A review of artificial intelligence to enhance the security of big data systems: state-of-art, methodologies, applications, and challenges
D Dai, S Boroomand - Archives of Computational Methods in Engineering, 2022 - Springer
Technological advancements modernize the way we live with the changes made both
globally and nationwide. These technological improvements also cause adverse effects in …
globally and nationwide. These technological improvements also cause adverse effects in …
A hybrid stock price index forecasting model based on variational mode decomposition and LSTM network
H Niu, K Xu, W Wang - Applied Intelligence, 2020 - Springer
Abstract Changes in the composite stock price index are a barometer of social and
economic development. To improve the accuracy of stock price index prediction, this paper …
economic development. To improve the accuracy of stock price index prediction, this paper …
A neural network based price sensitive recommender model to predict customer choices based on price effect
SS Chen, B Choubey, V Singh - Journal of Retailing and Consumer …, 2021 - Elsevier
The impact of price and price changes should not be ignored while designing algorithms for
predicting customer choice. Consumer preferences should be modeled with consideration of …
predicting customer choice. Consumer preferences should be modeled with consideration of …
Generic neural locomotion control framework for legged robots
M Thor, T Kulvicius… - IEEE transactions on …, 2020 - ieeexplore.ieee.org
In this article, we present a generic locomotion control framework for legged robots and a
strategy for control policy optimization. The framework is based on neural control and black …
strategy for control policy optimization. The framework is based on neural control and black …
Rutting prediction and analysis of influence factors based on multivariate transfer entropy and graph neural networks
The Rutting prediction model is an essential element of efficient pavement management
systems. Accuracy of commonly used predictive model necessitates knowledge of the input …
systems. Accuracy of commonly used predictive model necessitates knowledge of the input …
ROA and ROE forecasting in iron and steel industry using machine learning techniques for sustainable profitability
Return on equity (ROE) and return on assets (ROA) are important indicators that reveal the
sustainability of a company's profitability performance for both managers and investors. The …
sustainability of a company's profitability performance for both managers and investors. The …
Deep learning in structural bioinformatics: current applications and future perspectives
N Kumar, R Srivastava - Briefings in Bioinformatics, 2024 - academic.oup.com
In this review article, we explore the transformative impact of deep learning (DL) on
structural bioinformatics, emphasizing its pivotal role in a scientific revolution driven by …
structural bioinformatics, emphasizing its pivotal role in a scientific revolution driven by …
Experimental study of bubble growth on novel fin structures during pool boiling
Boiling heat transfer associated with phase change is perhaps one of the most efficient
cooling methodologies to manage extreme heat flux due to its large latent heat. Fin …
cooling methodologies to manage extreme heat flux due to its large latent heat. Fin …
The effectiveness of using a pretrained deep learning neural networks for object classification in underwater video
Video image processing and object classification using a Deep Learning Neural Network
(DLNN) can significantly increase the autonomy of underwater vehicles. This paper …
(DLNN) can significantly increase the autonomy of underwater vehicles. This paper …
Using an artificial neural network (ANN) for prediction of thermal degradation from kinetics parameters of vegetable fibers
Vegetal fibers are prominent reinforcements for polymer composite materials, considering
their properties and application possibilities. In particular, thermal degradation behavior is …
their properties and application possibilities. In particular, thermal degradation behavior is …