Results 101 to 110 of about 18,004 (194)
In view of the strong randomness and non-stationarity of complex system, this study suggests a hybrid multi-strategy prediction technique based on optimized hybrid denoising and deep learning.
Xinyi Xu (505530) +2 more
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Analyzing Reddit Data: Hybrid Model for Depression Sentiment using FastText Embedding
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)Depression, a prevalent mental condition globally, exerts a substantial influence on various aspects of human cognition, emotions, and behaviour.
Merinda Lestandy +3 more
doaj +1 more source
Ultra-short-term power load forecasting is beneficial to improve the economic efficiency of power systems and ensure the safe and stable operation of power grids.
Yuanfang Gou (18239273) +2 more
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Training and Validation loss for best-performing models (a) BiLSTM-GRU with Floret embedding on IJCNLP, (b) BiLSTM-GRU with Floret embedding on Jahangir, (c) BiLSTM-GRU with FastText embedding on MKPUCIT, and (d) BiLSTM-GRU with Floret embedding on UNER.
Emmanuel Soriano Flores (18271908) +7 more
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Application of CNN-BiLSTM Algorithm for Ethereum Price Prediction [PDF]
The volatile and dynamic Ethereum (ETH) market demands an accurate predictive model to support investment decision making. The complexity of ETH time series data and the influence of various external factors make price prediction a challenge in itself ...
Trimono Trimono +7 more
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Power load forecasting based on SMD-Informer-BiLSTM
Zhejiang dianliTo address the limitations of traditional methods—such as inadequate feature extraction and insufficient nonlinear processing capability in single deep learning models—this paper proposes a novel power load forecasting model integrating secondary modal ...
XU Shixin +5 more
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Научный диалогThis article addresses the challenge of analyzing evidentiality within media discourse by leveraging contemporary machine learning methods. The study’s relevance stems from the need to interpret implicit and context-dependent modal meanings that are ...
D. V. Kozlovsky
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Predicting Residential Electricity Consumption Using CNN-BiLSTM-SA Neural Networks
As global population growth and the use of household appliances increase, residential electricity consumption has surged, leading to challenges in maintaining a balanced electrical load.
Meng-Ping Wu, Fan Wu
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Journal of Applied Science and EngineeringWith the rapid development of social media, user-generated content (UGC) has become a valuable source for understanding user preferences and behaviors. However, traditional user behavior analysis methods mainly focus on behavioral data while ignoring the
Yanping Song
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Optimisasi Hyperparameter BiLSTM Menggunakan Bayesian Optimization untuk Prediksi Harga Saham
The accuracy of deep learning models in predicting dynamic and non-linear stock market data highly depends on selecting optimal hyperparameters. However, finding optimal hyperparameters can be costly in terms of the model's objective function, as it ...
Pasha, Muhammad Fermi +5 more
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