Bagging predictors

Cited by (1,119)

Items 1,001–1,100 of 1,119

1. An empirical study of latency in an emerging class of edge computing applications for wearable cognitive assistance
   Chen, Zhuo, Hu, Wenlu, Wang, Junjue, Zhao, Siyan, Amos, Brandon, Wu, Guanhang, Ha, Kiryong, Elgazzar, Khalid, Pillai, Padmanabhan, Klatzky, Roberta et al. · 2017 · Proceedings of the Second ACM/IEEE Symposium on Edge Computing
2. Random forest regression for magnetic resonance image synthesis
   Jog, Amod, Carass, Aaron, Roy, Snehashis, Pham, Dzung L., Prince, Jerry L. · 2017 · Medical Image Analysis
3. Digital Language Death
   Kornai, András · 2013 · PLoS ONE
4. Survival after valve replacement for aortic stenosis: Implications for decision making
   Mihaljevic, Tomislav, Nowicki, Edward R., Rajeswaran, Jeevanantham, Blackstone, Eugene H., Lagazzi, Luigi, Thomas, James, Lytle, Bruce W., Cosgrove, Delos M. · 2008 · The Journal of Thoracic and Cardiovascular Surgery
5. An integrated design of particle swarm optimization (PSO) with fusion of features for detection of brain tumor
   Sharif, Muhammad, Amin, Javaria, Raza, Mudassar, Yasmin, Mussarat, Satapathy, Suresh Chandra · 2020 · Pattern Recognition Letters
6. Surgery for permanent atrial fibrillation: impact of patient factors and lesion set
   Gillinov, A. Marc, Bhavani, Sekar, Blackstone, Eugene H., Rajeswaran, Jeevanantham, Svensson, Lars G., Navia, Jose L., Pettersson, B.Gösta, Sabik, Joseph F., Smedira, Nicholas G., Mihaljevic, Tomislav et al. · 2006 · The Annals of Thoracic Surgery
7. Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms
   Erdal, Halil Ibrahim, Karakurt, Onur · 2013 · Journal of Hydrology
8. 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
9. Explainable decision forest: Transforming a decision forest into an interpretable tree
   Sagi, Omer, Rokach, Lior · 2020 · Information Fusion
10. Mortality Risk Score Prediction in an Elderly Population Using Machine Learning
    Rose, Sherri · 2013 · American Journal of Epidemiology
11. Forecasting Murder Within a Population of Probationers and Parolees: A High Stakes Application of Statistical Learning
    Berk, Richard, Sherman, Lawrence, Barnes, Geoffrey, Kurtz, Ellen, Ahlman, Lindsay · 2009 · Journal of the Royal Statistical Society Series A: Statistics in Society
12. Brain–phenotype models fail for individuals who defy sample stereotypes
    Greene, Abigail S., Shen, Xilin, Noble, Stephanie, Horien, Corey, Hahn, C. Alice, Arora, Jagriti, Tokoglu, Fuyuze, Spann, Marisa N., Carrión, Carmen I., Barron, Daniel S. et al. · 2022 · Nature
13. Explainable AI and machine learning: performance evaluation and explainability of classifiers on educational data mining inspired career counseling
    Guleria, Pratiyush, Sood, Manu · 2023 · Education and Information Technologies
14. Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning
    Allen-Zhu, Zeyuan, Li, Yuanzhi · 2020 · arXiv
15. Internet Traffic Classification by Aggregating Correlated Naive Bayes Predictions
    Jun Zhang, Chao Chen, Yang Xiang, Wanlei Zhou, Yong Xiang · 2013 · IEEE Transactions on Information Forensics and Security
16. Social network analysis for customer churn prediction
    Verbeke, Wouter, Martens, David, Baesens, Bart · 2014 · Applied Soft Computing
17. Continued Intention to Use of M-Banking in Jordan by Integrating UTAUT, TPB, TAM and Service Quality with ML
    Abu-Taieh, Evon M., AlHadid, Issam, Abu-Tayeh, Sabah, Masa’deh, Ra’ed, Alkhawaldeh, Rami S., Khwaldeh, Sufian, Alrowwad, Ala’aldin · 2022 · Journal of Open Innovation: Technology, Market, and Complexity
18. Automated trading with performance weighted random forests and seasonality
    Booth, Ash, Gerding, Enrico, McGroarty, Frank · 2014 · Expert Systems with Applications
