[HTML][HTML] Artificial neural networks based optimization techniques: A review

MGM Abdolrasol, SMS Hussain, TS Ustun, MR Sarker… - Electronics, 2021 - mdpi.com
In the last few years, intensive research has been done to enhance artificial intelligence (AI)
using optimization techniques. In this paper, we present an extensive review of artificial …

[HTML][HTML] Data science in economics: comprehensive review of advanced machine learning and deep learning methods

S Nosratabadi, A Mosavi, P Duan, P Ghamisi, F Filip… - Mathematics, 2020 - mdpi.com
This paper provides a comprehensive state-of-the-art investigation of the recent advances in
data science in emerging economic applications. The analysis is performed on the novel …

[HTML][HTML] Financial time series forecasting with the deep learning ensemble model

K He, Q Yang, L Ji, J Pan, Y Zou - Mathematics, 2023 - mdpi.com
With the continuous development of financial markets worldwide to tackle rapid changes
such as climate change and global warming, there has been increasing recognition of the …

Groundwater quality evaluation using hybrid model of the multi-layer perceptron combined with neural-evolutionary regression techniques: case study of Shiraz plain

H Moayedi, M Salari, AA Dehrashid, BN Le - … Environmental Research and …, 2023 - Springer
In recent decades, qualitative and quantitative assessments of groundwater sources reveal
that efficient and accurate optimization approaches may assist in solving the multiple …

Employment of an electronic tongue combined with deep learning and transfer learning for discriminating the storage time of Pu-erh tea

Z Yang, N Miao, X Zhang, Q Li, Z Wang, C Li, X Sun… - Food Control, 2021 - Elsevier
Pu-erh tea is a famous Chinese fermented tea, and its quality and flavor are closely related
to the storage time used for its fermentation. This paper puts forward one method to …

[HTML][HTML] Research on the feasibility of applying GRU and attention mechanism combined with technical indicators in stock trading strategies

MC Lee - Applied Sciences, 2022 - mdpi.com
The vigorous development of Time Series Neural Network in recent years has brought many
potential possibilities to the application of financial technology. This research proposes a …

[HTML][HTML] Remaining Useful Life Prediction of Lithium-Ion Battery Using ICC-CNN-LSTM Methodology

C Rincón-Maya, F Guevara-Carazas… - Energies, 2023 - mdpi.com
In recent years, lithium-ion batteries have gained significant attention due to their crucial role
in various applications, such as electric vehicles and renewable energy storage. Accurate …

[HTML][HTML] A deep network-based trade and trend analysis system to observe entry and exit points in the forex market

AK Das, D Mishra, K Das, AK Mohanty, MA Mohammed… - Mathematics, 2022 - mdpi.com
In the Forex market, trend trading, where trend traders identify trends and attempt to capture
gains through the analysis of an asset's momentum in a particular direction, is a great way to …

Sulfur dioxide emissions in Portugal: Prediction, estimation and air quality regulation using machine learning

VM Ribeiro - Journal of Cleaner Production, 2021 - Elsevier
Latest reports of the European Environment Agency and Agência Portuguesa do Ambiente
raise a reasonable doubt on the satisfaction of 2030 targets imposed by supranational …

Stop-loss adjusted labels for machine learning-based trading of risky assets

Y Hwang, J Park, Y Lee, DY Lim - Finance Research Letters, 2023 - Elsevier
Since the rise of ML/AI, many researchers and practitioners have been trying to predict future
stock price movements. In actual implementations, however, stop-loss is widely adopted to …