Support-vector networks

Cited by (1,253)

Items 1,001–1,100 of 1,253

1. Credit scoring using the clustered support vector machine
   Terry Harris · 2014 · Expert Systems with Applications
2. MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans
   Mendrik, Adriënne M., Vincken, Koen L., Kuijf, Hugo J., Breeuwer, Marcel, Bouvy, Willem H., de Bresser, Jeroen, Alansary, Amir, de Bruijne, Marleen, Carass, Aaron, El-Baz, Ayman et al. · 2015 · Computational Intelligence and Neuroscience
3. Metaheuristic Algorithms in Modeling and Optimization
   Gandomi, Amir Hossein, Yang, Xin-She, Talatahari, Siamak, Alavi, Amir Hossein · 2013 · Metaheuristic Applications in Structures and Infrastructures
4. Deep learning and time series-to-image encoding for financial forecasting
   Barra, Silvio, Carta, Salvatore Mario, Corriga, Andrea, Podda, Alessandro Sebastian, Recupero, Diego Reforgiato · 2020 · IEEE/CAA Journal of Automatica Sinica
5. On the Feasibility of Internet-Scale Author Identification
   Narayanan, Arvind, Paskov, Hristo, Gong, Neil Zhenqiang, Bethencourt, John, Stefanov, Emil, Shin, Eui Chul Richard, Song, Dawn · 2012 · 2012 IEEE Symposium on Security and Privacy
6. Experimental Realization of Quantum Artificial Intelligence
   Zhaokai, Li, Xiaomei, Liu, Nanyang, Xu, jiangfeng, Du · 2014 · arXiv
7. Vision-Based Material Recognition for Automated Monitoring of Construction Progress and Generating Building Information Modeling from Unordered Site Image Collections
   Dimitrov, Andrey, Golparvar-Fard, Mani · 2014 · Advanced Engineering Informatics
8. Human Digital Twin for Fitness Management
   Barricelli, Barbara Rita, Casiraghi, Elena, Gliozzo, Jessica, Petrini, Alessandro, Valtolina, Stefano · 2020 · IEEE Access
9. Wheel Defect Detection With Machine Learning
   Krummenacher, Gabriel, Ong, Cheng Soon, Koller, Stefan, Kobayashi, Seijin, Buhmann, Joachim M. · 2018 · IEEE Transactions on Intelligent Transportation Systems
10. Machine Learning Using Combined Structural and Chemical Descriptors for Prediction of Methane Adsorption Performance of Metal Organic Frameworks (MOFs)
    Pardakhti, Maryam, Moharreri, Ehsan, Wanik, David, Suib, Steven L., Srivastava, Ranjan · 2017 · ACS Combinatorial Science
11. Development of convolutional neural network and its application in image classification: a survey
    Wang, Wei, Yang, Yujing, Wang, Xin, Wang, Weizheng, Li, Ji · 2019 · Optical Engineering
12. Human Activity Recognition via Hybrid Deep Learning Based Model
    Khan, Imran Ullah, Afzal, Sitara, Lee, Jong Weon · 2022 · Sensors
13. Facial Expression Recognition Using Facial Movement Features
    Zhang, Ligang, Tjondronegoro, Dian · 2011 · IEEE Transactions on Affective Computing
14. Artificial intelligence for aging and longevity research: Recent advances and perspectives
    Zhavoronkov, Alex, Mamoshina, Polina, Vanhaelen, Quentin, Scheibye-Knudsen, Morten, Moskalev, Alexey, Aliper, Alex · 2019 · Ageing Research Reviews
15. High-accuracy detection of early Parkinson’s disease through multimodal features and machine learning
    Prashanth, R., Dutta Roy, Sumantra, Mandal, Pravat K., Ghosh, Shantanu · 2016 · International Journal of Medical Informatics
16. Prediction of Employee Turnover in Organizations using Machine Learning Algorithms
    Punnoose, Rohit, Ajit, Pankaj · 2016 · International Journal of Advanced Research in Artificial Intelligence
17. Towards an intelligent framework for multimodal affective data analysis
    Poria, Soujanya, Cambria, Erik, Hussain, Amir, Huang, Guang-Bin · 2015 · Neural Networks
18. Deep learning for freezing of gait detection in Parkinson’s disease patients in their homes using a waist-worn inertial measurement unit
