College of Science and Technology

Center for Data Analytics
and Biomedical Informatics

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Center for Data Analytics and Biomedical Informatics

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WARNING: Posted article may not exactly replicate the final versions published in the journals/conference proceedings/books. Copyrights of papers of record are owned by the respective publishers.

Biomedical Informatics
Data Science

I. BIOMEDICAL INFORMATICS

  • Kim, S., Choi, B.K., Cho, J.S,, Huh, U., Shin, M-J,, Obradovic, Z., Rubin, D.J., Lee, J.L., Park, J-H. (2025) “Development of machine learning models with explainable AI for frailty risk prediction and their web-based application in community public health,” Frontiers in Public Health, 13:1698062. https://doi.org/10.3389/fpubh.2025.1698062
  • Alharbi, A., Aljurbua, R., Gupta, S., Otudi, H., Andjelkovic, J., Obradovic, Z. (2025) “Cross-Platform Spatiotemporal Sentiment Trends Analysis of COVID-19 Vaccine Discourse,” Social Network Analysis and Mining, Nov. 06 2025 15:104. https://doi.org/10.1007/s13278-025-01532-w (PDF)
  • Lee, B.E., Gardiner, H.M., Gadegbeku, C.A., Reese, P.P., Obradovic, Z., Fink, E.L., Gillespie, A. (2025) “A Cross-Sectional Social Network Analysis of Decision-Making About Recruiting a Living Donor for Kidney Transplantation,” Kidney Med. 2025 Aug 20; 7(10):101095, DOI: https://doi.org/10.1016/j.xkme.2025.101095
  • Norris, L.A., Stanojevic, M., Skriner, L.C., Chu, B.C., Aalberg, M., Silverman, W.K., Bodden, D., Piacentini, J.C., Obradovic, Z., & Kendall, P.C. (2025) “Using machine learning to predict treatment outcome in a concatenated dataset of youth anxiety treatments,” Child Psychiatry and Human Development, July 03 2025, https://doi.org/10.1007/s10578-025-01873-9 (PDF)
  • Abdel Hai, A., Weiner, M.G., Livshits, A., Brown, J.R., Paranjape, A.P., Hwang, W., Kirchner, L.H., Mathioudakis, N., French, E.K., Obradovic, Z., Rubin, D.J. (2024) “Domain Generalization for Enhanced Predictions of Hospital Readmission on Unseen Domains among Patients with Diabetes,” Artificial Intelligence in Medicine, 158 (2024) 103010, https://doi.org/10.1016/j.artmed.2024.103010
  • Calvelli, H., Gardiner, H., Gadegbeku, C., Reese, P., Obradovic, Z., Fink, E., Gillespie, A. (2024) “A Social Network Analysis of Hemodialysis Clinics: Attitudes Towards Living Donor Kidney Transplant Among Influential Patients,” February 2024, Kidney360. DOI:10.34067/KID.0000000000000383
  • Albarakati, N., Gillespie, A., Obradovic, Z. (2023) “Summarizing Multiple Networks Based on Their Underlying Clustering Structure to Guide a Joint Clustering of Hospitals Admissions,” Informatics in Medicine Unlocked. Vol. 39: 101243, 14 April 2023, DOI: 10.1016/j.imu.2023.101243. (PDF)
  • Stanojevic, M., Andjelkovic, J., Kasprowicz, A., Huuki, L.A., Chao, J., Hedges, S.B., Kumar, S., Obradovic, Z. (2023) “Discovering Research Articles Containing Evolutionary Timetrees by Machine Learning,” Bioinformatics, Vol. 39, Issue 1, Jan. 2023, btad035, DOI: 10.1093/bioinformatics/btad035. (PDF)
  • Aljurbua, R., Gillespie, A., Obradovic, Z. (2022) “The Company We Keep. Using Hemodialysis Social Network Data to Classify Patients’ Kidney
    Transplant Attitudes with Machine Learning Algorithms.” BMC Nephrology, Dec. 29; 23(1):414 DOI: 10.1186/s12882-022-03049-2.
  • Asadi, N., Olson, I., Obradovic, Z. (2022) “A Spatio-Temporal Transformer Framework for Learning Contextual Representation of fMRI Data,” Network Neuroscience, 1-26. DOI: 10.1162/netn_a_00281.
  • Andjelkovic, J., Ljubic, B., Abdel Hai, A., Stanojevic, M., Pavlovski, M., Diaz, W., Obradovic, Z. (2022) “Sequential Machine Learning in Prediction of Common Cancers,” Informatics in Medicine Unlocked, Vol. 30, 2022, 100928. S2352-9148(22)00076-4, DOI: 10.1016/j.imu.2022.100928.
  • Asadi, N., Olson, I., Obradovic, Z. (2021) “The Backbone Network of Dynamic Functional Connectivity,” Network Neuroscience, Nov. 20, 2021, 5 (4) pp851-873. https://doi.org/10.1101/2021.04.20.440711
  • Ljubic, B., Pavlovski, M., Roychoudhury, S., Van Neste C., Salhi, A., Essack, E., Bajic, V., Obradovic, Z. (2021) “Genes and Comorbidities of Thyroid Cancer,” Informatics in Medicine Unlocked, Vol. 25, pp. 100680. https://doi.org/10.1016/j.imu.2021.100680.
  • Gillespie, A., Fink, E., Gardiner, H., Gadegbeku, C.A., Reese, P.P., Obradovic, Z. (2021) “Does Whom Patients Sit Next to During Hemodialysis Affect Whether They Request a Living Donation?” Kidney 360, March 2021, 2(3), pp. 507-518. https://doi.org/10.34067/KID.0006682020.
  • Zhao, B., Katuwawala, A., Oldfield, C.J., Dunker, A.K., Faraggi, E., Gsponer, J., Kloczkowski, A., Malhis, N., Mirdita, M., Obradovic, Z., Soding, J., Steinegger, M., Zhou, Y., Kurgan, L. (2021) “DescribePROT: Database of Amino Acid-Level Protein Structure and Function Prediction,” Nucleic Acids Research., Vol. 49, Issue D298-D308. https://doi.org/10.1093/nar/gkaa931
  • Ljubic, B., Pavlovski, M., Alshehri, J., Roychoudhury, S., Bajic, V., Van Neste C., Obradovic, Z. (2020) “Comorbidity Network Analysis and Genetics of Colorectal Cancer,” Informatics in Medicine Unlocked, vol. 21, 100492, https://doi.org/10.1016/j.imu.2020.100492.
  • Ljubic, B., Roychoudhury, S., Cao, X., Pavlovski, M., Obradovic, S., Nair, R., Glass, L, Obradovic, Z. (2020) “Influence of Medical Domain Knowledge on Deep Learning for Alzheimer’s Disease Prediction,” Computer Methods and Programs in Biomedicine.
  • Ljubic, B., Abdel Hai, A., Stanojevic, M., Diaz, W., Polimac, D., Pavlovski, M., Obradovic, Z.  (2020) “Predicting Complications of Diabetes Mellitus Using Advanced Machine Learning Algorithms,” Journal of the American Medical Informatics Association. Vol. 27, Issue 9, Sept. 2020, pp. 1343-1351. https://doi.org/10.1093/jamia/ocaa120
  • Zhou, F., Gillespie, A., Gligorijevic, Dj., Gligorijevic, J., Obradovic, Z. (2020) “Use of Disease Embedding Technique to Predict the Risk of Progression to End-Stage Renal Disease,” Journal of Biomedical Informatics, vol. 105, 103409, May 2020. (PDF)
  • Gillespie,
    A., Gardiner, H.M., Fink, E.L., Reese, P.P., Gadegbeku, C.A., Obradovic, Z. (2020), “Does Sex, Race, and the Size of a Kidney Transplant Candidate’s Social Network Affect the Number of Living Donor Requests? A Multi-Center Social Network Analysis of Patients on the Kidney Transplant Waitlist,” Transplantation, Feb.10. https://doi.org/10.1097/TP.0000000000003167
  • Asadi, N., Wang, Y., Olson, I., Obradovic, Z. (2020) “A Heuristic Information Cluster Search Approach for Precise Functional Brain Mapping,” Human Brain Mapping, Feb 07, 1-18. (PDF)
  • Cao, X.H., Han, C., Glass, L.M., Kindman, A., Obradovic, Z. (2019) “Time-to-Event Estimation by Re-Defining Time,” Journal of Biomedical Informatics, Dec., vol. 100:103326. (PDF)
  • Ljubic, B., Gligorijevic, Dj, Gligorijevic, J., Pavlovski, M., Obradovic, Z. (2019) “Social Network Analysis for Better Understanding of Influenza,” Journal of Biomedical Informatics, vol. 93, May 2019, 103161. (PDF)
  • Gligorijevic, J., Gligorijevic, Dj, Pavlovski, M., Milkovitz, E., Glass, L., Grier, K., Vankireddy, P., Obradovic, Z. (2019) “Optimizing Clinical Trials Recruitment via Deep Learning,” Journal of the American Medical Informatics Association. (PDF)
  • Siddiqui, S. A., Zhang, Y., Lloret, J., Song, H., Obradovic, Z. (2019) “Pain-Free Blood Glucose Monitoring using Wearable Sensors: Recent Advancements and Future Prospects,” IEEE Reviews in Biomedical Engineering. (PDF)
  • Albarakati, N., Obradovic, Z. (2018) “Multi-Domain and Multi-View Networks Model for Clustering Hospital Admissions from the Emergency Department,” International Journal of Data Science and Analytics. (PDF)
  • Reljin I., Obradovic Z., Popovic M.B., Mladenov V. (2018) “New Methods for Analyzing Complex Biomedical Systems and Signals,” Complexity, vol 2018, Article ID 6405121, 2018. Doi: 10.1155/2018/6405121. (PDF)
  • Povalej Brzan P., Obradovic, Z., Stiglic, G. “Contribution of Temporal Data to Predictive Performance in 30-day Readmission of Morbidly Obese Patients,” PeerJ, 25 April, 2017 (PDF)
  • Stojkovic, I. Obradovic, Z., “Sparse Learning of the Disease Severity Score for High Dimensional Data,” Complexity, Volume 2017 (2017), Article ID 7120691, 11 pages https://doi.org/10.1155/2017/7120691. (PDF)
  • Povalej Brzan P., Obradovic, Z., Stiglic, G. “Contribution of Temporal Data to Predictive Performance in 30-day Readmission of Morbidly Obese Patients,” PeerJ, 25 April, 2017 (PDF)
  • Gillespie, A., Fink, E.L., Traino, H.M., Uversky, A., Bass, S.B., Greener, J., Hunt, J., Browne, H., Hammer, H., Reese, P.P., Obradovic, Z. “Hemodialysis Clinic Social Networks, Sex Differences, and Renal Transplantation,” American Journal of Transplantation, doi: 10.1111/ajt.14273. (PDF)
  • Radovic, M., Ghalwash, M., Filipovic, Obradovic, Z. (2017) “Minimum Redundancy Maximum Relevance Feature Selection Approach for Temporal Gene Expression Data,” BMC Bioinformatics, , 18:9, doi: 10-1186/s12859-016-1423-9. (PDF)
  • Jordanski, M, Radovic, M., Milosevic, Z., Filipovic, Obradovic, Z. “Machine Learning Approach for Predicting Wall Shear Distribution for Abdominal Aortic Aneurysm and Carotid Bifurcation Models,” IEEE Journal of Biomedical and Health Informatics, doi: 10.1109/JBHI.2016.2639818. (PDF)
  • Gligorijevic, Dj., Stojanovic, J., Obradovic, Z., (2016), “Disease Types Discovery from a Large Database of Inpatient Records: A Sepsis Study,” Methods, 111, 45-55. S1046-2023(16)30232-8, doi: doi:10.1016/j.ymeth.2016.07.021. (PDF)
  • Cao, X.H., Stojkovic, I., Obradovic, Z. (2016) “A Robust Data Scaling Algorithm to Improve Classification Accuracies in Biomedical Data,” BMC Bioinformatics, Sep 9; 17(1):359, doi: 10.1186/s12859-016-1236-x. (PDF)
  • Gligorijevic, Dj., Stojanovic, J., Djuric, N., Radosavljevic, V., Grbovic, M., Kulathinal, R.J., Obradovic, Z. (2016) “Large-Scale Discovery of Disease-Disease and Disease-Gene Associations,” Scientific Reports, Nature Publishing Group, 2016, Aug 31, 6:32404 doi 10.1038/srep32404. (PDF)
  • Feldman, K., Stiglic, G., Dasgupta, D., Kricheff, M., Obradovic, Z., Chawla, N. (2016) “Insights into Population Health Management Through Disease Diagnoses Networks,” Scientific Reports, Nature Publishing Groups, 2016, July 27, 6:30465 doi: 10.1038/srep30465. (PDF)
  • Stojanovic, J., Gligorijevic, Dj., Radosavljavic, V., Djuric, N., Grbovic, M., Obradovic, Z., (2016) “Modeling Healthcare Quality via Compact Representations of Electronic Health Records,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, Jul 14. doi:10.1109/TCBB.2016.2591523. (PDF)
  • Stojkovic, I, Ghalwash, M., Cao, X.H., Obradovic, Z. (2016) “Effectiveness of Multiple Blood-Cleansing Interventions in Sepsis, Characterized in Rats,” Scientific Reports, Nature Publishing Group, 2016, April 21, 6:24719 doi: 10.1038/srep24719. (PDF)
  • Ghalwash, M., Cao, X.H., Stojkovic, I., Obradovic, Z. (2016) “Structured Feature Selection using Coordinate Descent Optimization,” BMC Bioinformatics, 2016 17:158 doi:10.1186/s12859-016-0954-4. (PDF)
  • Weissgerber, T.L., Garovic, V.D., Milin-Lazovic, J.S., Winham, S.J., Obradovic, Z., Trzeciakowski, J.P., Milic, N.M. (2016) “Reinventing Biostatistics Education for Basic Scientists,” PLOS Biology, April 8 14(4): doi:10.1371/journal.pbio.1002430. (PDF)
  • Stiglic, G., Brzan, P.P., Fijacko, N., Fei, W., Delibasic, B., Kalousis, A., Obradovic, Z. “Comprehensible Predictive Modeling Using Regularized Logistic Regression and Comorbidity Based Features,” PLOS ONE,Dec 8, 2015, DOI: 10.1371/journal.pone.0144439 (PDF)
  • Ghalwash, M., Ramljak, D.,Obradovic, Z. (2015) “Patient-Specific Early Classification of Multivariate Observations,” International Journal of Data Mining and Bioinformatics, Vol. 11, No. 4, pp. 392-411 2015. (PDF)
  • Gillespie, A., Hammer, H., Kolenikov, S., Polychronopoulou, A., Ouzienko, V., Obradovic, Z., Urbanski, M.A., Browne, T., Silva, P. (2014) “Sex Differences and Attitudes toward Living Donor Kidney Transplantation among Urban Black Patients on Hemodialysis” Clinical Journal of the American Society of Nephrology, 9. (PDF)
  • Mathew, G., Obradovic, Z. (2013) “Distributed Privacy Preserving Decision Support System for Highly Imbalanced Clinical Data,” ACM Transactions on Management Information Systems.Volume 4 Issue 3 Article No. 12, October 2013 . (PDF)
  • Mathew, G., Obradovic, Z. (2013) “Dynamic Distributed Predictive Learning Models that Preserve Privacy for Hospitals with Insufficient Labeled Data,” Network Modeling Analysis in Health Informatics and Bioinformatics, Aug. 2013. (PDF)
  • Zhang, P., Cao, W., Obradovic, Z. (2013) “Learning by Aggregating Experts and Filtering Novices: A Solution to Crowdsourcing Problems in Bioinformatics,” BMC Bioinformatics 14 (Suppl 12):S5. (PDF)
  • Radosavljevic, V., Ristovski, K., Obradovic, Z. (2013) “A Data-Driven Acute Inflammation Therapy,” BMC Medical Genomics 6 (Suppl 3):S7. (PDF)
  • Tallarida, R. J., Midic, U., Lammare, N.S., Obradovic, Z. (2013) “A Search for Interaction Among Combinations of Drugs of Abuse and the Use of Isobolographic Analysis,” Journal of Clinical Pharmacy and Therapeutics 38(3):190-5 Jun 2013. (PDF) (Pubmed PMID: 23550787)
  • Lou, Q., Obradovic, Z. (2013) “Classifying Temporal Microarray Data by Selecting Informative Genes,” Journal of Bioinformatics and Computational Biology, 11(3):1341006, Jun 2013. (Pubmed PMID: 23796183)
  • Dunker A.K., Babu M., Barbar E., Blackledge M., Bondos S.E., Dosztányi Z., Dyson H.J., Forman-Kay J., Fuxreiter M., Gsponer J., Han K.-H., Jones D.T., Longhi S., Metallo S.J., Nishikawa K., Nussinov R., Obradovic Z., Pappu R., Rost B., Selenko P., Subramaniam V., Sussman J.L., Tompa P., Uversky V.N. (2013) “What’s in a name? Why these proteins are intrinsically disordered,” Intrinsically Disordered Proteins 1 (1) e24157 (PDF)
