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Multimodel Phishing URL Detection Using LSTM, Bidirectional LSTM, and GRU Models
Future Internet, 2022 In today’s world, phishing attacks are gradually increasing, resulting in individuals losing valuables, assets, personal information, etc., to unauthorized parties. In phishing, attackers craft malicious websites disguised as well-known, legitimate sites
Sanjiban Sekhar Roy +2 more
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Novel Dual Residual-Enhanced Deep Bidirectional LSTM Network for Soft Sensing of Rare Earth Component Content [PDF]
SensorsLong short-term memory (LSTM) networks demonstrate superior time-series feature extraction capabilities and have exhibited significant advantages in the soft sensing of key indicators in complex industrial processes.
Wenhao Dai +3 more
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Residual and bidirectional LSTM for epileptic seizure detection [PDF]
Frontiers in Computational NeuroscienceElectroencephalogram (EEG) plays a pivotal role in the detection and analysis of epileptic seizures, which affects over 70 million people in the world. Nonetheless, the visual interpretation of EEG signals for epilepsy detection is laborious and time-consuming.
Wang Wen-Feng
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Agriculture, 2022 Cattle behaviour is a significant indicator of cattle welfare. With the advancements in electronic equipment, monitoring and classifying multiple cattle behaviour patterns is becoming increasingly important in precision livestock management.
Yiqi Wu +5 more
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An Attention Enhanced Bidirectional LSTM for Early Forest Fire Smoke Recognition
IEEE Access, 2019 Detecting forest fire smoke during the initial stages is vital for preventing forest fire events. Recent studies have shown that exploring spatial and temporal features of the image sequence is important for this task.
Yichao Cao, Feng Yang, Xiaobo Lu
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Emoji Prediction Using Bi-Directional LSTM [PDF]
ITM Web of Conferences, 2023 Messengers and social media dominate today’s internet usage across the globe. For the large population, a typical day starts with messages flooding on mobiles, from simple good morning wishes, business meeting invites, reminders, and schedules for the ...
Kone Vinayak Sudhakar +4 more
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Real-Time Monitoring for Hydraulic States Based on Convolutional Bidirectional LSTM with Attention Mechanism [PDF]
Sensors, 2020 Jongpil Jeong, Jeong Jongpil
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BS-LSTM: An Ensemble Recurrent Approach to Forecasting Soil Movements in the Real World
Frontiers in Earth Science, 2021 Machine learning (ML) proposes an extensive range of techniques, which could be applied to forecasting soil movements using historical soil movements and other variables.
Praveen Kumar +4 more
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Peramalan Data Indeks Harga Konsumen Berbasis Time Series Multivariate Menggunakan Deep Learning
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2021 Multivariate Time Series based forecasting is a type of forecasting that has more than one criterion changes from time to time that it can forecast based on historical patterns of data sequences.
Soffa Zahara, Sugianto
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Machine Learning with Applications, 2022 Rainfall forecasting has gained utmost research relevance in recent times due to its complexities and persistent applications such as flood forecasting and monitoring of pollutant concentration levels, among others.
Ari Yair Barrera-Animas +5 more
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