Challenges and opportunities in applied machine learning
Abstract
Machine‐learning research is often conducted in vitro, divorced from motivating practical applications. A researcher might develop a new method for the general task of classification, then assess its utility by comparing its performance (for example, accuracy or AUC) to that of existing classification models on publicly available data sets. In terms of advancing machine learning as an academic discipline, this approach has thus far proven quite fruitful. However, it is our view that the most interesting open problems in machine learning are those that arise during its application to real‐world problems. We illustrate this point by reviewing two of our interdisciplinary collaborations, both of which have posed unique machine‐learning problems, providing fertile ground for novel research.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Runtime monitoring of ML applications, including selection of metrics, live monitoring of system behavior for automated responses, and dynamic monitoring for runtime verification and certification, is a maintenance challenge.
“-Runtime Monitoring: Selection of the metrics used for monitoring, live monitoring of system behavior that allows automated responses without direct human intervention, and dynamic monitoring for runtime verification and certification [17,19,24,30,47,48].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [17]
Challenges on the software structure of ML applications include complex software modules of ML algorithms, anti-patterns in ML applications, and various design issues in ML models (model selection, customization and reuse).
“-Software Structure: Challenges on the software structure of ML applications.This category includes the complex software modules of ML algorithms [14], anti-patterns in ML applications [17], and various design issues in ML models (model selection, customization and reuse) [16,34,58,59].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [17]
Design issues on data collection, pre-processing, cleaning, labeling and augmentation, including big data challenges, exist for ML applications.
“-Data Design: Design issues on data collection, pre-processing, cleaning, labeling and augmentation, including big data challenges [17,23,25-27, 31,33,41,44,74-76,105,114-116].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [17]
Data management for ML applications, including tools for data dependencies, automatic data validation and cleaning during runtime, and concept drift adaptation, is a maintenance challenge.
“-Data Management: Tools for data dependencies, automatic data validation and cleaning during runtime, and concept drift adaptation [17,23,60,69].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [17]
Risk management of the development, deployment and operation of ML applications is critical, but is rendered difficult by various uncertainties.
“-Risk Management: Risk management of the development, deployment and operation of ML applications is critical, but is rendered difficult by various uncertainties [17,24].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [17]
Technical debts may result in maintenance cost escalation.
“Technical debts which may result in maintenance cost escalation [17].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [17]
The main challenges in group dynamics and psychology are collaboration to ensure a successful project and adequate communication with customers.
“The main challenges in this category are collaboration to ensure a successful project and adequate communication with customers [17,18,21,27,40,59,83].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [17]
Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138 · see citing works
Evidence based medicine: what it is and what it isn't
Sackett, D. L., Rosenberg, W. M. C., Gray, J. A. M., Haynes, R. B., & Richardson, W. S. (1996) · BMJ · doi:10.1136/bmj.312.7023.71 · Cited by 15,769 · see citing works
