{"id":10,"date":"2020-09-25T18:24:44","date_gmt":"2020-09-25T18:24:44","guid":{"rendered":"https:\/\/sites.tntech.edu\/dtalbert\/?page_id=10"},"modified":"2023-11-21T22:22:52","modified_gmt":"2023-11-21T22:22:52","slug":"publications","status":"publish","type":"page","link":"https:\/\/sites.tntech.edu\/dtalbert\/douglas-a-talbert\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"\n<p><strong>2023<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Brown KE, Talbert S, Talbert DA. \u201cA QUEST for Model Assessment: Identifying Difficult Subgroups via Epistemic Uncertainty Quantification.\u201d <em>Proceedings of the<\/em> <em>American Medical Informatics Association 2023 Annual Symposium<\/em>, 2023.<\/li>\n\n\n\n<li>Phillips KL, Brown KE, Talbert S, Talbert DA. \u201cGroup Bias and the Complexity\/Accuracy Tradeoff in Machine Learning-Based Trauma Triage Models.\u201d <em>Florida Artificial Intelligence Research Society (FLAIRS-36)<\/em>, 2023. (<strong>Best Student Paper Award<\/strong>)<\/li>\n\n\n\n<li>Gannod M, Masto N, Owusu C, Highway C, Brown KE, Blake-Bradshaw A, Feddersen JC, Hagy HM, Talbert DA, Cohen B. \u201cSemantic Segmentation with Multispectral Satellite Images of Waterfowl Habitat.\u201d <em>Florida Artificial Intelligence Research Society (FLAIRS-36)<\/em>, 2023.<\/li>\n<\/ul>\n\n\n\n<p><strong>2022<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Stone GB, Talbert DA, Eberle W. A Survey of Scalable Reinforcement Learning. <em>International Journal of Intelligent Computing Research <\/em>(<em>IJICR<\/em>), 2022, 13(1).<\/li>\n\n\n\n<li>Boateng AB, Bruce JW, Talbert DA. Anomaly Detection for a Water Treatment System Based on One-class Neural Network. <em>IEEE Access<\/em>. 2022, 10. doi: 10.1109\/ACCESS.2022.3218624.<\/li>\n\n\n\n<li>Stone GB, Talbert DA, Eberle W. Utilizing Real-Time Strategy for Penetration Testing. <em>International Journal of Chaotic Computing<\/em> (<em>IJCC<\/em>), 2022, 8(1).<\/li>\n\n\n\n<li>Hasan MM, Talbert DA. \u201cMitigating the Rashomon Effect in Counterfactual Explanation: A Game-theoretic Approach.\u201d <em>Florida Artificial Intelligence Research Society (FLAIRS-35)<\/em>, 2022.<\/li>\n\n\n\n<li>Hasan MM, Talbert DA. \u201cData Augmentation using Counterfactuals: Proximity vs Diversity.\u201d <em>Florida Artificial Intelligence Research Society (FLAIRS-35)<\/em>, 2022.<\/li>\n\n\n\n<li>Brown KE, Talbert DA. \u201cUsing Explainable AI to Measure Feature Contribution to Uncertainty.\u201d <em>Florida Artificial Intelligence Research Society (FLAIRS-35)<\/em>, 2022.<\/li>\n\n\n\n<li>Brown KE, Talbert DA. \u201cA Simple Direct Uncertainty Quantification Technique Based on Machine Learning Regression.\u201d <em>Florida Artificial Intelligence Research Society (FLAIRS-35)<\/em>, 2022.<\/li>\n\n\n\n<li>Hines B, Talbert DA, Anton S. \u201cImproving Trust via XAI and Pre-Processing for Machine Learning of Complex Biomedical Datasets.\u201d <em>Florida Artificial Intelligence Research Society (FLAIRS-35)<\/em>, 2022.<\/li>\n\n\n\n<li>Manicavasaga R, Lamichhane PB, Kandel P, Talbert DA. \u201cDrug Repurposing for Rare Orphan Diseases Using Machine Learning Techniques.\u201d <em>Florida Artificial Intelligence Research Society (FLAIRS-35)<\/em>, 2022.