Random Forests

Cited by (1,834)

Items 1,401–1,500 of 1,834

1. Spatial+: A new cross-validation method to evaluate geospatial machine learning models
   Wang, Yanwen, Khodadadzadeh, Mahdi, Zurita-Milla, Raúl · 2023 · International Journal of Applied Earth Observation and Geoinformation
2. Reconstructing grassland fire history using sedimentary charcoal: Considering count, size and shape
   Leys, Berangere A., Commerford, Julie L., McLauchlan, Kendra K. · 2017 · PLOS ONE
3. SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018
   Tang, Guoqiang, Clark, Martyn P., Newman, Andrew J., Wood, Andrew W., Papalexiou, Simon Michael, Vionnet, Vincent, Whitfield, Paul H. · 2020 · Earth System Science Data
4. Determining the optimal features in freezing of gait detection through a single waist accelerometer in home environments
   Samà, Albert, Rodríguez-Martín, Daniel, Pérez-López, Carlos, Català, Andreu, Alcaine, Sheila, Mestre, Berta, Prats, Anna, Crespo, M. Cruz, Bayés, Àngels · 2018 · Pattern Recognition Letters
5. Deep learning-based identification of genetic variants: application to Alzheimer’s disease classification
   Jo, Taeho, Nho, Kwangsik, Bice, Paula, Saykin, Andrew J, For The Alzheimer’s Disease Neuroimaging Initiative · 2022 · Briefings in Bioinformatics
6. Decision tree and random forest implementations for fast filtering of sensor data
   Buschjager, Sebastian, Morik, Katharina · 2018 · IEEE Transactions on Circuits and Systems I Regular Papers
7. A machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma from preoperative diffusion-weighted imaging
   Kaissis, Georgios, Ziegelmayer, Sebastian, Lohöfer, Fabian, Algül, Hana, Eiber, Matthias, Weichert, Wilko, Schmid, Roland, Friess, Helmut, Rummeny, Ernst, Ankerst, Donna et al. · 2019 · European Radiology Experimental
8. EEG Window Length Evaluation for the Detection of Alzheimer’s Disease over Different Brain Regions
   Tzimourta, Katerina D., Giannakeas, Nikolaos, Tzallas, Alexandros T., Astrakas, Loukas G., Afrantou, Theodora, Ioannidis, Panagiotis, Grigoriadis, Nikolaos, Angelidis, Pantelis, Tsalikakis, Dimitrios G., Tsipouras, Markos G. · 2019 · Brain Sciences
9. Computational design of epitope-specific functional antibodies
   Nimrod, Guy, Fischman, Sharon, Austin, Mark, Herman, Asael, Keyes, Feenagh, Leiderman, Olga, Hargreaves, David, Strajbl, Marek, Breed, Jason, Klompus, Shelley et al. · 2018 · Cell Reports
10. Developing an item pool to assess processes of change in psychological interventions: The Process-Based Assessment Tool (PBAT)
    Ciarrochi, Joseph, Sahdra, Baljinder, Hofmann, Stefan G., Hayes, Steven C. · 2022 · Journal of Contextual Behavioral Science
11. Agricultural input shocks affect crop yields more in the high-yielding areas of the world
    Ahvo, Aino, Heino, Matias, Sandström, Vilma, Chrisendo, Daniel, Jalava, Mika, Kummu, Matti · 2023 · Nature Food
12. Chemometrics for Raman Spectroscopy Harmonization
    Barton, Bastian, Thomson, James, Lozano Diz, Enrique, Portela, Raquel · 2022 · Applied Spectroscopy
13. Applying random forests to identify biomarker panels in serum 2D-DIGE data for the detection and staging of prostate cancer
    Fan, Yue, Murphy, Thomas Brendan, Byrne, Jennifer C., Brennan, Lorraine, Fitzpatrick, John M., Watson, R. William G. · 2011 · Journal of Proteome Research