Advances in neural information processing systems 7
(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90463-0 · Cited by 14,759 · see citing works
He, H., & Garcia, E. A. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.239 · Cited by 10,359 · see citing works
Decision curve analysis: a novel method for evaluating prediction models
Vickers, A. J., & Elkin, E. B. (2006) · Medical Decision Making · doi:10.1177/0272989x06295361 · Cited by 6,341 · see citing works
(2009) · Minds at UW (University of Wisconsin) · Cited by 4,791 · see citing works
Data mining and knowledge discovery: making sense out of data
Feyyad, U. M. (1996) · IEEE Expert · doi:10.1109/64.539013 · Cited by 4,644 · see citing works
MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets
Friedl, M. A., Sulla‐Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A. M., & Huang, X. (2009) · Remote Sensing of Environment · doi:10.1016/j.rse.2009.08.016 · Cited by 3,501 · see citing works
Get another label? improving data quality and data mining using multiple, noisy labelers
Sheng, V. S., Provost, F. J., & Ipeirotis, P. G. (2008) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1401890.1401965 · Cited by 1,180 · see citing works
Identifying Mislabeled Training Data
Brodley, C. E., & Friedl, M. A. (1999) · Cited by 718 · see citing works
Self-citationEmploying EM and Pool-Based Active Learning for Text Classification
McCallum, A., & Nigam, K. (1998) · Cited by 689 · see citing works
Identifying Mislabeled Training Data
Brodley, C. E., & Friedl, M. A. (1999) · Journal of Artificial Intelligence Research · doi:10.1613/jair.606 · Cited by 612 · see citing works
Self-citationASSERT: A Physician-in-the-Loop Content-Based Retrieval System for HRCT Image Databases
Shyu, C., Brodley, C. E., Kak, A. C., Kosaka, A., Aisen, A. M., & Broderick, L. S. (1999) · Computer Vision and Image Understanding · doi:10.1006/cviu.1999.0768 · Cited by 398 · see citing works
Self-citationTemporal sequence learning and data reduction for anomaly detection
Lane, T. D. R., & Brodley, C. E. (1999) · ACM Transactions on Information and System Security · doi:10.1145/322510.322526 · Cited by 394 · see citing works
Self-citationAdvanced mean field methods: theory and practice
Opper, M., & Saad, D. (2001) · Cited by 385 · see citing works
Machine Learning from Imbalanced Data Sets 101
Provost, F. J. (2008) · Faculty Digital Archive (New York University Florence) · Cited by 378 · see citing works
Proceedings, Twentieth International Conference on Machine Learning
Fawcett, T. E., & Mishra, N. (2003) · AAAI Press eBooks · Cited by 374 · see citing works
Support Vector Machine Active Learning with Application sto Text Classification
Tong, S., & Koller, D. (2000) · International Conference on Machine Learning · Cited by 369 · see citing works
Friedl, M. A., Brodley, C. E., & Strahler, A. H. (1999) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/36.752215 · Cited by 311 · see citing works
Self-citationUser re-authentication via mouse movements
Pusara, M., & Brodley, C. E. (2004) · IEEE Symposium on Visualization for Cyber Security (VIZSEC) · doi:10.1145/1029208.1029210 · Cited by 287 · see citing works
Self-citationTutorial on Variational Approximation Methods
Jaakkola, T. (2001) · The MIT Press eBooks · doi:10.7551/mitpress/1100.003.0014 · Cited by 270 · see citing works
Unsupervised feature selection applied to content-based retrieval of lung images
Dy, J., Brodley, C. E., Kak, A. C., Broderick, L. S., & Aisen, A. M. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1182100 · Cited by 255 · see citing works
Self-citationProceedings of the Sixteenth International Conference on Machine Learning
Bratko, I., & Džeroski, S. (1999) · Cited by 254 · see citing works
Eliminating class noise in large datasets
Xingquan Zhu, A., Wu, X., & Chen, Q. (2003) · Cited by 252 · see citing works
