Random Forests

Cited by (1,868)

Items 1,201–1,300 of 1,868

1. Machine Learning in Software Defined Networks: Data collection and traffic classification
   Amaral, Pedro, Dinis, Joao, Pinto, Paulo, Bernardo, Luis, Tavares, Joao, Mamede, Henrique S. · 2016 · 2016 IEEE 24th International Conference on Network Protocols (ICNP)
2. General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models
   Molnar, Christoph, König, Gunnar, Herbinger, Julia, Freiesleben, Timo, Dandl, Susanne, Scholbeck, Christian A., Casalicchio, Giuseppe, Grosse-Wentrup, Moritz, Bischl, Bernd · 2022 · Lecture Notes in Computer Science
3. Generating 3D city models without elevation data
   Biljecki, Filip, Ledoux, Hugo, Stoter, Jantien · 2017 · Computers, Environment and Urban Systems
4. Reaching the end-game for GWAS: Machine learning approaches for the prioritization of complex disease loci
   Nicholls, Hannah L., John, Christopher R., Watson, David S., Munroe, Patricia B., Barnes, Michael R., Cabrera, Claudia P. · 2020 · Frontiers in Genetics
5. A Novel Approach to Monitor Rehabilitation Outcomes in Stroke Survivors Using Wearable Technology
   Patel, Shyamal, Hughes, Richard, Hester, Todd, Stein, Joel, Akay, Metin, Dy, Jennifer G., Bonato, Paolo · 2010 · Proceedings of the IEEE
6. An impact assessment of machine learning risk forecasts on parole board decisions and recidivism
   Berk, Richard · 2017 · Journal of Experimental Criminology
7. Comparison of the Predictive Performance and Interpretability of Random Forest and Linear Models on Benchmark Data Sets
   Marchese Robinson, Richard L., Palczewska, Anna, Palczewski, Jan, Kidley, Nathan · 2017 · Journal of Chemical Information and Modeling
8. Spatio-temporal downscaling of gridded crop model yield estimates based on machine learning
   Folberth, C., Baklanov, A., Balkovič, J., Skalský, R., Khabarov, N., Obersteiner, M. · 2019 · Agricultural and Forest Meteorology
9. AKIpredictor, an online prognostic calculator for acute kidney injury in adult critically ill patients: development, validation and comparison to serum neutrophil gelatinase-associated lipocalin
   Flechet, Marine, Güiza, Fabian, Schetz, Miet, Wouters, Pieter, Vanhorebeek, Ilse, Derese, Inge, Gunst, Jan, Spriet, Isabel, Casaer, Michaël, Van den Berghe, Greet et al. · 2017 · Intensive Care Medicine
10. Legal requirements on explainability in machine learning
    Bibal, Adrien, Lognoul, Michael, de Streel, Alexandre, Frénay, Benoît · 2021 · Artificial Intelligence and Law
11. Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry
    Nandy, Aditya, Duan, Chenru, Janet, Jon Paul, Gugler, Stefan, Kulik, Heather J. · 2018 · Industrial & Engineering Chemistry Research
12. Human Skin, Oral, and Gut Microbiomes Predict Chronological Age
    Huang, Shi, Haiminen, Niina, Carrieri, Anna-Paola, Hu, Rebecca, Jiang, Lingjing, Parida, Laxmi, Russell, Baylee, Allaband, Celeste, Zarrinpar, Amir, Vázquez-Baeza, Yoshiki et al. · 2020 · mSystems
13. Gotcha! Network-based fraud detection for social security fraud
    Van Vlasselaer, Véronique, Eliassi-Rad, Tina, Akoglu, Leman, Snoeck, Monique, Baesens, Bart · 2017 · Management Science
14. Social network analysis for customer churn prediction
    Verbeke, Wouter, Martens, David, Baesens, Bart · 2014 · Applied Soft Computing
15. Predicting suicides after outpatient mental health visits in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS)
    Kessler, R C, Stein, M B, Petukhova, M V, Bliese, P, Bossarte, R M, Bromet, E J, Fullerton, C S, Gilman, S E, Ivany, C, Lewandowski-Romps, L et al. · 2017 · Molecular Psychiatry