  • Oates, M.E., Romero, P., Ishida, T., Ghalwash, M., Mizianty, M.J., Xue, B. Dosztanyi, Z., Uversky, V.N., Obradovic, Z., Kurgan, L., Dunker, A.K., Gough, J. (2013) “D2P2: Database of Disordered Protein Predictions,” Nucleic Acids Research (Database Issue) Jan;41:D508-16. (PDF) (Pubmed PMID: 23203878)
  • Ghalwash, M, Obradovic, Z. (2012) ” Early Classification of Multivariate Temporal Observations by Extraction of Interpretable Shapelets,” BMC Bioinformatics. (PDF) (Pubmed PMID: 22873729)
  • Midic, U., Obradovic, Z. (2012) ” Intrinsic Disorder in Putative Protein Sequences,” 
 Proteome Science. vol. 10, suppl. 1. (PDF) (Pubmed PMID: 22759577)
  • Turan, N., Ghalwash, M.F., Katari, S., Coutifaris, C., Obradovic, Z., Sapienza, C. (2012) ” DNA Methylation Differences at Growth Related Genes Correlate with Birth Weight: A Molecular Signature Linked to Developmental Origins of Adult Disease,” 
BMC Medical Genomics, vol. 5. no 10. (PDF) (Pubmed PMID: 22498030)
  • Li, A., Ling, H., Obradovic, Z., Smith, D.J, Megalooikonomou, V. (2012) ” Learning Pair-Wise Gene Functional Similarity by Multiplex Gene Expression Maps,” BMC Bioinformatics vol. 13, suppl. 3. (PDF) (Pubmed PMID: 22536893)
  • Wang, D., Parkman, H.P., Jacobs, M.R., Mishra, A.K., Krynetskiy, E., Obradovic, Z. (2012) ” DNA microarray SNP associations with clinical efficacy and side effects of domperidone treatment for gastroparesis, ” Journal of Biomedical Informatics, vol. 45, no. 2, pp. 316-22. (PDF) (Pubmed PMID: 22179054)
  • Ghalwash, M., Dunker A.K. and Obradovic, Z. (2012) ” Uncertainty Analysis in Protein Disorder Prediction ” Molecular BioSystems , 2012, 8 (1), 381 – 391. (PDF) (Pubmed PMID: 22101336)
  • Zhang, P., Obradovic, Z. (2011) ” Unsupervised Integration of Multiple Protein Predictors: The Method and Evaluation on CASP7, CASP8 and CASP9 Data,” Proteome Science, vol. 8, suppl. 1, 2011. (PDF) (Pubmed PMID: 22166115)
  • Lee, YS, Vandevoort, CA, Gaughan, JP, Midic, U., Obradovic, Z., Latham, K.E. (2011) ” Extensive Effects of In Vitro Oocyte Maturation on Rhesus Monkey Cumulus Cell Transcriptome, ” Am J Physiol Endocrinol Metab July, vol. 30, no. 1, E 196-209 (PDF) (Pubmed PMID: 21487073)
  • Pavlovic-Lazetic, G., Mitic, N.S., Kovacevic, J.J., Obradovic, Z. Sasa N Malkov, S.N. and Milos V Beljanski, M.V. (2011) “Bioinformatics analysis of disordered proteins in prokaryotes” BMC Bioinformatics. (PDF) (Pubmed PMID: 21366926)
  • Potireddy, S, Midic, U., Liang, C.G., Obradovic, Z., Latham, K.E. (2010) “Positive and negative cis-regulatory elements directing postfertilization maternal mRNA translational control in mouse embryos,” Am J Physiol Cell Physiol 299. (Pubmed PMID: 20573994)
  • Garriga, J., Xie, H., Obradovic, Z., Grana, X. (2010) “Selective Control of Gene Expression by CDK9 in Human Cells,” Journal of Cellular Physiology, vol. 222(1):200-8. (PDF) (Pubmed PMID: 19780058)
  • Midic, U., Oldfield, C.J., Dunker, A.K., Obradovic, Z., Uversky, V.N. (2009) ” Unfoldomics of Human Genetic Diseases: Examples of Ordered and Intrinsically Disordered Members of the Human Diseasome, ” Protein and Peptide Letters, vol. 16, no. 12, pp. 1533-1547. (PDF) (Pubmed PMID: 20001916)
  • Uversky, V.N., Oldfield, C.J., Midic, U., Xie, H., Xue, B., Vucetic, S., Iakoucheva, L.M., Obradovic, Z., Dunker, A.K., (2009) “Unfoldomics of Human Diseases: Linking Protein Intrinsic Disorder with Diseases,” BMC Genomics, vol. 10 Suppl 1:S07. (PDF) (Pubmed PMID: 19594884)
  • Midic, U., Oldfield, C.J., Dunker, A.K., Obradovic, Z., Uversky, V.N. (2009) “Protein Disorder in the Human Deseasome: Unfoldomics of Human Genetic Diseases,” BMC Genomics, vol. 10 Suppl 1:S12. (PDF) (Pubmed PMID: 19594871)
  • Li, A., Xie H., Chin, M.H., Obradovic, Z., Smith, D.J., Megalooikonomou, V. (2009) “Analysis of Multiplex Gene Expression Maps Obtained by Voxelation,” BMC Bioinformatics, 10 Suppl 4:S10. (PDF) (Pubmed PMID: 19426449)
  • Dunker, K., Oldfield, C.J., Meng, J., Romero, P., Yang, J., Chen, J.W., Vacic, V., Obradovic, Z. and Uversky, V.N. (2008) “The Unfoldomics Decade: An Update on Intrinsically Disordered Proteins,” BMC Genomics, vol. 9 (Suppl 2):S1, 16. Sept.. (PDF) (Pubmed PMID: 18831774)
  • Megalooikonomou, V., Kontos, D., Pokrajac, D., Lazarevic, A., Obradovic, Z. (2008) “An Adaptive Partitioning Approach for Mining Discriminant Regions in 3D Image Data,” Journal of Intelligent Information Systems, vol 31, no. 3, pp. 217-242. (PDF)
  • Xu, Q., Canutescu, A., Wang, G., Shapavalov, M.V., Obradovic, Z. and Dunbrack, R.L. (2008) “Statistical Analysis of Interfaces in Crystals of Homologous Proteins,” J. Molecular Biology, vol. 381, pp. 487-507 . (PDF) (Pubmed PMID: 18599072)
  • Ren, S., Uversky, V.N., Chen, Z., Dunker, A.K. and Obradovic, Z. (2008) “Short Linear Motifs recognized by SH2, SH3 and Ser/Thr Kinase domains are conserved in disordered protein regions,” BMC Genomics, vol. 9 (Suppl 2):S26, 9. Sept. (PDF) (Pubmed PMID: 18831792)
  • Krynetskaia, N., Xie, X., Vucetic, S., Obradovic, Z., Krynetskiy, E. (2008), “High Mobility Group Protein B1 is an Activator of Apoptotic Response to Antimetabolite Drugs,” Molecular Pharmacology, Jan;73(1):260-9. (PDF) (Pubmed PMID: 17951356)
  • Xie, H., Vucetic, S. Iakoucheva L.M., Oldfield C.J., Dunker, A.K., Uversky, V.N. and Obradovic, Z. (2007) “Functional Anthology of Intrinsic Disorder. I. Biological Processes and Functions of Proteins with Long Disordered Regions,” Journal of Proteome Research, May;6(5):1882-98. (PDF) (Pubmed PMID: 17391014)
  • Vucetic, S., Xie, H., Iakoucheva L.M., Oldfield C.J., Dunker, A.K., Obradovic, Z. and Uversky, V.N. (2007) “Functional Anthology of Intrinsic Disorder. II. Cellular Components, Domains, technical Terms, Developmental processes,” Journal of Proteome Research, May 4;6(5):1899-1916. (PDF) (Pubmed PMID: 17391015)
  • Xie, H., Vucetic, S. Iakoucheva L.M., Oldfield C.J., Dunker, A.K., Obradovic, Z. and Uversky, V.N. (2007) “Functional Anthology of Intrinsic Disorder. III. Ligands, Postranslational Modifications and Diseases Associated with Intrinsically Disordered Proteins,” Journal of Proteome Research, May 4;6(5):1917-1932. (PDF) (Pubmed PMID: 17391016)
  • Radivojac, P., Iakoucheva, L.M., Oldfield C.J.,, Obradovic, Z., Uversky, V.N., Dunker A.K. (2007) “Intrinsic Disorder and Functional Proteomics,” Biophysical Journal, vol. 92, March 2007, pp. 1439-1456. (PDF) (Pubmed PMID: 17158572)
  • Midic, U. Dunker, K. and Obradovic, Z. (2007) “Exploring alternative knowledge representations for protein secondary-structure prediction,” Int’l Journal of Data Mining and Bioinformatics, 1(3):286-313. (Pubmed PMID: 18399076)
  • Sickmeier, M., Hamilton, A., LeGall, T. Vacic, V., Cortese, M.S., Uversky, V.N., Tompa, P., Obradovic, Z. and Dunker, A.K. (2007) “DisProt: The Database of Disordered Proteins,” Nucleic Acids Research, 35(Database issue):D786-93. (PDF) (Pubmed PMID: 17145717)
  • Xu, Q., Canutescu, A., Obradovic, Z. and Dunbrack, R.L. (2006) “ProtBuD: A Database of Biological Unit Structures of Protein Families and Superfamilies,” Bioinformatics, Dec 1;22(23):2876-82. (PDF) (Pubmed PMID: 17018535)
  • Han, B., Obradovic, Z., Hu, Z.Z., Wu, C. H. and Vucetic, S. (2006) “Substring Selection for Biomedical Document Classification,” Bioinformatics, Dec 1;22(23):2876-82. (PDF) (Pubmed PMID: 16837530)
  • Romero, P., Zaidi, S., Fang,Y.Y., Uversky, V.N., Radivojac, P., Oldfield, C., Cortese M., LeGall, T., Obradovic, Z. and Dunker, A.K. (2006)“Alternative Splicing in Concert with Protein Intrinsic Disorder Enables Increased Functional Diversity in Multicellular Organisms,” The Proceedings of the National Academy of Sciences, vol. 103, no. 22, 8390-8395, May 30. (PDF) (Pubmed PMID: 16717195)
  • Peng, K., Radivojac, P., Vucetic, S., Dunker, A.K. and Obradovic, Z. (2006) “Length-Dependent Prediction of Intrinsic Protein Disorder,” BMC Bioinformatics, vol. 7 (1), 208, April 17. (PDF) (Pubmed PMID: 16618368)
  • Radivojac, P., Vucetic, S., O’Connor, T.R., Uversky, V.N., Obradovic, Z. and Dunker, A.K. (2006) “Calmodulin Signaling: Analysis and Prediction of a Disorder-Dependent Molecular Recognition,” Proteins: Structure, Function and Bioinformatics, vol. 63(2), pp. 398-410, May 1. (PDF) (Pubmed PMID: 16493654)
  • Obradovic, Z., Peng, K., Vucetic, S., Radivojac, P., and Dunker, A.K. (2005) “Exploiting Heterogeneous Sequence Properties Improves Prediction of Protein Disorder,” Proteins: Structure, Function and Bioinformatics, vol. 61, Suppl. 7, pp. 176-182. (PDF) (Pubmed PMID: 16187360)
  • Peng, K., Vucetic, S., Radivojac, P., Brown, C.J., Dunker, A.K. and Obradovic, Z. (2005) “Optimizing Long Intrinsic Disorder Predictors with Protein Evolutionary Information,” Journal of Bioinformatics and Computational Biology, vol. 3, no. 1, pp. 35-60. (PDF) (Pubmed PMID: 15751111)
  • Vucetic, S., Obradovic, Z., Vacic, V., Radivojac, P., Peng, K., Lawson, J.D., Brown, C.J., Sikes, J.G., Newton, C. and Dunker, A.K. (2005) “Disprot: A Database of Protein Disorder,” Bioinformatics, vol 21, no. 1, pp. 137-40. (PDF) (Pubmed PMID: 15310560)
  • Pokrajac, D., Megalooikonomou, V., Lazarevic, A., Kontos, D. and Obradovic, Z. (2005) “Applying Spatial Distribution Analysis Techniques to Classification of 3D Medical Images,” International Journal Artificial Intelligence in Medicine, Vol. 33, No 3, pp. 261-80. (PDF) (Pubmed PMID: 15811790)
  • Romero, P., Obradovic, Z., and Dunker, A.K.(2004) “Natively Disordered Proteins: Functions and Predictions,” Applied Bioinformatics, 3(2-3), pp.105-13. (Pubmed PMID: 15693736)
  • Radivojac, P., Chawla, N. V., Dunker, A.K., and Obradovic, Z. (2004) “Classification and Knowledge Discovery in Protein Databases,” Journal of Biomedical Informatics, vol. 37, pp. 224-239. (PDF) (Pubmed PMID: 15465476)
  • Iakoucheva, L.M., Radivojac, P., Brown, C.J., O’Connor, T.R., Sikes, J.G., Obradovic, Z. and Dunker, A.K. (2004) “The Importance of Intrinsic Disorder for Protein Phosphorylation,” Nucleic Acids Research, vol. 32, no. 3, pp. 1037-1049. (PDF) (Pubmed PMID: 14960716)
  • Obradovic, Z, Peng, K, Vucetic, S., Radivojac, P., Brown, C., and Dunker, A.K. (2003) “Predicting Intrinsic Disorder from Amino Acid Sequence,” Proteins: Structure, Function and Genetics, vol. 53 Suppl 6, pp. 566-72. (PDF) (Pubmed PMID: 14579347)
  • Radivojac, P., Obradovic, Z., Smith D.K., Zhu, G., Vucetic, S., Brown, C., Lawson, J.D. and Dunker, A.K., (2003) “Protein flexibility and intrinsic disorder,” Protein Science, vol. 13, pp. 71-80. (PDF) (datasets) (Pubmed PMID: 14691223)
  • Vucetic, S., Brown C., Dunker A.K and Obradovic, Z. (2003) “Flavors of Protein Disorder,” Proteins: Structure, Function and Genetics, vol. 52. pp. 573-584 (PDF) (Pubmed PMID: 12910457)
  • Smith, D. K., Radivojac, P., Obradovic, Z., Dunker, A. K. and Zhu, G. (2003) “Improved Amino Acid Flexibility Parameters,” Protein Science, vol 12, pp. 1060-1072. (PDF) (Pubmed PMID: 12717028)
  • Iakoucheva, L.M., Brown, C.J., Lawson, J.D., Obradovic, Z. and Dunker A.K. (2002) “Intrinsic Disorder in Cell-signaling and Cancer-associated Proteins,” Journal of Molecular Biology, vol. 323, pp. 573-584. (PDF) (Pubmed PMID: 12381310)
  • Dunker, A.K., Brown, C.J., Lawson, J.D., Iakoucheva, L.M. and Obradovic, Z. (2002) “Intrinsic Disorder and Protein Function,” Biochemistry, May 28th, vol. 41, issue 21, pp. 6573 – 6582. (PDF) (Pubmed PMID: 12022860)
  • Dunker, A.K., Brown, C.J. and Obradovic, Z. (2002) “Identification and Functions of Usefully Disordered Proteins,” Advances in Protein Chemistry, vol. 62, pp. 25-49. (PDF) (Pubmed PMID: 12418100)
  • Dunker, A.K and Obradovic, Z. (2001) “The Protein Trinity – Linking Function and Disorder,” Nature Biotechnology, vol. 19, Sept., pp. 805-806. (PDF) (Pubmed PMID: 11533628)
  • Dunker A.K., Lawson J.D., Brown C.J., Romero P., Oh J., Oldfield C.J., Campen A.M., Ratlif, Hipps K.W., Ausio J., Nissen M.S., Reeves R., Kang C.H., Kissinger C.R., Bailey R.W., Griswold M.D., Chiu W., Garner E.C. and Obradovic Z. (2001) “Intrinsically Disordered Proteins,” Journal of Molecular Graphics and Modeling, vol. 19, pp. 28-61. (PDF) (Pubmed PMID: 11381529)
  • Romero, P., Obradovic, Z., Li, X., Garner, E., Brown, C.J. and Dunker, A.K. (2001) “Sequence Complexity and Disordered Protein,” Proteins: Structure, Function and Genetics, vol. 42, pp. 38-48. (PDF) (Pubmed PMID: 11093259)
  • Romero, P., Obradovic, Z and Dunker K. (2000) “Intelligent Data Analysis for Identifying Protein Disorder,” Issues on Application of Data Mining, Artificial Intelligence Review, Vol. 14, No. 6, S2, pp. 447-484. (PDF)
  • Romero, P., Obradovic, Z. and Dunker, A.K. (1999) “Folding Minimal Sequences: The Lower Bound for Sequence Complexity of Globular Proteins,” FEBS Letters. vol. 462, pp.363-367. (PDF) (Pubmed PMID: 10622726)
  • Dunker, A.K., Obradovic, Z., Romero, P., Kissinger, C. and Villafranca, J.E. (1997) “On the Importance of Being Disordered,” Protein Data Bank Quarterly Newsletter, Release no. 81, pp. 3-5.
  • Xie, H., Obradovic, Z. and Vucetic, S. (2009) “Mining of Microarray, Proteomics, and Clinical Data for Improved Identification of Chronic Fatigue Syndrome, ” chapter 9 in McConnell, P, Lim, S., and A.J. Cuticchia, Methods of Micorarray Data Analysis VI. (Scotts Valley, California: CreateSpace Publishing, 2009), pp. 119-127.
  • Xie, H., Midic, U., Vucetic, S. and Obradovic, Z. (2008) “Algorithmic Methods for the Analysis of Gene Expression Data,” chapter 4 in Handbook of Applied Algorithms: Solving Scientific, Engineering, and Practical Problems (eds. A. Nayak and I. Stojmenovic), Willey-IEEE Press, pp. 115-146. (PDF)
  • Uversky V.N., Radivojac, P., Iakoucheva, L.M., Obradovic, Z. and Dunker, A.K. (2007) “Prediction of Intrinsic Disorder and its Use in Functional Proteomics,” chapter 5 in Methods in Molecular Biology vol. 408: Gene Function Analysis (ed. M. Ochs), Humana Press Inc., Totowa, N.J. (PDF) (Pubmed PMID: 18314578)
  • Peng, K. Obradovic, Z. and Vucetic, S. (2006) “Supervised Learning under Sample Selection Bias from Protein Structure Databases,” in Advances in Applied and Computational Mathematics, Nova Science Publishers, pp. 153-170.
