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Forecasting Digital Storage Usage in Judiciary Information Systems Using Neural Network Models

https://doi.org/10.2139/ssrn.6460577
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/j.sysarc.2022.102604openalex-snapshot
CREW: Computation reuse and efficient weight storage for hardware-accelerated MLPs and RNNs
inferredcites →https://doi.org/10.1016/s0360-8352(98)00066-7openalex-snapshot
The use of ARIMA models for reliability forecasting and analysis
inferredcites →https://doi.org/10.1080/00031305.2017.1380080openalex-snapshot
Forecasting at Scale
inferredcites →https://doi.org/10.1080/07474938.2010.481556openalex-snapshot
An Empirical Comparison of Machine Learning Models for Time Series Forecasting
inferredcites →https://doi.org/10.1093/oso/9780198538493.001.0001openalex-snapshot
Neural Networks for Pattern Recognition
inferredcites →https://doi.org/10.3390/electronics12030650openalex-snapshot
Technical Study of Deep Learning in Cloud Computing for Accurate Workload Prediction
inferredcites →https://doi.org/10.48001/jofsn.2024.211-10openalex-snapshot
Comparative Analysis of LSTM, RNN, CNN and MLP Machine Learning Algorithms for Stock Value Prediction
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