16. Distilling the Knowledge From Handcrafted Features for Human Activity Recognition
    Chen, Zhenghua, Le Zhang, Cao, Zhiguang, Guo, Jing · 2018 · IEEE Transactions on Industrial Informatics
17. The tree cover and temperature disparity in US urbanized areas: quantifying the association with income across 5,723 communities
    McDonald, Robert I., Biswas, Tanushree, Sachar, Cedilla, Housman, Ian, Boucher, Timothy M., Balk, Deborah, Nowak, David, Spotswood, Erica, Stanley, Charlotte K., Leyk, Stefan · 2021 · PLOS ONE
18. A survey on aspect-based sentiment classification
    Brauwers, Gianni, Frasincar, Flavius · 2023 · ACM Computing Surveys
19. Feasibility of Reidentifying Individuals in Large National Physical Activity Data Sets From Which Protected Health Information Has Been Removed With Use of Machine Learning
    Na, Liangyuan, Yang, Cong, Lo, Chi-Cheng, Zhao, Fangyuan, Fukuoka, Yoshimi, Aswani, Anil · 2018 · JAMA Network Open
20. Semi-supervised machine-learning classification of materials synthesis procedures
    Huo, Haoyan, Rong, Ziqin, Kononova, Olga, Sun, Wenhao, Botari, Tiago, He, Tanjin, Tshitoyan, Vahe, Ceder, Gerbrand · 2019 · npj Computational Materials
21. Quantitative Toxicity Prediction Using Topology Based Multitask Deep Neural Networks
    Wu, Kedi, Wei, Guo-Wei · 2018 · Journal of Chemical Information and Modeling
22. Explainable artificial intelligence for cybersecurity: a literature survey
    Charmet, Fabien, Tanuwidjaja, Harry Chandra, Ayoubi, Solayman, Gimenez, Pierre-François, Han, Yufei, Jmila, Houda, Blanc, Gregory, Takahashi, Takeshi, Zhang, Zonghua · 2022 · Annals of Telecommunications
23. Identifying microRNA targets in different gene regions
    Xu, Wenlong, San Lucas, Anthony, Wang, Zixing, Liu, Yin · 2014 · BMC Bioinformatics
24. Radiogenomic analysis of hypoxia pathway is predictive of overall survival in Glioblastoma
    Beig, Niha, Patel, Jay, Prasanna, Prateek, Hill, Virginia, Gupta, Amit, Correa, Ramon, Bera, Kaustav, Singh, Salendra, Partovi, Sasan, Varadan, Vinay et al. · 2018 · Scientific Reports
25. SOPHIA: An Event-Based IoT and Machine Learning Architecture for Predictive Maintenance in Industry 4.0
    Calabrese, Matteo, Cimmino, Martin, Fiume, Francesca, Manfrin, Martina, Romeo, Luca, Ceccacci, Silvia, Paolanti, Marina, Toscano, Giuseppe, Ciandrini, Giovanni, Carrotta, Alberto et al. · 2020 · Information
26. Machine learning for inferring animal behavior from location and movement data
    Wang, Guiming · 2019 · Ecological Informatics
27. Traffic flow forecasting for urban work zones
    Hou, Yi, Edara, Praveen, Sun, Carlos · 2015 · IEEE Transactions on Intelligent Transportation Systems
28. Precision radiology: predicting longevity using feature engineering and deep learning methods in a radiomics framework
    Oakden-Rayner, Luke, Carneiro, Gustavo, Bessen, Taryn, Nascimento, Jacinto C., Bradley, Andrew P., Palmer, Lyle J. · 2017 · Scientific Reports
29. Interpretable classification models for recidivism prediction
    Zeng, Jiaming, Ustun, Berk, Rudin, Cynthia · 2017 · Journal of the Royal Statistical Society Series A: Statistics in Society
30. Automated trading with performance weighted random forests and seasonality
    Booth, Ash, Gerding, Enrico, McGroarty, Frank · 2014 · Expert Systems with Applications
31. Duodenal microbiota composition and mucosal homeostasis in pediatric celiac disease
    Cheng, Jing, Kalliomäki, Marko, Heilig, Hans GHJ, Palva, Airi, Lähteenoja, Hannu, de Vos, Willem M, Salojärvi, Jarkko, Satokari, Reetta · 2013 · BMC Gastroenterology
32. Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth
    Valindria, Vanya V., Lavdas, Ioannis, Bai, Wenjia, Kamnitsas, Konstantinos, Aboagye, Eric O., Rockall, Andrea G., Rueckert, Daniel, Glocker, Ben · 2017 · IEEE Transactions on Medical Imaging
33. An automated tuberculosis screening strategy combining X-ray-based computer-aided detection and clinical information
    Melendez, Jaime, Sánchez, Clara I., Philipsen, Rick H. H. M., Maduskar, Pragnya, Dawson, Rodney, Theron, Grant, Dheda, Keertan, van Ginneken, Bram · 2016 · Scientific Reports
34. The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence
    Watson, David · 2019 · Minds and Machines
35. Gene Essentiality Analyzed by In Vivo Transposon Mutagenesis and Machine Learning in a Stable Haploid Isolate of Candida albicans
    Segal, Ella Shtifman, Gritsenko, Vladimir, Levitan, Anton, Yadav, Bhawna, Dror, Naama, Steenwyk, Jacob L., Silberberg, Yael, Mielich, Kevin, Rokas, Antonis, Gow, Neil A. R. et al. · 2018 · mBio
36. Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study
    Maier, Oskar, Schröder, Christoph, Forkert, Nils Daniel, Martinetz, Thomas, Handels, Heinz · 2015 · PLOS ONE
37. A Machine Learning Approach for the Prediction of Formability and Thermodynamic Stability of Single and Double Perovskite Oxides
    Talapatra, Anjana, Uberuaga, Blas P., Stanek, Christopher R., Pilania, Ghanshyam · 2021 · Chemistry of Materials
38. Feature Importance in Gradient Boosting Trees with Cross-Validation Feature Selection
    Adler, Afek Ilay, Painsky, Amichai · 2022 · Entropy
39. Fast and precise single-cell data analysis using a hierarchical autoencoder
    Tran, Duc, Nguyen, Hung, Tran, Bang, La Vecchia, Carlo, Luu, Hung N., Nguyen, Tin · 2021 · Nature Communications
40. Machine learning algorithms to infer trait‐matching and predict species interactions in ecological networks
    Pichler, Maximilian, Boreux, Virginie, Klein, Alexandra‐Maria, Schleuning, Matthias, Hartig, Florian · 2020 · Methods in Ecology and Evolution
41. The ConTraSt database for analysing and comparing empirical studies of consciousness theories
    Yaron, Itay, Melloni, Lucia, Pitts, Michael, Mudrik, Liad · 2022 · Nature Human Behaviour
42. Forward Noise Adjustment Scheme for Data Augmentation
    Moreno-Barea, Francisco J., Strazzera, Fiammetta, Jerez, Jose M., Urda, Daniel, Franco, Leonardo · 2018 · 2018 IEEE Symposium Series on Computational Intelligence (SSCI)
43. Deep learning similarities from different representations of source code
    Tufano, Michele, Watson, Cody, Bavota, Gabriele, Di Penta, Massimiliano, White, Martin, Poshyvanyk, Denys · 2018 · Proceedings of the 15th International Conference on Mining Software Repositories
44. Flow infusion electrospray ionisation mass spectrometry for high throughput, non-targeted metabolite fingerprinting: a review
    Draper, John, Lloyd, Amanda J., Goodacre, Royston, Beckmann, Manfred · 2013 · Metabolomics
45. Computational psychotherapy research: scaling up the evaluation of patient–provider interactions
    Imel, Zac E., Steyvers, Mark, Atkins, David C. · 2015 · Psychotherapy
46. 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
47. Adaptive encrypted traffic fingerprinting with bi-directional dependence
    Al-Naami, Khaled, Chandra, Swarup, Mustafa, Ahmad, Khan, Latifur, Lin, Zhiqiang, Hamlen, Kevin, Thuraisingham, Bhavani · 2016 · Proceedings of the 32nd Annual Conference on Computer Security Applications
48. The Natural History of Severe Acute Liver Injury
    Koch, David G, Speiser, J L, Durkalski, V, Fontana, R J, Davern, T, McGuire, B, Stravitz, R T, Larson, A M, Liou, I, Fix, O et al. · 2017 · American Journal of Gastroenterology
