International Conference on Computer and Knowledge Engineering

Home / 15th International Conference on Computer and Knowledge Engineering

Adaptive Sliding Window Optimization for Multi-Dimensional Data Streams Using Reinforcement Learning

Authors :

Abolfazl Zarghani¹

1- Ferdowsi university of mashhad

Keywords :

Reinforcement Learning،Sliding Window،Multi-Dimensional Data Streams،Concept Drift،Dueling DQN

Abstract :

Multi-dimensional data streams from IoT, financial markets, and real-time analytics require adaptive processing due to their high velocity and complex dependencies. Fixed-size sliding windows struggle with concept drift and bursty patterns. We propose RL-Window, a reinforcement learning (RL) approach using a Dueling Deep Q-Network (DQN) with prioritized experience replay and noisy networks to dynamically optimize window sizes based on stream characteristics like variance, correlations, and spectral features. Evaluated on UCI HAR, PAMAP2, and Yahoo! Finance datasets, RL-Window achieves superior classification accuracy (up to 92.1\%), drift robustness (-3.2\% drop), and energy efficiency (1.1 mJ) compared to baselines like ADWIN [1] and CNN-Adaptive [2]. Comprehensive dataset characterization and qualitative analyses highlight its adaptability for real-time, resource-constrained applications. We employ dual rolling buffers (200-point temporal, 512-point spectral with 50\% overlap) to build the state; stability is reported as the standard deviation of selected window sizes (samples); and energy/latency are measured on Raspberry Pi 4 via inline power metering and end-to-end timing.

Papers List

List of archived papers

Improving Motor Imagery Classification in BCI Systems Using EMD and Multi-Layer CNNs

Reza Arghand - Ali Chaibakhsh - Moein Radman

Optimizing MR Image Registration for Accurate Brain Volume Measurement in Children with Autism Spectrum Disorder

Shiva Sanati - Mahdi Saadatmand

Data Clustering using Chimp Optimization Algorithm

SAYED PEDRAM HAERI BOROUJENI - ELNAZ PASHAEI

Adaptive Multi-Scale Attentional Network for Semantic Segmentation of Remote Sensing Images

Melika Zare - Sattar Hashemi

Towards Transparent and Accurate Story Point Estimation via Interpretable BERT-based Modeling

Seyed Emad Baradaran Hosseini - Maryam Khodabakhsh - Alireza Tajary - Seyedehfatemeh Karimi

Robust Distributed Learning over Heterogeneous Adaptive Networks based on Federated BSP Model

Fatemeh Barani - MohammadHafez Yari - Abdorreza Savadi - Hadi Sadoghi Yazdi

Financial Market Prediction Using Deep Neural Networks with Hardware Acceleration

Dara Rahmati - Mohammad Hadi Foroughi - Ali Bagherzadeh - Mehdi Foroughi - Saeid Gorgin

Non-Negative Matrix Factorization improves Residual Neural Networks

Hojjat Moayed

Human vs NotebookLM for Educational Podcasts: A Controlled Experiment on Two General Topics

Ali Banihashemi - Amirali Shahriary - Yadollah Yaghoobzadeh

Using Deep Learning for Classification of Lung Cancer on CT Images in Ardabil Province

Mohammad Ali Javadzadeh Barzaki - Jafar Abdollahi - Mohammad Negaresh - Maryam Salimi - Hadi Zolfeghari - Mohsen Mohammadi - Asma Salmani - Rona Jannati - Firouz Amani