    Camps, Julià, Samà, Albert, Martín, Mario, Rodríguez-Martín, Daniel, Pérez-López, Carlos, Moreno Arostegui, Joan M., Cabestany, Joan, Català, Andreu, Alcaine, Sheila, Mestre, Berta et al. · 2018 · Knowledge-Based Systems
19. modlAMP: Python for antimicrobial peptides
    Müller, Alex T, Gabernet, Gisela, Hiss, Jan A, Schneider, Gisbert · 2017 · Bioinformatics
20. A hybrid model approach for forecasting future residential electricity consumption
    Dong, Bing, Li, Zhaoxuan, Rahman, S.M. Mahbobur, Vega, Rolando · 2016 · Energy and Buildings
21. Accelerating antibiotic discovery through artificial intelligence
    Melo, Marcelo C. R., Maasch, Jacqueline R. M. A., de la Fuente-Nunez, Cesar · 2021 · Communications Biology
22. A carbon-fiber electrode array for long-term neural recording
    Guitchounts, Grigori, Markowitz, Jeffrey E, Liberti, William A, Gardner, Timothy J · 2013 · Journal of Neural Engineering
23. Multimodal Language Analysis with Recurrent Multistage Fusion
    Liang, Paul Pu, Liu, Ziyin, Bagher Zadeh, AmirAli, Morency, Louis-Philippe · 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
24. Bayer’s in silico ADMET platform: a journey of machine learning over the past two decades
    Göller, Andreas H., Kuhnke, Lara, Montanari, Floriane, Bonin, Anne, Schneckener, Sebastian, ter Laak, Antonius, Wichard, Jörg, Lobell, Mario, Hillisch, Alexander · 2020 · Drug Discovery Today
25. Deep learning models for traffic flow prediction in autonomous vehicles: a review, solutions, and challenges
    Miglani, Arzoo, Kumar, Neeraj · 2019 · Vehicular Communications
26. Artificial Intelligence and Machine Learning Tools for High-Performance Microalgal Wastewater Treatment and Algal Biorefinery: A Critical Review
    Oruganti, Raj Kumar, Biji, Alka Pulimoottil, Lanuyanger, Tiamenla, Show, Pau Loke, Sriariyanun, Malinee, Upadhyayula, Venkata K.K., Gadhamshetty, Venkataramana, Bhattacharyya, Debraj · 2023 · Science of The Total Environment
27. Reducing systematic review workload through certainty-based screening
    Miwa, Makoto, Thomas, James, O’Mara-Eves, Alison, Ananiadou, Sophia · 2014 · Journal of Biomedical Informatics
28. Machine learning meets volcano plots: computational discovery of cross-coupling catalysts
    Meyer, Benjamin, Sawatlon, Boodsarin, Heinen, Stefan, von Lilienfeld, O. Anatole, Corminboeuf, Clémence · 2018 · Chemical Science
29. Making deep neural networks right for the right scientific reasons by interacting with their explanations
    Schramowski, Patrick, Stammer, Wolfgang, Teso, Stefano, Brugger, Anna, Herbert, Franziska, Shao, Xiaoting, Luigs, Hans-Georg, Mahlein, Anne-Katrin, Kersting, Kristian · 2020 · Nature Machine Intelligence
30. Brain-wide Electrical Spatiotemporal Dynamics Encode Depression Vulnerability
    Hultman, Rainbo, Ulrich, Kyle, Sachs, Benjamin D., Blount, Cameron, Carlson, David E., Ndubuizu, Nkemdilim, Bagot, Rosemary C., Parise, Eric M., Vu, Mai-Anh T., Gallagher, Neil M. et al. · 2018 · Cell
31. Detecting Cars in UAV Images With a Catalog-Based Approach
    Moranduzzo, Thomas, Melgani, Farid · 2014 · IEEE Transactions on Geoscience and Remote Sensing
32. Synchronous brain activity across individuals underlies shared psychological perspectives
    Lahnakoski, Juha M., Glerean, Enrico, Jääskeläinen, Iiro P., Hyönä, Jukka, Hari, Riitta, Sams, Mikko, Nummenmaa, Lauri · 2014 · NeuroImage
33. PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING
    Lochner, Michelle, McEwen, Jason D., Peiris, Hiranya V., Lahav, Ofer, Winter, Max K. · 2016 · The Astrophysical Journal Supplement Series
34. Innovative Materials Science via Machine Learning
    Gao, Chaochao, Min, Xin, Fang, Minghao, Tao, Tianyi, Zheng, Xiaohong, Liu, Yangai, Wu, Xiaowen, Huang, Zhaohui · 2022 · Advanced Functional Materials