  • Albarakati, N., Otudi, H., Aljurbua, R., Obradovic, Z. (in press) “Discovering Linkages Among Multiple Disease Networks by Joint Clustering,” Proc. 17th Int’l Conf. Social Networks Analysis and Mining (ASONAM 2025), Niagara Falls, Canada, Aug. 2025.
  • Aljurbua, R., Gillespie, A., Alshehri, J., Alharbi, A., Albarakati, N., Obradovic, Z. (2024) “Node2VecFuseClassifier: Bridging Perspectives in Modeling Transplantation Attitudes Among Dialysis Patients,” 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI), Orlando, FL, USA, 2024, pp. 113-122, doi: 10.1109/ICHI61247.2024.00022
  • Abdel Hai, A., Weiner, M.G., Livshits, A., Brown, J.R., Paranjape, A.P., Obradovic, Z., Rubin, D.J. (2023) “Spatial Knowledge Transfer with Deep Adaptation Network for Predicting Hospital Readmission,” In: Juarez, J.M., Marcos, M., Stiglic, G., Tucker, A. (eds) Artificial Intelligence in Medicine. AIME 2023. Lecture Notes in Computer Science, vol 13897. Springer, Cham. DOI: 10.1007/978-3-031-34344-5_17. (PDF)
  • Stanojevic, M., Norris, L., Kendall, P., Obradovic, Z. (2022) “Predicting Anxiety Treatment Outcomes with Machine Learning,” Proc. 21st International Conference on Machine Learning and Applications, Special Session on Machine Learning in Health, Bahamas, December 2022, pp. 957-962, DOI: 10.1109/ICMLA55696.2022.00160.
  • Abdel Hai, A., Weiner, M.G., Paranjape, A.P., Livshits, A., Brown, J.R., Obradovic, Z., Rubin, D.J. (2022) “Deep Learning vs Traditional Models for Predicting Hospital Readmission among Patients with Diabetes,” Proc. AMIA 2022 Annual Symposium, Washington, D.C., Nov. 2022. (PDF)
  • Asadi, N., Wang,Y., Olson, I., Obradovic, Z. (2018) “A Greedy Best-First Search Algorithm for Accurate Functional Brain Mapping,” 2018 Conference on Cognitive Computational Neural Science, Philadelphia, PA, Sept. 2018. (PDF)
  • Cao, X.H., Han, C., Obradovic, Z. (2018) “Learning a Dynamic-based Representation for Multivariate Biomarker Time Series Classifications,” Proc. 6th IEEE Int’l Conf. on Healthcare Informatics (ICHI), New York, NY, June 2018. Best paper award. (PDF)
  • Gligorijevic, Dj., Stojanovic, J., Satz, W., Stojkovic, I., Schreyer, K., Del Portal, D., Obradovic, Z. (2018) “Deep Attention for Triage of Emergency Department Patients,” 2018 SIAM Int’l Conf. Data Mining, San Diego, CA, May 2018. (PDF)
  • Stojkovic, I., Obradovic, Z. (2017) “Predicting Sepsis Biomarker Progression under Therapy,” Proc. 30th IEEE Int’l Symp. Computer-Based Medical Systems, Thessaloniki, Greece, June, 2017. (PDF)
  • Albarakati, N., Obradovic, Z. (2017) “Disease-Based Clustering of Hospital Admission: Disease Network of Hospital Networks Approach,” Proc. 30th IEEE Int’l Symp. Computer-Based Medical Systems, Thessaloniki, Greece, June, 2017. (PDF)
  • Chen EY, Olson, I.R., Chein, J., McCloskey, M.S., Edwards MA, Mohamed, F.B., Hoge, W.S., Obradović Z., Olino, T.M. “An fMRI task as a test of long-term clinically significant weight loss,” Organization for Human Brain Mapping, Vancouver, Canada, June 2017.
  • Chen EY, Foster, G.D., Mohamed, F.B., Conklin, C.J., Hoge, W.S., Olson, I.R., Chein, J., McCloskey, M.S., Obradović Z., Olino, T.M., on behalf of Temple Eating Disorders program 2020: Arlt. M. “Can baseline resting state functional connectivity classify clinically significant weight loss 3 and 15 months later?,” Association for Psychological Science, Boston, USA, May 2017.
  • Radovic, M., Jordanski, M., Filipovic, N., Obradovic, Z. (2016) “T-Relief: Feature Selection for Temporal High-Dimensional Gene Expression Data,” Proc. 2nd European Alliance for Innovation International Conf. on Future Access Enabler of Ubiquitous and Intelligent Infrastructure, Belgrade, Serbia, Oct. 2016 (PDF)
  • Vukicevic, M., Radovanovic, S., Stiglic, G., Delibasic, B., Van Poucke S., Obradovic, Z. (2016) “A Data and Knowledge Driven Randomization Technique for Privacy-Preserving Data Enrichment in Hospital Readmission Prediction,” 5th Workshop on Data Mining for Medicine and Healthcare, 2016 SIAM Int’l Conf. Data Mining (SDM), 10 – 18 Miami, FL, May 2016. (PDF)
  • Ramljak, D., Davey, A., Uversky, A., Roychoudhury, S., Obradovic, Z. (2015) “Casting a Wider Net: Data Driven Discovery of Proxies for Target Diagnoses,” AMIA 2015 Annual symposium, San Francisco, Nov. 14 – 18 2015, pp. 1047-1055 (PDF)
  • Roychoudhury, S., Ghalwash, M., Obradovic, Z. (2015) “False Alarm Suppression in Early Prediction of Cardiac Arrhythmia,”Proc. 15th IEEE International Conference on Bioinformatics and Bioengineering, Belgrade, Serbia, Nov. 2015. (PDF)
  • Cao, X.H., Obradovic, Z. (2015) “A Robust Data Scaling Algorithm for Gene Expression Classification,”Proc. 15th IEEE International Conference on Bioinformatics and Bioengineering, Belgrade, Serbia, Nov. 2015. (PDF)
  • Vukicevic, M., Radovanovic, S., Kovacevic, A., Sliglic, G., Obradovic, Z. (2015) “Improving hospital readmission prediction using domain knowledge based virtual examples,” Proc. the 10th Conf. on Knowledge Management in Organization, Maribor, Slovenia, August, 2015. (PDF)
  • Radovanovic, S., Vukicevic, M., Kovacevic, A., Sliglic, G., Obradovic, Z. (2015) “Domain knowledge based hierarchical feature selection for 30-day hospital readmission prediction” Proc. AIME 2015, the 15th Conference on Artificial Intelligence in Medicine, Pavia, Italy, June, 2015. (PDF)
  • Ramljak, D., Davey, A., Uversky, A., Roychoudhury, S., Obradovic, Z. (2015) “Hospital Corners and Wrapping Patients in Markov Blankets,” 4th Workshop on Data Mining for Medicine and Healthcare, 2015 SIAM International Conference on Data Mining, Vancouver, Canada, April 30 – May 02, 2015 (PDF)
  • Gligorijevic, Dj., Stojanovic, J., Obradovic, Z. (2015) ” Improving Confidence while Predicting Trends in Temporal Disease Networks,” 4th Workshop on Data Mining for Medicine and Healthcare, 2015 SIAM International Conference on Data Mining, Vancouver, Canada, April 30 – May 02, 2015 (PDF)
  • Polychronopoulou, A., Obradovic, Z. (2014) “Hospital Pricing Estimation by Gaussian Conditional Random Fields Based Regression on Graphs” Proc. 2014 IEEE International Conference on Bioinformatics and Biomedicine, Belfast, UK, Nov. 2014. (PDF)
  • Stiglic, G., Wang, F., Davey, A., and Obradovic, Z. (2014) “Readmission Classification Using Stacked Regularized Logistic Regression Models,” Proc. AMIA 2014 Annual Symposium, Washington, DC, Nov. 2014. (PDF)
  • Cao, X.H., Stojkovic, I., and Obradovic, Z. (2014) “Predicting Sepsis Severity from Limited Temporal Observations,” Proc. Discovery Science 2014, Bled, Slovenia, Oct. 2014. (PDF)
  • Ghalwash, M., Radosavljevic, V, and Obradovic, Z. (2014) “Utilizing Temporal Patterns for Estimating Uncertainty in Interpretable Early Decision Making,” Proc. 20th ACM SIGKDD Conf. on Knowledge Discovery and Data Mining, New York, NY, Aug. 2014. (PDF)
  • Ghalwash, M., and Obradovic, Z. (2014) “A Data-Driven Model for Optimizing Therapy Duration for Septic Patients,” Proc. 14th SIAM Int’l Conf. Data Mining, 3rd Workshop on Data Mining for Medicine and Healthcare , Philadelphia, April 2014. (PDF)
  • Ghalwash, M., Radosavljevic, V, and Obradovic, Z. (2013)”Extraction of Interpretable Multivariate Patterns for Early Diagnostic,” Proc. 2013 IEEE International Conference on Data Mining (ICDM’13), Dallas, TX, Dec. 2013. (PDF)
  • Zhang, P., Agarwal, P. and Obradovic, Z.,(2013) “Computational Drug Repositioning by Ranking and Integrating Multiple Data Sources,” Proc. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Prague, Czech Republic Sept. 2013 (PDF)
  • Stiglic, G., Davey, A., Obradovic, Z.,(2013) “Temporal Evaluation of Risk Factors for Acute Myocardial Infarction Readmissions,” Proc. 2013 IEEE International Conference on Healthcare Informatics, Workshop on Hospital Readmission Prediction and Clinical Risk Management, Philadelphia 2013 (PDF)
  • Ghalwash, M., Radosavljevic, V., Obradovic, Z.,(2013) “Early Diagnosis and Its Benefits in Sepsis Blood Purification Treatment,” Proc. 2013 IEEE International Conference on Healthcare Informatics, International Workshop on Data Mining for Healthcare, Philadelphia 2013 (PDF)
  • Mathew, G., Obradovic, Z.,(2013) “Improving Computational Efficiency for Personalized Medical Applications in Mobile Cloud Computing Environment,” Proc. 2013 IEEE International Conference on Healthcare Informatics, The First Workshop on Mobile Cloud Computing in Healthcare, Philadelphia, 2013 (PDF)
  • Radosavljevic, V., Ristovski, K., Obradovic, Z. (2013) “Gaussian Conditional Random Fields for Modeling Patient’s Response in Acute Inflammation Treatment,” Int’l Conf. Machine Leaning 2013 workshop on Machine Learning for System Identification, Atlanta, GA. (PDF)
  • Ristovski, K., Radosavljevic, V., Obradovic, Z. (2013) “Prediction of Patient’s Response to An Acute Inflammation Treatment by Mixture of Experts,” Proc. 10th Int’l Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics,, Nice, France, June 2013. (PDF)
  • Mathew, G., Obradovic, (2013) “Auto-reduction of Features for Containing Communication Costs in a Distributed Privacy-Preserving Clinical Decision Support System,” Proc. 3rd IEEE Int’l Conf. Computational Advances in Bio and Medical Sciences, New Orleans, LA, June 2013. (PDF)
  • Lou, Q., Obradovic, Z. (2012) “Analysis of Temporal High-Dimensional Gene Expression Data for Identifying Informative Biomarker Candidates,” Proc. 2012 IEEE International Conference on Data Mining, Brussels, Belgium, Dec. 2012. (PDF)
  • Mathew, G., Obradovic, Z. (2012) “Distributed Privacy Preserving Decision System for Predicting Hospitalization Risk in Hospitals,” Proc. 11th International Conference on Machine Learning and Applications: Machine Learning in Health Informatics Workshop, Boca Raton, Florida, Dec. 2012. (PDF)
  • Slivka, J., Zhang, P., Kovacevic, A., Konjovic, Z., Obradovic, Z. (2012) “Semi-Supervised Learning on Single-view Datasets by Integration of Multiple Co-Trained Classifiers,” Proc. 11th International Conference on Machine Learning and Applications, Boca Raton, Florida, Dec. 2012. (PDF)
  • Ghalwash, M., Ramljak, D., Obradovic, Z. (2012) ” Early Classification of Multivariate Time Series Using a Hybrid HMM/SVM model,” Proc. 2012 IEEE International Conference on Bioinformatics and Biomedicine, Philadelphia, PA, Oct. 2012. (PDF)
  • Radosavljevic, V., Ristovski, K., Obradovic, Z. (2012) ” A Data Mining Approach for Optimization of Acute Inflammation Therapy,” Proc. 2012 IEEE International Conference on Bioinformatics and Biomedicine, Philadelphia, PA, Oct. 2012. (PDF)
  • Zhang, P., Obradovic, Z. (2012) ” Integration of Multiple Annotators by Aggregating Experts and Filtering Novices,” Proc. 2012 IEEE International Conference on Bioinformatics and Biomedicine, Philadelphia, PA, Oct. 2012. (PDF)
  • Lou, Q., Obradovic, Z. (2012) ” Predicting Viral Infection by Selecting Informative Biomarkers From Temporal High-Dimensional Gene Expression Data ” , Proc. 2012 IEEE International Conference on Bioinformatics and Biomedicine, Philadelphia, PA, Oct. 2012. (PDF)
  • Ristovski, K., Radosavljevic, V., Obradovic, Z. (2012) “Kernel-based Characterization of Dynamics in a Heterogeneous Population of Septic Patients Under Therapy,” Int’l Conf. Machine Leaning 2012 workshop on Machine Learning for Clinical Data Analysis, Edinburgh, Scotland. (PDF)
  • Stiglic, G., Pernek, I., Kokol, P., Obradovic, Z. (2012) ” Disease Prediction Based on Prior Knowledge,” ACM SIGKDD Workshop on Health Informatics, in conjunction with the 18th SIGKDD Conf. Knowledge Discovery and Data Mining, Beijing, China, Aug. 2012. (PDF)
  • Lou, Q, Parkman, H.P. , Jacobs, M.R., Krynetskiy, E. and Obradovic, Z. (2012) ” Exploring Genetic Variability in Drug Therapy by Selecting a Minimum Subset of the Most Informative Single Nucleotide Polymorphisms through Approximation of a Markov Blanket in a Kernel-induced Space,” Proc. 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, San Diego, CA, May 2012. (PDF)
  • Vukicevic, M., Delibasic, B., Obradovic, Z., Jovanovic, M., Suknovic, M. (2012) ” A Method for Design of Data-tailored Partitioning Algorithms for Optimizing the Number of Clusters in Microarray Analysis,” Proc. 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, San Diego, CA, May 2012. (PDF)
  • Vukicevic, M., Delibasic, B., Jovanovic, M., Suknovic, M., Obradovic, Z. (2011) ” Internal Evaluation Measures as Proxies for External Indices in Clustering Gene Expression Data,” Proc. 2011 IEEE International Conference on Bioinformatics and Biomedicine, Atlanta, GA, Nov. 2011. (PDF)
  • Midic, U., Obradovic, Z. (2011) ” Intrinsic Disorder in Putative Protein Sequences,” Proc. 2011 IEEE International Conference on Bioinformatics and Biomedicine, Atlanta, GA, Nov. 2011. (PDF)
  • Zhang, P., Obradovic, Z. (2011) “Learning from Inconsistent and Unreliable Annotators by a Gaussian Mixture Model and Bayesian Information Criteria,” Proc.European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Athens, Greece, Sept. 2011 , Part III, Lecture Notes in Artificial Intelligence 6913, Springer-Verlag, Berlin Heidelberg, pp. 553-568. (video lecture) (PDF)
  • Mathew, G., Obradovic, Z. (2011) “Constraint Graphs as Security Filters for Privacy Assurance in Medical Transactions,” Proc. ACM Conference on Bioinformatics, Computational Biology and Biomedicine, Chicago, IL, August, 2011. (PDF)
  • Li, A., Lin, H., Obradovic, Z., Smith, D.J, Megalooikonomou, V. (2011) ” Identifying Pair-wise Gene Functional Similarity by Multiplex Gene Expression Maps and Supervised Learning,” Proc. ACM Conference on Bioinformatics, Computational Biology and Biomedicine, Chicago, IL, August, 2011 (PDF)
  • Zhang, P., Obradovic, Z. (2010) “Unsupervised Integration of Multiple Protein Disorder Predictors,” Proc. IEEE International Conference on Bioinformatics and Biomedicine, Hong Kong. (PDF)
  • Li, A., Xie, H., Obradovic, Z., Smith, D.J, Megalooikonomou, V. (2010) “Identify Gene Functions using Functional Expression Profiles obtained by Voxelation,” ACM International Conference on Bioinformatics and Computational Biology, Niagara Falls, Aug. 2010. (PDF)
