[HTML][HTML] Forecasting: theory and practice

F Petropoulos, D Apiletti, V Assimakopoulos… - International Journal of …, 2022 - Elsevier
Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

Computational methods and tools in antimicrobial peptide research

PGA Aronica, LM Reid, N Desai, J Li… - Journal of Chemical …, 2021 - ACS Publications
The evolution of antibiotic-resistant bacteria is an ongoing and troubling development that
has increased the number of diseases and infections that risk going untreated. There is an …

[HTML][HTML] A deep learning technique for intrusion detection system using a Recurrent Neural Networks based framework

SM Kasongo - Computer Communications, 2023 - Elsevier
In recent years, the spike in the amount of information transmitted through communication
infrastructures has increased due to the advances in technologies such as cloud computing …

[HTML][HTML] Price forecasting through neural networks for crude oil, heating oil, and natural gas

B **, X Xu - Measurement: Energy, 2024 - Elsevier
Building price projections of various energy commodities has long been an important
endeavor for a wide range of participants in the energy market. We study the forecast …

Oil price forecasting: A hybrid GRU neural network based on decomposition–reconstruction methods

S Zhang, J Luo, S Wang, F Liu - Expert Systems with Applications, 2023 - Elsevier
Significant fluctuations in the price of crude oil in recent years make accurate price
estimations of critical importance. A reliable method for crude oil price forecasting is …

A comparative analysis of gradient boosting algorithms

C Bentéjac, A Csörgő, G Martínez-Muñoz - Artificial Intelligence Review, 2021 - Springer
The family of gradient boosting algorithms has been recently extended with several
interesting proposals (ie XGBoost, LightGBM and CatBoost) that focus on both speed and …

An optimized XGBoost method for predicting reservoir porosity using petrophysical logs

S Pan, Z Zheng, Z Guo, H Luo - Journal of Petroleum Science and …, 2022 - Elsevier
To overcome the deficiencies of current porosity prediction methods, the XGBoost algorithm
is introduced to construct a model for porosity prediction, and the obtained model is …

Enhancing intrusion detection: a hybrid machine and deep learning approach

M Sajid, KR Malik, A Almogren, TS Malik… - Journal of Cloud …, 2024 - Springer
The volume of data transferred across communication infrastructures has recently increased
due to technological advancements in cloud computing, the Internet of Things (IoT), and …

A combined architecture of multivariate LSTM with Mahalanobis and Z-Score transformations for oil price forecasting

S Urolagin, N Sharma, TK Datta - Energy, 2021 - Elsevier
Oil price plays a vital role in a country's economy. Oil price forecasting helps in making better
economic planning and decisions. The fluctuation in the oil price occurs due to several …

Evaluation of different boosting ensemble machine learning models and novel deep learning and boosting framework for head-cut gully erosion susceptibility

W Chen, X Lei, R Chakrabortty, SC Pal… - Journal of …, 2021 - Elsevier
The objective of this study is to assess the gully head-cut erosion susceptibility and identify
gully erosion prone areas in the Meimand watershed, Iran. In recent years, this study area …