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Deep learning in food authenticity: Recent advances and future trends
Background The development of fast, efficient, accurate, and reliable techniques and
methods for food authenticity identification is crucial for food quality assurance. Traditional …
methods for food authenticity identification is crucial for food quality assurance. Traditional …
Differential privacy in deep learning: A literature survey
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 …
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
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 …
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
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 …
ensuring the security and privacy of interconnected systems. As cyberattacks become more …
Gelu activation function in deep learning: a comprehensive mathematical analysis and performance
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 …
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
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 …
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
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 …
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
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 …
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
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 …
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 …
scale feature aggregation convolutional neural network (MFACNN) is proposed to address …