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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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. …”

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. …”

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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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”.…”

Unsupervised learning based performance analysis of n-support vector regression for speed prediction of a large road network by Asif, M. T., Oran, A., Fathi, E., Xu, M., Dhanya, M. M., Mitrovic, N., Jaillet, P., Dauwels, Justin, Goh, Chong Yang

“…Previous studies have shown that data driven machine learning methods like support vector regression (SVR) can effectively and accurately perform this task. …”
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Learning driver-specific behavior for overtaking : a combined learning framework by Lu, Chao, Wang, Huaji, Lv, Chen, Gong, Jianwei, Xi, Junqiang, Cao, Dongpu

“…However, traditional offline learning methods lack the ability to adapt to individual driving behavior. …”
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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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Using machine learning to generate novel hypotheses: increasing optimism about COVID-19 makes people less willing to justify unethical behaviors by Sheetal, Abhishek, Feng, Zhiyu, Savani, Krishna

“…The findings suggest that optimism can help reduce unethicality, and they document the utility of machine-learning methods for generating novel hypotheses.…”
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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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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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Three-dimensional Softmax mechanism guided bidirectional GRU networks for hyperspectral remote sensing image classification by Wu, Guoqiang, Ning, Xin, Hou, Luyang, He, Feng, Zhang, Hengmin, Shankar, Achyut

“…The recent years have witnessed the potentials of deep learning methods have shown great promise in the hyperspectral image classification due to their ability to model complex structures and extract multiple features in an end-to-end fashion. …”
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Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures by Belov, Vladimir, Erwin-Grabner, Tracy, Aghajani, Moji, Aleman, Andre, Amod, Alyssa R., Basgoze, Zeynep, Benedetti, Francesco, Besteher, Bianca, Bülow, Robin, Ching, Christopher R. K., Connolly, Colm G., Cullen, Kathryn, Davey, Christopher G., Dima, Danai, Dols, Annemiek, Evans, Jennifer W., Fu, Cynthia H. Y., Gonul, Ali Saffet, Gotlib, Ian H., Grabe, Hans J., Groenewold, Nynke, Hamilton, J. Paul, Harrison, Ben J., Ho, Tiffany C., Mwangi, Benson, Jaworska, Natalia, Jahanshad, Neda, Klimes-Dougan, Bonnie, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Li, Meng, Linden, David E. J., MacMaster, Frank P., Mehler, David M. A., Melloni, Elisa, Mueller, Bryon A., Ojha, Amar, Oudega, Mardien L., Penninx, Brenda W. J. H., Poletti, Sara, Pomarol-Clotet, Edith, Portella, Maria J., Pozzi, Elena, Reneman, Liesbeth, Sacchet, Matthew D., Sämann, Philipp G., Schrantee, Anouk, Sim, Kang, Soares, Jair C., Stein, Dan J., Thomopoulos, Sophia I., Uyar-Demir, Aslihan, van der Wee, Nic J. A., van der Werff, Steven J. A., Völzke, Henry, Whittle, Sarah, Wittfeld, Katharina, Wright, Margaret J., Wu, Mon-Ju, Yang, Tony T., Zarate, Carlos, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., Goya-Maldonado, Roberto

“…Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.…”
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