14. Universal Approach for Structural Interpretation of QSAR/QSPR Models
    Polishchuk, Pavel G., Kuz'min, Victor E., Artemenko, Anatoly G., Muratov, Eugene N. · 2013 · Molecular Informatics
15. Maize yield prediction at an early developmental stage using multispectral images and genotype data for preliminary hybrid selection
    Danilevicz, Monica F., Bayer, Philipp E., Boussaid, Farid, Bennamoun, Mohammed, Edwards, David · 2021 · Remote Sensing
16. XAI Evaluation: Evaluating Black-Box Model Explanations for Prediction
    Zhang, Yuyi, Xu, Feiran, Zou, Jingying, Petrosian, Ovanes L., Krinkin, Kirill V. · 2021 · 2021 II International Conference on Neural Networks and Neurotechnologies (NeuroNT)
17. Metastatic heterogeneity of the consensus molecular subtypes of colorectal cancer
    Eide, Peter W., Moosavi, Seyed H., Eilertsen, Ina A., Brunsell, Tuva H., Langerud, Jonas, Berg, Kaja C. G., Røsok, Bård I., Bjørnbeth, Bjørn A., Nesbakken, Arild, Lothe, Ragnhild A. et al. · 2021 · npj Genomic Medicine
18. MACE prediction of acute coronary syndrome via boosted resampling classification using electronic medical records
    Huang, Zhengxing, Chan, Tak-Ming, Dong, Wei · 2017 · Journal of Biomedical Informatics
19. BioReader: a text mining tool for performing classification of biomedical literature
    Simon, Christian, Davidsen, Kristian, Hansen, Christina, Seymour, Emily, Barnkob, Mike Bogetofte, Olsen, Lars Rønn · 2019 · BMC Bioinformatics
20. Analyzing walking route choice through built environments using random forests and discrete choice techniques
    Tribby, Calvin P, Miller, Harvey J, Brown, Barbara B, Werner, Carol M, Smith, Ken R · 2017 · Environment and Planning B: Urban Analytics and City Science
21. Survival Prediction Models since Liver Transplantation - Comparisons between Cox Models and Machine Learning Techniques
    Kantidakis, Georgios, Putter, Hein, Lancia, Carlo, Boer, Jacob de, Braat, Andries E., Fiocco, Marta · 2020 · BMC Medical Research Methodology
22. Integration of data mining classification techniques and ensemble learning to identify risk factors and diagnose ovarian cancer recurrence
    Tseng, Chih-Jen, Lu, Chi-Jie, Chang, Chi-Chang, Chen, Gin-Den, Cheewakriangkrai, Chalong · 2017 · Artificial Intelligence in Medicine
23. Assessing carbon emissions from road transport through traffic flow estimators
    Nocera, Silvio, Ruiz-Alarcón-Quintero, Cayetano, Cavallaro, Federico · 2018 · Transportation Research Part C: Emerging Technologies
24. Machine Learning-Based Differentiation of Benign and Premalignant Colorectal Polyps Detected with CT Colonography in an Asymptomatic Screening Population: A Proof-Of-Concept Study
    Grosu, Sergio, Wesp, Philipp, Graser, Anno, Maurus, Stefan, Schulz, Christian, Knösel, Thomas, Cyran, Clemens C., Ricke, Jens, Ingrisch, Michael, Kazmierczak, Philipp M. · 2021 · Radiology
25. An Aggregated-Based Deep Learning Method for Leukemic B-lymphoblast Classification
    Kasani, Payam Hosseinzadeh, Park, Sang-Won, Jang, Jae-Won · 2020 · Diagnostics
26. Listening to Lions: Animal-Borne Acoustic Sensors Improve Bio-logger Calibration and Behaviour Classification Performance
    Wijers, Matthew, Trethowan, Paul, Markham, Andrew, du Preez, Byron, Chamaillé-Jammes, Simon, Loveridge, Andrew, Macdonald, David · 2018 · Frontiers in Ecology and Evolution
