Deep learning in food authenticity: Recent advances and future trends

Z Deng, T Wang, Y Zheng, W Zhang, YH Yun - Trends in Food Science & …, 2024‏ - Elsevier
Background The development of fast, efficient, accurate, and reliable techniques and
methods for food authenticity identification is crucial for food quality assurance. Traditional …

Differential privacy in deep learning: A literature survey

K Pan, YS Ong, M Gong, H Li, AK Qin, Y Gao - Neurocomputing, 2024‏ - Elsevier
The widespread adoption of deep learning is facilitated in part by the availability of large-
scale data for training desirable models. However, these data may involve sensitive …

Mathematical analysis and performance evaluation of the gelu activation function in deep learning

M Lee - Journal of Mathematics, 2023‏ - Wiley Online Library
Selecting the most suitable activation function is a critical factor in the effectiveness of deep
learning models, as it influences their learning capacity, stability, and computational …

[HTML][HTML] A federated learning-based zero trust intrusion detection system for Internet of Things

D Javeed, MS Saeed, M Adil, P Kumar, A Jolfaei - Ad Hoc Networks, 2024‏ - Elsevier
The rapid expansion of Internet of Things (IoT) devices presents unique challenges in
ensuring the security and privacy of interconnected systems. As cyberattacks become more …

Gelu activation function in deep learning: a comprehensive mathematical analysis and performance

M Lee - arxiv preprint arxiv:2305.12073, 2023‏ - arxiv.org
Selecting the most suitable activation function is a critical factor in the effectiveness of deep
learning models, as it influences their learning capacity, stability, and computational …

[HTML][HTML] A comparative analysis of multi-label deep learning classifiers for real-time vehicle detection to support intelligent transportation systems

D Shokri, C Larouche, S Homayouni - Smart cities, 2023‏ - mdpi.com
An Intelligent Transportation System (ITS) is a vital component of smart cities due to the
growing number of vehicles year after year. In the last decade, vehicle detection, as a …

A novel data-driven deep learning approach for wind turbine power curve modeling

Y Wang, X Duan, R Zou, F Zhang, Y Li, Q Hu - Energy, 2023‏ - Elsevier
Existing wind turbine power curve (WTPC) models have limited performance in capturing the
complex relationship between wind speed and wind power due to their inadequate …

Deep learning techniques with genomic data in cancer prognosis: a comprehensive review of the 2021–2023 literature

M Lee - Biology, 2023‏ - mdpi.com
Simple Summary The ongoing advancements in deep learning, notably its use in predicting
cancer survival through genomic data analysis, calls for an up-to-date review. This paper …

A novel AI-based model for real-time flooding image recognition using super-resolution generative adversarial network

YF Zeng, MJ Chang, GF Lin - Journal of Hydrology, 2024‏ - Elsevier
Intensified climate change in recent years has had a global impact, leading to increased
precipitation events of short duration and high intensity. This phenomenon poses a severe …

Rolling bearing fault diagnosis method based on MTF-MFACNN

C Lei, C Miao, H Wan, J Zhou, D Hao… - Measurement Science …, 2023‏ - iopscience.iop.org
A rolling bearing fault diagnosis method based on the Markov transition field (MTF) and multi-
scale feature aggregation convolutional neural network (MFACNN) is proposed to address …