19. Binary Ensemble Neural Network: More Bits per Network or More Networks per Bit?
    Zhu, Shilin, Dong, Xin, Su, Hao · 2019 · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
20. The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence
    Watson, David · 2019 · Minds and Machines
21. Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study
    Maier, Oskar, Schröder, Christoph, Forkert, Nils Daniel, Martinetz, Thomas, Handels, Heinz · 2015 · PLOS ONE
22. Feature Importance in Gradient Boosting Trees with Cross-Validation Feature Selection
    Adler, Afek Ilay, Painsky, Amichai · 2022 · Entropy
23. 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)
24. Modeling CLV: A test of competing models in the insurance industry
    Donkers, Bas, Verhoef, Peter C., de Jong, Martijn G. · 2007 · Quantitative Marketing and Economics
25. 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
26. Evaluation of a Previously Suggested Plasma Biomarker Panel to Identify Alzheimer's Disease
    Björkqvist, Maria, Ohlsson, Mattias, Minthon, Lennart, Hansson, Oskar · 2012 · PLoS ONE
27. An experimental evaluation of ensemble methods for EEG signal classification
    Sun, Shiliang, Zhang, Changshui, Zhang, Dan · 2007 · Pattern Recognition Letters
28. Network inference with Granger causality ensembles on single-cell transcriptomics
    Deshpande, Atul, Chu, Li-Fang, Stewart, Ron, Gitter, Anthony · 2022 · Cell Reports
29. 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
30. CyberLearning: Effectiveness analysis of machine learning security modeling to detect cyber-anomalies and multi-attacks
    Sarker, Iqbal H. · 2021 · Internet of Things
31. Patient length of stay and mortality prediction: A survey
    Awad, Aya, Bader–El–Den, Mohamed, McNicholas, James · 2017 · Health Services Management Research
32. 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
33. Multivariate data analysis for neuroimaging data: overview and application to Alzheimer’s disease
    the Alzheimer’s Disease Neuroimaging Initiative, Habeck, Christian, Stern, Yaakov · 2010 · Cell Biochemistry and Biophysics
34. 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
35. Flexible neural trees ensemble for stock index modeling
    Chen, Yuehui, Yang, Bo, Abraham, Ajith · 2007 · Neurocomputing
36. A Survey on Differentially Private Machine Learning [Review Article]
    Gong, Maoguo, Xie, Yu, Pan, Ke, Feng, Kaiyuan, Qin, A.K. · 2020 · IEEE Computational Intelligence Magazine
37. A literature survey and experimental evaluation of the state-of-the-art in uplift modeling: A stepping stone toward the development of prescriptive analytics
    Devriendt, Floris, Moldovan, Darie, Verbeke, Wouter · 2018 · Big Data
38. Deep learning-based classification of posttraumatic stress disorder and depression following trauma utilizing visual and auditory markers of arousal and mood
    Schultebraucks, Katharina, Yadav, Vijay, Shalev, Arieh Y., Bonanno, George A., Galatzer-Levy, Isaac R. · 2022 · Psychological Medicine
39. Boosting Insights in Insurance Tariff Plans with Tree-Based Machine Learning Methods
    Henckaerts, Roel, Côté, Marie-Pier, Antonio, Katrien, Verbelen, Roel · 2021 · North American Actuarial Journal
40. An Analysis of ISO 26262: Machine Learning and Safety in Automotive Software
    Salay, Rick, Queiroz, Rodrigo, Czarnecki, Krzysztof · 2018 · SAE Technical Paper Series
41. Advancing predictive modeling in archaeology: an evaluation of regression and machine learning methods on the grand staircase-escalante national monument
    Yaworsky, Peter M., Vernon, Kenneth B., Spangler, Jerry D., Brewer, Simon C., Codding, Brian F. · 2020 · PLOS One
42. The Computerized Adaptive Diagnostic Test for Major Depressive Disorder (CAD-MDD): A Screening Tool for Depression
    Gibbons, Robert D., Hooker, Giles, Finkelman, Matthew D., Weiss, David J., Pilkonis, Paul A., Frank, Ellen, Moore, Tara, Kupfer, David J. · 2013 · The Journal of Clinical Psychiatry
43. Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory
    Kruppa, Jochen, Liu, Yufeng, Biau, Gérard, Kohler, Michael, König, Inke R., Malley, James D., Ziegler, Andreas · 2014 · Biometrical Journal
44. Sample Subset Optimization Techniques for Imbalanced and Ensemble Learning Problems in Bioinformatics Applications
    Pengyi Yang, Yoo, Paul D., Fernando, Juanita, Zhou, Bing B., Zili Zhang, Zomaya, Albert Y. · 2014 · IEEE Transactions on Cybernetics
45. Selecting Representative Data Sets
    Borovicka, Tomas, Jirina, Marcel, Kordik, Pavel, Jiri, Marcel · 2012 · Advances in Data Mining Knowledge Discovery and Applications
46. Detecting, Tracing, and Monitoring Architectural Tactics in Code
    Mirakhorli, Mehdi, Cleland-Huang, Jane · 2016 · IEEE Transactions on Software Engineering
47. CYCLoPs: A Comprehensive Database Constructed from Automated Analysis of Protein Abundance and Subcellular Localization Patterns in Saccharomyces cerevisiae
    Koh, Judice L Y, Chong, Yolanda T, Friesen, Helena, Moses, Alan, Boone, Charles, Andrews, Brenda J, Moffat, Jason · 2015 · G3 Genes|Genomes|Genetics
48. Detecting potential labeling errors in microarrays by data perturbation
    Malossini, Andrea, Blanzieri, Enrico, Ng, Raymond T. · 2006 · Bioinformatics
49. 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
50. Surgery for paroxysmal atrial fibrillation in the setting of mitral valve disease: a role for pulmonary vein isolation?
