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Process‐Guided Deep Learning Predictions of Lake Water Temperature
Process‐Guided Deep Learning Predictions of Lake Water Temperature
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Process‐Guided Deep Learning Predictions of Lake Water Temperature

Read, Jordan S. ; Jia, Xiaowei ; Willard, Jared ; Appling, Alison P. ; Zwart, Jacob A. ; Oliver, Samantha K. ; Karpatne, Anuj ; Hansen, Gretchen J. A. ; Hanson, Paul C. ; Watkins, William ; Steinbach, Michael ; Kumar, Vipin

Water resources research, 2019-11, Vol.55 (11), p.9173-9190 [Periódico revisado por pares]

Washington: John Wiley & Sons, Inc

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2
Brain Tumor/Mass Classification Framework Using Magnetic-Resonance-Imaging-Based Isolated and Developed Transfer Deep-Learning Model
Brain Tumor/Mass Classification Framework Using Magnetic-Resonance-Imaging-Based Isolated and Developed Transfer Deep-Learning Model
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Brain Tumor/Mass Classification Framework Using Magnetic-Resonance-Imaging-Based Isolated and Developed Transfer Deep-Learning Model

Alanazi, Muhannad Faleh ; Ali, Muhammad Umair ; Hussain, Shaik Javeed ; Zafar, Amad ; Mohatram, Mohammed ; Irfan, Muhammad ; AlRuwaili, Raed ; Alruwaili, Mubarak ; Ali, Naif H ; Albarrak, Anas Mohammad

Sensors (Basel, Switzerland), 2022-01, Vol.22 (1), p.372 [Periódico revisado por pares]

Switzerland: MDPI AG

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3
Understanding, Investigating, and promoting deep learning in language education: A survey on chinese college students' deep learning in the online EFL teaching context
Understanding, Investigating, and promoting deep learning in language education: A survey on chinese college students' deep learning in the online EFL teaching context
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Understanding, Investigating, and promoting deep learning in language education: A survey on chinese college students' deep learning in the online EFL teaching context

Jiang, Ruihong

Frontiers in psychology, 2022-09, Vol.13, p.955565-955565 [Periódico revisado por pares]

Frontiers Media S.A

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4
A comprehensive review on ensemble deep learning: Opportunities and challenges
A comprehensive review on ensemble deep learning: Opportunities and challenges
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A comprehensive review on ensemble deep learning: Opportunities and challenges

Mohammed, Ammar ; Kora, Rania

Journal of King Saud University. Computer and information sciences, 2023-02, Vol.35 (2), p.757-774 [Periódico revisado por pares]

Elsevier B.V

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5
Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment
Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment
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Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment

Bui, Dieu Tien ; Tsangaratos, Paraskevas ; Nguyen, Viet-Tien ; Liem, Ngo Van ; Trinh, Phan Trong

Catena (Giessen), 2020-05, Vol.188, p.104426, Article 104426 [Periódico revisado por pares]

Elsevier B.V

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6
Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques
Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques
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Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques

Afan, Haitham Abdulmohsin ; Ibrahem Ahmed Osman, Ahmedbahaaaldin ; Essam, Yusuf ; Ahmed, Ali Najah ; Huang, Yuk Feng ; Kisi, Ozgur ; Sherif, Mohsen ; Sefelnasr, Ahmed ; Chau, Kwok-wing ; El-Shafie, Ahmed

Engineering applications of computational fluid mechanics, 2021-01, Vol.15 (1), p.1420-1439 [Periódico revisado por pares]

Hong Kong: Taylor & Francis

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7
Amino Acid Encoding for Deep Learning Applications
Amino Acid Encoding for Deep Learning Applications
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Amino Acid Encoding for Deep Learning Applications

ElAbd, Hesham ; Bromberg, Yana ; Hoarfrost, Adrienne ; Lenz, Tobias ; Wendorff, Mareike

BMC bioinformatics, 2020-06, Vol.21 (1), p.235-235, Article 235 [Periódico revisado por pares]

2230 Support: BMC

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8
Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection
Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection
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Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection

Ghorbanzadeh, Omid ; Blaschke, Thomas ; Gholamnia, Khalil ; Meena, Sansar Raj ; Tiede, Dirk ; Aryal, Jagannath

Remote sensing (Basel, Switzerland), 2019-01, Vol.11 (2), p.196 [Periódico revisado por pares]

Basel: MDPI AG

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9
Deep convolutional neural networks for mammography: advances, challenges and applications
Deep convolutional neural networks for mammography: advances, challenges and applications
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Deep convolutional neural networks for mammography: advances, challenges and applications

Abdelhafiz, Dina ; Yang, Clifford ; Ammar, Reda ; Nabavi, Sheida

BMC bioinformatics, 2019-06, Vol.20 (Suppl 11), p.281-281, Article 281 [Periódico revisado por pares]

England: BioMed Central Ltd

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10
ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation
ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation
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ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation

Khurshid, Shaan ; Friedman, Samuel ; Reeder, Christopher ; Di Achille, Paolo ; Diamant, Nathaniel ; Singh, Pulkit ; Harrington, Lia X ; Wang, Xin ; Al-Alusi, Mostafa A ; Sarma, Gopal ; Foulkes, Andrea S ; Ellinor, Patrick T ; Anderson, Christopher D ; Ho, Jennifer E ; Philippakis, Anthony A ; Batra, Puneet ; Lubitz, Steven A

Circulation (New York, N.Y.), 2022-01, Vol.145 (2), p.122-133 [Periódico revisado por pares]

United States

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