  • Li, A., Obradovic, Z., Smith, D.J., Bodenreider, O., Megalooikonomou, V. (2009) “Mining Association Rules among Gene Functions in Clusters of Similar Gene Expression Maps,” Workshop on Data Mining in Functional Genomics at the IEEE International Conference on Bioinformatics and Biomedicine, Washington D.C., November 2009. (PDF)
  • Midic U, Dunker A.K., and Obradovic, Z. (2009) “Protein Sequence Alignment and Intrinsic Disorder: A Substitution Matrix for an Extended Alphabet,” Proc. Workshop on Statistical and Relational Learning and Mining in Bioinformatics at the 15th ACM SIGKDD Conf. Knowledge Discovery and Data Mining, Paris, France, June 2009. (PDF)
  • Jianjiong Gao, Ganesh Kumar Agrawal, Jay J. Thelen, Zoran Obradovic, A. Keith Dunker, and Dong Xu. (2009) ” A New Machine Learning Approach for Protein Phosphorylation Site Prediction in Plants. ” Lecture Notes in Bioinformatics (LNBI 5462): Proceeding of the First International Conference on Bioinformatics and Computational Biology (BICoB), April 2009, New Orleans, USA, pp. 18-29. (PDF)
  • Ren, S. and Obradovic, Z. (2008) “Improvement of Survival Prediction from Gene Expression Profiles by Mining of Prior Knowledge,” Proc. IEEE Int’l Conf. on Bioinformatics and Biomedicine, Philadelphia, Nov. 2008. (PDF)
  • An L., Xie H., Chin M., Obradovic Z., Smith D., Megalooikonomou V., (2008) “Analysis of Multiplex Gene Expression Maps Obtained by Voxelation”Proc. IEEE Int’l Conf. on Bioinformatics and Biomedicine, Philadelphia, Nov. 2008. (PDF)
  • Dunker, K., Oldfield, C.J., Meng, J. Romero, P., Yang, J., Obradovic, Z. and Uversky, V.N. (2007) “Intrinsically Disordered Proteins: An Update,” Proc. IEEE 7th Int’l Symp. Bioinformatics and Bioengineering, Harvard Medical School, Cambridge, MA, 2007, pp. 49-58. (PDF)
  • Xie, H., Obradovic, Z. and Vucetic, S. (2006) “Mining of Microarray, Proteomics, and Clinical Data for Improved Identification of Chronic Fatigue Syndrome,” Proc. Sixth International Conference for the Critical Assessment of Microarray Data Analysis, Durham, NC, 2006. (PDF)
  • Midic, U., Dunker, K. and Obradovic, Z. (2005) “Improving Protein Secondary-Structure Prediction by Predicting Ends of Secondary-Structure Segments,” Proc. 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, San Diego, CA, pp. 490-497. (PDF)
  • Peng, K, Vucetic, S. and Obradovic, Z. (2005) “Correcting Sampling Bias in Structural Genomics through Iterative Selection of Underrepresented Targets,” Proc. 5th SIAM Int’l Conf. on Data Mining, Newport Beach, CA, pp.621-625. (PDF)
  • Xie, H., Vucetic, S., Sun, H., Hedge, P and Obradovic, Z. (2004) “Characterization of Gene Functional Expression Profiles of Plasmodium Falciparum,” Proc. 5th Conf. on Critical Assessment of Microarray Data Analysis, Durham, North Carolina. (PDF)
  • Radivojac, P., Obradovic, Z., Dunker, A.K. and Vucetic, S. (2004) “Feature Selection Filters Based on Permutation Test,” Proc. 15th European Conference on Machine Learning, Pisa, Italy. (PDF)
  • Peng, K., Obradovic, Z. and Vucetic, S., (2004) “Towards Efficient Learning of Neural Network Ensembles from Arbitrarily Large Datasets,” Proc. 16th European Conf. on Artificial Intelligence, Valencia, Spain, pp. 623-627. (PDF)
  • Pokrajac, D., Lazarevic, A., Singleton, T. and Obradovic, Z. (2004) “Localized Neural Network Based Distributional Learning for Knowledge Discovery in Protein Databases,” Proc. Int’l Joint Conf. Neural Networks, Budapest, Hungary. (PDF)
  • Peng, K., Obradovic, Z. and Vucetic, S., (2004) “Exploring Bias in the Protein Data Bank Using Contrast Classifiers,” Proc. 9th Pacific Symposium on Biocomputing, Hawaii, pp. 435-446. (PDF)
  • Kontos, D., Megalooikonomou, V., Pokrajac, D., Lazarevic, A., Obradovic, Z., Ford, J., Makedon, F. and Saykin, A.J. (2004) “Extraction of Discriminative Functional MRI Activation Patterns and an Application to Alzheimer’s Disease,” Proc. 7th Int’l Conf. on Medical Image Computing and Computer-Assisted Intervention, Lecture Notes in Computer Science series, Springer, Saint-Malo, France, Lecture Notes in Computer Science 3217, Vol. 2, pp. 727-735. (PDF)
  • Peng, K., Vucetic, S., Han, B., Xie H. and Obradovic, Z. (2003) “Exploiting Unlabeled Data for Improving Accuracy of Predictive Data Mining,” Proc. 3rd IEEE Int’l Conf. Data Mining, Melbourne, Fl, pp. 267-274. (PDF)
  • Han, B., Vucetic, S. and Obradovic, Z. (2003) “Reranking Medline Citations by Relevance to a Difficult Biological Query,” Proc. IASTED Int’l Conf. Neural Networks and Computational Intelligence, Cancun, Mexico, pp. 38-43. (PDF)
  • Vucetic, S., Pokrajac, D., Xie H. and Obradovic, Z. (2003) “Detection of Underrepresented Biological Sequences Using Class-Conditional Distribution Models,” Proc. Third SIAM Int’l Conf. on Data Mining, San Francisco, CA, pp. 279-283. (PDF)
  • Radivojac, P., Obradovic, Z., Brown, C.J. and Dunker, A.K. (2003) “Prediction of Boundaries Between Intrinsically Ordered and Disordered Protein Regions,” Proc. 8th Pacific Symposium on Biocomputing, Hawaii, pp. 216-227. (PDF) (Pubmed PMID: 12603030)
  • Radivojac, P., Obradovic, Z., Brown, C.J. and Dunker, A.K. (2002) “Improving Sequence Alignments for Intrinsically Disordered Proteins,” Proc. 7th Pacific Symposium on Biocomputing, Hawaii, pp. 589-600. (PDF) (Pubmed PMID: 11928510)
  • Dunker, A.K., Brown. C.J, Lawson, J.D., Iakoucheva-Sebat, L.M., Vucetic, S. and Obradovic, Z. (2002) “The Protein Trinity: Structure/Function Relationships that Include Intrinsic Disorder,” Proc. 2002 Miami Nature Biotechnology Winter Symp., The Scientific Word, 2(S2), 49-50. (PDF)
  • Megalooikonomou, V., Pokrajac, D., Lazarevic, A., and Obradovic, Z. (2002) “Effective Classification of 3D Image Data using Partitioning Methods,” Proc. SPIE Visualization and Data Analysis 2002 Conf., San Jose, CA, pp. 62-73. (PDF)
  • Vucetic, S., Radivojac, P., Dunker, A.K., Brown, C.J. and Obradovic, Z. (2001) “Methods for Improving Protein Disorder Prediction,” Proc. 2001 IEEE/INNS International Joint Conference on Neural Networks, Washington D.C., vol. 4, pp. 2718-2723. ISBN: 0-7803-7044-9 (PDF)
  • Williams, R.M., Obradovic, Z., Mathura, V., Braun, W., Garner, E.C., Young, J., Takayama, S., Brown, C.J. and Dunker A.K. (2001) “The Protein Non-Folding Problem: Amino Acid Determinants of Intrinsic Order and Disorder,” Proc. 6th Pacific Symposium on Biocomputing, Maui, Hawaii, pp. 89-100. (PDF) (Pubmed PMID: 11262981)
  • Lazarevic, A., Pokrajac, D., Megalooikonomou, V. and Obradovic, Z. (2001) “Distinguishing Among 3-D Distributions for Brain Image Data Classification,” Proc. 4th International Conference of Neural Networks and Expert Systems in Medicine and Health Care, Milos Island, Greece, pp. 389-396. (PDF)
  • Pokrajac, D., Lazarevic, A., Megalooikonomou, V. and Obradovic, Z. (2001) “Classification of Brain Image Data using Measures of Distributional Distance,” Human Brain Mapping, Brighton, UK. (PDF)
  • Dunker, A.K., Obradovic, Z., Romero, P., Garner, E.C and Brown, C.J. (2000) “Intrinsic Protein Disorder in Complete Genomes,” In S. Miyano and T. Takagi (editors) Proc. Genome Informatics 11, Tokyo, Japan, pp. 161-171. (PDF) (Pubmed PMID: 11700597)
  • Li, X., Obradovic, Z., Brown, C. J., Garner, E. C., Keith A. K. (2000) “Comparing Predictors of Disordered Protein,” In S. Miyano and T. Takagi (editors) Proc. Genome Informatics 11, Tokyo, Japan, pp. 172-184. (PDF) (Pubmed PMID: 11700598)
  • Li, X., Rani, M., Romero, P., Obradovic, Z. and Dunker, A.K. (1999) “Predicting Protein Disordered Regions for N-, C- and Internal Regions,” In S. Miyano & T. Takagi (editors) Proc. Genome Informatics 10, Tokyo, Japan, pp. 30-40. (PDF) (Pubmed PMID: 11072340)
  • Garner, E., Romero, P., C.J. Brown, Obradovic, Z. and Dunker, A.K. (1999) “Predicting Binding Regions within Disordered Proteins,” In S. Miyano & T. Takagi (editors) Proc. Genome Informatics 10, Tokyo, Japan, pp. 41-50. (PDF) (Pubmed PMID: 11072341)
  • Xie, Q., Arnold, G.E., Romero, P., Obradovic, Z., Garner, E and Dunker, A.K. (1998) “The Sequence Attribute Method for Determining Relationships Between Sequence and Protein Disorder,” In S. Miyano & T. Takagi (editors) Proc. Genome Informatics 1998, Tokyo, Japan, pp. 193-200. (PDF) (Pubmed PMID: 11072335)
  • Garner, E., Cannon, P., Romero, P., Obradovic, Z. and Dunker, A.K. (1998) “Predicting Disordered Regions from Amino Sequence: Common Theme Despite Differing Structural Characterization,” In S. Miyano & T. Takagi (editors) Proc. Genome Informatics 1998,Tokyo, Japan, pp. 201-213. (PDF) (Pubmed PMID: 11072336)
  • Rani, M., Romero, P., Obradovic, Z. and Dunker, A.K. (1998) “Annotation of PDB with respect to Disordered Regions in Proteins,” In S. Miyano & T. Takagi (editors) Proc. Genome Informatics 1998, Tokyo, Japan, pp. 240-241. (PS)
  • Romero, P., Obradovic, Z., Kissinger, C., Villafranca, J.E., Garner, E., Guilliot, S. and Dunker, A.K. (1998) “Thousands of Proteins Likely to Have Long Disordered Regions,” Proc. Pacific Symposium on Biocomputing, Maui, Hawaii, vol. 3, pp. 435-446. (PDF) (Pubmed PMID: 9697202)
  • Dunker, A.K., Garner E., Guilliot S., Romero P., Albrecht K., Hart J., Obradovic Z., Kissinger C., and Villafranca, J.E., (1998) “Protein Disorder and the Evolution of Molecular Recognition: Theory, Predictions and Observations,” Proc. Pacific Symposium on Biocomputing, Maui, Hawaii, vol. 3, pp. 471-482. (PDF) (Pubmed PMID: 9697205)
  • Romero, P., Obradovic, Z and Dunker A.K. (1997) “Sequence Data Analysis for Long Disordered Regions Prediction in the Calcineurin Family,” In S. Miyano & T. Takagi (editors) Proc. Genome Informatics 1997, Tokyo, Japan, pp. 110-125. (PDF) (Pubmed PMID: 11072311)
  • Romero, P., Obradovic, Z., Kissinger, C., Villafranca, J.E. and Dunker, A.K. (1997) “Identifying Disordered Regions in Proteins from Amino Acid Sequence,” Proc. IEEE Int. Conf. on Neural Networks, Houston, TX, vol. 1, pp. 90-95. (PDF)

II. DATA SCIENCE

  • Aljurbua R, Alshehri J, Gupta S, Alharbi A, Obradovic Z (2025) Leveraging multi-modal data for early prediction of severity in forced transmission outages with hierarchical spatiotemporal multiplex networks. PLoS One 20(6): e0326752. https://doi.org/10.1371/journal.pone.0326752 (PDF)
  • Alharbi A, Aljurbua R, Gupta S, Obradovic Z (2025) TriLex: A fusion approach for unsupervised sentiment analysis of short texts. PLoS ONE 20(4): e0317100. https://doi.org/10.1371/journal.pone.0317100 (PDF)
  • Aljurbua, R., Alshehri, J., Alharbi, A., Power, W., Obradovic, Z. (2023) “Social Media Sensors for Weather-Caused Outage Prediction Based on
    Spatio-Temporal Multiplex Network Representation,” IEEE Access, Vol. 11, pp. 125883-125896, 2023, DOI: 10.1109/ACCESS.2023.3327444. (PDF)
  • Alqudah, M., Obradovic, Z. (2023) “Enhancing Weather-Related Outage Prediction and Precursor Discovery through Attention-Based Multi-Level
    Modeling,” IEEE Access, vol. 11, pp. 94840-94851, ISSN 2169-3536, DOI: 10.1109/ACCESS.2023.3303110. (PDF)
  • Polychronopoulou, A., Alshehri, J., Obradovic, Z. (2023) “Quantum-Inspired Measures of Network Distinguishability,” Social Networks Analysis and Mining, 13, 69 (2023), DOI: 10.1007/s13278-023-01069-w. (PDF)
  • Alshehri J., Pavlovski, M., Dragut, E., Obradovic, Z. (2023) “Aligning Comments to News Articles on a Budget,” IEEE Access. Vol. 11, pp. 18900-18909, ISSN 2169-3536, DOI: 10.1109/ACCESS.2023.3247948. (PDF)
  • Alqudah, M., Kezunovic, M., Obradovic, Z. (2023) “Automated Power System Fault Prediction and Precursor Discovery Using Multi-modal Data,” IEEE Access. Vol. 11, pp. 7283-7296, ISSN 2169-3536. DOI: 10.1109/ACCESS.2022.3233219. (PDF)
  • Baembitov, R., Kezunovic, M., Brewster, K., Obradovic, Z. (2023) “Incorporating Wind Modeling into Electric Grid Outage Risk Prediction and Mitigation Solution,” IEEE Access. Vol. 11, pp. 4373 -4380, ISSN 2169-3536. DOI: 10.1109/ACCESS.2023.3234984. (PDF)
  • Basic, M., Arsic, B., Obradovic, Z. (2022) “Another Estimation of Laplacian Spectrum of the Kronecker Product of Graphs,” Information Sciences, Vol. 609, Sept. 2022, pp. 605-625. DOI: 10.1016/j.ins.2022.07.082.
  • Delibasic, B., Radovanovic, S., Jovanovic, M., Obradovic, Z., Suknovic, M., Lojic, R. (2022) “A Study on Ski Groups Size and their Relationship to the Risk of Injury,” Journal of Sports Engineering and Technology, Aug, 2022. DOI: 10.1177/17543371221118193.
  • Zhou, F., Gao, S., Ni, L., Pavlovski, M., Dong, Q., Obradovic, Z., Qian, W. (2022) “Dynamic Self-paced Sampling Ensemble for Highly Imbalanced
    and Class-overlapped Data Classification,” Data Mining and Knowledge Discovery. DOI: 10.1007/s10618-022-00838-z. (PDF)
  • Alqudah, M., Pavlovski, M., Dokic, T., Kezunovic, M., Obradovic, Z. (2021) “Fault Detection Utilizing Convolution Neural Network on Timeseries Synchrophasor Data From Phasor Measurement Units,” IEEE Transactions on Power Systems. https://doi.org/10.1109/TPWRS.2021.3135336.
  • Abdel Hai, A., Dokic, T., Pavlovski, M., Mohamed, T., Saranovic, D., Alqudah, M., Kezunovic, M., Obradovic, Z. (2021) “Transfer Learning for Event Detection from PMU Measurements with Scarce Labels,” IEEE Access, Vol. 9, Sept. 10, 2021, pp. 127420-127432. https://doi.org/10.1109/ACCESS.2021.3111727.
  • Pavlovski, M., Alqudah, M., Dokic, T., Abdel Hai, A., Kezunovic, M., Obradovic, Z. (2021) “Hierarchical Convolutional Neural Networks for Event Classification on PMU Measurements,” IEEE Transactions on Instrumentation & Measurement. Vol. 70, 2021. https://doi.org/10.1109/TIM.2021.3115583.
  • Asadi, N., Olson, I., Obradovic, Z. (2021) “The Backbone Network of Dynamic Functional Connectivity,” Network Neuroscience, Nov. 20, 2021, 5 (4) pp851-873. https://doi.org/10.1101/2021.04.20.440711.
  • Saranovic, D., Pavlovski, M., Power, W., Stojkovic, I., Obradovic, Z. (2021) “Interception of Automated Adversarial Drone Swarms in Partially Observed Environments,” Integrated Computer-Aided Engineering., 28 (4), pp. 1-14. https://doi.org/10.3233/ICA-210653.