<\/li>\n\n\n\n<li>Gothard AT, Talbert DA, Anton S, \u201cToward early damage detection of total knee arthroplasty implants via convolutional neural networks.\u201d <em>Health Monitoring of Structural and Biological Systems XVI<\/em>. Vol. 12048. SPIE, 2022.<\/li>\n<\/ul>\n\n\n\n<p><strong>2021<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Sun L, Talbert DA. \u201cEffects of \u03b1,\u03b2 and Patch Size on Performance of Layer-wise Relevance Propagation.\u201d <em>Proceedings of the 17<sup>th<\/sup> International Conference on Data Science (ICDATA),<\/em> 2021.<\/li>\n\n\n\n<li>Brown KE, Talbert S, Talbert DA. \u201cThe Uncertainty of Counterfactuals.\u201d <em>Florida Artificial Intelligence Research Society (FLAIRS-34)<\/em>, 2021.<\/li>\n\n\n\n<li>Hasan MM, Talbert DA. \u201cCounterfactual Examples for Data Augmentation: A Case Study.\u201d <em>Florida Artificial Intelligence Research Society (FLAIRS-34)<\/em>, 2021.<\/li>\n<\/ul>\n\n\n\n<p><strong>2020<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Kamal M, Talbert DA. &#8220;Toward Never-Ending Learning for Malware Analysis.&#8221; 2020 IEEE International Conference on Big Data (Big Data), 2020.<\/li>\n\n\n\n<li>Martindale N, Muhammad I, Talbert DA. Ensemble-Based Online Machine Learning Algorithms and Ensembles for Network Intrusion Detection Systems Using Streaming Data. <em>Information<\/em>. 2020, 11(6). <\/li>\n\n\n\n<li>Frey L, Talbert DA. Artificial Intelligence Pipeline to Bridge the Gap between Bench Researchers and Clinical Researchers in Precision Medicine. Med One. 2020;5:e200001. https:\/\/doi.org\/10.20900\/mo20200001. <\/li>\n\n\n\n<li>Brown KE, Bhuiyan FA, Talbert DA. \u201cUncertainty Quantification in Multimodal Ensembles of Deep Learners.\u201d Florida Artificial Intelligence Research Society (FLAIRS-33), 2020. (<strong>Best Student Paper Award<\/strong>)<\/li>\n\n\n\n<li>Bhuiyan FA, Brown KE, Sharif MB, Johnson Q, Talbert DA. \u201cAssessing Modality Selection Heuristics to Improve Multimodal Machine Learning for Malware Detection.\u201d Florida Artificial Intelligence Research Society (FLAIRS-33), 2020.<\/li>\n\n\n\n<li>Shafee A, Baza M, Talbert DA, Fouda MM, Nabil M, Mahmoud M. \u201cMimic Learning to Generate a Shareable Network Intrusion Detection Model.\u201d Proceedings of the 17th Annual IEEE Consumer Communications &amp; Networking Conference, 2020.<\/li>\n\n\n\n<li>Ayub MA, Johnson W, Talbert DA, Siraj A. \u201cModel Evasion Attack on Intrusion Detection Systems using Adversarial Machine Learning.\u201d 54th Annual Conference on Information Sciences and Systems (CISS), 2020.<\/li>\n<\/ul>\n\n\n\n<p><strong>2019<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Maynard DS, Bradford MA, Covey KR, Lindner D, Glaeser J, Talbert DA, Tinker PJ, Walker DM, Crowther TW. Consistent trade-offs in fungal trait expression across broad spatial scales. <em>Nature microbiology<\/em>. 2019 Feb 25:1. <\/li>\n\n\n\n<li>Brown KE, Talbert DA. \u201cHeuristically Reducing the Cost of Correlation-Based Feature Selection.\u201d ACM Southeast Conference (ACMSE 2019), 2019. (<strong>Best Paper Runner-up Award<\/strong>)<\/li>\n\n\n\n<li>Bhuiyan F, Sharif B, Tinker PJ, Eberle W, Talbert DA, Ghafoor S, Frey L. \u201cGene Selection and Clustering of Breast Cancer Data.\u201d Florida Artificial Intelligence Research Society (FLAIRS-32), 2019.