    Gillinov, A. Marc, Bakaeen, Faisal, McCarthy, Patrick M., Blackstone, Eugene H., Rajeswaran, Jeevanantham, Pettersson, Gosta, Sabik, Joseph F., Najam, Farzad, Hill, Kathleen M., Svensson, Lars G. et al. · 2006 · The Annals of Thoracic Surgery
51. 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
52. 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
53. Assessing the Capacity of Social Determinants of Health Data to Augment Predictive Models Identifying Patients in Need of Wraparound Social Services
    Kasthurirathne, Suranga N, Vest, Joshua R, Menachemi, Nir, Halverson, Paul K, Grannis, Shaun J · 2018 · Journal of the American Medical Informatics Association
54. Estimating residential energy consumption in metropolitan areas: A microsimulation approach
    Zhang, Wenwen, Robinson, Caleb, Guhathakurta, Subhrajit, Garikapati, Venu M., Dilkina, Bistra, Brown, Marilyn A., Pendyala, Ram M. · 2018 · Energy
55. Conserved rules govern genetic interaction degree across species
    Koch, Elizabeth N, Costanzo, Michael, Bellay, Jeremy, Deshpande, Raamesh, Chatfield-Reed, Kate, Chua, Gordon, D'Urso, Gennaro, Andrews, Brenda J, Boone, Charles, Myers, Chad L · 2012 · Genome Biology
56. 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)
57. A machine learning approach to live migration modeling
    Jo, Changyeon, Cho, Youngsu, Egger, Bernhard · 2017 · Proceedings of the 2017 Symposium on Cloud Computing
58. Applying multiple classifiers and non-linear dynamics features for detecting sleepiness from speech
    Krajewski, Jarek, Schnieder, Sebastian, Sommer, David, Batliner, Anton, Schuller, Björn · 2012 · Neurocomputing
59. Dynamic-based damage identification using neural network ensembles and damage index method
    Dackermann, Ulrike, Li, Jianchun, Samali, Bijan · 2010 · Advances in Structural Engineering
60. Uncertainty Assessment of Hyperspectral Image Classification: Deep Learning vs. Random Forest
    Shadman Roodposhti, Majid, Aryal, Jagannath, Lucieer, Arko, Bryan, Brett A. · 2019 · Entropy
61. Transfer Learning Algorithm of P300-EEG Signal Based on XDAWN Spatial Filter and Riemannian Geometry Classifier
    Li, Feng, Xia, Yi, Wang, Fei, Zhang, Dengyong, Li, Xiaoyu, He, Fan · 2020 · Applied Sciences
62. Ensemble Deep Learning for Biomedical Time Series Classification
    Jin, Lin-peng, Dong, Jun · 2016 · Computational Intelligence and Neuroscience
63. A Decision-Support Tool for Renal Mass Classification
    Kunapuli, Gautam, Varghese, Bino A., Ganapathy, Priya, Desai, Bhushan, Cen, Steven, Aron, Manju, Gill, Inderbir, Duddalwar, Vinay · 2018 · Journal of Digital Imaging
64. A review of machine learning prediction methods for anxiety disorders
    Pintelas, Emmanuel G., Kotsilieris, Theodore, Livieris, Ioannis E., Pintelas, Panagiotis · 2018 · Proceedings of the 8th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion
65. Active and semi-supervised learning for object detection with imperfect data
    Rhee, Phill Kyu, Erdenee, Enkhbayar, Kyun, Shin Dong, Ahmed, Minhaz Uddin, Jin, Songguo · 2017 · Cognitive Systems Research
66. Comparing Churn Prediction Techniques and Assessing Their Performance: A Contingent Perspective
    Tamaddoni, Ali, Stakhovych, Stanislav, Ewing, Michael · 2016 · Journal of Service Research
67. Impact of nutritional state on lung transplant outcomes
    Chamogeorgakis, Themistokles, Mason, David P., Murthy, Sudish C., Thuita, Lucy, Raymond, Daniel P., Pettersson, Gösta B., Blackstone, Eugene H. · 2013 · The Journal of Heart and Lung Transplantation
68. Temporal feature aggregation with attention for insider threat detection from activity logs