  • Kezunovic, M., Pinson, P., Obradovic, Z., Grijalva, S., Hong, T., Bessa, R. (2020) “Big Data Analytics for Future Electricity Grids,” Electric Power Systems Research, SI: Proc. 21st Power Systems Computation Conference (PSCC 2020), Vol. 189, Dec. 2020, 106788. https://doi.org/10.1016/j.epsr.2020.106788
  • He, L., Han, C., Mukherjee, A., Obradovic, Z., Dragut, E. (2019) “On the Dynamics of User Engagement in News Comment Media,” WIREs Data Mining and Knowledge Discovery. (PDF)
  • Singh, L., Deshpande, A., Zhou, W., Banerjee, A., Bowers, A., Friedler, S., Jagodish, H.V., Karypis, G., Obradovic, Z., Vullikanti, A., Zuo, W. (2019)” NSF BIGDATA PI Meeting-Domain-Specific Research Directions and Data Sets,” ACM SIGMOD Record Vol. 47, Issue 3, pp. 32-35. (PDF)
  • Jelisavcic, V., Stojkovic, I., Milutinovic, V., Obradovic, Z. (2018) “Learning of Scale-Free Networks based on Cholesky Factorization,” International Journal of Intelligent Systems, Vol 33, Issue 6, June 2018; pp. 1322-1339. (PDF)
  • Negri T.T., Zhou, F., Obradovic, Z., Gonzaga, A. “Extended Color Local Mapped Pattern for Color Texture Classification under Varying Illumination,” Journal of Electronic Imaging, 2018. (PDF)
  • Rege, A., Obradovic, Z., Asadi, N., Parker, E., Pandit, R. Masceri, N., Singer, B. (2018) “Predicting Adversarial Cyber Intrusion Stages Using Autoregressive Neural Networks,” IEEE Intelligent Systems , PP(99):1-1, January 2018. (PDF)
  • Delibasic, B., Radovanovic, S., Jovanovic, M., Obradovic, Z., Suknovic, M. “Ski Injury Predictive Analytics from Massive Ski Lift Transportation Data,” Journal of Sports Engineering and Technology, Sept. 4, 2017 (PDF)
  • Vujicic, T., Glass, J., Zhou, F., Obradovic, Z. “Gaussian Conditional Random Fields Extended for Directed Graphs,” Machine Learning. (PDF)
  • Glass, J., Obradovic, Z. (2017) “Structured Regression on Multi-Scale Networks,” IEEE Intelligent Systems, Vol. 32, Issue 2, Mar-April, 2017, pp. 23-30. (PDF)
  • Milutinovic, V., Furht, B., Obradovic, Z., Korolija, N. (2016) “Advances in High Performance Computing and Related Issues,” Mathematical Problems in Engineering, vol. 2016, Article ID 2632306.
  • Delibasic, B., Markovic, P., Delias, P. Obradovic, Z. (2016) “Mining Skier Transportation Patterns from Ski Resort Lift Usage Data,” IEEE Trans. Human-Machine Systems. pp 1 – 6 (PDF)
  • Stiglic, G., Brzan, P.P., Fijacko, N., Fei, W., Delibasic, B., Kalousis, A., Obradovic, Z. “Comprehensible Predictive Modeling Using Regularized Logistic Regression and Comorbidity Based Features,” PLOS ONE,Dec 8, 2015, DOI: 10.1371/journal.pone.0144439 (PDF)
  • Delibasic. B.,, Obradovic, Z., (2015) “Identifying high-number-cluster structures in RFID ski lift gates entrance data,” Annals of Data Science, 2(2), pp. 145-155 (PDF)
  • Das, D., Ganguly, A., Obradovic, Z., (2015) “A Bayesian sparse generalized linear model with an application to multi-scale covariate discovery for observed rainfall extremes over United States,” IEEE Transactions on Geosciences and Remote Sensing, vol. 53, issue 12, pp. 6689 – 6702, Dec. 2015 (PDF)
  • Mathew, G., Obradovic, Z. (2015) “A Distributed Decision Support Algorithm that Preserves Personal Privacy,” Journal of Intelligent Information Systems., Feb 2015, vol. 44, no. 1, pp. 107-132. (PDF)
  • Das, D., Dy, J., Ross, J, Obradovic, Z., Ganguly, A. (2014) “Non-parametric Bayesian mixture of sparse regressions with application towards feature selection for statistical downscaling,” Nonlinear Processes in Geophysics, 21, 1145-1157, 2014. (PDF)
  • Djuric, N., Radosavljevic, V., Obradovic, Z., Vucetic, S. (2014) “Gaussian Conditional Random Fields for Aggregation of Operational Aerosol Retrievals,” IEEE Geoscience and Remote Sensing Letters, Volume:12 , Issue: 4, 2014 (PDF)
  • Uversky, A., Ramljak, D., Radosavljevic, V., Ristovski, K., Obradovic, Z. (2014) “Panning for Gold – Using Variograms to Select Useful Connections in a Temporal Multigraph Setting,” Social Network Analysis and Mining, 4:211, July 2014 (PDF)
  • Ouzienko, V., Obradovic, Z. (2013) “Imputation of Missing Links and Attributes in Longitudinal Social Surveys,” Machine Learning Journal Oct. 2013. (PDF)
  • Boyd, S.L., Hoffman, D.A., Obradovic, Z., Ristovski, K. (2013) ” Building a Taxonomy of Litigation: Clusters of Causes of Action in Federal Complaints,” Journal of Empirical Legal Studies. vol. 10, no. 2, June 2013, pp. 253-287. (PDF)
  • Suknovic, M., Delibasic, B., Jovanovic, M., Vukicevic, M., Becajski-Vujaklija, D., Obradovic, Z. (2012) ” Reusable components in decision trees induction algorithms, ” Computational Statistics, vol. 27, no. 1, pp. 127-148. (PDF)
  • Ristovski, K., Vucetic, S. and Obradovic, Z. (2012) “Uncertainty Analysis of Neural Network-Based Aerosol Retrieval,” IEEE Transactions on Geoscience and Remote Sensing, the Space Technology Special Issue, vol. 50, no. 2, pp. 409-414. (PDF)
  • Mennis, J., Harris, P., Obradovic, Z., Izenman, A., Grunwald, H., and Lockwood, B., (2011) “The effect of neighborhood characteristics and spatial spillover on urban juvenile delinquency and recidivism,” The Professional Geographer, vol. 63, no. 2, pp. 161-173 (PDF)
  • Ouzienko, V., Guo, Y., Obradovic, Z. (2011) ” A Decoupled Exponential Random Graph Model for Prediction of Structure and Attributes in Temporal Social Networks,” Statistical Analysis and Data Mining Journal vol. 4, no. 5, pp. 470-486. (PDF)
  • Izenman, A., Harris, P., Mennis, J., Jupin, J., Obradovic, Z. (2011) ” Local Spatial Biclustering and Prediction of Urban Juvenile Delinquency and Recidivism,” Statistical Analysis and Data Mining Journal, vol. 4, no. 3, pp. 259-275. (PDF)
  • Harris, P., Mennis, J., Obradovic, Z., Izenman, A., Grunwald, H. (2011) “The Coaction of Neighborhood and Individual Effects on Juvenile Recidivism,” Cityscape, vol. 13, no. 3., 2011, pp. 33-55. (PDF)
  • Delibasic, B., Jovanovic, M., Vukicevic, M., Suknovic, M., Obradovic, Z. (2011) ” Component-based decision trees for classification,” Intelligent Data Analysis, vol. 15, no. 5, pp. 671-69 3 (PDF)
  • Radosavljevic, V., Vucetic, S., Obradovic, Z. (2010) “A Data Mining Technique for Aerosol Retrieval Across Multiple Accuracy Measures,” IEEE Geoscience and Remote Sensing Letters, vol. 7, no.2, pp. 411-415. (PDF)
  • Vucetic, S., Han, B., Mi, W., Li. Z., Obradovic, Z. (2008) “A Data Mining Approach for the Validation of Aerosol Retrievals,” IEEE Geoscience and Remote Sensing Letters, vol. 5, no. 1, pp. 113-117. (PDF)
  • Han, B., Vucetic, S., Braverman, A. and Obradovic, Z. (2006) “A Statistical Complement to Deterministic Algorithms for Retrieving Aerosol Optical Thickness from Radiance Data,” Engineering Applications of Artificial Intelligence, vol. 19, no. 7, pp. 787-795. (PDF)
  • Jones, P.R., Schwartz, D., Schwartz, I.M., Obradovic, Z., Jupin, J., (2006) “Risk Classification and Juvenile Dispositions: What is the State of the Art?” Temple Law Review, vol. 79, no. 2. (PDF)
  • Vucetic, S. and Obradovic, Z. (2005) “Collaborative Filtering Using a Regression-Based Approach,” Knowledge and Information Systems, Vol. 7, No. 1, pp. 1-22. (PDF)
  • Pokrajac, D., Milutinovich, J. and Obradovic, Z. (2005) “Toward Neural Network-Based Profit Optimization,” Facta Universitatis, Series Economics and Organization, vol. 2, no. 3, pp. 261-275. (PS)
  • Pokrajac, D., Hoskinson, R.L. and Obradovic, Z. (2003) “Modeling Spatial-Temporal Data with a Short Observation History,” Knowledge and Information Systems. Vol. 5, pp. 368-386. (PDF)
  • Pokrajac, D., Fiez, T. and Obradovic, Z. (2002) “A Data Generator for Evaluating Spatial Issues in Precision Agriculture,” Precision Agriculture. Vol 3, no.3, pp. 259-282. (PS)
  • Lazarevic, A. and Obradovic, Z. (2002) “Knowledge Discovery in Multiple Spatial Databases,” Neural Computing and Applications, vol 10. no. 4, pp. 339-350. (PDF)
  • Lazarevic, A. and Obradovic, Z. (2002) “Boosting Algorithms for Parallel and Distributed Learning,” Distributed and Parallel Databases: An International Journal, Special Issue on Parallel and Distributed Data Mining, vol. 2, pp. 203-229. (PDF)
  • Lazarevic, A. and Obradovic, Z. (2001) “Adaptive Boosting Techniques in Heterogeneous and Spatial Databases,” Intelligent Data Analysis, Vol. 5, pp.1-24. (PDF)
  • Pokrajac, D., Lazarevic, A. and Obradovic, Z. (2001) “Exploration-Exploitation Trade-Off in Machine Learning,” Facta Universitatis, Ser. Elec. and Energ., vol. 14, no. 1, pp. 67-90. 
  • Obradovic, Z. and Srikumar, R. (2001) “Parallelizing Design of Application Tailored Neural Networks,” Facta Universitatis, Ser. Mathematics and Informatics, vol. 16, pp. 97-108. (PDF)
  • Obradovic, Z. and Srikumar, R. (2000) “Constructive Neural Networks Design Using Genetic Optimization,” Facta Universitatis, Ser. Mathematics and Informatics, vol. 15, pp. 133-146 (PDF)
  • Vucetic, S., Fiez, T. and Obradovic, Z. (2000) “Analyzing the Influence of Data Aggregation and Sampling Density on Spatial Estimation,” Water Resources Research, Vol. 36 , No. 12 , pp. 3721-3731. (PDF)
  • Vucetic, S., Obradovic, Z. and Tomsovic, K. (2001) “Price-Load Relationships in California’s Electricity Market,” IEEE Trans. on Power Systems, Vol. 16, No. 2, pp. 280-286. (PDF)
  • Obradovic, Z. and Mehr, I., (1996) “Parallel Neural Network Learning Through Repetitive Bounded Depth Trajectory Branching,” Neural, Parallel and Scientific Computations, vol. 4, no. 4, pp. 475-491.
  • Obradovic, Z. and Chenoweth, T., (1996) “Selection of Learning Algorithms for Trading Systems Based on Biased Estimators.” Heuristics, The Journal of Intelligent Technologies, vol. 9, no. 1, pp. 9-21. (PDF)
  • Chenoweth, T., Obradovic, Z. and Lee, S., (1996) “Embedding Technical Analysis into Neural Network Based Trading Systems,” Applied Artificial Intelligence, Taylor & Francis, Washington D.C., vol. 10, no. 6., pp. 523-541. (PDF)
  • Drossu, R. and Obradovic, Z., (1996) “Regime Signaling Techniques for Non-stationary Time Series Forecasting.” Journal of Computational Intelligence in Finance, Finance & Technology Publishing, vol. 4, no. 5, pp. 7-15.
  • Drossu, R. and Obradovic, Z., (1996) “Rapid Design of Neural Networks for Time Series Prediction,” IEEE Computational Science and Engineering, vol. 3, no. 2, pp. 78-89. (PDF)
  • Chenoweth, T. and Obradovic, Z., (1996) “A Multi-Component Nonlinear Prediction System for the S&P 500 Index,” Neurocomputing, vol. 10, no. 3, pp. 275-290. (PDF)
  • Drossu, R., Obradovic, Z. and Fletcher, J. (1996) “A Flexible Graphical User Interface for Embedding Heterogeneous Neural Network Simulators,” IEEE Trans. on Education, special issue on Applications of Information Technology, volume 39, no. 3, pp. 367-374. (PDF)
  • Obradovic, Z., (1996) “Computing with Nonmonotone Multivalued Neurons,” Multiple Valued Logic, vol. 1, no. 4, pp. 271-294. (PDF)
  • Fletcher, J. and Obradovic, Z., (1995) “A Discrete Approach to Constructive Neural Network Learning,” Neural, Parallel and Scientific Computations, vol. 3, no. 3, pp. 307-320. (PDF)
  • Chenoweth, T. and Obradovic, Z., (1995) “An Explicit Feature Selection Strategy for Predictive Models of the S&P 500 Index,” Journal of Computational Intelligence in Finance, Finance & Technology Publishing, vol. 3, no. 6, pp. 14-21. (PDF)
  • Perez, L.G., Flechsig, A.J., Meador, J.L. and Obradovic, Z., (1994) “Training an Artificial Neural Network to Discriminate Between Magnetizing Inrush and Internal Faults,” IEEE Trans. on Power Delivery, vol. 9, no. 1., pp. 434-441. (PDF)
  • Obradovic, Z. and Parberry, I. (1994) “Learning with Discrete Multi-Valued Neurons,” Journal of Computer and System Sciences, vol. 49, no. 2, pp. 375-390. (PDF)
  • Fletcher, J. and Obradovic, Z. (1993) “Combining Prior Symbolic Knowledge and Constructive Neural Networks,” Connection Science: Journal of Neural Computing, Artificial Intelligence and Cognitive Research, vol. 5, nos. 3 & 4, pp. 365-375. (PDF)
  • Obradovic, Z. and Parberry, I. (1992) “Computing with Discrete Multi-Valued Neurons,” Journal of Computer and System Sciences, vol. 45, no. 3, pp. 471-492.
  • Obradovic, Z. and Yan, P., (1990) “Small Depth Polynomial Size Neural Networks,” Neural Computation, vol. 2, no. 4, pp. 402-404.
  • Obradovic, Z., Potkonjak, M. and Obradovic, M. (1987) “Design of Efficient Algorithms for VLSI Systolic Arrays,” Informatica, vol. 21, pp. 153-159.
  • Stanojevic, M., Alshehri, J., Obradovic, Z. (2021) “High Performance Computing for Understanding Natural Language,” chapter 10 in Handbook of Research on Methodologies and Applications of Supercomputing, Editors: Milutinovic, V. and Kotlar, M., IGI Global. ISBN13: 9781799871569. (PDF)
  • Kezunovic, M., Obradovic, Z., Dokic, T., Zhang, B., Stojanovic, J., Dehghanian, P., Chen, P.C., (2017) “Predicting Spatiotemporal Impact of Weather on Power Systems Using Big Data Science, ” in Data Science and Big Data: An Environment of Computational Intelligence, Editors: Pedrycz, W. and Chen, S.M., Springer. (PDF)
  • Harris, P., Mennis, J., Obradovic, Z., Izenman, A., Grunwald, H., Lockwood, B., Jupin J., and Chisholm, L.,(2009) “Investigating the Simultaneous Effects of Individual, Neighborhood, and Program Effects on Juvenile Recidivism Using GIS and Spatial Statistics.” Research report, the U.S. Department of Justice, National Institute of Justice.
  • Han, B., Obradovic, Z. and Vucetic, S. (2008) “Using Statistical Methods to Improve Efficiency and Accuracy of Aerosol Retrievals,” Chapter 7 in Discrete and Computational Mathematics, Nova Science Publishers, Editors: F. Liu, Gaston M. N’Guerekata, D. Pokrajac, X. Shi, J. Sun, X. Xia, pp. 93-106.
  • Schwartz, I.M., Jones, P.R., Schwartz, D., Obradovic, Z. (2008) “Improving Social Work Through the Use of Technology and Advanced Research Methods,” chapter 13 in Child Welfare Research: Advances for Practice and Policy (eds. Lindsey, D. and Shlonsky, A.) Oxford, pp. 214-230.
  • Obradovic, Z. and Vucetic, S. (2004) “Challenges in Scientific Data Mining: Heterogeneous, Biased, and Large Sample,” a peer reviewed book chapter at The Next Generation Data Mining (editors: H. Kargupta, A. Joshi, K. Sivakumar, Y. Yesha), AAAI/MIT Press, pp. 381-401. (PS)
  • Drossu, R. and Obradovic, Z. (2000) “Data Mining Techniques for Designing Efficient Neural Network Time Series Predictors,” peer reviewed book chapter no. 10 in Cloete, I. and Zurada, J. Knowledge-Based Neurocomputing, MIT Press, ISBN 0-262-03274-0, pp. 325-368. (PDF)
  • Romero, P., Obradovic, Z. and Fletcher J. (2000) “Integration of Heterogeneous Sources of Partial Domain Knowledge,” peer reviewed book chapter no. 7 in Cloete, I. and Zurada, J. Knowledge-Based Neurocomputing, MIT Press, pp. 217-250. (PDF)
  • Drossu, R. and Obradovic, Z. (1997) “An Analysis of the INFFC Cotton Futures Time Series: Lower Bounds and Testbed Design Recommendations,” in Caldwell, B. R., (editor) Nonlinear Financial Forecasting: The First Nonlinear Financial Forecasting Competition, Finance & Technology Publishing, pp. 241-261. (PDF)
  • Drossu, R. and Obradovic, Z. (1996) “Prediction Horizon Effects on Stochastic Modeling Hints for Neural Networks,” In P.E. Keller, S.Hashem, L.J. Kangas, and R. T. Kouzes (editors) Applications of Neural Networks in Environment, Energy, and Health
  • Drossu, R., Lakshman, T.V., Obradovic, Z. and Raghavendra C.S., (1995) “Single and Multiple Frame Video Traffic Prediction Using Neural Network Models,” In Raghavan S.V. and Jain B.N. (editors) Computer Networks, Architecture and Applications, Chapman & Hall, 1995, chapter 9, pp. 146-158. (PDF)
  • Shrivastava, A., Gupta, S., Obradovic, Z. (in press) “Real-time path planning for UAVs in windy environments without computational fluid dynamics,” Proc. 40th Annual AAAI Conference on Artificial Intelligence, Singapore, January 2026.
