Advances in Sparse and Low Rank Matrix Optimization for Machine Learning Applications by Johnson, Nicholas André G.

“…Moreover, our approach outperforms benchmark methods when used as part of a multi-label learning algorithm. …”
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Speeding up deep neural network training with decoupled and analytic learning by Zhuang, Huiping

“…In this thesis, we explore methods in two different areas, i.e., decoupled learning and analytic learning, in order to reduce the training time. …”
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Collision avoidance for automated guided vehicles using deep reinforcement learning by Qin, Yifan

“…While centralized collision avoidance methods for multi-robot systems exist and they are often more accurate and error-free, decentralized methods have the potential to reduce the prohibitive computation where each robot generates paths without observing other robots’ states. …”
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Driver behavior profiling: An investigation with different smartphone sensors and machine learning by Ferreira Junior, Jair, Carvalho, Eduardo, Ferreira, Bruno V., de Souza, Cleidson, Suhara, Yoshihiko, Pentland, Alex, Pessin, Gustavo

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Deep graph contrastive learning model for drug-drug interaction prediction by Jiang, Zhenyu, Gong, Zhi, Dai, Xiaopeng, Zhang, Hongyan, Ding, Pingjian, Shen, Cong

“…In this paper, we propose a novel method, which is a deep graph contrastive learning model for drug-drug interaction prediction (DeepGCL for brevity). …”
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Machine learning algorithm for electroencephalography (EEG) based brain signal analysis by Teo, Jeffrey Eng Hock

“…The final stage is to use various classification methods to classify the data and test the accuracy of the models. …”
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A novel reinforcement learning model for post-incident malware investigations by Dunsin, Dipo, Ghanem, Mohamed Chahine, Ouazzane, Karim, Vassilev, Vassil

“…The experimental results demonstrate that RL improves malware detection rates compared to conventional methods, with the RL model's performance varying depending on the complexity and learning rate of the environment. …”
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Opportunities for machine learning to accelerate halide-perovskite commercialization and scale-up by Kumar, Rishi E., Tiihonen, Armi, Sun, Shijing, Fenning, David P., Liu, Zhe, Buonassisi, Tonio

“…In this perspective, we review practical challenges hindering the commercialization of halide perovskites, and discuss how machine-learning (ML) tools could help: (1) active-learning algorithms that blend institutional knowledge and human expertise could help stabilize and rapidly update baseline manufacturing processes; (2) ML-powered metrology, including computer imaging, could help narrow the performance gap between large- and small-area devices; and (3) inference methods could help accelerate root-cause analysis by reconciling multiple data streams and simulations, focusing research effort on areas with highest probability for improvement. …”
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Human comfort in indoor environment : a review on assessment criteria, data collection and data analysis methods by Song, Ying, Mao, Fubing, Liu, Qing

“…Through the survey, we find that sensor technology has been widely used in the data collection for various types of comfort, while so far the machine learning approaches are mainly applied in the area of thermal comfort study. …”
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PLATON: top-down R-tree packing with learned partition policy by Yang, Jingyi, Cong, Gao

“…To address the limitations of existing R-tree packing methods, we propose PLATON, a top-down R-tree packing method with learned partition policy that explicitly optimizes the query performance with regard to the given data and workload instance. …”
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Review and empirical analysis of machine learning-based software effort estimation by Rahman, Mizanur, Sarwar, Hasan, Abdul Kader, Md, Goncalves, Teresa C.F., Ting, Tin Tin

“…The literature review employed a systematic approach to identify relevant research on machine learning techniques for SEE. Additionally, comparative experiments were conducted using five commonly employed Machine Learning (ML) methods: K-Nearest Neighbor, Support Vector Machine, Random Forest, Logistic Regression, and LASSO Regression. …”
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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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Precision indoor location tracking using RSSI fingerprinting and machine learning by Kuan, Jeff Chow Zhi

“…In this project, the objective is to determine the effectiveness of using fingerprinting method with machine learning for indoor Wi-Fi localization. …”
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SimSC: a simple framework for semantic correspondence with temperature learning by Li, X, Han, K, Wan, X, Prisacariu, VA

“…This module is trained together with the backbone and the temperature is updated online. We evaluate our method on three public datasets and demonstrate that we can achieve accuracy on par with state-of-the-art methods under the same backbone without using a learned matching head. …”

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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Machine learning-based approaches for large-scale temporal data analytics by Seyed Ali Majid Zonoozi

“…The proposed concept tracking method applies to problem settings that cannot be handled by existing concept drift and stream mining methods, and outperforms popular unsupervised baselines from the wider Data Mining and Machine Learning literature. …”
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3-way respiratory sound classification using machine learning algorithms by Tu, Yixuan

“…This project mainly focuses on two types of classification algorithms, one is to project the processed audio signal to a set of spatial points and use geometrical distance to divide them into three parts, the other is to use machine learning algorithms. For geometrical distance, I tried three types of distances: Minkowski, Manhattan and Euclidean distance; for machine learning algorithms, I tried two widely used methods: Support Vector Machine and Convolutional Neural Network, while the SVM part I tried both Gaussian Core and linear SVM. …”
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Efficient and privacy-preserving feature importance-based vertical federated learning by Li, Anran, Huang, Jiahui, Jia, Ju, Peng, Hongyi, Zhang, Lan, Tuan, Luu Anh, Yu, Han, Li, Xiang-Yang

“…Vertical Federated Learning (VFL) enables multiple data owners, each holding a different subset of features about a largely overlapping set of data samples, to collaboratively train a global model. …”
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Sensor drift detection framework for water systems with probabilistic machine learning by Hoang, Thu Minh

“…Anomaly detection via advanced Machine Learning (ML) models available nowadays can be a solution to this sensor drift problem because the models are fast, accurate, and able to give long-term prediction. …”
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Deep learning approach to cervical segmentation from routine CT images by Duan, Yiming

“…Yet, compared to these holistic segmentation targets, another equally important class of human tissues, the bone, has a more pronounced se quential character. Traditional 3D methods are unable to learn the exact position of bone joints due to the limitation of local similarity and numbers of param eters. …”
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