Active Learning with Multiple Views
Muslea, I., Minton, S., & Knoblock, C. A. (2006) · Journal of Artificial Intelligence Research · doi:10.1613/jair.2005 · Cited by 238 · see citing works
Dönmez, P., & Carbonell, J. (2008) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1458082.1458165 · Cited by 227 · see citing works
Identifying and eliminating mislabeled training instances
Brodley, C. E., & Friedl, M. A. (1996) · Cited by 223 · see citing works
Self-citationExperiments with Noise Filtering in a Medical Domain
Gamberger, D., Lavrač, N., & Grošelj, C. (1999) · Cited by 173 · see citing works
Ensemble Methods for Noise Elimination in Classification Problems
Verbaeten, S., & Assche, A. V. (2003) · Lecture notes in computer science · doi:10.1007/3-540-44938-8_32 · Cited by 172 · see citing works
Aisen, A. M., Broderick, L. S., Winer-Muram, H. T., Brodley, C. E., Kak, A. C., Pavlopoulou, C., Dy, J., Shyu, C., & Marchiori, A. (2003) · Radiology · doi:10.1148/radiol.2281020126 · Cited by 169 · see citing works
Self-citationFinding anomalous periodic time series
Rebbapragada, U. D., Protopapas, P., Brodley, C. E., & Alcock, C. R. (2008) · Machine Learning · doi:10.1007/s10994-008-5093-3 · Cited by 156 · see citing works
Self-citationTemporal sequence learning and data reduction for anomaly detection
Lane, T. D. R., & Brodley, C. E. (1998) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/288090.288122 · Cited by 151 · see citing works
Self-citationIdentifying and Handling Mislabelled Instances
Muhlenbach, F., Lallich, S., & Zighed, A. D. (2003) · Journal of Intelligent Information Systems · doi:10.1023/a:1025832930864 · Cited by 134 · see citing works
Predictive application-performance modeling in a computational grid environment
Kapadia, N. H., Fortes, J. A. B., & Brodley, C. E. (2003) · ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC) · doi:10.1109/hpdc.1999.805281 · Cited by 131 · see citing works
Self-citationActive learning for biomedical citation screening
Wallace, B. C., Small, K., Brodley, C. E., & Trikalinos, T. A. (2010) · Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1835804.1835829 · Cited by 123 · see citing works
Self-citationClass Noise Mitigation Through Instance Weighting
Rebbapragada, U. D., & Brodley, C. E. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74958-5_71 · Cited by 95 · see citing works
Self-citationASSERT: A PHYSICIAN-IN-THE-LOOP CONTENT-BASED IMAGE RETRIEVAL SYSTEM FOR HRCT IMAGE DATABASES
Shyu, C., Brodley, C. E., & Kak, A. C. (1999) · Cited by 77 · see citing works
Self-citationNoise elimination in inductive concept learning: A case study in medical diagnosis
Gamberger, D., Nada Lavrač, A., & Džeroski, S. (1996) · Lecture notes in computer science · doi:10.1007/3-540-61863-5_47 · Cited by 75 · see citing works
Why label when you can search?
Attenberg, J., & Provost, F. J. (2010) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1835804.1835859 · Cited by 75 · see citing works
Behavioral authentication of server flows
Early, J. P., Brodley, C. E., & Rosenberg, C. (2004) · Annual Computer Security Applications Conference · doi:10.1109/csac.2003.1254309 · Cited by 68 · see citing works
Self-citationSevere Class Imbalance: Why Better Algorithms Aren’t the Answer
Drummond, C., & Holte, R. C. (2005) · Lecture notes in computer science · doi:10.1007/11564096_52 · Cited by 67 · see citing works
Who Should Label What? Instance Allocation in Multiple Expert Active Learning
Wallace, B., Small, K., Brodley, C. E., & Trikalinos, T. A. (2011) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972818.16 · Cited by 65 · see citing works
Self-citationLomasky, R., Brodley, C. E., Aernecke, M. J., Walt, D. R., & Friedl, M. A. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74958-5_63 · Cited by 64 · see citing works
Self-citationVisualization and interactive feature selection for unsupervised data