  • Andjelkovic, J., Gupta, S., Stojkovic, I., Obradovic. Z., (in press) “A Topic Modeling Graph Approach for Prediction of Social Media Users’ Emotional Response to Mass Shootings,” Proc. 14th Int’l Conf, on Complex Networks and their Applications, Binghamton, NY, Dec. 2025.
  • Saranovic, D., Obradovic, Z., Baembitov, R., Kezunovic, M. (in press) “Granular Spatio-Temporal Grid-based Machine Learning for Forced Outage Prediction in Electrical Networks,” Proc. 59th IEEE Hawaii International Conference on System Science (HICSS), 2026.
  • Andjelkovic, J., Gupta, S., Alharbi, A., Obradovic, Z. (in press) “Decoding Social Media Sentiment Before Events Occur Through Sentence-Level Embeddings and Graph Learning,” Proc. 24th IEEE Int’l Conf. on Machine Learning and Applications, Boca Raton, Florida, Dec. 2025.
  • Gupta, S., Andjelkovic, J., Shrivastava, A., Muto, A., Obradovic, Z. (in press) “Physics-Informed Neural Networks for Soil Moisture Modeling and Landslide Assessment,” Proc. 24th IEEE Int’l Conf. on Machine Learning and Applications, Boca Raton, Florida, Dec. 2025
  • Baembitov, R., Karmacharya, A.M., Kezunovic, M., Saranovic, D., Obradovic, Z. (2025) “Interpretability of the ML-Based State of Risk Predictions for the Electric Grid Forced Outages,” Proc. IFAC Workshop on Smart Energy Systems for Efficient and Sustainable Smart Grids and Smart Cities, Bari, Italy, June, 2025. Elsevier open access journal IFAC-PapersOnLine, 59(9), 181-186. (PDF)
  • Aljurbua, R., Baembitov R., Petridis, C., Kezunovic, M., Obradovic, Z. (2025) “Spatiotemporal Multiplex Network Model for Predicting Forced Outage Severity in Distribution Grids,” Proc. 26th Engineering Applications of Neural Networks (EANN), Limassol, Cyprus, June 2025. Springer Nature Switzerland AG 2025. L. Iliadis et al. (Eds.): EANN 2025, CCIS 2582, pp. 165–178, 2025. https://doi.org/10.1007/978-3-031-96199-1_13 (PDF)
  • Gupta, S., Otudi, H., Abdel Hai, A., Aljurbua, R., Andjelkovic, J., Alharbi, A., Obradovic, Z. (2025) “Harnessing Machine Learning for Rain-Induced Landslide Detection and Analysis,” Proc. 26th Engineering Applications of Neural Networks (EANN), Limassol, Cyprus, June 2025. Springer Nature Switzerland AG 2025, L. Iliadis et al. (Eds.): EANN 2025, CCIS 2582, pp. 94–108, 2025. https://doi.org/10.1007/978-3-031-96199-1_8 (PDF)
  • Otudi, H., Gupta, S., Abdel Hai, A., Aljurbua, R., Alharbi, A., Obradovic, Z. (2025) “Utilizing Multiple Data Sources to Improve Prediction of Severe Weather Events through Spatio-Temporal Analysis,” Proc. 26th Engineering Applications of Neural Networks (EANN), Limassol, Cyprus, June 2025. L. Iliadis et al. (Eds.): EANN 2025, CCIS 2581, pp. 267–280, 2025. https://doi.org/10.1007/978-3-031-96196-0_20 (PDF)
  • Lambe, Z., Alshehri, S., Obradovic, Z. (2025) “Disaster Data Driven Forecasting for Preemptive Crisis Response,” Proc. 21st Int’l Conf. on Artificial Intelligence Applications and Innovations (AIAI), Limassol, Cyprus, June 2025. Springer Nature Switzerland AG 2025, I. Maglogiannis et al. (Eds.): AIAI 2025, IFIP AICT 756, pp. 126–140, 2025. https://doi.org/10.1007/978-3-031-96228-8_10
  • Power, W., Gupta, S., Obradovic, Z., (2025) “Summarizing Online Discussions with Prototype Relation Networks Using Large Language Models,” Proc.21st Int’l Conf. on Artificial Intelligence Applications and Innovations (AIAI), Limassol, Cyprus, June 2025, Springer Nature Switzerland AG 2025, I. Maglogiannis et al. (Eds.): AIAI 2025, IFIP AICT 758, pp. 389–402, 2025. https://doi.org/10.1007/978-3-031-96235-6_28 (PDF)
  • Shrivastava, A., Petridis, C., Vacic, M., Obradovic, Z. (2025) “Autonomous Navigation in Swarm of UAVs Using Spatio Temporal Data and Constrained-Reinforcement Learning,” Proc. 26th Engineering Applications of Neural Networks (EANN), Limassol, Cyprus, June 2025. Springer Nature Switzerland AG 2025, L. Iliadis et al. (Eds.): EANN 2025, CCIS 2582, pp. 54–67, 2025. https://doi.org/10.1007/978-3-031-96199-1_5
  • Petridis, C., Shrivastava, A., Vacic, M., Obradovic, Z. (2025) “PixelPath: Predicting UAV Trajectories in GPS-Restricted Environments Using Image Feature Extraction and Machine Learning,” Proc. 21st Int’l Conf. on Artificial Intelligence Applications and Innovations (AIAI), Limassol, Cyprus, June 2025. Springer Nature Switzerland AG 2025, I. Maglogiannis et al. (Eds.): AIAI 2025, IFIP AICT 757, pp. 270–284, 2026. https://doi.org/10.1007/978-3-031-96231-8_20
  • Aljurbua, R., Alshehri, J., Gupta, S., Alharbi, A., Obradovic, Z. (2025) “Early Prediction of Power Outage Duration through Hierarchical Spatiotemporal Multiplex Networks, In: Cherifi, H., Donduran, M., Rocha, L.M., Cherifi, C., Varol, O. (eds) Complex Networks & Their Applications XIII. COMPLEX NETWORKS 2024. Studies in Computational Intelligence, vol 1189, pp. 320-334, 2025, Springer Nature Switzerland, Cham. https://doi.org/10.1007/978-3-031-82435-7_26 (PDF)
  • Sutan, J.K., Alshehri, J., Aljurbua, R., Obradovic, Z. (2025) “ClusterLDA: Clustering-Based Topic Extraction for Online News,” Proc. WiDS PSU 2025: Eighth IEEE International Conference of Women in Data Science, Prince Sultan University, pp. 145-150, IEEE Explore, DOI: 10.1109/WiDS-PSU64963.2025.00039.
  • Baembitov, R., Karmacharya, A.M., Kezunovic, M., Saranovic, D., Obradovic, Z. (2025), “Effect of Lightning Features on Predicting Outages Related to Thunderstorms in Distribution Grids,” Proc. 58th IEEE Hawaii International Conference on System Science (HICSS), 2978-2987, https://hdl.handle.net/10125/109202.
  •  Gupta, S., Abdel Hai, A., Alharbi, A., Otudi, H., Obradovic, Z. (2024) “Exploring Topic-Related User Experiences through Social Graph,” Proc. 32nd European Conference on Information Systems (ECIS), 14. Paphos, Cyprus, June 2024. https://aisel.aisnet.org/ecis2024/track09_coghbis/track09_coghbis/14 (PDF)
  • Otudi, H., Gupta, S., Obradovic, Z. (2024) “Leveraging Diverse Data Sources for Enhanced Prediction of Severe Weather-Related Disruptions Across Different Time Horizons,” in: Iliadis, L., Maglogiannis, I., Papaleonidas, A., Pimenidis, E., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2024. Communications in Computer and Information Science, vol 2141. Springer, Cham. https://doi.org/10.1007/978-3-031-62495-7_17, pp. 220-234.
  • Power, W., Obradovic, Z. (2024) “Generating Profiles of News Commentators with Language Models,” Proc. 20th International Conference on Artificial Intelligence Applications and Innovations, Corfu, Greece, June 2024. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Avlonitis, M., Papaleonidas, A. (eds) Artificial Intelligence Applications and Innovations. AIAI 2024. IFIP Advances in Information and Communication Technology, vol 712. Springer, Cham. https://doi.org/10.1007/978-3-031-63215-0_4, pp. 47-59.
  • Gupta, S., Alshehri, J., Abdel Hai, A., Otudi, H., Obradovic, Z. (2024) “From Tweets to Reddit: Leveraging Semi-Supervised Domain Adaptation for Improving Data Filtering,” In: Maglogiannis, I., Iliadis, L., Macintyre, J., Avlonitis, M., Papaleonidas, A. (eds) Artificial Intelligence Applications and Innovations. AIAI 2024. IFIP Advances in Information and Communication Technology, vol 714. Springer, Cham. https://doi.org/10.1007/978-3-031-63223-5_22, pp. 290-304. (PDF)
  • Alharbi, A., Abdel Hai, A., Aljurbua, R., Obradovic, Z. (2024) “AI-Driven Sentiment Trend Analysis: Enhancing Topic Modeling Interpretation with ChatGPT,” In:
    Maglogiannis, I., Iliadis, L., Macintyre, J., Avlonitis, M., Papaleonidas, A. (eds) Artificial Intelligence Applications and Innovations. AIAI 2024. IFIP Advances in Information and Communication Technology, vol 712. Springer, Cham. https://doi.org/10.1007/978-3-031-63215-0_1, pp. 3-17. (PDF)
  • Otudi, H., Gupta, S., Albarakati, N., Obradovic, Z. (2024) “Classifying Severe Weather Events by Utilizing Social Sensor Data and Social Network Analysis,” Proc. 2023 IEEE/ACM Int’l Conf. on Advances in Social Networks Analysis and Mining, Kusadasi, Turkey, Nov. 2023. pp. 64-71.  ACM paper number 13713.8; Published 15 March 2024,  https://doi.org/10.1145/3625007.3627298
  • Power, W., Obradovic, Z. (2024) “Understanding Online Attitudes with Pre-Trained Language Models,” 14th International Workshop on Mining and Analyzing Social Networks for Decision Support (MSNDS), in Proc. 2023 IEEE/ACM Int’l Conf. on Advances in Social Networks Analysis and Mining. Kusadasi, Turkey, Nov. 2023. pp. 745-752. ACM Paper number 13713.12; Published 15 March 2024,  https://doi.org/10.1145/3625007.3627302
  • Baembitov, R., Kezunovic, M., Saranovic, D., Obradovic, Z. (2024) “Sensitivity Analysis of Machine Learning Algorithms for Outage Risk Prediction,” Proc. 57th IEEE Hawaii International Conference on System Science (HICSS), January 2024, pp. 3150-3159, ISBN 978-0-9981331-7-1 https://scholarspace.manoa.hawaii.edu/server/api/core/bitstreams/9fc2488c-b52c-4826-97d4-c8551bc58e59/content
  • Otudi, H., Mohamed, T., Hu, Y., Kezunovic, Hu, Y., M., Obradovic, Z. (2023) “Training Machine Learning Models with Simulated Data for Improved Line Fault Events Classification from 3-Phase PMU Field Recordings,” Proc. 56th IEEE Hawaii International Conference on System Science (HICSS), Maui, January 2023, pp. 2641-2650,  https://hdl.handle.net/10125/102957. (PDF)
  • Abdel Hai, A., Mohamed, T., Pavlovski, M., Kezunovic, M., Obradovic, Z. (2022) “Transfer Learning on Phasor Measurement Data from a Power System to Detect Events in Another System,” Proc. 21st International Conference on Machine Learning and Applications, Special Session on Machine Learning in Energy, Bahamas, December 2022, pp. 1567-1572. (PDF)
  • Alshehri, J., Stanojevic, M., Dragut, E., Obradovic, Z. (2022) “On Label Quality in Class Imbalance Setting – A Case Study,” Proc. 21st International Conference on Machine Learning and Applications, Special Session on Natural Language Processing, Bahamas, December 2022, pp. 1666-1671. (PDF)
  • Markovic, T., Devedzic, V., Zhou, F., Obradovic, Z. (2022) “Software package for regression algorithms based on Gaussian Conditional Random Fields,” Proc. 21st International Conference on Machine Learning and Applications, Special Session on Machine Learning for Graphs, Bahamas, December 2022, pp. 1121-1128. (PDF)
  • Shen, C., Han, C., He, L., Mukherjee, A., Obradovic, Z., Dragut, E. (2022) “Session-based News Recommendation from Temporal User Commenting Dynamics,” Proc. 2022 IEEE/ACM Int’l Conf. on Advances in Social Networks Analysis and Mining, Istanbul, Turkey, Nov. 2022. https://par.nsf.gov/servlets/purl/10378398.
  • Mohamed, T., Y., Kezunovic, M., Obradovic, Z., Hu, Y., Cheng, Z. (2022) “Application of Machine Learning to Oscillation Detection using PMU Data based on Prony Analysis,” Proc. 2022 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Novi Sad, Serbia, October 2022. DOI: 10.1109/ISGT-Europe54678.2022.9960589.
  • Alshehri, J., Stanojevic, M., Khan, P., Rapp, P., Dragut, E., Obradovic, Z. (2022) “MultiLayerET: A Unified Representation of Entities and Topics Using Multilayer Graphs,” Proc. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Grenoble, France, September 2022. (PDF)
  • Kezunovic, M., Obradovic, Z., Hu, Y. (2022) “Automated System-wide Event Detection and Classification Using Machine Learning on Synchrophasor Data,” Proc. CIGRE Technical Exhibition 2022, C2 Power System Operation and Control, PS1 – System Control Room Preparedness: Today and in the Future, Paris, France, Aug. 2022. Reference: C2-10224_2022. (PDF)
  • Kezunovic, M., Obradovic, Z., Hu, Y. (2022) “Use of Machine Learning on PMU Data for Transmission System Fault Analysis,” Proc. CIGRE Technical Exhibition 2022, B5 Protection and Automation, PS2 – Applications of Emerging Technology for Protection, Automation and Control, Paris, France, Aug. 2022. (PDF)
  • Aleksic, N., Pajic, E., Obradovic, P., Power, W., Misic, M., Obradovic, Z. (2022) “Modelling Subreddit Interactions by Activity Overlap,” Proc. Serbian Int’l Conf. Applied Artificial Intelligence, Kragujevac, May 2022. The Best Student Paper Award. (PDF)
  • Roychoudhury, S. Zhou, F., Obradovic, Z. (2022) “Leveraging Dependencies among Learned Temporal Subsequences,” Proc. 22nd SIAM Int’l Conf. Data Mining (SDM 2022), Alexandria, VA, May 2022. DOI: 10.1137/1.9781611977172.57.
  • Dokic, T., Baembitov, R., Abdel Hai, A., Cheng, Z., Hu, Y., Kezunovic, M., Obradovic, Z. (2022) “Machine Learning Using a Simple Feature for Detecting Multiple Types of Events From PMU Data,” Proc. IEEE Int’l Conf. on Smart Grid Synchronized Measurement and Analytics – SGSMA 2022, Split, Croatia, May 2022. DOI: 10.1109/SGSMA51733.2022.9806000.
  • Cheng, Z., Hu, Y., Obradovic, Z., Kezunovic, M. (2022) “Using Synchrophasor Status Word as Data Quality Indicator: What to Expect in the Field?,” Proc. IEEE Int’l Conf. on Smart Grid Synchronized Measurements and Analytics – SGSMA, Split, Croatia, May 2022. DOI: 10.1109/SGSMA51733.2022.9806010.
  • Li X., Pavlovski M., Zhou F., Dong Q., Qian W., Obradovic, Z., (2022) “Supervised Multi-view Latent Space Learning by Jointly Preserving Similarities across Views and Samples,” Proc. 27th Int’l Conf. on Database Systems for Advanced Application (DASFAA), Database Systems for Advanced Applications, Lecture Notes in Computer Science, Vol. 13246, pp. 689-696, April 2022. (PDF)
  • Otudi, H., Dokic, T. Mohamed, T., Hu, Y., Kezunovic, M., Obradovic, Z. (2022) “Line Faults Classification Using Machine Learning On Three phases Voltages Extracted from Large Dataset of PMU Measurements,” Proc. 55th IEEE Hawaii International Conference on System Science (HICSS), January 2022.  https://scholarspace.manoa.hawaii.edu/items/5a3fa2c2-d182-4be5-8517-bb64ec0d69cf.
  • Baembitov, R., Kezunovic, M., Obradovic, Z. “Graph Embeddings for Outage Prediction,” Proc. 2021 North American Power Symposium (NASP), 2021, pp 1-6, https://doi.org/10.1109/NAPS52732.2021.9654696.