35. Improving propensity score estimators’ robustness to model misspecification using super learner.
    Pirracchio, Romain, Petersen, Maya L., van der Laan, Mark · 2015 · American Journal of Epidemiology
36. Delusions and the role of beliefs in perceptual inference
    Schmack, Katharina, Gòmez-Carrillo de Castro, Ana, Rothkirch, Marcus, Sekutowicz, Maria, Rössler, Hannes, Haynes, John-Dylan, Heinz, Andreas, Petrovic, Predrag, Sterzer, Philipp · 2013 · The Journal of Neuroscience
37. Extreme Learning Machine-Based Classification of ADHD Using Brain Structural MRI Data
    Peng, Xiaolong, Lin, Pan, Zhang, Tongsheng, Wang, Jue · 2013 · PLoS ONE
38. Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis
    Wu, Yunyi, Wang, Guanyu · 2018 · International Journal of Molecular Sciences
39. DeepMicro: Deep representation learning for disease prediction based on microbiome data
    Oh, Min, Zhang, Liqing · 2020 · Scientific Reports
40. Internal representation of task rules by recurrent dynamics: the importance of the diversity of neural responses
    Rigotti, Mattia · 2010 · Frontiers in Computational Neuroscience
41. A systematic literature review of automatic Alzheimer’s disease detection from speech and language
    Petti, Ulla, Baker, Simon, Korhonen, Anna · 2020 · Journal of the American Medical Informatics Association
42. Predictive Modeling of the Hospital Readmission Risk from Patients’ Claims Data Using Machine Learning: A Case Study on COPD
    Min, Xu, Yu, Bin, Wang, Fei · 2019 · Scientific Reports
43. Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects
    Liu, Yiming, Tan, Xinyu, Liang, Jie, Han, Hongwei, Xiang, Peng, Yan, Wensheng · 2023 · Advanced Functional Materials
44. Conversational Agents: Goals, Technologies, Vision and Challenges
    Allouch, Merav, Azaria, Amos, Azoulay, Rina · 2021 · Sensors
45. A survey on aspect-based sentiment classification
    Brauwers, Gianni, Frasincar, Flavius · 2023 · ACM Computing Surveys
46. Machine-learning based design of active composite structures for 4D printing
    Hamel, Craig M, Roach, Devin J, Long, Kevin N, Demoly, Frédéric, Dunn, Martin L, Qi, H Jerry · 2019 · Smart Materials and Structures
47. Quantitative change of EEG and respiration signals during mindfulness meditation
    Ahani, Asieh, Wahbeh, Helane, Nezamfar, Hooman, Miller, Meghan, Erdogmus, Deniz, Oken, Barry · 2014 · Journal of NeuroEngineering and Rehabilitation
48. A blockchain-based fog computing framework for activity recognition as an application to e-healthcare services
    Islam, Naveed, Faheem, Yasir, Din, Ikram Ud, Talha, Muhammad, Guizani, Mohsen, Khalil, Mudassir · 2019 · Future Generation Computer Systems
49. Microbiota and cancer immunotherapy: in search of microbial signals
    Gharaibeh, Raad Z, Jobin, Christian · 2019 · Gut
50. Automated trading with performance weighted random forests and seasonality
    Booth, Ash, Gerding, Enrico, McGroarty, Frank · 2014 · Expert Systems with Applications
51. Support Vector Machines with the Ramp Loss and the Hard Margin Loss
    Brooks, J. Paul · 2011 · Operations Research
52. Predicting Travel Mode of Individuals by Machine Learning
    Omrani, Hichem · 2015 · Transportation Research Procedia
53. Insights into multimodal imaging classification of ADHD
    Colby, John B., Rudie, Jeffrey D., Brown, Jesse A., Douglas, Pamela K., Cohen, Mark S., Shehzad, Zarrar · 2012 · Frontiers in Systems Neuroscience