Dy, J., & Brodley, C. E. (2000) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/347090.347168 · Cited by 64 · see citing works
Self-citationInteractive Content-Based Image Retrieval Using Relevance Feedback
MacArthur, S. D., Brodley, C. E., Kak, A. C., & Broderick, L. S. (2002) · Computer Vision and Image Understanding · doi:10.1006/cviu.2002.0977 · Cited by 61 · see citing works
Self-citationLearning to Schedule Straight-Line Code
Moss, J. E. B., Utgoff, P. E., Cavazos, J., Precup, D., Stefanović, D., Brodley, C. E., & Scheeff, D. (1997) · Cited by 60 · see citing works
Self-citationA Unified Approach to Active Dual Supervision for Labeling Features and Examples
Attenberg, J., Melville, P., & Provost, F. J. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15880-3_9 · Cited by 59 · see citing works
Applying classification algorithms in practice
Brodley, C. E., & Smyth, P. (1997) · Statistics and Computing · doi:10.1023/a:1018557312521 · Cited by 57 · see citing works
Self-citationGoal-directed classification using linear machine decision trees
Draper, B. A., Brodley, C. E., & Utgoff, P. E. (1994) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.310684 · Cited by 57 · see citing works
Self-citationPenalized Probabilistic Clustering
Lu, Z., & Leen, T. K. (2007) · Neural Computation · doi:10.1162/neco.2007.19.6.1528 · Cited by 55 · see citing works
Proceedings of the Seventh SIAM International Conference on Data Mining
Apte, C., Liu, B., & Parthasarathy, S. (2007) · Society for Industrial and Applied Mathematics eBooks · Cited by 43 · see citing works
Brodley, C. E., Kak, A. C., Shyu, C., Dy, J., Broderick, L. S., & Aisen, A. M. (1999) · National Conference on Artificial Intelligence · Cited by 41 · see citing works
Self-citationA noise filtering method using neural networks
Zeng, X., & Martinez, T. R. (2003) · doi:10.1109/scima.2003.1215926 · Cited by 38 · see citing works
Kernel Based Detection of Mislabeled Training Examples
Valizadegan, H., & Tan, P. (2007) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972771.28 · Cited by 36 · see citing works
An algorithm for correcting mislabeled data
Zeng, X., & Martinez, T. R. (2001) · Intelligent Data Analysis · doi:10.3233/ida-2001-5605 · Cited by 34 · see citing works
Improving Classification by Removing or Relabeling Mislabeled Instances
Lallich, S., Muhlenbach, F., & Zighed, D. A. (2002) · Lecture notes in computer science · doi:10.1007/3-540-48050-1_3 · Cited by 28 · see citing works
Distinguishing mislabeled data from correctly labeled data in classifier design
Venkataraman, S., Metaxas, D., Fradkin, D., Kulikowski, C. A., & Muchnik, I. B. (2005) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2004.52 · Cited by 23 · see citing works
Identifying mislabeled training examples in ILP Classification Problems
Verbaeten, S. (2002) · Lirias (KU Leuven) · Cited by 20 · see citing works
Correlation Clustering for Learning Mixtures of Canonical Correlation Models
Fern, X. Z., Brodley, C. E., & Friedl, M. A. (2005) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972757.39 · Cited by 19 · see citing works
Self-citationFocusing attention on objects of interest using multiple matched filters
Stough, T., & Brodley, C. E. (2001) · IEEE Transactions on Image Processing · doi:10.1109/83.908516 · Cited by 13 · see citing works
Self-citationRedefining class definitions using constraint-based clustering
Preston, D. R., Brodley, C. E., Khardon, R., Sulla‐Menashe, D., & Friedl, M. A. (2010) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1835804.1835908 · Cited by 12 · see citing works
Self-citationImproving onboard analysis of Hyperion images by filtering mislabeled training data examples
Rebbapragada, U. D., Mandrake, L., Wagstaff, K. L., Gleeson, D. F., Castaño, R., Chien, S., & Brodley, C. E. (2009) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero.2009.4839580 · Cited by 12 · see citing works
Self-citationGenerating High-Quality Training Data for Automated Land-Cover Mapping