<\/li>\n\n\n\n<li>Roberts JT, Talbert DA. \u201cBiologically Extending the Gen 2 ANN Model.\u201d Florida Artificial Intelligence Research Society (FLAIRS-32), 2019.<\/li>\n<\/ul>\n\n\n\n<p><strong>2018<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Walker DM, Murray CM, Talbert DA, Tinker P, Graham SP, Crowther TW. A salamander&#8217;s top down effect on fungal communities in a detritivore ecosystem. <em>FEMS Microbiology Ecology<\/em>. 2018 Sep 20;94(12):fiy168.<\/li>\n\n\n\n<li>Singh R, Graves JA, Talbert DA, Eberle W. \u201cPrefix and Suffix Sequential Pattern Mining.\u201d In: P. Perner (Ed.): MLDM 2018, LNAI 10934, pp. 296\u2013311, 2018. (https:\/\/doi.org\/10.1007\/978-3-319-96136-1_24)<\/li>\n\n\n\n<li>Gannod G, Eberle W, Cooke R, Talbert DA, Hagler K, Opp K, Baniya, J. \u201cEstablishing an Agile Mindset and Culture for Workforce Preparedness: A Baseline Study.\u201d In 2018 Frontiers in Education Conference, 2018. Publ by IEEE.<\/li>\n\n\n\n<li>Singh R, Graves J, Talbert DA, \u201cSubgroup Discovery in Sequential Databases.\u201d Florida Artificial Intelligence Research Society (FLAIRS-31), 2018.<\/li>\n\n\n\n<li>Singh R, Graves JA, Waitman LR, Talbert DA. \u201cFinding a Balance Between Interestingness and Diversity in Sequential Pattern Mining.\u201d Proceedings of the 14th International Conference on Data Science (ICDATA), 2018.<\/li>\n\n\n\n<li>Hossain MM, Talbert DA, Ghafoor SK, Kannan R. \u201cFAWCA: A Flexible-greedy Approach to find well-tuned CNN Architecture for Image Recognition Problem.\u201d Proceedings of the 14th International Conference on Data Science (ICDATA), 2018.<\/li>\n<\/ul>\n\n\n\n<p><strong>2017<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Talbert DA, Tinker P, Crowther T, Walker D. \u201cUsing Machine Learning to Understand Top-Down Effects in an Ecosystem: Opportunities, Challenges, and Lessons Learned.\u201d <em>Proceedings of the<\/em> <em>Florida Artificial Intelligence Research Society (FLAIRS)<\/em>, 2017.<\/li>\n\n\n\n<li>Paudel R, Eberle W, Talbert DA. \u201cDetection of Anomalous Activity in Diabetic Patients using Graph-Based Approach.\u201d <em>Proceedings of the<\/em> <em>Florida Artificial Intelligence Research Society (FLAIRS)<\/em>, 2017.<\/li>\n\n\n\n<li>Thakkar N, Mookiah L, Talbert DA, Eberle W. \u201cAnomalies in Students Enrollment using Visualization.\u201d <em>Proceedings of the<\/em> <em>Florida Artificial Intelligence Research Society (FLAIRS)<\/em>, 2017.<\/li>\n<\/ul>\n\n\n\n<p><strong>2016<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Cruz D, Talbert DA, Eberle W, Biernacki J. \u201cA Neural Network Approach for Predicting Microstructure Development in Cement.\u201d <em>18<sup>th<\/sup> International Conference on Artificial Intelligence (ICAI&#8217;16)<\/em>, July 2016.<\/li>\n<\/ul>\n\n\n\n<p><strong>2015<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Neely R, Cleghern Z, Talbert DA. \u201cUsing Subgroup Discovery Metrics to Mine Interesting Subgraphs.\u201d <em>Proceedings of the<\/em> <em>Florida Artificial Intelligence Research Society (FLAIRS)<\/em>, 2015.<\/li>\n<\/ul>\n\n\n\n<p><strong>2013<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Eberle W, Talbert DA, Simpson E, Roberts L, and Pope A, \u201cUsing Machine Learning and Predictive Modeling to Assess Admission Policies and Standards.