54. Classification of depression patients and normal subjects based on electroencephalogram (EEG) signal using alpha power and theta asymmetry
    Mahato, Shalini, Paul, Sanchita · 2020 · Journal of Medical Systems
55. Detection of passion fruits and maturity classification using Red-Green-Blue Depth images
    Tu, Shuqin, Xue, Yueju, Zheng, Chan, Qi, Yu, Wan, Hua, Mao, Liang · 2018 · Biosystems Engineering
56. Evaluation of Machine Learning Algorithms in Spatial Downscaling of MODIS Land Surface Temperature
    Li, Wan, Ni, Li, Li, Zhao-Liang, Duan, Si-Bo, Wu, Hua · 2019 · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
57. 3D deep convolutional neural networks for amino acid environment similarity analysis
    Torng, Wen, Altman, Russ B. · 2017 · BMC Bioinformatics
58. Mapping functional humoral correlates of protection against malaria challenge following RTS,S/AS01 vaccination
    Suscovich, Todd J., Fallon, Jonathan K., Das, Jishnu, Demas, Allison R., Crain, Jonathan, Linde, Caitlyn H., Michell, Ashlin, Natarajan, Harini, Arevalo, Claudia, Broge, Thomas et al. · 2020 · Science Translational Medicine
59. Atypical diffusion tensor hemispheric asymmetry in autism
    Lange, Nicholas, DuBray, Molly B., Lee, Jee Eun, Froimowitz, Michael P., Froehlich, Alyson, Adluru, Nagesh, Wright, Brad, Ravichandran, Caitlin, Fletcher, P. Thomas, Bigler, Erin D. et al. · 2010 · Autism Research
60. GeoAI for Large-Scale Image Analysis and Machine Vision: Recent Progress of Artificial Intelligence in Geography
    Li, Wenwen, Hsu, Chia-Yu · 2022 · ISPRS International Journal of Geo-Information
61. HABNet: Machine Learning, Remote Sensing-Based Detection of Harmful Algal Blooms
    Hill, Paul R., Kumar, Anurag, Temimi, Marouane, Bull, David R. · 2020 · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
62. Promises, Pitfalls, and Basic Guidelines for Applying Machine Learning Classifiers to Psychiatric Imaging Data, with Autism as an Example
    Kassraian-Fard, Pegah, Matthis, Caroline, Balsters, Joshua H., Maathuis, Marloes H., Wenderoth, Nicole · 2016 · Frontiers in Psychiatry
63. Surveying the reach and maturity of machine learning and artificial intelligence in astronomy
    Fluke, Christopher J., Jacobs, Colin · 2020 · WIREs Data Mining and Knowledge Discovery
64. Deep Learning Approaches for Detecting Freezing of Gait in Parkinson’s Disease Patients through On-Body Acceleration Sensors
    Sigcha, Luis, Costa, Nélson, Pavón, Ignacio, Costa, Susana, Arezes, Pedro, López, Juan Manuel, De Arcas, Guillermo · 2020 · Sensors
65. A survey on trust evaluation based on machine learning
    Wang, Jingwen, Jing, Xuyang, Yan, Zheng, Fu, Yulong, Pedrycz, Witold, Yang, Laurence T. · 2021 · ACM Computing Surveys
66. Developing a Multi-Dose Computational Model for Drug-Induced Hepatotoxicity Prediction Based on Toxicogenomics Data
    Su, Ran, Wu, Huichen, Xu, Bo, Liu, Xiaofeng, Wei, Leyi · 2019 · IEEE/ACM Transactions on Computational Biology and Bioinformatics
67. Orchestrating the Development Lifecycle of Machine Learning-based IoT Applications: A Taxonomy and Survey
    Qian, Bin, Su, Jie, Wen, Zhenyu, Jha, Devki Nandan, Li, Yinhao, Guan, Yu, Puthal, Deepak, James, Philip, Yang, Renyu, Zomaya, Albert Y. et al. · 2021 · ACM Computing Surveys
68. Light attention predicts protein location from the language of life
    Stärk, Hannes, Dallago, Christian, Heinzinger, Michael, Rost, Burkhard · 2021 · Bioinformatics Advances
69. Automated diagnoses of attention deficit hyperactive disorder using magnetic resonance imaging
