Reinforcement learning for neural architecture search: A review
Deep neural networks are efficient and flexible models that perform well for a variety of tasks
such as image, speech recognition and natural language understanding. In particular …
such as image, speech recognition and natural language understanding. In particular …
A comprehensive survey on optimizing deep learning models by metaheuristics
Deep neural networks (DNNs), which are extensions of artificial neural networks, can learn
higher levels of feature hierarchy established by lower level features by transforming the raw …
higher levels of feature hierarchy established by lower level features by transforming the raw …
Dropout vs. batch normalization: an empirical study of their impact to deep learning
Overfitting and long training time are two fundamental challenges in multilayered neural
network learning and deep learning in particular. Dropout and batch normalization are two …
network learning and deep learning in particular. Dropout and batch normalization are two …
Image synthesis in multi-contrast MRI with conditional generative adversarial networks
Acquiring images of the same anatomy with multiple different contrasts increases the
diversity of diagnostic information available in an MR exam. Yet, the scan time limitations …
diversity of diagnostic information available in an MR exam. Yet, the scan time limitations …
An automatic plant leaf disease identification using DenseNet-121 architecture with a mutation-based henry gas solubility optimization algorithm
S Nandhini, K Ashokkumar - Neural Computing and Applications, 2022 - Springer
Farmers are struggling to provide the fast-growing population with sufficient agricultural
products, while plant diseases result in devastating food loss. The billions of dollars spent by …
products, while plant diseases result in devastating food loss. The billions of dollars spent by …
Multi-task convolutional neural networks outperformed random forest for map** soil particle size fractions in central Iran
Abstract Knowledge about the spatial distribution of soil particle size fractions (PSF) is
critical for sustainable management and resource assessment of the agricultural regions …
critical for sustainable management and resource assessment of the agricultural regions …
Hyperparameter optimization of deep neural network using univariate dynamic encoding algorithm for searches
YJ Yoo - Knowledge-Based Systems, 2019 - Elsevier
This paper proposes a method to find the hyperparameter tuning for a deep neural network
by using a univariate dynamic encoding algorithm for searches. Optimizing hyperparameters …
by using a univariate dynamic encoding algorithm for searches. Optimizing hyperparameters …
Transfer learning-based convolutional neural networks with heuristic optimization for hand gesture recognition
Human action recognition (HAR) has a considerable place in scientific studies. Additionally,
hand gesture recognition, which is a subcategory of HAR, plays an important role in …
hand gesture recognition, which is a subcategory of HAR, plays an important role in …
Development of an energy consumption prediction model for battery electric vehicles in real-world driving: a combined approach of short-trip segment division and …
Y Pan, W Fang, W Zhang - Journal of Cleaner Production, 2023 - Elsevier
Due to the excellent energy-saving and environmental protection features, electric vehicles
(EVs) are gaining significant market penetration, especially in densely populated urban …
(EVs) are gaining significant market penetration, especially in densely populated urban …
Facilitating database tuning with hyper-parameter optimization: a comprehensive experimental evaluation
Recently, using automatic configuration tuning to improve the performance of modern
database management systems (DBMSs) has attracted increasing interest from the …
database management systems (DBMSs) has attracted increasing interest from the …