Machine learning approaches in Brillouin distributed fiber optic sensors

C Karapanagiotis, K Krebber - Sensors, 2023 - mdpi.com
This paper presents reported machine learning approaches in the field of Brillouin
distributed fiber optic sensors (DFOSs). The increasing popularity of Brillouin DFOSs stems …

Develo** Cryptocurrency Trading Strategy Based on Autoencoder-CNN-GANs Algorithms

Z Hu, R Yu, Z Zhang, H Zheng, Q Liu… - arxiv preprint arxiv …, 2024 - arxiv.org
This paper leverages machine learning algorithms to forecast and analyze financial time
series. The process begins with a denoising autoencoder to filter out random noise …

Integrating Textual Analytics with Time Series Forecasting Models: Enhancing Predictive Accuracy in Global Energy and Commodity Markets

J Rao, Q Zhang, S Liu, X Liu - Innovations in Applied Engineering and …, 2023 - ojs.sgsci.org
This study presents a comprehensive framework for predicting crude oil prices by integrating
textual features extracted from news headlines into a time series forecasting model. The …

Pipeline degradation evaluation based on distributed fiber sensors and convolutional neural networks (CNNs)

Z Wu, Q Wang, AV Gribok, KP Chen - Optical Fiber Sensors, 2022 - opg.optica.org
Pipeline Degradation Evaluation Based on Distributed Fiber Sensors and Convolutional Neural
Networks (CNNs) Page 1 Pipeline Degradation Evaluation Based on Distributed Fiber Sensors …

Detection of ultrasonic guided waves using fiber optical sensors toward nondestructive evaluation

Q Wang, K Zhao, S Zhong, X Yi, J Zhao… - Optical Fiber …, 2022 - opg.optica.org
Conference title, upper and lower case, bolded, 18 point type, centered Page 1 Detection of
Ultrasonic Guided Waves Using Fiber Optical Sensors Toward Nondestructive Evaluation Qirui …

Enhancing Financial Forecasting Models with Textual Analysis: A Comparative Study of Decomposition Techniques and Sentiment-Driven Predictions

Q Zhang, J Rao - Innovations in Applied Engineering and Technology, 2022 - ojs.sgsci.org
Financial time series data are inherently complex, encompassing various components such
as trends, seasonal patterns, and irregular fluctuations. This paper presents a …

Machine Learning in Action: Topic-Centric Sentiment Analysis and Its Applications

J Rao - Available at SSRN, 2024 - papers.ssrn.com
This article discusses topic-level sentiment analysis using machine learning techniques
such as topic modeling and Latent Dirichlet allocation (LDA). Topic modeling is an …