    Eloyan, Ani, Muschelli, John, Nebel, Mary Beth, Liu, Han, Han, Fang, Zhao, Tuo, Barber, Anita D., Joel, Suresh, Pekar, James J., Mostofsky, Stewart H. et al. · 2012 · Frontiers in Systems Neuroscience
70. Automated detection of contractual risk clauses from construction specifications using bidirectional encoder representations from transformers (BERT)
    Moon, Seonghyeon, Chi, Seokho, Im, Seok-Been · 2022 · Automation in Construction
71. Machine Learning in Pharmacometrics: Opportunities and Challenges
    McComb, Mason, Bies, Robert, Ramanathan, Murali · 2022 · British Journal of Clinical Pharmacology
72. Building type classification using spatial and landscape attributes derived from LiDAR remote sensing data
    Lu, Zhenyu, Im, Jungho, Rhee, Jinyoung, Hodgson, Michael · 2014 · Landscape and Urban Planning
73. Cyberthreat Detection from Twitter using Deep Neural Networks
    Dionisio, Nuno, Alves, Fernando, Ferreira, Pedro M., Bessani, Alysson · 2019 · 2019 International Joint Conference on Neural Networks (IJCNN)
74. Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review
    Yan, Melissa Y, Gustad, Lise Tuset, Nytrø, Øystein · 2022 · Journal of the American Medical Informatics Association
75. Identifying Neuroimaging Markers of Motor Disability in Acute Stroke by Machine Learning Techniques
    Rehme, A. K., Volz, L. J., Feis, D.-L., Bomilcar-Focke, I., Liebig, T., Eickhoff, S. B., Fink, G. R., Grefkes, C. · 2015 · Cerebral Cortex
76. Predicting students’ academic performance by using educational big data and learning analytics: evaluation of classification methods and learning logs.
    Huang, Anna Y. Q., Lu, Owen H. T., Huang, Jeff C. H., Yin, C. J., Yang, Stephen J. H. · 2020 · Interactive Learning Environments
77. Adaptive Bitrate Selection: A Survey
    Sani, Yusuf, Mauthe, Andreas, Edwards, Christopher · 2017 · IEEE Communications Surveys & Tutorials
78. Rapid on-site identification of pesticide residues in tea by one-dimensional convolutional neural network coupled with surface-enhanced Raman scattering
    Zhu, Jiaji, Sharma, Arumugam Selva, Xu, Jing, Xu, Yi, Jiao, Tianhui, Ouyang, Qin, Li, Huanhuan, Chen, Quansheng · 2021 · Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy
79. A mouse tissue atlas of small noncoding RNA
    Isakova, Alina, Fehlmann, Tobias, Keller, Andreas, Quake, Stephen R. · 2020 · Proceedings of the National Academy of Sciences
80. Security intrusion detection using quantum machine learning techniques
    Kalinin, Maxim, Krundyshev, Vasiliy · 2022 · Journal of Computer Virology and Hacking Techniques
81. Prediction of arrival times of freight traffic on US railroads using support vector regression
    Barbour, William, Martinez Mori, Juan Carlos, Kuppa, Shankara, Work, Daniel B. · 2018 · Transportation Research Part C: Emerging Technologies
82. Evolution and impact of bias in human and machine learning algorithm interaction
    Sun, Wenlong, Nasraoui, Olfa, Shafto, Patrick · 2020 · PLOS ONE
83. From Bit to Bedside: A Practical Framework for Artificial Intelligence Product Development in Healthcare
    Higgins, David, Madai, Vince I. · 2020 · Advanced Intelligent Systems
84. Statistical stopping criteria for automated screening in systematic reviews
    Callaghan, Max W, Müller-Hansen, Finn · 2020 · Systematic Reviews
85. A CNN-SVM combined model for pattern recognition of knee motion using mechanomyography signals