    Pal, Preetam, Chattopadhyay, Pratik, Swarnkar, Mayank · 2023 · Expert Systems with Applications
69. An integrated approach of ensemble learning methods for stock index prediction using investor sentiments
    Deng, Shangkun, Zhu, Yingke, Yu, Yiting, Huang, Xiaoru · 2024 · Expert Systems with Applications
70. Machine-learning models for combinatorial catalyst discovery
    Landrum, Gregory A, Penzotti, Julie E, Putta, Santosh · 2005 · Measurement Science and Technology
71. Deep Big Multilayer Perceptrons for Digit Recognition
    Cireşan, Dan Claudiu, Meier, Ueli, Gambardella, Luca Maria, Schmidhuber, Jürgen · 2012 · Lecture Notes in Computer Science
72. Ensemble learning of rule-based evolutionary algorithm using multi-layer perceptron for supporting decisions in stock trading problems
    Mabu, Shingo, Obayashi, Masanao, Kuremoto, Takashi · 2015 · Applied Soft Computing
73. Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach
    Fraccaro, Paolo, Nicolo, Massimo, Bonetto, Monica, Giacomini, Mauro, Weller, Peter, Traverso, Carlo Enrico, Prosperi, Mattia, OSullivan, Dympna · 2015 · BMC Ophthalmology
74. A fast method of steel surface defect detection using decision trees applied to LBP based features
    Aghdam, Sina Rezaei, Amid, Ehsan, Imani, Mohammad Faghih · 2012 · 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA)
75. Artificial intelligence in radiotherapy
    Li, Guangqi, Wu, Xin, Ma, Xuelei · 2022 · Seminars in Cancer Biology
76. On the effect of calibration in classifier combination
    Bella, Antonio, Ferri, Cèsar, Hernández-Orallo, José, Ramírez-Quintana, María José · 2013 · Applied Intelligence
77. Data-driven reverse engineering of signaling pathways using ensembles of dynamic models
    Henriques, David, Villaverde, Alejandro F., Rocha, Miguel, Saez-Rodriguez, Julio, Banga, Julio R. · 2017 · PLOS Computational Biology
78. Do Salient Social Norms Moderate Mortality Salience Effects? A (Challenging) Meta-Analysis of Terror Management Studies
    Schindler, Simon, Hilgard, Joe, Fritsche, Immo, Burke, Brian, Pfattheicher, Stefan · 2023 · Personality and Social Psychology Review
79. Using machine learning techniques to develop forecasting algorithms for postoperative complications: protocol for a retrospective study
    Fritz, Bradley A, Chen, Yixin, Murray-Torres, Teresa M, Gregory, Stephen, Ben Abdallah, Arbi, Kronzer, Alex, McKinnon, Sherry Lynn, Budelier, Thaddeus, Helsten, Daniel L, Wildes, Troy S et al. · 2018 · BMJ Open
80. Pretransplant gastroesophageal reflux compromises early outcomes after lung transplantation
    Murthy, Sudish C., Nowicki, Edward R., Mason, David P., Budev, Marie M., Nunez, Anthony I., Thuita, Lucy, Chapman, Jeffrey T., McCurry, Kenneth R., Pettersson, Gösta B., Blackstone, Eugene H. · 2011 · The Journal of Thoracic and Cardiovascular Surgery
81. Predicting employee absenteeism for cost effective interventions
    Lawrance, Natalie, Petrides, George, Guerry, Marie-Anne · 2021 · Decision Support Systems
82. A Q-learning-based multi-agent system for data classification
    Pourpanah, Farhad, Tan, Choo Jun, Lim, Chee Peng, Mohamad-Saleh, Junita · 2017 · Applied Soft Computing
83. Classification of BGP anomalies using decision trees and fuzzy rough sets
    Li, Yan, Xing, Hong-Jie, Hua, Qiang, Wang, Xi-Zhao, Batta, Prerna, Haeri, Soroush, Trajkovic, Ljiljana · 2014 · 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
84. Artificial Intelligence to Prevent Mobile Heart Failure Patients Decompensation in Real Time: Monitoring-Based Predictive Model
    Larburu, Nekane, Artetxe, Arkaitz, Escolar, Vanessa, Lozano, Ainara, Kerexeta, Jon · 2018 · Mobile Information Systems