27. Is There More Violence in the Middle?
    Jones, Zachary M., Lupu, Yonatan · 2018 · American Journal of Political Science
28. Prediction of hypoglycemia during aerobic exercise in adults with type 1 diabetes
    Reddy, Ravi, Resalat, Navid, Wilson, Leah M., Castle, Jessica R., El Youssef, Joseph, Jacobs, Peter G. · 2019 · Journal of Diabetes Science and Technology
29. Logic models to predict continuous outputs based on binary inputs with an application to personalized cancer therapy
    Knijnenburg, Theo A., Klau, Gunnar W., Iorio, Francesco, Garnett, Mathew J., McDermott, Ultan, Shmulevich, Ilya, Wessels, Lodewyk F. A. · 2016 · Scientific Reports
30. Analyzing Raman spectroscopic data
    Ryabchykov, Oleg, Guo, Shuxia, Bocklitz, Thomas · 2019 · Physical Sciences Reviews
31. Machine Learning Approach to Delineate the Impact of Material Properties on Solar Cell Device Physics
    Islam, Md. Shafiqul, Islam, Md. Tohidul, Sarker, Saugata, Jame, Hasan Al, Nishat, Sadiq Shahriyar, Jani, Md. Rafsun, Rauf, Abrar, Ahsan, Sumaiyatul, Shorowordi, Kazi Md., Efstathiadis, Harry et al. · 2022 · ACS Omega
32. Slic-Seg: A minimally interactive segmentation of the placenta from sparse and motion-corrupted fetal MRI in multiple views
    Wang, Guotai, Zuluaga, Maria A., Pratt, Rosalind, Aertsen, Michael, Doel, Tom, Klusmann, Maria, David, Anna L., Deprest, Jan, Vercauteren, Tom, Ourselin, Sébastien · 2016 · Medical Image Analysis
33. Metabolic Networks and Metabolites Underlie Associations Between Maternal Glucose During Pregnancy and Newborn Size at Birth.
    Scholtens, Denise M., Bain, James R., Reisetter, Anna C., Muehlbauer, Michael J., Nodzenski, Michael, Stevens, Robert D., Ilkayeva, Olga, Lowe, Lynn P., Metzger, Boyd E., Newgard, Christopher B. et al. · 2016 · Diabetes
34. The climate sensitivity of carbon, timber, and species richness covaries with forest age in boreal–temperate North America
    Thom, Dominik, Golivets, Marina, Edling, Laura, Meigs, Garrett W., Gourevitch, Jesse D., Sonter, Laura J., Galford, Gillian L., Keeton, William S. · 2019 · Global Change Biology
35. Body Parts Dependent Joint Regressors for Human Pose Estimation in Still Images
    Dantone, Matthias, Gall, Juergen, Leistner, Christian, Van Gool, Luc · 2014 · IEEE Transactions on Pattern Analysis and Machine Intelligence
36. Geographically Weighted Machine Learning and Downscaling for High-Resolution Spatiotemporal Estimations of Wind Speed
    Li, Lianfa · 2019 · Remote Sensing
37. Subpallial Enhancer Transgenic Lines: a Data and Tool Resource to Study Transcriptional Regulation of GABAergic Cell Fate
    Silberberg, Shanni N., Taher, Leila, Lindtner, Susan, Sandberg, Magnus, Nord, Alex S., Vogt, Daniel, Mckinsey, Gabriel L., Hoch, Renee, Pattabiraman, Kartik, Zhang, Dongji et al. · 2016 · Neuron
38. Automated quantification of cerebral edema following hemispheric infarction: application of a machine-learning algorithm to evaluate CSF shifts on serial head CTs
    Chen, Yasheng, Dhar, Rajat, Heitsch, Laura, Ford, Andria, Fernandez-Cadenas, Israel, Carrera, Caty, Montaner, Joan, Lin, Weili, Shen, Dinggang, An, Hongyu et al. · 2016 · NeuroImage: Clinical
39. Automatic detection of articulation disorders in children with cleft lip and palate
    Maier, Andreas, Hönig, Florian, Bocklet, Tobias, Nöth, Elmar, Stelzle, Florian, Nkenke, Emeka, Schuster, Maria · 2009 · The Journal of the Acoustical Society of America