Rebbapragada, U. D., Lomasky, R., Brodley, C. E., & Friedl, M. A. (2008) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2008.4779779 · Cited by 8 · see citing works
Self-citationProgressive refinement for support vector machines
Wagstaff, K. L., Kocurek, M., Mazzoni, D., & Tang, B. (2009) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-009-0149-y · Cited by 8 · see citing works
Strategic targeting of outliers for expert review
Brodley, C. E., & Rebbapragada, U. D. (2010) · Cited by 8 · see citing works
Self-citation
Flagged first, then by citations
2 of the 67 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Open challenges for data stream mining research
Krempl, G., Žliobaitė, I., Brzeziński, D., Hüllermeier, E., Last, M., Lemaire, V., Noack, T., Shaker, A., Sievi, S., Spiliopoulou, M., et al. (2014) · ACM SIGKDD Explorations Newsletter · doi:10.1145/2674026.2674028 · Cited by 295 · see citing works
Sharma, A., Mukhopadhyay, T., Rangappa, S. M., Siengchin, S., & Kushvaha, V. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09700-9 · Cited by 295 · see citing works
Mahroof, K. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2018.11.008 · Cited by 223 · see citing works
A Survey for Cervical Cytopathology Image Analysis Using Deep Learning
Rahaman, M. M., Li, C., Wu, X., Yao, Y., Hu, Z., Jiang, T., Li, X., & Qi, S. (2020) · IEEE Access · doi:10.1109/access.2020.2983186 · Cited by 152 · see citing works
Multi-Objective Hyperparameter Optimization in Machine Learning—An Overview
Karl, F., Pielok, T., Moosbauer, J., Pfisterer, F., Coors, S., Binder, M., Schneider, L., Thomas, J., Richter, J., Lang, M., et al. (2023) · ACM Transactions on Evolutionary Learning and Optimization · doi:10.1145/3610536 · Cited by 133 · see citing works
McKinnel, D. R., Dargahi, T., Dehghantanha, A., & Choo, K. R. (2019) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2019.02.022 · Cited by 89 · see citing works
Software engineering challenges for machine learning applications: A literature review
Kumeno, F. (2020) · Intelligent Decision Technologies · doi:10.3233/idt-190160 · Cited by 81 · see citing works
Kumar, S., Raut, R. D., Narwane, V. S., Narkhede, B. E., & Muduli, K. (2021) · International Journal of Productivity and Performance Management · doi:10.1108/ijppm-10-2020-0511 · Cited by 72 · see citing works
A neural network based distributed intrusion detection system on cloud platform
Li, Z., Sun, W., & Wang, L. (2012) · IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS) · doi:10.1109/ccis.2012.6664371 · Cited by 55 · see citing works
Active cleaning of label noise
Ekambaram, R., Fefilatyev, S., Shreve, M., Kramer, K., Hall, L., Goldgof, D. B., & Kasturi, R. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.09.020 · Cited by 49 · see citing works
Machine learning techniques for the automation of literature reviews and systematic reviews in EFSA
Jaspers, S., De Troyer, E., & Aerts, M. (2018) · EFSA Supporting Publications · doi:10.2903/sp.efsa.2018.en-1427 · Cited by 36 · see citing works
ASCENT: Active Supervision for Semi-Supervised Learning
Li, Y., Wang, Y., Yu, D., Ye, N., Hu, P., & Zhao, R. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2897307 · Cited by 33 · see citing works
Breast Cancer Identification Using Machine Learning
Jia, X., Sun, X., & Zhang, X. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/8122895 · Cited by 25 · see citing works
Filho, M. G., de Almeida, S. V. Q., Lage, M., Osiro, L., Lima, B., & Callefi, M. H. B. M. (2025) · Enterprise Information Systems · doi:10.1080/17517575.2025.2458685 · Cited by 23 · see citing works
Hopf, K., Weigert, A., & Staake, T. (2022) · Journal of Decision System · doi:10.1080/12460125.2022.2059172 · Cited by 14 · see citing works
Diraz, T. A., Mollah, M., & Anzum, K. M. T. (2026) · Journal of Open Innovation Technology Market and Complexity · doi:10.1016/j.joitmc.2026.100728 · Cited by 8 · see citing works