85. Enhancement of TCP over wired/wireless networks with packet loss classifiers inferred by supervised learning
    El Khayat, Ibtissam, Geurts, Pierre, Leduc, Guy · 2010 · Wireless Networks
86. On to the next chapter for crop breeding: Convergence with data science
    Ersoz, Elhan S., Martin, Nicolas F., Stapleton, Ann E. · 2020 · Crop Science
87. ToxCast EPA in Vitro to in Vivo Challenge: Insight into the Rank-I Model
    Novotarskyi, Sergii, Abdelaziz, Ahmed, Sushko, Yurii, Körner, Robert, Vogt, Joachim, Tetko, Igor V. · 2016 · Chemical Research in Toxicology
88. Exploratory Data Mining Techniques (Decision Tree Models) for Examining the Impact of Internet-Based Cognitive Behavioral Therapy for Tinnitus: Machine Learning Approach (Preprint)
    Rodrigo, Hansapani, Beukes, Eldré W, Andersson, Gerhard, Manchaiah, Vinaya · 2021
89. Linking surface hydrodynamics to planktonic ecosystem: the case study of the ichthyoplanktonic assemblages in the Central Mediterranean Sea
    Cuttitta, Angela, Torri, Marco, Zarrad, Rafik, Zgozi, Salem, Jarboui, Othman, Quinci, Enza Maria, Hamza, Mohamed, Abdulfatah, Elfetori, Haddoud, Daw, El Turki, Akram et al. · 2018 · Hydrobiologia
90. Sample Subset Optimization for Classifying Imbalanced Biological Data
    Yang, Pengyi, Zhang, Zili, Zhou, Bing B., Zomaya, Albert Y. · 2011 · Lecture Notes in Computer Science
91. Evaluation of Pattern Recognition Methods for Head Gesture-Based Interface of a Virtual Reality Helmet Equipped with a Single IMU Sensor
    Hachaj, Tomasz, Piekarczyk, Marcin · 2019 · Sensors
92. Ready, Steady, Go AI: A practical tutorial on fundamentals of artificial intelligence and its applications in phenomics image analysis
    Nakhle, Farid, Harfouche, Antoine L. · 2021 · Patterns
93. Artificial intelligence and machine learning: Definition of terms and current concepts in critical care research
    Sun, Kai, Roy, Arkajyoti, Tobin, Joshua M. · 2024 · Journal of Critical Care
94. Discriminating between empirical studies and nonempirical works using automated text classification
    Langlois, Alexis, Nie, Jian‐Yun, Thomas, James, Hong, Quan Nha, Pluye, Pierre · 2018 · Research Synthesis Methods
95. Integrating Federated Learning for Improved Counterfactual Explanations in Clinical Decision Support Systems for Sepsis Therapy
    Düsing, Christoph, Cimiano, Philipp, Rehberg, Sebastian, Scherer, Christiane, Kaup, Olaf, Köster, Christiane, Hellmich, Stefan, Herrmann, Daniel, Meier, Kirsten Laura, Claßen, Simon et al. · 2024 · Artificial Intelligence in Medicine
96. Ensemble system based on genetic algorithm for stock market forecasting
    Gonzalez, Rafael Thomazi, Padilha, Carlos Alberto, Barone, Dante Augusto Couto · 2015 · 2015 IEEE Congress on Evolutionary Computation (CEC)
97. A two‐stage forecasting approach for short‐term intermodal freight prediction
    Moscoso‐López, José Antonio, Turias, Ignacio, Jiménez‐Come, Maria Jesús, Ruiz‐Aguilar, Juan Jesús, Cerbán, María del Mar · 2019 · International Transactions in Operational Research
98. Computational Barthel Index: an automated tool for assessing and predicting activities of daily living among nursing home patients
    Wojtusiak, Janusz, Asadzadehzanjani, Negin, Levy, Cari, Alemi, Farrokh, Williams, Allison E. · 2021 · BMC Medical Informatics and Decision Making
99. Involving Users to Improve the Collaborative Logical Framework
    Santos, Olga C., Boticario, Jesus G. · 2014 · The Scientific World Journal
100. Hypothesis Transfer Learning for Efficient Data Computing in Smart Cities Environments
     Valerio, Lorenzo, Passarella, Andrea, Conti, Marco · 2016 · 2016 IEEE International Conference on Smart Computing (SMARTCOMP)