Using AI / machine learning to solve real world problems by Lok, Ignatius Zhengrong

“…With the development of technology, it is common to see machine learning methods used and adopted to help solve real-world problems of both individuals and well as large corporations. …”
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Cross-modal retrieval: a review of methodologies, datasets, and future perspectives by Han, Zhichao, Azman, Azreen Bin, Rina Binti Mustaffa, Mas, Binti Khalid, Fatimah

“…Currently, the most popular deep learning methods have achieved remarkable results in the field of data processing and graphics. …”
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Microencapsulation of ciplukan (Physalis angulata L.) extract as food ingredients: Effect of water ratio and maltodextrin concentration variables on product characteristics by Iwansyah, Ade Chandra, Wardhani, Riuh, Darsih, Cici, Kurniawan, Taufik, Ariani, Dini, Andriana, Yusuf, Karim, Mirwan Ardiansyah, Indriati, Ashri, Luthfiyanti, Rohmah, Hazrulrizawati, Abd Hamid

“…The present work evaluated the characteristics of ciplukan (Physalis angulata L.) microcapsule extracts prepared by spray drying method. Different water ratios namely X1 (1:2), X2 (1:5), and X3 (1:10), and maltodextrin concentrations namely Y1 (5%) and Y2 (10%) were applied in a spray drying system to produce microcapsule extracts. …”
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Investigation of the sequential accelerator on the perceptron for pattern recognition by Sirote Saetang.

“…Machine Learning methods have been widely used in recent years in many areas, such as object recognition, autonomous vehicle, recovery rate of symptoms and stock prediction. …”
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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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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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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. …”

Measuring the predictability of life outcomes with a scientific mass collaboration by Salganik, Matthew J., Lundberg, Ian, Kindel, Alexander T., Ahearn, Caitlin E., Al-Ghoneim, Khaled, Almaatouq, Abdullah, Altschul, Drew M., Brand, Jennie E., Carnegie, Nicole Bohme, Compton, Ryan James, Datta, Debanjan, Davidson, Thomas, Filippova, Anna, Gilroy, Connor, Goode, Brian J., Jahani, Eaman, Kashyap, Ridhi, Kirchner, Antje, McKay, Stephen, Morgan, Allison C., Pentland, Alex, Polimis, Kivan, Raes, Louis, Rigobon, Daniel E., Roberts, Claudia V., Stanescu, Diana M., Suhara, Yoshihiko, Usmani, Adaner, Wang, Erik H., Adem, Muna, Alhajri, Abdulla, AlShebli, Bedoor, Amin, Redwane, Amos, Ryan B., Argyle, Lisa P., Baer-Bositis, Livia, Buchi, Moritz, Chung, Bo-Ryehn, Eggert, William, Faletto, Gregory, Fan, Zhilin, Freese, Jeremy, Gadgil, Tejomay, Gagne ́, Josh, Gao, Yue, Halpern-Manners, Andrew, Hashim, Sonia P., Hausen, Sonia, He, Guanhua, Higuera, Kimberly, Hogan, Bernie, Horwitz, Ilana M., Hummel, Lisa M., Jain, Naman, Jin, Kun, Jurgens, David, Kaminski, Patrick, Karapetyan, Areg, Kim, E. H., Leizman, Ben, Liu, Naijia, Moser, Malte, Mack, Andrew E., Mahajan, Mayank, Mandell, Noah, Marahrens, Helge, Mercado-Garcia, Diana, Mocz, Viola, Mueller-Gastell, Katariina, Musse, Ahmed, Niu, Qiankun, Nowak, William, Omidvar, Hamidreza, Or, Andrew, Ouyang, Karen, Pinto, Katy M., Porter, Ethan, Porter, Kristin E., Qian, Crystal, Rauf, Tamkinat, Sargsyan, Anahit, Schaffner, Thomas, Schnabel, Landon, Schonfeld, Bryan, Sender, Ben, Tang, Jonathan D., Tsurkov, Emma, van Loon, Austin, Varol, Onur, Wang, Xiafei, Wang, Zhi, Wang, Julia, Wang, Flora, Weissman, Samantha, Whitaker, Kirstie, Wolters, Maria K., Woon, Wei Lee, Wu, James, Wu, Catherine, Yang, Kengran, Yin, Jingwen, Zhao, Bingyu, Zhu, Chenyun, Brooks-Gunn, Jeanne, Engelhardt, Barbara E., Hardt, Moritz, Knox, Dean, Levy, Karen, Narayanan, Arvind, Stewart, Brandon M., Watts, Duncan J., McLanahan, Sara

“…Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. …”
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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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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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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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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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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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Estimation of diaphragm wall deflections for deep braced excavation in anisotropic clays using ensemble learning by Zhang, Runhong, Wu, Chongzhi, Goh, Anthony Teck Chee, Böhlke, Thomas, Zhang, Wengang

“…Surrogate models were developed via ensemble learning methods (ELMs), including the eXtreme Gradient Boosting (XGBoost), and Random Forest Regression (RFR) to predict the maximum lateral wall deformation (δhmax). …”
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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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Applications of deep learning to neurodevelopment in pediatric imaging: achievements and challenges by Hu, Mengjiao, Nardi, Cosimo, Zhang, Haihong, Ang, Kai Keng

“…We first introduce the commonly used deep learning methods and architectures in neuroimaging, such as convolutional neural networks, auto-encoders, and generative adversarial networks. …”
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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. …”

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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