\u201d <em>9th Annual National Symposium on Student Retention<\/em>, November 2013.<\/li>\n<\/ul>\n\n\n\n<p><strong>2011<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Talbert DA, Honeycutt MB, Talbert SR. \u201cA Machine Learning and Data Mining Framework to Enable Evolutionary Improvement in Trauma Triage.\u201d <em>International Conference on Machine Learning and Data Mining<\/em>, 2011.<\/li>\n\n\n\n<li>Sherrill B, Eberle W, Talbert DA. \u201cAnalysis of Student Data for Retention Using Data Mining Techniques.\u201d <em>National Symposium on Student Retention<\/em>, 2011.<\/li>\n<\/ul>\n\n\n\n<p><strong>2010<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Honeycutt MB, Kolpack J, Honeycutt N, Talbert DA. \u201cOn the utility of HCI practices in improving a commercial information retrieval system.\u201d <em>Conference on Information Systems Applied Research<\/em>, 2010. (<strong>Distinguished Paper Award<\/strong>)<\/li>\n<\/ul>\n\n\n\n<p><strong>2007<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Talbert SR, Talbert DA. \u201cA Comparison of a Decision Tree Induction Algorithm with the ACS Guidelines for Trauma Triage.\u201d <em>Proceedings of the American Medical Informatics Association 2007 Annual Symposium<\/em>, p. 1127, 2007.<\/li>\n\n\n\n<li>Ey JL, Walker A, Talbert DA. \u201cThe dGrail Toolkit for Iterative Deterministic Record Linkage.\u201d <em>Proceedings of the American Medical Informatics Association 2007 Annual Symposium<\/em>, p. 951, 2007.<\/li>\n<\/ul>\n\n\n\n<p><strong>2006<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Rosenbloom ST, Chiu KW, Byrne DW, Talbert DA, Neilson EG, Miller RA. \u201cInterventions to regulate ordering of serum magnesium levels: an unintended consequence of decision support.\u201d <em>Journal of the American Medical Informatics Association<\/em>, 2005, 12: 546-553. <\/li>\n\n\n\n<li>Rosenbloom ST, Geissb\u00fchler AJ, Dupont WD, Giuse DA, Talbert DA, Tierney WM, Plummer WD, Stead WW, Miller RA. \u201cEffect of CPOE user interface design on utilization of education decision support during patient care.\u201d Journal of the American Medical Informatics Association, 2005, 12: 458-473. <\/li>\n\n\n\n<li>Basu C, Merrell MA, Fellbaum C, Talbert DA, Kolpack J, Honeycutt M, Alonso R, Bloom J. &#8220;Automated Knowledge Discovery System (AKDS): A Business Case Study in Ontology Development and Use.&#8221; <em>Proceedings of the 1st Workshop on Formal Ontologies Meet Industry<\/em>, 2005.<\/li>\n<\/ul>\n\n\n\n<p><strong>2004<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Hulgan T, Rosenbloom ST, Hargrove F, Talbert DA, Arbogast PG, Bansal P, Miller RA, Kernodle DS. \u201cOral quinolone dosing in hospitalized patients: an evaluation of a computerized decision support intervention.\u201d <em>Journal of Internal Medicine<\/em>, 2004, 256: 349-357. <\/li>\n\n\n\n<li>Neilson E, Johnson K, Rosenbloom T, Dupont W, Talbert DA, Kaiser A, Miller R. \u201cThe impact of peer management on testing behavior.\u201d <em>Annals of Internal Medicine<\/em>, 2004, 141: 196-204. <\/li>\n\n\n\n<li>Rosenbloom ST, Aronsky D, Talbert DA, Miller RA. \u201cClinicians&#8217; perceptions of clinical decision support integrated into computerized provider order-entry.