A review on big data based on deep neural network approaches

M Rithani, RP Kumar, S Doss - Artificial Intelligence Review, 2023 - Springer
Big data analytics has become a significant trend for many businesses as a result of the
daily acquisition of enormous volumes of data. This information has been gathered because …

Real-time forecasting of time series in financial markets using sequentially trained dual-LSTMs

K Gajamannage, Y Park, DI Jayathilake - Expert Systems with Applications, 2023 - Elsevier
Financial markets are highly complex and volatile; thus, accurate forecasting of such
markets is vital to make early alerts about crashes and subsequent recoveries. People have …

Environment sound event classification with a two-stream convolutional neural network

X Dong, B Yin, Y Cong, Z Du, X Huang - IEEE Access, 2020 - ieeexplore.ieee.org
In recent years, with the construction of intelligent cities, the importance of environmental
sound classification (ESC) research has become increasingly prominent. However, due to …

Non-invasive cuff-less blood pressure estimation using a hybrid deep learning model

S Yang, Y Zhang, SY Cho, R Correia… - Optical and Quantum …, 2021 - Springer
Conventional blood pressure (BP) measurement methods have different drawbacks such as
being invasive, cuff-based or requiring manual operations. There is significant interest in the …

Deep domain adaptation to predict freezing of gait in patients with Parkinson's disease

VG Torvi, A Bhattacharya… - 2018 17th IEEE …, 2018 - ieeexplore.ieee.org
Freezing of gait (FoG) is a common gait impairment in patients with advanced Parkinson's
disease (PD), which manifests as sudden difficulties in starting or continuing locomotion …

Light-weight student LSTM for real-time wildfire smoke detection

M Jeong, MJ Park, J Nam, BC Ko - Sensors, 2020 - mdpi.com
As the need for wildfire detection increases, research on wildfire smoke detection combining
low-cost cameras and deep learning technology is increasing. Camera-based wildfire …

Data reduction and reconstruction of wind turbine wake employing data driven approaches

M Geibel, G Bangga - Energies, 2022 - mdpi.com
Data driven approaches are utilized for optimal sensor placement as well as for velocity
prediction of wind turbine wakes. In this work, several methods are investigated for suitability …

[PDF][PDF] End-to-End Speech Command Recognition with Capsule Network.

J Bae, DS Kim - Interspeech, 2018 - isca-archive.org
In recent years, neural networks have become one of the common approaches used in
speech recognition (SR), with SR systems based on Convolutional Neural Networks (CNNs) …

Code-switched automatic speech recognition in five South African languages

A Biswas, E Yılmaz, E van der Westhuizen… - Computer Speech & …, 2022 - Elsevier
Most automatic speech recognition (ASR) systems are optimised for one specific language
and their performance consequently deteriorates drastically when confronted with …

[PDF][PDF] An evaluation on speech recognition technology based on machine learning

T Kumar, SS Rajest, KO Villalba-Condori… - Webology, 2022 - researchgate.net
Speech is the basic way of interaction between the listener to the speaker by voice or
expression. Humans can easily understand the speakers' message, but machines can't …