    Wu, Haifeng, Huang, Qing, Wang, Daqing, Gao, Lifu · 2018 · Journal of Electromyography and Kinesiology
86. The neural basis for a persistent internal state in Drosophila females
    Deutsch, David, Pacheco, Diego, Encarnacion-Rivera, Lucas, Pereira, Talmo, Fathy, Ramie, Clemens, Jan, Girardin, Cyrille, Calhoun, Adam, Ireland, Elise, Burke, Austin et al. · 2020 · eLife
87. Current approaches for choosing feature selection and learning algorithms in quantitative structure–activity relationships (QSAR)
    Khan, Pathan Mohsin, Roy, Kunal · 2018 · Expert Opinion on Drug Discovery
88. Deep learning versus traditional machine learning methods for aggregated energy demand prediction
    Paterakis, Nikolaos G., Mocanu, Elena, Gibescu, Madeleine, Stappers, Bart, van Alst, Walter · 2017 · 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)
89. Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study
    Qureshi, Muhammad Naveed Iqbal, Min, Beomjun, Jo, Hang Joon, Lee, Boreom · 2016 · PLOS ONE
90. Cortical thickness predicts the first onset of major depression in adolescence
    Foland‐Ross, Lara C., Sacchet, Matthew D., Prasad, Gautam, Gilbert, Brooke, Thompson, Paul M., Gotlib, Ian H. · 2015 · International Journal of Developmental Neuroscience
91. Three-Dimensional spatially resolved geometrical and functional models of human liver tissue reveal new aspects of NAFLD progression
    Segovia-Miranda, Fabián, Morales-Navarrete, Hernán, Kücken, Michael, Moser, Vincent, Seifert, Sarah, Repnik, Urska, Rost, Fabian, Brosch, Mario, Hendricks, Alexander, Hinz, Sebastian et al. · 2019 · Nature Medicine
92. Machine Learning for Security in Vehicular Networks: A Comprehensive Survey
    Talpur, Anum, Gurusamy, Mohan · 2022 · IEEE Communications Surveys & Tutorials
93. Stock Market Prediction with High Accuracy using Machine Learning Techniques
    Bansal, Malti, Goyal, Apoorva, Choudhary, Apoorva · 2022 · Procedia Computer Science
94. RETRACTED ARTICLE: Automatic sleep staging in obstructive sleep apnea patients using photoplethysmography, heart rate variability signal and machine learning techniques
    Uçar, Muhammed Kürşad, Bozkurt, Mehmet Recep, Bilgin, Cahit, Polat, Kemal · 2018 · Neural Computing and Applications · Retracted
95. Explaining Outliers by Subspace Separability
    Micenkova, Barbora, Ng, Raymond T., Dang, Xuan-Hong, Assent, Ira · 2013 · 2013 IEEE 13th International Conference on Data Mining
96. Steel surface defects recognition based on multi-type statistical features and enhanced twin support vector machine
    Chu, Maoxiang, Gong, Rongfen, Gao, Song, Zhao, Jie · 2017 · Chemometrics and Intelligent Laboratory Systems
97. Radiomics in radiooncology – challenging the medical physicist
    Peeken, Jan C., Bernhofer, Michael, Wiestler, Benedikt, Goldberg, Tatyana, Cremers, Daniel, Rost, Burkhard, Wilkens, Jan J., Combs, Stephanie E., Nüsslin, Fridtjof · 2018 · Physica Medica
98. A peek into the black box: exploring classifiers by randomization
    Henelius, Andreas, Puolamäki, Kai, Boström, Henrik, Asker, Lars, Papapetrou, Panagiotis · 2014 · Data Mining and Knowledge Discovery
99. Sequence-based predictive modeling to identify cancerlectins
    Lai, Hong-Yan, Chen, Xin-Xin, Chen, Wei, Tang, Hua, Lin, Hao · 2017 · Oncotarget
100. Disease Prediction via Graph Neural Networks
     Sun, Zhenchao, Yin, Hongzhi, Chen, Hongxu, Chen, Tong, Cui, Lizhen, Yang, Fan · 2021 · IEEE Journal of Biomedical and Health Informatics