40. No-Reference Video Quality Estimation Based on Machine Learning for Passive Gaming Video Streaming Applications
    Barman, Nabajeet, Jammeh, Emmanuel, Ghorashi, Seyed Ali, Martini, Maria G. · 2019 · IEEE Access
41. Machine Learning in Human Olfactory Research
    Lötsch, Jörn, Kringel, Dario, Hummel, Thomas · 2019 · Chemical Senses
42. Using a combined computational-experimental approach to predict antibody-specific B cell epitopes
    Sela-Culang, Inbal, Benhnia, Mohammed Rafii-El-Idrissi, Matho, Michael H., Kaever, Thomas, Maybeno, Matt, Schlossman, Andrew, Nimrod, Guy, Li, Sheng, Xiang, Yan, Zajonc, Dirk et al. · 2014 · Structure
43. Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives
    Cardoso-Fernandes, Joana, Teodoro, Ana C., Lima, Alexandre, Perrotta, Mônica, Roda-Robles, Encarnación · 2020 · Applied Sciences
44. Best Prompts for Text-to-Image Models and How to Find Them
    Pavlichenko, Nikita, Ustalov, Dmitry · 2023 · Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
45. Tumour gene expression signature in primary melanoma predicts long-term outcomes
    Garg, Manik, Couturier, Dominique-Laurent, Nsengimana, Jérémie, Fonseca, Nuno A., Wongchenko, Matthew, Yan, Yibing, Lauss, Martin, Jönsson, Göran B., Newton-Bishop, Julia, Parkinson, Christine et al. · 2021 · Nature Communications
46. Long-term annual mapping of four cities on different continents by applying a deep information learning method to landsat data
    Lyu, Haobo, Lu, Hui, Mou, Lichao, Li, Wenyu, Wright, Jonathon, Li, Xuecao, Li, Xinlu, Zhu, Xiao, Wang, Jie, Yu, Le et al. · 2018 · Remote Sensing
47. Spatial prediction of demersal fish diversity in the Baltic Sea: comparison of machine learning and regression-based techniques
    Smoliński, Szymon, Radtke, Krzysztof · 2017 · ICES Journal of Marine Science
48. Single cell dynamics of tumor specificity vs bystander activity in CD8+ T cells define the diverse immune landscapes in colorectal cancer
    Borràs, Daniel Morales, Verbandt, Sara, Ausserhofer, Markus, Sturm, Gregor, Lim, Jinyeong, Verge, Gil Arasa, Vanmeerbeek, Isaure, Laureano, Raquel S., Govaerts, Jannes, Sprooten, Jenny et al. · 2023 · Cell Discovery
49. Improving Cancer Outreach Effectiveness Through Targeting and Economic Assessments: Insights from a Randomized Field Experiment
    Chen, Yixing, Lee, Ju-Yeon, Sridhar, Shrihari (Hari), Mittal, Vikas, McCallister, Katharine, Singal, Amit G. · 2020 · Journal of Marketing
50. Feature extraction for robust physical activity recognition
    Zhu, Jiadong, San-Segundo, Rubén, Pardo, José M. · 2017 · Human-centric Computing and Information Sciences
51. “Engagement vs. performance: using electronic portfolios to predict first semester engineering student retention,”
    Aguiar, Everaldo, Chawla, Nitesh V., Brockman, Jay, Ambrose, G. Alex, Goodrich, Victoria · 2014 · Proceedings of the Fourth International Conference on Learning Analytics And Knowledge
52. Effectiveness of a Smartphone App With a Wearable Activity Tracker in Preventing the Recurrence of Mood Disorders: Prospective Case-Control Study (Preprint)
    Cho, Chul-Hyun, Lee, Taek, Lee, Jung-Been, Seo, Ju Yeon, Jee, Hee-Jung, Son, Serhim, An, Hyonggin, Kim, Leen, Lee, Heon-Jeong · 2020
53. A demonstration of conservation genomics for threatened species management
    Wright, Belinda R., Farquharson, Katherine A., McLennan, Elspeth A., Belov, Katherine, Hogg, Carolyn J., Grueber, Catherine E. · 2020 · Molecular Ecology Resources
54. Standard Plane Localization in Ultrasound by Radial Component Model and Selective Search
