Results 21 to 30 of about 118,357 (248)

Deep learning-based gas-phase chemical kinetics kernel emulator: Application in a global air quality simulation case

Frontiers in Environmental Science, 2022
The global atmospheric chemical transport model has become a key technology for air quality forecast and management. However, precise and rapid air quality simulations and forecast are frequently limited by the model’s computational performance.
Zixi Wang, Zixi Wang, Jie Li, Jie Li, Jie Li, Lin Wu, Lin Wu, Mingming Zhu, Mingming Zhu, Yujing Zhang, Yujing Zhang, Zhilan Ye, Zhilan Ye, Zifa Wang, Zifa Wang, Zifa Wang   +15 more
doaj   +1 more source

EfficientRMT-Net—An Efficient ResNet-50 and Vision Transformers Approach for Classifying Potato Plant Leaf Diseases

Italian National Conference on Sensors, 2023
The primary objective of this study is to develop an advanced, automated system for the early detection and classification of leaf diseases in potato plants, which are among the most cultivated vegetable crops worldwide. These diseases, notably early and
Kashif Shaheed, Imran Qureshi, Fakhar Abbas, Sohail Jabbar, Qaisar Abbas, Hafsa Ahmad, M. Z. Sajid   +6 more
semanticscholar   +1 more source

SPEAKER IDENTIFICATION SYSTEM USING AUDIO SIGNAL AND DEEP LEARNING METHOD [PDF]

Proceedings on Engineering Sciences
Automatic Speaker Identification (ASI) does not result in high accuracy, so it is essential to develop a highly accurate Speaker Identification (SI) system. Artificial Intelligence has shown remarkable improvement in the development of such systems using
Neelam Nehra , Pardeep Sangwan , Neelu Trivedi   +2 more
doaj   +1 more source

A Novel Weight-Shared Multi-Stage CNN for Scale Robustness [PDF]

, 2019
Convolutional neural networks (CNNs) have demonstrated remarkable results in image classification for benchmark tasks and practical applications. The CNNs with deeper architectures have achieved even higher performance recently thanks to their robustness
Matsubara, Takashi, Takahashi, Ryo, Uehara, Kuniaki   +2 more
core   +2 more sources

Why ResNet Works? Residuals Generalize [PDF]

IEEE Transactions on Neural Networks and Learning Systems, 2020
Residual connections significantly boost the performance of deep neural networks. However, there are few theoretical results that address the influence of residuals on the hypothesis complexity and the generalization ability of deep neural networks.
Fengxiang He, Tongliang Liu, Dacheng Tao
openaire   +3 more sources

A Deep Learning Review of ResNet Architecture for Lung Disease Identification in CXR Image

Applied Sciences, 2023
The lungs are two of the most crucial organs in the human body because they are connected to the respiratory and circulatory systems. Lung cancer, COVID-19, pneumonia, and other severe diseases are just a few of the many threats. The patient is subjected
Syifa Auliyah Hasanah, Anindya Apriliyanti Pravitasari, A. S. Abdullah, Intan Nurma Yulita, Mohammad Hamid Asnawi   +4 more
semanticscholar   +1 more source

COVID-ResNet: COVID-19 Recognition Based on Improved Attention ResNet

Electronics, 2023
COVID-19 is the most widespread infectious disease in the world. There is an incubation period in the early stage of infection. At present, there are some difficulties in the diagnosis of COVID-19. Medical image analysis based on computed tomography (CT) images is an important tool for clinical diagnosis. However, the lesion size of COVID-19 is smaller,
Tao Zhou, Xiaoyu Chang, Yuncan Liu, Xinyu Ye, Huiling Lu, Fuyuan Hu   +5 more
openaire   +1 more source

Short-Term Load Forecasting and Associated Weather Variables Prediction Using ResNet-LSTM Based Deep Learning

IEEE Access, 2023
Short-term load forecasting is mainly utilized in control centers to explore the changing patterns of consumer loads and predict the load value at a certain time in the future. It is one of the key technologies for the smart grid implementation. The load
Xinfang Chen, Weiran Chen, V. Dinavahi, Yiqing Liu, Jilin Feng   +4 more
semanticscholar   +1 more source

SpikingResformer: Bridging ResNet and Vision Transformer in Spiking Neural Networks [PDF]

Computer Vision and Pattern Recognition
The remarkable success of Vision Transformers in Artificial Neural Networks (ANNs) has led to a growing interest in incorporating the self-attention mechanism and transformer-based architecture into Spiking Neural Networks (SNNs).
Xinyu Shi, Zecheng Hao, Zhaofei Yu
semanticscholar   +1 more source

Wider or Deeper: Revisiting the ResNet Model for Visual Recognition [PDF]

Pattern Recognition, 2016
The community has been going deeper and deeper in designing one cutting edge network after another, yet some works are there suggesting that we may have gone too far in this dimension.
Zifeng Wu, Chunhua Shen, Anton van den Hengel   +2 more
semanticscholar   +1 more source