49. Logistic regression has similar performance to optimised machine learning algorithms in a clinical setting: application to the discrimination between type 1 and type 2 diabetes in young adults
    Lynam, Anita L., Dennis, John M., Owen, Katharine R., Oram, Richard A., Jones, Angus G., Shields, Beverley M., Ferrat, Lauric A. · 2020 · Diagnostic and Prognostic Research
50. Mapping Multiplex Hubs in Human Functional Brain Networks
    De Domenico, Manlio, Sasai, Shuntaro, Arenas, Alex · 2016 · Frontiers in Neuroscience
51. Feature Selection via Coalitional Game Theory
    Cohen, Shay, Dror, Gideon, Ruppin, Eytan · 2007 · Neural Computation
52. Efficient Parameter Estimation Enables the Prediction of Drug Response Using a Mechanistic Pan-Cancer Pathway Model
    Fröhlich, Fabian, Kessler, Thomas, Weindl, Daniel, Shadrin, Alexey, Schmiester, Leonard, Hache, Hendrik, Muradyan, Artur, Schütte, Moritz, Lim, Ji-Hyun, Heinig, Matthias et al. · 2018 · Cell Systems
53. A Comprehensive Study of Activity Recognition Using Accelerometers
    Twomey, Niall, Diethe, Tom, Fafoutis, Xenofon, Elsts, Atis, McConville, Ryan, Flach, Peter, Craddock, Ian · 2018 · Informatics
54. 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
55. Integrating machine learning into item response theory for addressing the cold start problem in adaptive learning systems
    Pliakos, Konstantinos, Joo, Seang-Hwane, Park, Jung Yeon, Cornillie, Frederik, Vens, Celine, Van den Noortgate, Wim · 2019 · Computers & Education
56. Predicting Defective Lines Using a Model-Agnostic Technique
    Wattanakriengkrai, Supatsara, Thongtanunam, Patanamon, Tantithamthavorn, Chakkrit, Hata, Hideaki, Matsumoto, Kenichi · 2022 · IEEE Transactions on Software Engineering
57. Text mining approach to predict hospital admissions using early medical records from the emergency department
    Lucini, Filipe R., Fogliatto, Flavio S., da Silveira, Giovani J.C., Neyeloff, Jeruza L., Anzanello, Michel J., Kuchenbecker, Ricardo S., Schaan, Beatriz D. · 2017 · International Journal of Medical Informatics
58. Interpretable machine learning for weather and climate prediction: A review
    Yang, Ruyi, Hu, Jingyu, Li, Zihao, Mu, Jianli, Yu, Tingzhao, Xia, Jiangjiang, Li, Xuhong, Dasgupta, Aritra, Xiong, Haoyi · 2024 · Atmospheric Environment
59. A Random Forests Approach to Predicting Clean Energy Stock Prices
    Sadorsky, Perry · 2021 · Journal of Risk and Financial Management
60. 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
61. Who Can Tolerate a Marginal Kidney? Predicting Survival after Deceased Donor Kidney Transplant by Donor-Recipient Combination
    Bae, Sunjae, Massie, Allan B., Thomas, Alvin G., Bahn, Gahyun, Luo, Xun, Jackson, Kyle R., Ottmann, Shane E., Brennan, Daniel C., Desai, Niraj M., Coresh, Josef et al. · 2019 · American Journal of Transplantation
62. The Early Whole-Blood Transcriptional Signature of Dengue Virus and Features Associated with Progression to Dengue Shock Syndrome in Vietnamese Children and Young Adults
    Hoang, Long Truong, Lynn, David J., Henn, Matt, Birren, Bruce W., Lennon, Niall J., Le, Phuong Thi, Duong, Kien Thi Hue, Nguyen, Tham Thi Hong, Mai, Lanh Ngoc, Farrar, Jeremy J. et al. · 2010 · Journal of Virology
63. 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
64. The successful discrimination of depression from EEG could be attributed to proper feature extraction and not to a particular classification method
    Čukić, Milena, Stokić, Miodrag, Simić, Slobodan, Pokrajac, Dragoljub · 2020 · Cognitive Neurodynamics
65. High-accuracy detection of malaria vector larval habitats using drone-based multispectral imagery