Social Media and Psychological Disorder
Ismail, N. H., Du, M., & Hu, X. (2019) · Springer eBooks · doi:10.1007/978-3-030-14714-3_9 · Cited by 8 · see citing works
Investigating the Disagreement Between Clinicians’ Ratings of Patients in ICUs
Rogers, S., Sleeman, D. H., & Kinsella, J. (2013) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2013.2252182 · Cited by 5 · see citing works
Perception of rhythmic agency for conversational labeling
Yu, C. G., Blackwell, A. F., & Cross, I. R. M. (2021) · Human-Computer Interaction · doi:10.1080/07370024.2021.1877541 · Cited by 3 · see citing works
Big Data Analysis for M2M Networks:Research Challenges and Open Research Issues
Tuna, G., Daş, R., Ramakrishnan, B., & Kılıçaslan, Y. (2017) · International Journal of Computer Networks And Applications · doi:10.22247/ijcna/2017/41308 · Cited by 2 · see citing works
Costas, J., Pastor, R., Gerbolés, C. G.-C., & Díez, R. P. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-86623-4_37 · Cited by 1 · see citing works
Hossain, M. M., Zhou, H., & Zheng, J. (2025) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/03611981251374995 · Cited by 1 · see citing works
Sardhara, T., Dadsena, R., Aydin, R. C., Hilgers, R.-D., Horn, L., Schulz, J. B., Reetz, K., Romanzetti, S., Dogan, I., Lischewski, S. A., et al. (2025) · Machine Learning with Applications · doi:10.1016/j.mlwa.2025.100790 · Cited by 1 · see citing works
An RML-Based Asymmetric Loss Support Vector Machine for Robust and Imbalanced Classification
Chen, Y.-N., Qi, K., & Tang, L. (2026) · Journal of the Operations Research Society of China · doi:10.1007/s40305-025-00662-4 · Cited by 1 · see citing works
Learning with Augmented Multi-Instance View
Zhu, Y., Wu, J., Jiang, Y., & Zhou, Z. (2014) · Cited by 1 · see citing works
Machine learning in health informatics: making better use of domain experts
Brodley, C. E., & Wallace, B. (2012) · Cited by 1 · see citing works
Self-citationYoon, A. (2019) · Research Repository (Delft University of Technology)
Morabito, V. (2016) · Springer eBooks · doi:10.1007/978-3-319-26874-3_1
Design and Analysis of Multipliers for DNN application using approximate 4:2 Compressors
Anjankar, S., Gillurkar, H., Joshi, P. U., & Dwaramwar, P. A. (2022) · INTERNATIONAL JOURNAL OF NEXT-GENERATION COMPUTING · doi:10.47164/ijngc.v13i5.918
Towards Smart Data Mining Support Based on Annotation Beneficial Grouping Method
Kavitha, S., Pushpavathi, K., & Mahalakshmi, S. (2023) · International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE) · doi:10.1109/iitcee57236.2023.10091053
Leveraging AI in Golang: Building Intelligent Applications with Go
Deva, S. (2025) · European Journal of Computer Science and Information Technology · doi:10.37745/ejcsit.2013/vol13n214868
The Six Gestalts: Reframing Hard and Soft Skills into Systemic Roles for Professional Growth
Gerbolés, C. G.-C., Costas, J., Díez, R. P., & Pastor-Climent, R. (2026) · Dirección y Organización · doi:10.37610/88.727
- Authors
- Brodley, C. E., Rebbapragada, U., Small, K., & Wallace, B. C.
- Affiliations
- Tufts University (US)
- Jet Propulsion Laboratory (US)
- Tufts Medical Center (US)
- Venue
- AI Magazine
- Publisher
- Wiley
- Published
- 2012-03
- Volume
- 33
- Issue
- 1
- Pages
- 11-24
- ISSN
- 0738-4602 (ISSN-L)
- 2371-9621
- OpenAlex
- W2130834086
- Cited by
- 32
- References
- 67
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 1 · 2023: 2 · 2022: 4 · 2021: 2 · 2020: 2 · 2019: 4 · 2018: 2 · 2017: 1 · 2016: 1 · 2015: 1 · 2014: 2 · 2013: 1 · 2012: 2
- FWCI
- 1.89
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- AI Magazine (publishedVersion)
- Funders
- National Science Foundation (IIS‐0803409, IIS‐0713259, R01HS0 18494)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- applied machine learning, real-world applications, interdisciplinary collaboration, practical problems, classification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
