Search Results - victor~ :: UTM Library Massive Scholar Tracking

121

Wind power load factor in the United Kingdom by Constable, J, Aoyama, H

Published 2009

“…These load factors are published in a convenient and where necessary corrected form by the Renewable Energy Foundation. …”

Journal article

122

Understanding Indexing Efficiency for Approximate Nearest Neighbor Search in High-dimensional Vector Databases by Qin, Yuting

Published 2024

“…Deep learning has transformed almost all types of data (e.g., images, videos, documents) into high-dimension vectors, which in turn forms Vector Databases as the data engines of various applications. …”

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Thesis

123

Echocardiogram Vector Embeddings Via R3D Transformer for the Advancement of Automated Echocardiography by Chung, Daniel J, Lee, Somin Mindy, Kaker, Vasu, Zhao, Yongyi, Bin, Irbaz, Perera, Sudheesha, Sasankan, Prabhu, Tang, George, Kazzi, Brigitte, Kuo, Po-Chih, Celi, Leo A, Kpodonu, Jacques

Published 2025

“…Automated echocardiography models are an attractive solution for high-variance human EF estimation, and key to this goal are echocardiogram vector embeddings, which are a critical resource for computational researchers. …”

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Article

124

New multipurpose M13Ke phage display vector for screening and developing biosensors by Fang, Yue Qin

Published 2010

“…We aim to engineer a multipurpose M13KE phage display vectors which contains an additional gene III for peptide or protein display to create biosensors for glucose blood sugar levels to help fight diabetes. …”

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Thesis

125

Iterative sparse matrix vector multiplication (SpMV) over GF(2) with CUDA by Prashanth Srinivas G S.

Published 2011

“…To fulfil such purposes Iterative Sparse Matrix Vector Multiplication (SpMV) algorithms need to be designed using useful languages like CUDA that enable seamless communication with GPUs and this is the primary intent of this project.…”

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Final Year Project (FYP)

126

Robust training algorithm of neural network and support vector machine for pattern recognition and control by Hu, Wenjie.

Published 2008

“…Traditional neural network approaches have suffered from difficulties with gener-alization ability and the over- fitting problem always plagues artificial lintelligence researchers.The support vector machines (SVMs)is a new and very promising classification technique developed by Vapnik and his research group.…”

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Thesis

127

Estimating risk factor time paths among people with Type 2 Diabetes and QALY gains from risk factor management by Gao, N, Dakin, HA, Holman, RR, Lim, L-L, Leal, J, Clarke, P

Published 2024

“…<p><strong>Objectives:</strong> Most type 2 diabetes simulation models utilise equations mapping out lifetime trajectories of risk factors [e.g. glycated haemoglobin (HbA1c)]. Existing equations, using historic data or assuming constant risk factors, frequently underestimate or overestimate complication rates. …”

Journal article

128