    Carrasco-Escobar, Gabriel, Manrique, Edgar, Ruiz-Cabrejos, Jorge, Saavedra, Marlon, Alava, Freddy, Bickersmith, Sara, Prussing, Catharine, Vinetz, Joseph M., Conn, Jan E., Moreno, Marta et al. · 2019 · PLOS Neglected Tropical Diseases
66. From the Well to the Bottle: Identifying Sources of Microplastics in Mineral Water
    Weisser, Jana, Beer, Irina, Hufnagl, Benedikt, Hofmann, Thomas, Lohninger, Hans, Ivleva, Natalia P., Glas, Karl · 2021 · Water
67. Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models
    Kumar, Manish, Thenmozhi, M. · 2014 · International Journal of Banking, Accounting and Finance
68. Integro: Leveraging Victim Prediction for Robust Fake Account Detection in OSNs
    Boshmaf, Yazan, Logothetis, Dionysios, Siganos, Georgos, Leria, Jorge, Lorenzo, Jose, Ripeanu, Matei, Beznosov, Konstantin · 2015 · Proceedings 2015 Network and Distributed System Security Symposium
69. 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
70. An experimental evaluation of ensemble methods for EEG signal classification
    Sun, Shiliang, Zhang, Changshui, Zhang, Dan · 2007 · Pattern Recognition Letters
71. The accuracy of passive phone sensors in predicting daily mood
    Pratap, Abhishek, Atkins, David C., Renn, Brenna N., Tanana, Michael J., Mooney, Sean D., Anguera, Joaquin A., Areán, Patricia A. · 2019 · Depression and Anxiety
72. Noninvasive Precision Screening of Prostate Cancer by Urinary Multimarker Sensor and Artificial Intelligence Analysis
    Kim, Hojun, Park, Sungwook, Jeong, In Gab, Song, Sang Hoon, Jeong, Youngdo, Kim, Choung-Soo, Lee, Kwan Hyi · 2021 · ACS Nano
73. Is hydrothermal treatment coupled with carbon capture and storage an energy-producing negative emissions technology?
    Cheng, Fangwei, Porter, Michael D., Colosi, Lisa M. · 2020 · Energy Conversion and Management
74. 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
75. A deep learning framework for audio deepfake detection
    Khochare, Janavi, Joshi, Chaitali, Yenarkar, Bakul, Suratkar, Shraddha, Kazi, Faruk · 2022 · Arabian Journal for Science and Engineering
76. Autonomous active recognition and unfolding of clothes using random decision forests and probabilistic planning
    Doumanoglou, Andreas, Kargakos, Andreas, Kim, Tae-Kyun, Malassiotis, Sotiris · 2014 · 2014 IEEE International Conference on Robotics and Automation (ICRA)
77. Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery
    Adans-Dester, Catherine, Hankov, Nicolas, O’Brien, Anne, Vergara-Diaz, Gloria, Black-Schaffer, Randie, Zafonte, Ross, Dy, Jennifer, Lee, Sunghoon I., Bonato, Paolo · 2020 · npj Digital Medicine
78. Machine Learning in Pharmacometrics: Opportunities and Challenges
    McComb, Mason, Bies, Robert, Ramanathan, Murali · 2022 · British Journal of Clinical Pharmacology
79. CyberLearning: Effectiveness analysis of machine learning security modeling to detect cyber-anomalies and multi-attacks
    Sarker, Iqbal H. · 2021 · Internet of Things
80. Patient length of stay and mortality prediction: A survey
    Awad, Aya, Bader–El–Den, Mohamed, McNicholas, James · 2017 · Health Services Management Research
81. What Works Best When? A Systematic Evaluation of Heuristics for Max-Cut and QUBO
    Dunning, Iain, Gupta, Swati, Silberholz, John · 2018 · INFORMS Journal on Computing
82. Predictive mapping of reef fish species richness, diversity and biomass in Zanzibar using IKONOS imagery and machine-learning techniques
    Knudby, Anders, LeDrew, Ellsworth, Brenning, Alexander · 2010 · Remote Sensing of Environment
83. 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
84. Photoplethysmography signal processing and synthesis