\u201d <em>International Journal of Medical Informatics<\/em>, 2004, 73: 433-441. <\/li>\n<\/ul>\n\n\n\n<p><strong>2001<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Bansal P, Aronsky D, Talbert DA, Miller RA. \u201cThe effect of a computer-based intervention on the appropriate use of arterial blood gas.\u201d <em>Proceedings of the<\/em> <em>American Medical Informatics Association 2001 Annual Symposium<\/em>, pp. 32-36, 2001.<\/li>\n<\/ul>\n\n\n\n<p><strong>2000<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Starmer J, Talbert DA, Miller RA. \u201cExperience using a programmable rules engine to implement a complex medical protocol during order entry.\u201d <em>Proceedings of the<\/em> <em>American Medical Informatics Association 2000 Annual Symposium<\/em>, pp. 829-832, 2000.<\/li>\n\n\n\n<li>Talbert DA, Fisher D. \u201cAn empirical analysis of techniques for constructing and searching k-dimensional trees.\u201d <em>Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining<\/em>, pp. 26-33, 2000.<\/li>\n<\/ul>\n\n\n\n<p><strong>1999<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Talbert DA, Fisher D. \u201cExploiting sample-data distributions to reduce the cost of nearest-neighbor searches with kd-trees.\u201d Proceedings of the Third Symposium on Intelligent Data Analysis, pp. 407-414, 1999.<\/li>\n\n\n\n<li>Talbert DA, Fisher D. \u201cOPT-KD: an algorithm for optimizing kd-trees.\u201d Proceeding of the Sixteenth International Conference on Machine Learning, pp. 398-405, 1999.<\/li>\n<\/ul>\n\n\n\n<p><strong>1998<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Frey L, Li C, Talbert DA, Fisher D. \u201cA review of the fourteenth international conference on machine learning.\u201d <em>Intelligent Data Analysis<\/em>, 1998, 2: 245-255.<\/li>\n<\/ul>\n\n\n\n<p><strong>1997<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Fisher D, Talbert DA, \u201cInference using probabilistic concept trees.\u201d <em>Proceeding of the Sixth International Workshop on Artificial Intelligence and Statistics<\/em>, pp. 191-202, 1997.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>2023 2022 2021 2020 2019 2018 2017 2016 2015 2013 2011 2010 2007 2006 2004 2001 2000 1999 1998 1997<\/p>\n","protected":false},"author":149,"featured_media":0,"parent":18,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-10","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/sites.tntech.edu\/dtalbert\/wp-json\/wp\/v2\/pages\/10","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.tntech.edu\/dtalbert\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sites.tntech.edu\/dtalbert\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sites.tntech.edu\/dtalbert\/wp-json\/wp\/v2\/users\/149"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.tntech.edu\/dtalbert\/wp-json\/wp\/v2\/comments?post=10"}],"version-history":[{"count":3,"href":"https:\/\/sites.tntech.edu\/dtalbert\/wp-json\/wp\/v2\/pages\/10\/revisions"}],"predecessor-version":[{"id":49,"href":"https:\/\/sites.tntech.edu\/dtalbert\/wp-json\/wp\/v2\/pages\/10\/revisions\/49"}],"up":[{"embeddable":true,"href":"https:\/\/sites.tntech.edu\/dtalbert\/wp-json\/wp\/v2\/pages\/18"}],"wp:attachment":[{"href":"https:\/\/sites.tntech.edu\/dtalbert\/wp-json\/wp\/v2\/media?parent=10"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}