End‐To‐End Deep Learning Temperature Prediction Algorithms of a Phase Change Materials From Experimental Photos by Mohammad Hassan Ranjbar, Kobra Gharali, Artie Ng

“…Initially, the networks were built using different convolutional layers and weights for feature extraction, and then the fully connected layers extracted the temperature profiles of the PCM. …”
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LM-CNN-based Automatic Cost Calculation Model for Power Transmission and Transformation Projects by Xiaolin WU, Ling LUAN, Lianwu PAN, Hailong LI

“…Finally, in view of the big difference between the expected output and the actual output, the Levenberg-Marquart algorithm is utilized to optimize the weight parameters of the convolutional neural network to complete the model training. …”
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Research of Fault Feature Extraction and Diagnosis of Planetary Gear Train based on SPS and CNN by Pan Zheng, Jianhua Zhou, Sujie Gao, Ben Chen, Xiangxiong Liu, Shijing Wu

“…The vibration signals of the planetary gearbox under different operating conditions and different fault states are further collected，and the extracted fault features are input into the convolutional neural network for fault identification. …”
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Exoplanet Classification Through Vision Transformers with Temporal Image Analysis by Anupma Choudhary, Sohith Bandari, B. S. Kushvah, C. Swastik

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Dynamic deep learning based super-resolution for the shallow water equations by Maximilian Witte, Fabrício R Lapolli, Philip Freese, Sebastian Götschel, Daniel Ruprecht, Peter Korn, Christopher Kadow

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Apple Watercore Grade Classification Method Based on ConvNeXt and Visible/Near-Infrared Spectroscopy by Chunlin Zhao, Zhipeng Yin, Yushuo Tan, Wenbin Zhang, Panpan Guo, Yaxing Ma, Haijian Wu, Ding Hu, Quan Lu

“…Next, methods such as the Gramian Angular Summation Field (GASF), Gram Angular Difference Field (GADF), and Markov Transition Field (MTF) were applied to transform the one-dimensional spectral data into two-dimensional images. …”
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Application of machine learning for brain tumor diagnosis using magnetic resonance images: a comparative analysis by Patel Rahul kumar-Manilal, D. J. Shah

“…The study described applies and evaluates three different methods. The study applied and evaluated three different methods for identifying brain tumors: a self-defined a support vector machine (SVM), a Random forest (RF), and a convolution neural network (CNN). …”
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Deep Learning Based Breast Cancer Detection Using Decision Fusion by Doğu Manalı, Hasan Demirel, Alaa Eleyan

“…First, SVM distinguishes between different tumor types using local binary pattern (LBP) features. …”
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Super‐resolution reconstruction algorithm for medical images by fusion of wavelet transform and multi‐scale adaptive feature selection by QiaoSu Wang, Qiaomei Ma

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Blind Interleaver Recognition Using Deep Learning Techniques by Nayim Ahamed, Swaminathan R., B. Naveen

“…This paper explores the application of deep learning to recognize four different interleavers such as block, convolutional, helical, and random especially in non-cooperative environments. …”
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Deep Learning Integrating Scale Conversion and Pedo‐Transfer Function to Avoid Potential Errors in Cross‐Scale Transfer by Peijun Li, Yuanyuan Zha, Yonggen Zhang, Chak‐Hau Michael Tso, Sabine Attinger, Luis Samaniego, Jian Peng

“…The proposed method uses the convolutional neural network (CNN) as a cross‐scale transfer approach to directly map soil/landscape static properties to soil hydraulic parameters across different spatial scales. …”
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Denoising of Heart Sounds Using Lightweight FCNs and Spectrograms With and Without Context by Declan Duggan, Andriy Temko, Volodymyr Sarana, Andreea Factor, Emanuel Popovici

“…We tested all models with different contamination types at different signal-to-noise ratios (SNRs), and found that the DWC gave an overall average improvement of 10.322 dB, with average increases ranging from 6.151 dB to 14.479 dB. …”
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Environmental sound classification system by I. N. Zhuk

“…Classification model includes classic convolutional neuron network architectures. Experiments based on different architectures of convolutional neural networks and proposed feature extraction method. …”
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Computer-aided diagnosis of hepatic cystic echinococcosis based on deep transfer learning features from ultrasound images by Miao Wu, Chuanbo Yan, Gan Sen

“…And each subtype has different treatment methods. An accurate diagnosis is the prerequisite for effective HCE treatment. …”
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Exploring Transfer Learning for Anthropogenic Geomorphic Feature Extraction from Land Surface Parameters Using UNet by Aaron E. Maxwell, Sarah Farhadpour, Muhammad Ali

“…Transfer learning between the different geomorphic datasets offered minimal benefits. …”
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S2-IFNet: A spatial-semantic information fusion network integrated with boundary feature enhancement for forest land extraction from Sentinel-2 data by Junyang Xie, Mengyao Zhang, Hao Wu, Anqi Lin, Marcos Adami, Abdul Rashid Mohamed Shariff, Yahui Guo

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Associations of greenhouse gases, air pollutants and dynamics of scrub typhus incidence in China: a nationwide time-series study by Haoyue Cao, Jianqiang Han, Weiming Hou, Juxiang Yuan

“…The Quantile-based G Computation (qgcomp) model revealed age-specific differences in susceptibility to environmental factors. …”
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CNN-based salient features in HSI image semantic target prediction by Vishal Srivastava, Bhaskar Biswas

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An Optimized 1-D CNN-LSTM Approach for Fault Diagnosis of Rolling Bearings Considering Epistemic Uncertainty by Onur Can Kalay

“…From this standpoint, the present study combined a 1-D convolutional neural network (1-D CNN) with a long short-term memory (LSTM) algorithm for classifying different ball-bearing health conditions. …”
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Target recognition in diverse synthetic aperture radar image datasets with low size weight and power processing hardware by Richard O. Lane, Wendy J. Holmes, Timothy Lamont‐Smith

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