    Mejía-Mejía, Elisa, Allen, John, Budidha, Karthik, El-Hajj, Chadi, Kyriacou, Panicos A., Charlton, Peter H. · 2022 · Photoplethysmography
85. 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
86. Folding clothes autonomously: A complete pipeline
    Doumanoglou, Andreas, Stria, Jan, Peleka, Georgia, Mariolis, Ioannis, Petrik, Vladimir, Kargakos, Andreas, Wagner, Libor, Hlavac, Vaclav, Kim, Tae-Kyun, Malassiotis, Sotiris · 2016 · IEEE Transactions on Robotics
87. Mining the human gut microbiota for effector strains that shape the immune system
    Ahern, Philip P., Faith, Jeremiah J., Gordon, Jeffrey I. · 2014 · Immunity
88. ABangle: characterising the VH-VL orientation in antibodies
    Dunbar, J., Fuchs, A., Shi, J., Deane, C. M. · 2013 · Protein Engineering Design and Selection
89. Automatic Fetal Head Circumference Measurement in Ultrasound Using Random Forest and Fast Ellipse Fitting
    Li, Jing, Wang, Yi, Lei, Baiying, Cheng, Jie-Zhi, Qin, Jing, Wang, Tianfu, Li, Shengli, Ni, Dong · 2018 · IEEE Journal of Biomedical and Health Informatics
90. Predicting student dropout in subscription-based online learning environments: the beneficial impact of the logit leaf model
    Coussement, Kristof, Phan, Minh, De Caigny, Arno, Benoit, Dries F., Raes, Annelies · 2020 · Decision Support Systems
91. Finding missed cases of familial hypercholesterolemia in health systems using machine learning
    Banda, Juan M., Sarraju, Ashish, Abbasi, Fahim, Parizo, Justin, Pariani, Mitchel, Ison, Hannah, Briskin, Elinor, Wand, Hannah, Dubois, Sebastien, Jung, Kenneth et al. · 2019 · npj Digital Medicine
92. 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
93. EXplainable Artificial Intelligence (XAI)—From Theory to Methods and Applications
    Ortigossa, Evandro S., Gonçalves, Thales, Nonato, Luis Gustavo · 2024 · IEEE Access
94. Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models
    Andaur Navarro, Constanza L., Damen, Johanna A.A., van Smeden, Maarten, Takada, Toshihiko, Nijman, Steven W.J., Dhiman, Paula, Ma, Jie, Collins, Gary S., Bajpai, Ram, Riley, Richard D. et al. · 2023 · Journal of Clinical Epidemiology
95. Insights into myalgic encephalomyelitis/chronic fatigue syndrome phenotypes through comprehensive metabolomics
    Nagy-Szakal, Dorottya, Barupal, Dinesh K., Lee, Bohyun, Che, Xiaoyu, Williams, Brent L., Kahn, Ellie J. R., Ukaigwe, Joy E., Bateman, Lucinda, Klimas, Nancy G., Komaroff, Anthony L. et al. · 2018 · Scientific Reports
96. Identifying incipient dementia individuals using machine learning and amyloid imaging
    Mathotaarachchi, Sulantha, Pascoal, Tharick A., Shin, Monica, Benedet, Andrea L., Kang, Min Su, Beaudry, Thomas, Fonov, Vladimir S., Gauthier, Serge, Rosa-Neto, Pedro · 2017 · Neurobiology of Aging
97. Artificial Intelligence–Based Classification of Breast Lesions Imaged With a Multiparametric Breast MRI Protocol With Ultrafast DCE-MRI, T2, and DWI
    Dalmiş, Mehmet U., Gubern-Mérida, Albert, Vreemann, Suzan, Bult, Peter, Karssemeijer, Nico, Mann, Ritse, Teuwen, Jonas · 2019 · Investigative Radiology
98. 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
99. Chemistry-informed machine learning prediction of compressive strength for alkali-activated materials
    Zhang, Lei V., Marani, Afshin, Nehdi, Moncef L. · 2022 · Construction and Building Materials
100. Interactions between white-tailed deer density and the composition of forest understories in the northern United States
     Russell, Matthew B., Woodall, Christopher W., Potter, Kevin M., Walters, Brian F., Domke, Grant M., Oswalt, Christopher M. · 2017 · Forest Ecology and Management