Autoencoders and their applications in machine learning: a survey
Autoencoders have become a hot researched topic in unsupervised learning due to their
ability to learn data features and act as a dimensionality reduction method. With rapid …
ability to learn data features and act as a dimensionality reduction method. With rapid …
CNO-LSTM: A chaotic neural oscillatory long short-term memory model for text classification
N Shi, Z Chen, L Chen, RST Lee - IEEE Access, 2022 - ieeexplore.ieee.org
Long Short-Term Memory (LSTM) networks are unique to exercise data in its memory cell
with long-term memory as Natural Language Processing (NLP) tasks have inklings of …
with long-term memory as Natural Language Processing (NLP) tasks have inklings of …
Forecasting vertical profiles of ocean currents from surface characteristics: A multivariate multi-head convolutional neural network–long short-term memory approach
S Kar, JR McKenna, G Anglada, V Sunkara… - Journal of Marine …, 2023 - mdpi.com
While study of ocean dynamics usually involves modeling deep ocean variables, monitoring
and accurate forecasting of nearshore environments is also critical. However, sensor …
and accurate forecasting of nearshore environments is also critical. However, sensor …
[HTML][HTML] How can we detect news surrounding community safety crisis incidents in the internet? Experiments using attention-based Bi-LSTM models
Reports related to community safety crisis incidents are being escalated and shared on
social media and other online digital platforms. These reports must be addressed quickly to …
social media and other online digital platforms. These reports must be addressed quickly to …
LSTM based forecasting of the next day's values of ionospheric total electron content (TEC) as an earthquake precursor signal
The sudden vibrations that occur due to the fractures in the Earth's crust, spreading in waves
and shaking the Earth's surface, are natural disaster that causes significant loss of life and …
and shaking the Earth's surface, are natural disaster that causes significant loss of life and …
Voice Conversion Augmentation for Speaker Recognition on Defective Datasets
Modern speaker recognition system relies on abundant and balanced datasets for
classification training. However, diverse defective datasets, such as partially-labelled, small …
classification training. However, diverse defective datasets, such as partially-labelled, small …
Identification and Prediction of Casing Collar Signal Based on CNN-LSTM
J Jing, Y Qin, X Zhu, H Shan, P Peng - Arabian Journal for Science and …, 2024 - Springer
To address the issue of casing collar localization and ensure the accuracy of well depth
measurement, a method combining convolutional neural networks (CNNs) and long short …
measurement, a method combining convolutional neural networks (CNNs) and long short …
Efficient Real-Time Smart Keyword Spotting Using Spectrogram-Based Hybrid CNN-LSTM for Edge System
Keyword Spotting (KWS) is the task of recognizing spoken command words from a
database. With recent application human-machine interactions, KWS systems require real …
database. With recent application human-machine interactions, KWS systems require real …
SpectroNet: A low complexity CNN-LSTM architecture for keyword spotting application
C Amadeus, I Syafalni, N Sutisna… - 2023 IEEE 66th …, 2023 - ieeexplore.ieee.org
Keyword spotting is a problem in Natural Language Processing which aim is to classify
spoken words from a dataset. With the recent emergence of CNN, researchers have …
spoken words from a dataset. With the recent emergence of CNN, researchers have …
Daily solar radiation prediction using LSTM Neural Networks
The integration of solar energy with the smart grids and existing infrastructure makes it a cost-
effective and environmentally-friendly solution to address the growing energy need. To …
effective and environmentally-friendly solution to address the growing energy need. To …