  • Polychronopoulou, A., Obradovic, Z., Alshehri, J. (2021) “Distinguishability of Graphs: A Case for Quantum-Inspired Measures,” Proc. 2021 IEEE/ACM Int’l Conf. on Advances in Social Networks Analysis and Mining, Nov. 2021, pp. 48-55. DOI: 10.1145/3487351.3488330. (PDF)
  • Polychronopoulou, A., Obradovic, Z., Zhou, F. (2021) “Cosine Similarity for Multiplex Network Summarization,” Proc. 2021 IEEE/ACM Int’l Conf. on Advances in Social Networks Analysis and Mining, Nov. 2021. (PDF)
  • Alshehri, J., Stanojevic, M., Dragut, E., Obradovic, Z., (2021) “Stay on Topic, Please: Aligning User Comments to the Content of a News Article,” Proc. 43rd Annual BCS-IRSG European Conference on Information Retrieval, March-April 2021. (PDF)
  • Baembitov, R., Dokic, T., Kezunovic, M., Hu, Y., Obradovic, Z., (2021) “Fast Extraction and Characterization of Fundamental Frequency Events from a Large PMU Dataset using Big Data Analytics,” Proc. 54th IEEE Hawaii International Conference on System Science (HICSS), January 2021. pp. 3195-3294, 10.24251/HICSS.2021.389. (PDF)
  • Pavlovski, M., Gligorijevic, J., Stojkovic, I., Agrawal, S., Komirishetty, S., Gligorijevic, Dj., Bhamidipati, N., Obradovic, Z. (2020) “Time-Aware User Embeddings as a Service,” Proc. 26th ACM SIGKDD Conf. Knowledge Discovery and Data Mining (KDD 2020), San Diego, Aug. 2020, pp. 3194-3202. https://doi.org/10.1145/3394486.3403371
  • Kezunovic, M., Dokic, T., Obradovic, Z., Pavlovski, M., Said, R., (2020) “Big Data Analytics for Predictive Lightning Outage Management Using Spatially Aware Logistic Regression Model,” Proc. CIGRE Technical Exhibition 2020, E-session SC D2 Information Systems and Telecommunication, PS-1: Impact of Emerging Information and Communication technologies on Electric Power Utilities, D2-101, Paris, France, Aug. 2020. (PDF)
  • Power, W., Pavlovski, M., Saranovic, D., Stojkovic, I., Obradovic, Z. (2020) “Autonomous Navigation for Drone Swarms in GPS-Denied Environments using Structured Learning,” Proc. 16th IFIP WG 12.5 Int’l Conf. Artificial Intelligence Applications and Innovations (AIAI), Neos Marmaras, Greece, June 2020, Part II, pp. 219-231, Springer. The Best Student Paper Award. (PDF)
  • Alqudah, M., Dokic, T., Kezunovic, M., Obradovic, Z. (2020) “Prediction of Solar Radiation Based on Spatial and Temporal Embeddings for Solar Generation Forecast,” Proc. 53th IEEE Hawaii International Conference on System Science (HICSS), Maui, Hawaii, January 2020. (PDF)
  • Stanojevic, M., Alshehri, J., Obradovic Z. (2019) “Surveying Public Opinion Using Label Prediction on Social Media Data,” The 2019 IEEE/ACM Int’l Conf. Social Networks Analysis and Mining (ASONAM 2019), Vancouver, CA, Aug. 2019 (PDF)
  • Han, C., Albarakati, N., Cao, X.H., Obradovic, Z. (2019) “A Distributable Convex Approach for Graph Structure Discovery,” 15th International Workshop on Mining and Learning with Graphs (MLG) 2019, held in conjunction with 25th ACM SIGKDD Conf. Knowledge Discovery and Data Mining (KDD 2019), Anchorage, Aug. 2019. (PDF)
  • Stanojevic, M., Alshehri, J., Dragut, E., Obradovic, Z. (2019) “Biased News Data Influence on Classifying Social Media ,” rd Int’l Workshop on Recent Trends in News Information Retrieval (NewsIR 2019), collocated with 42nd Int’l ACM SIGIR Conf. on Research Development in Information retrieval, Paris, July, 2019. (PDF)
  • Stojkovic, I., Jelisavcic, V., Gligorijevic, J., Gligorijevic, Dj., Obradovic, Z. (2019) “Decomposition Based Reparameterization for Efficient Estimation of Sparse Gaussian Conditional Random Fields,” 36th International Conference on Machine Learning (ICML) Workshop on Tractable Probabilistic Modeling (TMP), Long Beach, CA, June 2019. (PDF)
  • Asadi, N., Rege, A., Obradovic, Z. (2019) “Pattern Discovery in Intrusion Chains and Adversarial Movement,” Proc. IEEE Cyber Science 2019 Conference, University of Oxford, UK, June 2019. (PDF)
  • Roychoudhury, S. Zhou, F., Obradovic, Z. (2019) “Leveraging Subsequence-orders for Univariate and Multivariate Time-series Classification,” Proc. 19th SIAM Int’l Conf. Data Mining, Calgary, Canada, May 2019. (PDF)
  • Han, C., Cao, X.H., Stanojevic, M., Ghalwash, M., Obradovic, Z. (2019) “Temporal Graph Regression via Structure-Aware Intrinsic Representation Learning,” Proc. 19th SIAM Int’l Conf. Data Mining, Calgary, Canada, May 2019. (PDF)
  • Dokic, T., Pavlovski, M., Gligorijevic, Dj., Kezunovic, M., Obradovic, Z. (2019) “Spatially Aware Ensemble-Based Learning to Predict Weather-Related Outages in Transmission,” Proc. 52nd IEEE Hawaii International Conference on System Sciences (HICSS), Maui, Hawaii, January 2019. (PDF)
  • Kaplan, A., Cao, X., Dai, T., Obradovic, Z., Perez, T., Cromley, J.G., Mara, K., Balsai, M. (2019) “Modeling Semester-Long Recursive Dynamics of the Expenctancy-Value Motivation System among Undergraduate Biology Students,” Symp. Embracing and Modeling the Complex Dynamics of Motivation and Engagement: Contextual, Temporal, Dynamic, and Systematic, The Annual meeting of the American Educational Research Association, Toronto, Canada, April 2019. (PDF)
  • Tadic, P., Asadi, N., Popovic, N., Obradovic, Z. (2018) “Improving the Efficiency of the Support Vector Decomposition Machine,” Proc. IEEE 14th Symp. Neural Networks and Applications, Belgrade, Serbia, Nov. 2018.
  • Pavlovski, M., Zhou, F., Arsov, N., Kocarev, Lj., Obradovic, Z. “Generalization-Aware Structured Regression towards Balancing Bias and Variance,” Proc. 27th International Joint Conference on Artificial Intelligence (IJCAI), Stockholm, Sweden, July 2018, pp. 2616-2622. (PDF)
  • Asadi, N., Rege, A., Obradovic, Z. (2018) “Assessment of Group Dynamics During Cyber Crime Through Temporal Network Topology,” 2018 Int’l Conf. on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulations, Washington DC, July 2018. (PDF)
  • Gligorijevic, Dj., Gligorijevic, J., Raghuveer, A., Grbovic, M., Obradovic, Z. (2018) “Modeling Mobile User Actions for Purchase Recommendations using Deep Memory Networks,” Proc. 41st Int’l ACM SIGIR Conf. on Research and Development in Information Retrieval, Ann Arbor, MI, July 2018. (PDF)
  • Asadi, N., Rege, A., Obradovic, Z. (2018) “Analysis of Adversarial Movement Through Characteristics of Graph Topological Ordering,” Proc. IEEE Int’l Conf. on Cyber Situational Awareness, Data Analytics and Assessment (Cyber SA), Glasgow, Scotland, June 2018. (PDF)
  • Cao, X., Obradovic, Z., Kim, K. (2018) “A Simple yet Effective Model for Zero-Shot Learning,” Proc. IEEE Winter Conf. on Applications of Computer Vision, Lake Tahoe, Nevada, March 2018. (PDF)
  • Kezunovic, M., Obradovic, Z., Dokic, T., Roychoudhury, S. (2018) “Systematic Framework for Integration of Weather Data into Prediction Models for the Electric Grid Outage and Asset Management Applications,” Proc. 51st IEEE Hawaii International Conference on System Sciences (HICSS), Big Island, Hawaii, January 2018, pp. 2737-2746. (PDF)
  • Kaplan, A., Cao, X., Dai, T., Obradovic, Z., Perez, T., Cromley, J. G., Mara, K., Balsai, M. J. (2018) “Motivation as a complex system: Semester-long recursive dynamics of expectancy-value constructs in undergraduate biology,” The Annual Conference of the American Educational Research Association, NY, NY, April 2018. (PDF)
  • Stojkovic, I. Ghalwash, M., Obradovic, Z. “Ranking Based Multitask Learning of Scoring Functions,” Proc. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Skopje, Macedonia, September 2017. (PDF)
  • Pavlovski, M., Zhou, F., Stojkovic, I., Kocarev, Lj., Obradovic, Z. “Adaptive Skip-Train Structured Regression for Temporal Networks,” Proc. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Skopje, Macedonia, September 2017. (PDF)
  • Roychoudhury, S. Ghalwash, M., Obradovic, Z. “Cost Sensitive Time-Series Classification,” Proc. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Skopje, Macedonia, September 2017. (PDF)
  • Stojkovic, I., Jelisavcic, V., Milutinovic, V., Obradovic, Z. “Fast Sparse Gaussian Markov Random Fields Learning Based on Cholesky Factorization,” Proc. 26th International Joint Conference on Artificial Intelligence (IJCAI), pp 2758 – 2764 Melbourne, Australia, August 2017. (PDF)
  • Rege, A., Obradovic, Z., Asadi, N., Parker, B., Masceri, N., E., Singer, . Pandit, R. “Using a Real-Time Cybersecurity Exercise Case Study to Understand Temporal Characteristics of Cyberattacks,” Proc. 2017 Int’l Conf. Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior representation in Modeling and Simulation (SBP-BRiMS), Washington DC, July 2017. (PDF)
  • Rege, A., Obradovic, Z., Asadi, N., Singer, B., Masceri, N., Heath, Q. “A Temporal Assessment of Cyber Intrusion Chains Using Multidisciplinary Frameworks and Methodologies,” Proc. Int’l Conf. Cyber Situational Awareness, Data Analytics and Assesment (Cyber SA 2017), London, UK, June 2017. (PDF)
  • Han, C, Ghalwash, M., Obradovic, Z. (2017) “Continuous Conditional Dependent Network for Structured Regression,” Proc. 31st AAAI Conference on Artificial Intelligence (AAAI-17), San Francisco, CA, February 2017, 1962-1968. (PDF)
  • Negri, T.T., Zhou, F., Obradovic, Z., Gonzaga, A. (2017) “A Robust Descriptor for Color Texture Classification Under Varying Illumination,” 12th Int’l Conf. Computer Vision Theory and Applications, , Porto, Portugal, Feb. 2017,pp 378 – 388. (PDF)
  • Zhou, F., Ghalwash, M., Obradovic, Z. (2016) “A Fast Structured regression for Large Networks,” Proc. 2016 IEEE International Conference on Big Data, Washington, DC, Dec. 2016, pp 106 – 115. (PDF)
  • Mirowski, T., Roychoudhury, S., Zhou, F., Obradovic, Z. (2016) “Predicting Poll Trends using Twitter and Multivariate Time-series Classification,” Proc. 8th Int’l Conf. Social Informatics (SocInfo), Seattle, WA, Nov. 2016. (PDF)
  • Stojanovic, J., Gligorijevic, Dj., Obradovic, Z. (2016) “Modeling Customer Engagement from Partial Observations,” Proc. 25th Int’l Conf. Information and Knowledge Management (CIKM-16), Indianapolis, IN, Oct. 2016. (PDF)
  • Glass, J. and Obradovic, Z., (2016) “Shape Invariant Formulation for Changepoint Models in Multiple Dimensions,”Proc. 22th European Conf. Artificial Intelligence (ECAI-16), Aug. 2016, Hague, Holland. , Sept. 2016, Frontiers in Artificial Intelligence and Applications, Vol 285, pp. 1688-1689. (PDF)
  • Stojkovic, I., Jelisavcic, V., Milutinovic, V., Obradovic, Z. “Distance Based Modeling of Interactions in Structured Regression,” Proc. 25th International Joint Conference on Artificial Intelligence (IJCAI), New York, NY, July 2016, pp. 2032 – 2038 (PDF)
  • Han, C, Zhang, S., Ghalwash, M., Vucetic, S, Obradovic, Z. (2016) “Joint Learning of Representation and Structure for Sparse Regression on Graphs,” Proc. 16th SIAM Int’l Conf. Data Mining (SDM), 846 – 854 Miami, FL, May 2016. (PDF)
  • Polychronopoulou, A, Obradovic, Z. (2016) “Structured Regression on Multilayer Networks,” Proc. 16th SIAM Int’l Conf. Data Mining (SDM), 612 – 620, Miami, FL, May 2016. (PDF)
  • Gligorijevic, Dj, Stojanovic, J., Obradovic, Z. (2016) “Uncertainty Propagation in Long-term Structured Regression on Evolving Networks,” Proc. Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), 1603-1610, Phoenix, AZ, February 2016. (PDF)
  • Glass, J., Ghalwash, M., Vukicevic, M., Obradovic, Z. (2016) “Extending the Modeling Capacity of Gaussian Conditional Random Fields while Learning Faster,” Proc. Thirtieth AAAI Conference on Artificial Intelligence,(AAAI-16),1596 – 1602, 2016 Phoenix, AZ, February 2016. (PDF)
  • Dokic, T. Dehghanian, P., Chen, P.-C, Kezunovic, M., Medina-Cetina, Z., Stojanovic, J., Obradovic, Z. (2016) “Risk Assessment of a Transmission Line Insulation Breakdown due to Lightning and Severe Weather,” Proc. HICCS – Hawaii International Conference on System Science, Kauai, Hawaii, January 2016.pp. 2488 – 2497 (PDF)
  • Stojanovic, J., Jovanovic, M., Gligorijevic, Dj., Obradovic, Z. (2015) ” Semi-supervised learning for structured regression on partially observed attributed graphs” Proceedings of the 2015 SIAM International Conference on Data Mining (SDM 2015) Vancouver, Canada, April 30 – May 02, 2015 (PDF)
  • Radosavljevic, V., Vucetic, S., Obradovic, Z. (2014) “Neural Gaussian Conditional Random Fields,” Proc. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Nancy, France, September, 2014. (PDF)
  • Slivka, J., Nikolic, M., Ristovski, K., Radosavljevic, V., Obradovic, Z. (2014) “Distributed Gaussian Conditional Random Fields Based Regression for Large Evolving Graphs,” Proc. 14th SIAM Int’l Conf. Data Mining Workshop on Mining Networks and Graphs, Philadelphia, April 2014. (PDF)
  • Uversky, A., Ramljak, D., Radosavljevic, V., Ristovski, K., Obradovic, Z. (2013) “Which Links Should I Use? A Variogram Based Selection of Relationship Measures for Prediction of Node Attributes in Temporal Multigraphs,” Proc. 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Niagara Falls, Canada, Aug. 2013. (PDF)
  • Ristovski, K., Radosavljevic, V., Vucetic, S., Obradovic, Z. (2013) “Continuous Conditional Random Fields for Efficient Regression in Large Fully Connected Graphs,” Proc. The Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI-13), Bellevue, Washington, July 2013. (PDF)
  • Das, D., Ganguly, A., Obradovic, Z. (2013) “A Sparse Bayesian Model for Dependence Analysis of Extremes: Climate Applications,” ” Int’l Conf. Machine Leaning 2013 workshop on Inferning: Interactions between Inference and Learning, Atlanta, GA, June 2013. (PDF)
  • Delibasic, B., Obradovic, Z. (2013) “A DSS for injury prevention in ski resorts based on spatio-temporal RFID data from ski gates,” Proc. European Working Group Decision Support Systems Workshop, Thessaloniki, May 29th – 31st, 2013.
