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Full text release has been delayed at the author's request until August 16, 2026

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Time Serials Data Processing with Neural Networks: From Classification to Decision Making

Wang, Xufei
http://orcid.org/0000-0003-3761-7197
http://rave.ohiolink.edu/etdc/view?acc_num=case1718016427300005

Abstract Details

2024, Doctor of Philosophy, Case Western Reserve University, EECS - Computer Engineering.
This dissertation demonstrates the efficacy of neural networks in processing time series data, particularly through the lens of Human Activity Recognition (HAR) and Congestion Control (CC). The study is anchored in the detailed exploration of fundamental neural architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and advanced reinforcement learning techniques, which underpin the subsequent application-specific innovations. For data classification, the focus is on HAR, leveraging the ubiquitous presence of mobile devices equipped with accelerometers. This research introduces the Personalized Recurrent Neural Network (PerRNN), which uses spatiotemporal predictive learning to dynamically segment and interpret human activity from accelerometer data. Our PerRNN model, tested on the WISDM dataset, has been shown to significantly enhance activity recognition accuracy, achieving 96.44%, a marked improvement over existing methods. In the area of decision-making, we address the challenges of congestion control in dynamic and unpredictable mobile network environments. The Fair and Friendly Congestion Control (FFCC) algorithm, developed through Meta-Reinforcement Learning, optimizes network performance by prioritizing low latency and Quality of Experience (QoE). FFCC not only surpasses traditional Congestion Control Algorithms (CCAs) in critical performance metrics but also demonstrates adaptability to real-world network fluctuations, making it ideal for applications in complex and mobile settings. This dissertation underscores the transformative potential of neural networks in enhancing both classification accuracy and decision-making efficacy in time series data processing. By tailoring neural network architectures to specific challenges, this work not only advances theoretical knowledge but also catalyzes significant practical improvements in handling time-dependent data.
Pan Li (Committee Chair)
An Wang (Committee Member)
Yu Yin (Committee Member)
Daniel Saab (Committee Member)
71 p.
Computer Engineering; Computer Science
Congestion Control, Reinforcement Learning, Meta-Learning, Video Streaming

Recommended Citations

Citations

  • Wang, X. (2024). Time Serials Data Processing with Neural Networks: From Classification to Decision Making [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1718016427300005

    APA Style (7th edition)

  • Wang, Xufei. Time Serials Data Processing with Neural Networks: From Classification to Decision Making. 2024. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1718016427300005.

    MLA Style (8th edition)

  • Wang, Xufei. "Time Serials Data Processing with Neural Networks: From Classification to Decision Making." Doctoral dissertation, Case Western Reserve University, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=case1718016427300005

    Chicago Manual of Style (17th edition)

case1718016427300005
© 2024, some rights reserved.Creative Commons License Time Serials Data Processing with Neural Networks: From Classification to Decision Making by Xufei Wang is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.