An analysis on the traffic processing efficiency of a combination of serial and parallel bottlenecks by Quek, Wei-Liang, Chung, Ning Ning, Chew, Lock Yue

Get full text
Get full text

Machine learning baseline energy model (MLBEM) to evaluate prediction performances in building energy consumption by Mustapa, Rijalul Fahmi, Hairuddin, Muhammad Asraf, Mohd Nordin, Atiqah Hamizah, Dahlan, Nofri Yenita, Mohd Yassin, Ihsan, Khirul Ashar, Nur Dalila

“…The hours and temperature are considered as independent variables to be tested with residual error evaluations, whilst the correlation coefficient, coefficient of determination, and training time are also takeninto account. Three models with different categories involving Long Short-Term Memory (LSTM), Support Vector Regression (SVR), and AutoRegressive Integrated Moving Average with Exogenous inputs (ARIMAX) were compared, concluding that SVR was the best and can be used as a universal model in the Machine Learning Baseline Energy Model (MLBEM) studies. …”
Get full text

Sentiment analysis and topic modelling of 2024 U.S. and Indonesian election tweets: a study of political discourse and public opinion by Widawati, Elisia Brispalma

“…This study investigates the effectiveness of various deep learning architectures and statistical models in both sentiment analysis and the temporal analysis of online public discourse through topic modelling and sentiment forecasting of tweets related to the 2024 Indonesian and U.S. elections. …”
Get full text

Landscape Water Storage and Subsurface Correlation from Satellite Surface Soil Moisture and Precipitation Observations by Short Gianotti, Daniel J., Salvucci, Guido D., Akbar, Ruzbeh, McColl, Kaighin A., Cuenca, Richard, Entekhabi, Dara

Get full text

Searching for beyond the Standard Model physics using the improved description of 100Mo 2𝜈𝛽𝛽 decay spectral shape with CUPID-Mo by Augier, C., Barabash, A. S., Bellini, F., Benato, G., Beretta, M., Bergé, L., Billard, J., Borovlev, Yu. A., Cardani, L., Casali, N., Cazes, A., Celi, E., Chapellier, M., Chiesa, D., Dafinei, I., Danevich, F. A., De Jesus, M., Dixon, T., Dumoulin, L., Eitel, K.

“…These can be used to search for Beyond Standard Model (BSM) physics via 2𝜈𝛽𝛽 decay spectral distortions. 100Mo has a natural advantage due to its relatively short half-life, allowing higher 2𝜈𝛽𝛽 decay statistics at equal exposures compared to the other isotopes. …”
Get full text

Foundations of Computer Vision / by Torralba, Antonio, author 655233, Isola, Phillip, author 655234, Freeman, William T., author 655235, The MIT Press (Online service) 655237

“…Foundations of Computer Vision covers topics not standard in other texts, including transformers, diffusion models, statistical image models, issues of fairness and ethics, and the research process. …”
Get full text
Get full text

Towards understanding genome regulation via high-resolution analysis of chromatin accessibility by Dai, L

“…Avocato employs a simple but useful fragment size filtering strategy, retaining only short fragments from high-quality scATAC-seq data, which successfully prioritises functional SNP variants hidden in the raw signals. …”

Annual dilated convolution neural network for newbuilding ship prices forecasting by Gao, Ruobin, Liu, Jiahui, Bai, Xiwen, Yuen, Kum Fai

“…Although the researchers from forecasting and shipping have shown that the machine learning models outperform statistical ones, convolution neural networks are not investigated. …”
Get full text

Hidden Influence in Dynamic Networks by Erhardt, Keeley Donovan

“…I begin with a foundational overview of graph learning techniques and the specific models utilized in my work. …”
Get full text
Get full text

Estimating evolutionary and demographic parameters via ARG-derived IBD by Huang, Z, Kelleher, J, Chan, Y, Balding, D

“…Computational cost limits model complexity in our approach, but we are able to incorporate unknown nuisance parameters and model misspecification, still finding improved parameter inference.…”

Graph neural network for traffic forecasting: the research progress by Jiang, Weiwei, Luo, Jiayun, He, Miao, Gu, Weixi

“…Various forecasting methods have been proposed in the literature, including statistical models, shallow machine learning models, and deep learning models. …”
Get full text

Creep and shrinkage performance of kenaf bio fibrous concrete composite by Ogunbode, Ezekiel Babatunde

Get full text

Transforming kernel-based learners to incorporate domain knowledge from climate science by Bouabid, S

“…<p>In the face of persistent modelling and observational challenges in climate science, which hinder our understanding of the climate system, statistical machine learning has emerged as a potential ally in recent years. …”

Modelling and integrating of experimental analysis for predicting the parameters of kenaf fibre-reinforced concrete beam-column joint by Ayeni, Ige Samuel, Mohamad Jamaludin, Yatim, Abdul Shukor Lim, Nor Hasanah

“…Deep learning could predict precisely concrete structure parameters. …”
Get full text

Decentralised finance and automated market making by Monga, M

“…We do not specify a model for the stochastic processes observed by LPs, and use a long short-term memory (LSTM) neural network to approximate the optimal liquidity provision strategy.…”

Derivation of extreme non-gaussian stochastic offshore structural responses using finite memory nonlinear system by Mukhlas, Nurul ’Azizah

Get full text

Digital Anatomy : Applications of Virtual, Mixed and Augmented Reality / by Uhl, Jean-François, editor 643187, Jorge, J. A. (Joaquim Armando), editor 643188, Lopes, Daniel Simões, editor 643190, Campos, Pedro F., editor 643191

Essays on oil price fluctuations, financial markets and international trade by Le, Thai Ha

“…Oil price shocks appear to have a statistically significant and positive impact on real gold returns contemporaneously. …”
Get full text

"Data comes from the real world": A Constructionist Approach to Mainstreaming K12 Data Science Education by Ravi, Prerna, Parks, Robert, Masla, John, Abelson, Hal, Breazeal, Cynthia

Get full text

Diagrammatic Representation and Inference : 12th International Conference, Diagrams 2021, Virtual, September 28-30, 2021, Proceedings / by Diagrams (Conference) (12th : 2021 : Online) 643173, Basu, Amrita, 1953-, editor 643175, Stapleton, Gem, editor 643176, Linker, Sven, editor 643177, Legg, Catherine, editor 643178, Manalo, Emmanuel, editor 643179, Viana, Petrucio, editor 643180