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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From distraction to interaction: investigating learner engagement challenges in virtual classrooms by Irfan, Muhammad, Patel, Preeti, Hassan, Bilal

“…COVID-19, in particular, necessitated a global and swift adaptation by all teaching institutions to virtual learning methods. For universities, the transition to online learning predominantly focused on migrating teaching content, leaving online pedagogy, social interactions and informal learning areas, largely unattended. …”
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Machine learning meta-analysis identifies individual characteristics moderating cognitive intervention efficacy for anxiety and depression symptoms by Richter, T, Shani, R, Tal, S, Derakshan, N, Cohen, N, Enock, PM, McNally, RJ, Mor, N, Daches, S, Williams, AD, Yiend, J, Carlbring, P, Kuckertz, JM, Yang, W, Reinecke, A, Beevers, CG, Bunnell, BE, Koster, EHW, Zilcha-Mano, S, Okon-Singer, H

“…This research is a pre-registered individual-level meta-analysis to identify factors contributing to cognitive training efficacy for anxiety and depression symptoms. Machine learning methods, alongside traditional statistical approaches, were employed to analyze 22 datasets with 1544 participants who underwent working memory training, attention bias modification, interpretation bias modification, or inhibitory control training. …”

Glass box and black box machine learning approaches to exploit compositional descriptors of molecules in drug discovery and aid the medicinal chemist by Robson, B, Cooper, R

“…There are usually more inactive compounds by orders of magnitude, often a problem for machine learning methods. However, the approaches used here appear to work well for such “real world data”.…”

Active learning with applications in biomedical document annotation by Han, Xu

“…We also apply our active learning method for the task of named entity recognition. …”
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Output-weighted and relative entropy loss functions for deep learning precursors of extreme events by Rudy, Samuel H., Sapsis, Themistoklis P.

“…Such problems present a challenging task for data-driven modelling, with many naive machine learning methods failing to predict or accurately quantify such events. …”
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Portfolio Optimization Using a Hybrid Machine Learning Stock Selection Model by Masuda, Joshua S.

“…Additionally, two hybrid machine learning methods are used for prediction: CNN-LSTM and BiLSTM-BO-LightGBM. …”
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Forgery localization in images by Nur Dilah Binte Zaini

“…Late fusion is implemented to combine the confidence scores of the predicted class for each classifier. Simple machine learning methods have been carried out to implement image forgery detection and deep fake detection in this paper. …”
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Deep features based real-time SLAM by Syed Ariff Syed Hesham

“…This project implements a near real-time stereo SLAM system designed to operate effectively in extreme conditions using Deep Learning methods. It employs a Parallel Tracking-and-Mapping approach, making use of stereo constraints to ensure robust initialization and accurate scale recovery while maintaining real-time performance. …”
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Acne Severity Classification on Mobile Devices using Lighweight Deep Learning Approach by Nor Surayahani Suriani, Nor Surayahani Suriani, Ahmad Tarmizi, Syaidatus Syahira, Hj Mohd, Mohd Norzali, Mohd Shah, Shaharil

“…Most of the deep learning methods require devices with high computational resources which hardly implemented in mobile applications. …”
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Multimodal deception detection in videos by Syazwan Bin Jainal

“…There have been many approaches to the problem of deception detection, which include psychological, physiological and even machine learning methods. Deception detection has been successful in high-stakes situations, like courtrooms, where subjects are put under a stressful situation and experiments have yielded an accuracy of over 90%. …”
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Mobile robots autonomous exploration through deep reinforcement learning by Yin, Hanqiu

“…The algorithm adopts a deep learning method to effectively extract the environment features and automatically updates the strategy by interacting with the environment. …”
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