    Ni, Dong, Yang, Xin, Chen, Xin, Chin, Chien-Ting, Chen, Siping, Heng, Pheng Ann, Li, Shengli, Qin, Jing, Wang, Tianfu · 2014 · Ultrasound in Medicine & Biology
55. A machine learning approach to improve congestion control over wireless computer networks
    Geurts, P., El Khayat, I., Leduc, G. · 2004 · Fourth IEEE International Conference on Data Mining (ICDM'04)
56. An Untargeted Metabolomics Approach to Characterize Short-Term and Long-Term Metabolic Changes after Bariatric Surgery
    Narath, Sophie H., Mautner, Selma I., Svehlikova, Eva, Schultes, Bernd, Pieber, Thomas R., Sinner, Frank M., Gander, Edgar, Libiseller, Gunnar, Schimek, Michael G., Sourij, Harald et al. · 2016 · PLOS ONE
57. Data analysis with Shapley values for automatic subject selection in Alzheimer’s disease data sets using interpretable machine learning
    Bloch, Louise, Friedrich, Christoph M., for the Alzheimer’s Disease Neuroimaging Initiative · 2021 · Alzheimer's Research & Therapy
58. Text mining to support abstract screening for knowledge syntheses: a semi-automated workflow
    Pham, Ba’, Jovanovic, Jelena, Bagheri, Ebrahim, Antony, Jesmin, Ashoor, Huda, Nguyen, Tam T., Rios, Patricia, Robson, Reid, Thomas, Sonia M., Watt, Jennifer et al. · 2021 · Systematic Reviews
59. Propensity score adjustment using machine learning classification algorithms to control selection bias in online surveys
    Ferri-García, Ramón, Rueda, María del Mar · 2020 · PLOS ONE
60. Closed-Loop Neural Prostheses With On-Chip Intelligence: A Review and a Low-Latency Machine Learning Model for Brain State Detection
    Zhu, Bingzhao, Shin, Uisub, Shoaran, Mahsa · 2021 · IEEE Transactions on Biomedical Circuits and Systems
61. Deep learning of brain lesion patterns and user-defined clinical and MRI features for predicting conversion to multiple sclerosis from clinically isolated syndrome
    Yoo, Youngjin, Tang, Lisa Y. W., Li, David K. B., Metz, Luanne, Kolind, Shannon, Traboulsee, Anthony L., Tam, Roger C. · 2019 · Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization
62. Emergent behaviour and neural dynamics in artificial agents tracking odour plumes
    Singh, Satpreet H., van Breugel, Floris, Rao, Rajesh P. N., Brunton, Bingni W. · 2023 · Nature Machine Intelligence
63. MedGCN: Medication recommendation and lab test imputation via graph convolutional networks
    Mao, Chengsheng, Yao, Liang, Luo, Yuan · 2022 · Journal of Biomedical Informatics
64. Happiness Recognition from Mobile Phone Data
    Bogomolov, Andrey, Lepri, Bruno, Pianesi, Fabio · 2013 · 2013 International Conference on Social Computing
65. Genome-wide association studies and the genetic dissection of complex traits
    Sebastiani, Paola, Timofeev, Nadia, Dworkis, Daniel A., Perls, Thomas T., Steinberg, Martin H. · 2009 · American Journal of Hematology
66. A multiple combined method for rebalancing medical data with class imbalances
    Wang, Yun-Chun, Cheng, Ching-Hsue · 2021 · Computers in Biology and Medicine
67. Lattice Thermal Conductivity: An Accelerated Discovery Guided by Machine Learning
    Jaafreh, Russlan, Kang, Yoo Seong, Hamad, Kotiba · 2021 · ACS Applied Materials & Interfaces
68. Sensing Interruptibility in the Office: A Field Study on the Use of Biometric and Computer Interaction Sensors
    Züger, Manuela, Müller, Sebastian C., Meyer, André N., Fritz, Thomas · 2018 · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems
69. Comparative analysis of emotion estimation methods based on physiological measurements for real-time applications
    Kukolja, Davor, Popović, Siniša, Horvat, Marko, Kovač, Bernard, Ćosić, Krešimir · 2014 · International Journal of Human-Computer Studies
70. Classification and Prognosis Prediction from Histopathological Images of Hepatocellular Carcinoma by a Fully Automated Pipeline Based on Machine Learning
    Liao, Haotian, Xiong, Tianyuan, Peng, Jiajie, Xu, Lin, Liao, Mingheng, Zhang, Zhen, Wu, Zhenru, Yuan, Kefei, Zeng, Yong · 2020 · Annals of Surgical Oncology
71. DNA Modification Study of Major Depressive Disorder: Beyond Locus-by-Locus Comparisons
    Oh, Gabriel, Wang, Sun-Chong, Pal, Mrinal, Chen, Zheng Fei, Khare, Tarang, Tochigi, Mamoru, Ng, Catherine, Yang, Yeqing A., Kwan, Andrew, Kaminsky, Zachary A. et al. · 2015 · Biological Psychiatry
72. Advancements and challenges in the application of artificial intelligence in civil engineering: a comprehensive review
    Harle, Shrikant M. · 2024 · Asian Journal of Civil Engineering
73. Calibrating random forests for probability estimation
    Dankowski, Theresa, Ziegler, Andreas · 2016 · Statistics in Medicine
74. GeoF: Geodesic Forests for Learning Coupled Predictors
    Kontschieder, Peter, Kohli, Pushmeet, Shotton, Jamie, Criminisi, Antonio · 2013 · 2013 IEEE Conference on Computer Vision and Pattern Recognition
75. Automated distant galaxy merger classifications from Space Telescope images using the Illustris simulation
    Snyder, Gregory F, Rodriguez-Gomez, Vicente, Lotz, Jennifer M, Torrey, Paul, Quirk, Amanda C N, Hernquist, Lars, Vogelsberger, Mark, Freeman, Peter E · 2019 · Monthly Notices of the Royal Astronomical Society
76. Meaningless comparisons lead to false optimism in medical machine learning
    DeMasi, Orianna, Kording, Konrad, Recht, Benjamin · 2017 · PLoS ONE
77. Uncertainty Assessment of Hyperspectral Image Classification: Deep Learning vs. Random Forest
    Shadman Roodposhti, Majid, Aryal, Jagannath, Lucieer, Arko, Bryan, Brett A. · 2019 · Entropy
78. Analysis of chemometric models applied to Raman spectroscopy for monitoring key metabolites of cell culture
    Rafferty, Carl, Johnson, Kjell, O'Mahony, Jim, Burgoyne, Barbara, Rea, Rosemary, Balss, Karin M. · 2020 · Biotechnology Progress
79. Improving random forests by neighborhood projection for effective text classification
    Salles, Thiago, Gonçalves, Marcos, Rodrigues, Victor, Rocha, Leonardo · 2018 · Information Systems
80. Machine Learning Predictions of a Multiresolution Climate Model Ensemble
    Anderson, Gemma J., Lucas, Donald D. · 2018 · Geophysical Research Letters
81. Machine-learning-based prediction models for high-need high-cost patients using nationwide clinical and claims data
    Osawa, Itsuki, Goto, Tadahiro, Yamamoto, Yuji, Tsugawa, Yusuke · 2020 · npj Digital Medicine
82. Using sentiment analysis to predict opinion inversion in Tweets of political communication
    Matalon, Yogev, Magdaci, Ofir, Almozlino, Adam, Yamin, Dan · 2021 · Scientific Reports
83. Using Wearables and Machine Learning to Enable Personalized Lifestyle Recommendations to Improve Blood Pressure
    Chiang, Po-Han, Wong, Melissa, Dey, Sujit · 2021 · IEEE Journal of Translational Engineering in Health and Medicine
84. Taking Advantage of Multitask Learning for Fair Classification
    Oneto, Luca, Doninini, Michele, Elders, Amon, Pontil, Massimiliano · 2019 · Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society
