmsley, R, Chan, S, Smith-Byrne, K, Ramakrishnan, R, Woodward, M, Rahimi, K, Dwyer, T, Bennett, D, Doherty, A

“…</p> <p><strong>Conclusion</strong> Machine-learning methods classified movement behaviours accurately in free-living accelerometer data. …”

Deep learning with constrained data resource by Mao, Jiangtian

“…The method can achieve a better accuracy compared to simple full-supervised learning methods, especially the problem becomes to a one-shotting problem.…”
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An Intelligent Cooperative Control Architecture by How, Jonathan, Choi, Han-Lim, Undurti, Aditya, Redding, Joshua

“…This paper presents an extension of existing cooperative control algorithms that have been developed for multi-UAV applications to utilize real-time observations and/or performance metric(s) in conjunction with learning methods to generate a more intelligent planner response. …”
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Deep Reinforcement Learning in complex environments by Nardelli, N

“…The presence of multiple agents breaks some of the key assumptions that provide necessary stability to standard learning methods, creating unique and interesting problems. …”

Precious metal price prediction using deep neural networks by Peng, Zhiling

“…Keywords: gold price prediction, deep learning methods, regression models, the LSTM network, the Bi-LSTM model, multiple factors…”
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Commodity price prediction using neural networks by Seah, Isaac Zhe Hao

“…Furthermore, another form of supervised learning method: Support Vector Network(SVM), is briefly visited. …”
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QR-code based real-time interactive learning in iOS by Teng, Shi Xuan

“…The final year project titled “QR-Code Based Real-Time Interactive Learning in iOS” aims to develop a mobile application for Apple iOS Devices (iPhone, iPad) to enhance the learning methods, in-class participation, engagement and interaction between the lecturer and the students. …”
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Enhanced extreme learning machines for image classification by Cui, Dongshun

“…Among numerous machine learning methods, we choose the Extreme Learning Machine (ELM) for our image classification applications. …”
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Host genetics maps to behaviour and brain structure in developmental mice by Asbury, S, Lai, JKY, Rilett, KC, Haqqee, Z, Darwin, BC, Ellegood, J, Lerch, JP, Foster, JA

“…The influence of genetics, sex, and early life stress on behaviour and neuroanatomy was determined using traditional statistical and machine learning methods. Analytical results demonstrated that neuroanatomical diversity was primarily associated with genotype whereas behavioural phenotypic diversity was observed to be more susceptible to gene-environment variation. …”

Detecting Human Memory Processes via Bio-Signals by Abdelrahman, Mona Magdy

“…Using this data, we propose multi-modal, machine learning methods to predict and evaluate whether a user is in a cognitive state of learning, recognition, or recall. …”
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Image artefact removal using deep learning by Sanchari, Das

“…This report implements variations of the deep learning methods, namely a combination of the AR CNN and DnCNN in the form of a Residual AR CNN, a GAN which uses PatchGAN for its discriminator and a Residual GAN which uses residual learning. …”
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Machine learning for anomaly detection on intelligent transportation time series data by Lin, Yuxuan

“…Experimental results have shown that the proposed algorithm performs better than several other machine learning methods.…”
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3D Modelling Using Machine Learning Technique by Zhao, Haolong

“…The objective of this project is to perform 3D modeling using machine learning techniques, extensive research on 3D modeling and machine learning techniques were conducted. Machine learning methods are classified as the image rending-based methods, it has the features of low cost, flexible in application, easy to set up, which are desired in most of the application scenarios. …”
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Visual analytics using artificial intelligence : visual events classifier using deep learning by Ong, Kian Kuan

“…As Deep learning emerges from Machine learning to become a leading technology in today’s day and age, there have been many attempts at integrating Deep learning methods into day today applications. Out of these applications, image recognition is the area of interest in which this project aims to elaborate on. …”
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A survey of few-shot learning for biomedical time series by Li, C, Denison, T, Zhu, T

“…This survey provides a comprehensive review and comparison of few-shot learning methods for biomedical time series applications. …”

Machine learning techniques for the prediction of indoor gamma-ray dose rates - strengths, weaknesses and implications for epidemiology by Kendall, GM, Appleton, JD, Chernyavskiy, P, Arsham, A, Little, MP

“…The use of machine learning methods results in significantly improved predictions over earlier models. …”

Constrained neuro fuzzy inference methodology for explainable personalised modelling with applications on gene expression data by Singh, Balkaran, Doborjeh, Maryam, Doborjeh, Zohreh, Budhraja, Sugam, Tan, Samuel, Sumich, Alexander, Goh, Wilson, Lee, Jimmy, Lai, Edmund, Kasabov, Nikola

“…Thus far, most machine learning methods applied to gene expression datasets, including deep neural networks, lack personalised interpretability. …”
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Weighted online sequential extreme learning machine for class imbalance learning by Lin, Zhiping, Mirza, Bilal., Toh, Kar-Ann.

“…Most of the existing sequential learning methods for class imbalance learn data in chunks. …”
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Comparison of different binary classification models on radiomic features by Loo, Bryan Kun Hao

“…By applying different machine learning methods to the abundance of data provided by radiomic features, it will assist in carrying out cancer detection, prognosis as well as the prediction of treatment response. …”
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Interactive learning on ECG by Zhu, Yu Ting

“…This report demonstrates the various interactive learning methods with the implementation of hardware components and software development. …”
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