85. A Mobile Health Solution Complementing Psychopharmacology-Supported Smoking Cessation: Randomized Controlled Trial
    Carrasco-Hernandez, Laura, Jódar-Sánchez, Francisco, Núñez-Benjumea, Francisco, Moreno Conde, Jesús, Mesa González, Marco, Civit-Balcells, Antón, Hors-Fraile, Santiago, Parra-Calderón, Carlos Luis, Bamidis, Panagiotis D, Ortega-Ruiz, Francisco · 2020 · JMIR mHealth and uHealth
86. Variables shaping coastal adaptation decisions to armor, nourish, and retreat in North Carolina
    Siders, A.R., Keenan, Jesse M. · 2020 · Ocean & Coastal Management
87. Machine Learning for Predicting Adherence to Internet-Delivered Psychotherapy for Symptoms of Depression and Anxiety after Myocardial Infarction: Insights from the U-CARE Heart Trial (Preprint)
    Wallert, John, Gustafson, Emelie, Held, Claes, Madison, Guy, Norlund, Fredrika, von Essen, Louise, Olsson, Erik Martin Gustaf · 2018
88. Missing the missing values: The ugly duckling of fairness in machine learning
    Fernando, Martínez‐Plumed, Cèsar, Ferri, David, Nieves, José, Hernández‐Orallo · 2021 · International Journal of Intelligent Systems
89. AI-driven design optimization for sustainable buildings: a systematic review
    Manmatharasan, Piragash, Bitsuamlak, Girma, Grolinger, Katarina · 2025 · Energy and Buildings
90. Witnessing Violence Toward Siblings: An Understudied but Potent Form of Early Adversity
    Teicher, Martin H., Vitaliano, Gordana D. · 2011 · PLoS ONE
91. Chemometric-Guided Approaches for Profiling and Authenticating Botanical Materials
    Abraham, Evelyn J., Kellogg, Joshua J. · 2021 · Frontiers in Nutrition
92. Predictive modeling of treatment resistant depression using data from STAR*D and an independent clinical study
    Nie, Zhi, Vairavan, Srinivasan, Narayan, Vaibhav A., Ye, Jieping, Li, Qingqin S. · 2018 · PLOS ONE
93. African elephants address one another with individually specific name-like calls
    Pardo, Michael A., Fristrup, Kurt, Lolchuragi, David S., Poole, Joyce H., Granli, Petter, Moss, Cynthia, Douglas-Hamilton, Iain, Wittemyer, George · 2024 · Nature Ecology & Evolution
94. Empirical asset pricing via machine learning: evidence from the European stock market
    Drobetz, Wolfgang, Otto, Tizian · 2021 · Journal of Asset Management
95. It is Not “Accuracy vs. Explainability”—We Need Both for Trustworthy AI Systems
    Petkovic, Dragutin · 2023 · IEEE Transactions on Technology and Society
96. Including information about co-morbidity in estimates of disease burden: results from the World Health Organization World Mental Health Surveys
    Alonso, J., Vilagut, G., Chatterji, S., Heeringa, S., Schoenbaum, M., Bedirhan Üstün, T., Rojas-Farreras, S., Angermeyer, M., Bromet, E., Bruffaerts, R. et al. · 2011 · Psychological Medicine
97. Distributed defect recognition on steel surfaces using an improved random forest algorithm with optimal multi-feature-set fusion
    Wang, Yalin, Xia, Haibing, Yuan, Xiaofeng, Li, Ling, Sun, Bei · 2018 · Multimedia Tools and Applications
98. All Together Now: A Perspective on the Netflix Prize
    Bell, Robert M., Koren, Yehuda, Volinsky, Chris · 2010 · CHANCE
99. Automated machine learning with interpretation: A systematic review of methodologies and applications in healthcare
    Yuan, Han, Yu, Kunyu, Xie, Feng, Liu, Mingxuan, Sun, Shenghuan · 2024 · Medicine Advances
100. Cyberbullying detection from tweets using deep learning
     Bharti, Shubham, Yadav, Arun Kumar, Kumar, Mohit, Yadav, Divakar · 2022 · Kybernetes