  • Das, D., Kodra, E., Obradovic, Z., Ganguly, A. (2012) ” Mining Extremes: Severe Rainfall and Climate Change,” Proc. 20th European Conf. Artificial Intelligence (ECAI-12) , Aug. 2012, Montpellier, France. (PDF)
  • Das, D., Ganguly, A., Banerjee, A., Obradovic, Z. (2012) ” Towards understanding dominant processes in complex dynamical systems: Case of precipitation extremes, ” ACM SIGKDD Sixth International Workshop on Knowledge Discovery from Sensor Data, in conjunction with the 18th SIGKDD Conf. Knowledge Discovery and Data Mining, pp 16-24, Beijing, China, Aug. 2012. (PDF)
  • Das, D., Kodra, E., Ganguly, A., Obradovic, Z. (2012) ” Mining Extreme Values: Climate and Natural Hazard” ACM SIGKDD Workshop on Data Mining Applications In Sustainability, in conjunction with the 18th SIGKDD Conf. Knowledge Discovery and Data Mining, Beijing, China, Aug. 2012. (PDF)
  • Das, D., Ganguly, A., Chatterjee, S., Kumar, V., Obradovic, Z. (2012) “Spatially Penalized Regression for Dependence Analysis and Prediction of Rare Events: A Case for Precipitation Extremes,” ACM SIGKDD Workshop on Data Mining Applications In Sustainability, in conjunction with the 18th SIGKDD Conf. Knowledge Discovery and Data Mining, Beijing, China, Aug. 2012. (PDF)
  • Das, D., Ganguly, A., Chatterjee, Z., Kumar, V., Obradovic, Z. (2012) ” Spatially Penalized Regression for Dependence Analysis of Rare Events: A Study in Precipitation Extremes,” Proc. IEEE Int’l Geoscience and Remote Sensing Symposium, July 2012, Munich, Germany. (PDF)
  • Jupin, J., Shi, J. and Obradovic, Z. (2012) “Understanding Cloud Data Using Approximate String Matching and Edit Distance,” Proc. 2nd International Workshop on Sustainable HPC Cloud at the IEEE Supercomputing Conference 2012, Salt Lake City, Utah, Nov. 2012. (PDF)
  • Lou, Q., Obradovic, Z. (2012) ” Margin-Based Feature Selection in Incomplete Data, ” Proc. the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12), July 2012, Toronto, Ontario, Canada. (PDF)
  • Mathew, G., Obradovic, Z. (2011) ” A Privacy-preserving Framework for Distributed Clinical Decision Support ” Proc. IEEE International Conference on Computational Advances in Bio and Medical Sciences, Orlando, Florida (PDF)
  • Ouzienko, V., Obradovic, Z. (2011) ” Imputation of Missing Links and Attributes in Longitudinal Social Surveys,” Proc. 2011 IEEE International Conference on Data Mining Workshop on Data Mining in Networks, Vancouver, Canada, Dec. 2011. (PDF)
  • Lou, Q., Obradovic, Z. (2011) “Modeling Multivariate Spatio-Temporal Data with Large Gaps,” Proc. 22nd International Joint Conference on Artificial Intelligence, Barcelona, Spain, July 2011. (PDF)
  • Delibasic, B., Jovanovic, Vukicevic, Suknovic, M., Kirchner, K., Ruhland, J., Obradovic, Z. (2011) ” A decision support architecture for data mining based on reusable components (patterns) ” Proc. Workshop of European Working Group on Decision Support Systems, University of London, June 2011
  • Jun, G., Ghosh, J., Radosavljevic, V., Obradovic, Z. (2010) “Predicting Ground-Based Aerosol Optical Depth with Satelite Immages via Gausian Processes,” Proc. International Conference on Knowledge Discovery and Information Retrieval, Valencia, Spain. (PDF)
  • Lou, Q., Obradovic, Z. (2010) “Feature Selection by Approximating the Markov Blanket in a Kernel-Induced Space,” Proc. 19th European Conf. on Artificial Intelligence, August, Lisbon, Portugal. (PDF)
  • Ristovski, K., Das, D. Ouzienko, V., Guo, Y., Obradovic, Z. (2010) “Regression Learning with Multiple Noisy Oracles,” Proc. 19th European Conf. on Artificial Intelligence, August, Lisbon, Portugal. (PDF)
  • Ouzienko, V., Guo, Y., Obradovic, Z. (2010) “Prediction of Attributes and Links in Temporal Social Networks,” Proc. 19th European Conf. on Artificial Intelligence, August, Lisbon, Portugal. (PDF)
  • Radosavljevic, V., Obradovic, Z., Vucetic, S. (2010) “Continuous Conditional Random Fields for Regression in Remote Sensing,” Proc. 19th European Conf. on Artificial Intelligence, August, Lisbon, Portugal. (PDF)
  • Mathew, G. and Obradovic, Z. (2010) “Vocabularies in Collaboration Channels,” Proc. IEEE 6th Int.’l Conf. on Collaborative Computing: Networking, Applications and Worksharing, Chicago, IL. (PDF)
  • Das, D., Obradovic, Z., Vucetic, S. (2009) “Active Selection of Sensor Sites in Remote Sensing Applications,” IEEE International Conference on Data Mining, December, Miami, FL. (PDF)
  • Radosavljevic, V., Vucetic, S., Obradovic, Z. (2009) “Reduction of Ground-Based Sensor Sites for Spatio-Temporal Analysis of Aerosols,” Proc. 3rd International Workshop on Knowledge Discovery from Sensor Data at the 15th ACM SIGKDD Conf. Knowledge Discovery and Data Mining, Paris, France, June 2009. (PDF)
  • Ristovski, K., Vucetic, S., Obradovic, Z. (2009) “Evaluation of a Neural Networks based Approach for Aerosol Optical Depth Retrieval and Uncertainty Estimation,” Proc. Int’l Conf. on Space Technology, Thessaloniki, Greece, Aug. 2009. (PDF)
  • Ayuyev, V., Jupin, J., Harris, P. and Obradovic, Z. (2009) “Dynamic Clustering Based Estimation of Missing Values in Mixed Type Data,” Proc. 11th Int’l Conf. on Data Warehousing and Knowledge Discovery, Linz, Austria, Sept. 2009, pp. 366-377 (PDF)
  • Das, D., Radosavljevic, V., Vucetic, S., Obradovic, Z. (2008) “Reducing Need for Collocated Ground and Satellite based Observations in Statistical Aerosol Optical Depth Estimation,” IEEE Int’l Geoscience and Remote Sensing Symposium, July, Boston, MA. (PDF)
  • Radosavljevic, V., Vucetic, S., Obradovic, Z. (2008) “Spatio-Temporal Partitioning for Improving Aerosol Prediction Accuracy,” Proc. Eight SIAM Int’l Conf. on Data Mining, April 24-26, 2008, Atlanta, GA, USA. (PDF)
  • Zhuang, W., Radosavljevic, Han, B., Obradovic, Z., Vucetic, S. (2008) “Aerosol Optical Depth Prediction from Satellite Observations by Multiple Instance Regression,” Proc. Eight SIAM Int’l Conf. on Data Mining,, Atlanta, GA, USA, 2008. (PDF)
  • Radosavljevic, V., Vucetic, S., Obradovic, Z. (2007) “Aerosol Optical Depth Retrieval by Neural Network Ensembles with Adaptive Cost Function,” Proc. 10th Int’l Conf. Engineering Applications of Neural Networks,” Thessaloniki, Greece, Aug. 2007, pp. 266-275. (PDF)
  • Han, B., Obradovic, Z, Li, Z. and Vucetic, S., (2006) “Data Mining Support for Improvement of MODIS Aerosol Retrievals,” Proc. IEEE Int’l Geoscience and Remote Sensing Symp., Denver, CO, Aug. 2006. (PDF)
  • Obradovic, Z, Han, B., Xu, Q., Li, Y., Braverman, A., Li, Z. and Vucetic, S. (2006) “Data Mining Support for Aerosol Retrieval and Analysis – Project Summary,” NASA Data Mining Workshop, Pasadena, CA, May 2006.
  • Qin, Y. and Obradovic, Z. (2006) “Efficient Learning from Massive Spatial-Temporal Data through Selective Support Vector Propagation,” Proc. 16th European Conf. on Artificial Intelligence, Italy. (PDF)
  • Song, M., Song, I.Y., Allen, R.B and Obradovic, Z. (2006) “Improving Retrieval Performance by Automatic Query Expansion with Keyphrases and POS Phrase Categorization, Proc. 6th ACM/IEEE-CS Joint Conf. Digital Libraries, Chapel Hill, NC. (PDF)
  • Han, B., Vucetic, S., Braverman, A. and Obradovic, Z (2005) “Integration of Deterministic and Statistical Algorithms for Aerosol Retrieval,” Proc. International Conference on Novel Applications of Neural Networks in Engineering, Lillie, France, Aug. 2005, pp. 85-92. (PDF)
  • Han, B., Vucetic, S., Braverman, A. and Obradovic, Z. (2005) “Construction of an accurate geospatial predictor by fusion of global and local models,” Proc. IEEE 8th International Conference on Information Fusion, B.11.2 pp. 1-8, Philadelphia, PA, July 2005. (PDF)
  • Xu, Q., Han, B., Li, Y., Braverman, A., Obradovic, Z. and Vucetic, S. (2005) “Improving aerosol retrieval performance by integrating AERONET, MISR, and MODIS data products,” Proc. IEEE 8th International Conference on Information Fusion, B.11.3 pp. 1-8, Philadelphia, PA, July 2005. (PDF)
  • Radivojac, P., Sivalingam, K. and Obradovic, Z. (2003) “Learning from Class-Imbalanced Data in Wireless Sensor Networks,” Proc. IEEE Semiannual Vehicular Technology Conference Fall 2003, Orlando, Fl. (PDF)
  • Pokrajac, D., Hoskinson, R., Lazarevic, A., Obradovic, Z. (2002) “Spatial-Temporal Techniques for Prediction and Compression of Soil Fertility Data,” Proc. 6th International Conference on Precision Agriculture, Minneapolis, MN. (PDF)
  • Hoskinson, R., Pokrajac, D., Obradovic, Z., Lazarevic, A. (2002) “The Unpredictability of Soil Fertility across Space and Time,” Proc. 6th International Conference on Precision Agriculture, Minneapolis, MN. (PDF)
  • Jovanovic, N., Milutinovic, V. and Obradovic, Z. (2002) “Foundations of Predictive Data Mining,” Proc IEEE 6th Conf. on Neural Networks Applications in Electrical Engineering, Belgrade, Yugoslavia, pp. 53-58. (PDF)
  • Pokrajac, D. and Obradovic, Z. (2001) “Neural Network-Based Method for Site-Specific Fertilization Recommendation,” Proc. Society for Engineering in Agricultural, Food, and Biological Systems (ASAE) Annual International Meeting, 2001.
  • Pokrajac, D. and Obradovic, Z. (2001) “Neural network-based software for fertilizer optimization in precision farming,” Proc. 2001 IEEE/INNS International Joint Conference on Neural Networks, Washington D.C. (PDF)
  • Pokrajac, D. and Obradovic, Z. (2001) “Improved Spatial-Temporal Forecasting through Mining,” Proc. First SIAM Int’l Conf. on Data Mining,, April 5-7, 2001, Chicago, USA. (PDF)
  • Vucetic, S. and Obradovic, Z. (2001) “Classification on data with biased class distribution,” Proc. 12th European Conf. on Machine Learning, Freiburg, Germany, pp. 527-538. (PDF)
  • Lazarevic, A. and Obradovic, Z. (2001) “Data Reduction using Multiple Models Integration,” Principles of Knowledge Discovery in Databases, Proc. 5th European Conf., Freiburg, Germany, pp. 301-313. (PDF)
  • Lazarevic, A. and Obradovic, Z. (2001) “The Distributed Boosting Algorithm,” Proc. 7th ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining, San Francisco, CA, pp. 311-316. (PDF)
  • Lazarevic, A. and Obradovic, Z. (2001) “The Effective Pruning of Neural Network Ensembles,” Proc. 2001 IEEE/INNS International Joint Conference on Neural Networks, Washington D.C., pp. 796-801. (PDF)
  • Lazarevic, A. and Obradovic, Z. (2001) “Boosting Localized Classifiers in Heterogeneous Databases,” Proc. First SIAM Int’l Conf. on Data Mining, April 5-7, Chicago, USA. (PDF)
  • Lazarevic, A., Pokrajac, D., and Obradovic, Z. (2000) “Distributed Clustering and Local Regression for Knowledge Discovery in Multiple Spatial Databases,” Proc. 8th European Symposium on Artificial Neural Networks, Bruges, Belgium, April 2000, pp. 129-134. (PDF)
  • Vucetic S. and Obradovic Z. (2000) “A Regression-Based Approach for Scaling-Up Personalized Recommender Systems in E-Commerce,” Web Mining for E-Commerce Workshop at the Sixth ACM SIGKDD Inl’l Conf. on Knowledge Discovery and Data Mining, Boston, MA. (PS)
  • Vucetic, S. and Obradovic, Z. (2000) “Performance Controlled Data Reduction for Knowledge Discovery in Distributed Databases,” Proc. Pacific-Asia Conference on Knowledge Discovery and Data Mining, Kyoto, Japan, April 2000, Computer Science Editorial 3, Springer-Verlag, pp. 29-39. (PDF)
  • Vucetic S. and Obradovic Z. (2000) “Discovering Homogeneous Regions in Spatial Data through Competition,” Machine Learning: Proc. of the 17th Int’l. Conf., Stanford, CA, June 2000, pp. 1095-1102. (PS)
  • Pokrajac D. and Obradovic Z. (2000) “Combining Regressive and Auto-Regressive Models for Spatial-Temporal Prediction,” Machine Learning of Spatial Knowledge Workshop at the 17th Int’l. Conf. on Machine Learning, Stanford, CA, June 2000. (PDF)
  • Pokrajac, D. and Obradovic, Z. (2000) “Learning Heterogeneous Functions from Sparse and Non-Uniform Samples,” Proc. IEEE-INNS-ENNS Int’l Joint Conf. on Neural Networks, Como, Italy, July 2000. (PDF)
  • Pokrajac, D., Obradovic, Z. and Fiez, T. (2000) “Understanding the Influence of Noise, Sampling Density and Data Distribution on Spatial Prediction Accuracy,” Track on Simulation Methodology and Control Engineering and Artificial Intelligence, R. V. Landeghem (Ed.): Proc. 14th European Simulation Multiconference – Simulation and Modeling: Enablers for a Better Quality of Life, May 23-26, 2000, Ghent, Belgium. SCS Europe 2000, ISBN 1-56555-204-0, pp. 706-708. (PDF)
  • Pokrajac, D., Fiez, T. and Obradovic, Z. (2000) “A Tool for Controlled Knowledge Discovery in Spatial Domains,” Track on Simulation Methodology, Tools and Standards, R. V. Landeghem (Ed.): Proc. 14th European Simulation Multiconference – Simulation and Modeling: Enablers for a Better Quality of Life, May 23-26, 2000, Ghent, Belgium. SCS Europe 2000, ISBN 1-56555-204-0, pp. 26-32. (PDF)
  • Lazarevic, A. Fiez, T. and Obradovic, Z. (2000) “Adaptive Boosting for Spatial Functions with Unstable Driving Attributes,” Proc. Pacific-Asia Conference on Knowledge Discovery and Data Mining, Kyoto, Japan, April 2000, Computer Science Editorial 3, Springer-Verlag, pp. 329-340. (PDF)
  • Lazarevic, A. Fiez, T. and Obradovic, Z. (2000) “A Software System for Spatial Data Analysis and Modeling,” Proc. Data Mining Minitrack at the IEEE Hawaii Int’l Conf. On System Sciences, IEEE Computer Society Press, January 2000. (PDF)
  • Vucetic, S. and Obradovic, Z. (2000) “A Constructive Competitive Regression Method for Analysis and Modeling of Non-stationary Time Series,” Proc. the First Int’l Workshop on Computational Intelligence in Economics and Finance at the Fifth Int’l Conf. On Information Science, Atlantic City, N.Y., USA, vol. 2, pp. 978-981. (PS)
  • Obradovic, Z. and Tomsovic, K. (1999) “Time Series Method for Forecasting Electricity Market Pricing” in Intelligent Systems in Electricity Market Modeling session, IEEE Power Engineering Society 1999 Summer Meeting, Edmonton, Canada. (PDF)
  • Pokrajac, D., Lazarevic, A., Vucetic, S., Fiez T. and Obradovic Z. (1999) “Image Processing in Precision Agriculture,” Proc. IEEE Int’l Conf. on Telecommunications in Modern Satellite, Cable and Broadcasting Services, Nis, Yugoslavia, October 1999, IEEE Press, v.2, pp. 616-619. (PDF)
  • Pokrajac, D., Fiez, T., Obradovic, D., Kwek, S. and Obradovic, Z. (1999) “Distribution Comparison for Site-Specific Regression Modeling in Agriculture,’ Proc. IEEE/INNS Int’l Joint Conf. on Neural Networks, IEEE Press, ISBN 0-7803-5532-6, Washington D.C., July 1999, No. 346, Session 10.9. (PDF)
  • Lazarevic, A., Xu, X., Fiez, T. and Obradovic, Z. (1999) “Clustering-Regression-Ordering Steps for Knowledge Discovery in Spatial Databases,” Proc. IEEE/INNS Int’l Joint Conf. on Neural Networks, IEEE Press, ISBN 0-7803-5532-6, Washington D.C., July 1999, No. 345, Session 8.1B. (PDF)
  • Vucetic, S., Fiez, T. and Obradovic, Z. (1999) “A Data Partitioning Scheme for Spatial Regression,” Proc. IEEE/INNS Int’l Joint Conf. on Neural Networks, IEEE Press, ISBN 0-7803-5532-6,Washington D.C., July 1999, No. 348, Session 8.1A. (PDF)
  • Obradovic, D. and Obradovic Z. (1999) “Efficient Probability Density Balancing for Supporting Distributed Knowledge Discovery in Large Databases,” Proc. IEEE/INNS Int’l Joint Conf. on Neural Networks, IEEE Press, ISBN 0-7803-5532-6, Washington D.C., No. 347, Session 8.1B. (PDF)
  • Ngom A., Obradovic, Z. and Stojmenovic, I. (1998) “Minimization of Multivalued Multithreshold Perceptrons Using Genetic Algorithms,” The 28th IEEE Int’l. Symp. On Multiple-Valued Logic, Fukuoka, Japan, pp. 209-214. (PDF)
  • Drossu, R. and Obradovic, Z., (1997) “INFFC Data Analysis: Lower Bounds and Testbed Design Recommendations,” Proc. 1997 Computational Intelligence in Financial Engineering, New York, N.Y., pp. 71-74. (PDF)
  • Drossu, R. and Obradovic, Z., (1997) “Regime Signaling Techniques for Non-stationary Time Series Forecasting,” Proc. Chaotic and Complex Systems Minitrack at the 30-th Hawaii Int’l Conf. on System Sciences, IEEE Computer Society Press, vol. 5, pp. 530- 538. (PDF)
  • Obradovic, Z. and Chenoweth, T. (1996) “Selection of Learning Algorithms for Trading Systems Based on Biased Estimators – An Abstract,” Working Notes of the 1996 AAAI Workshop on Integrating Multiple Learned Models for Improving and Scaling Machine Learning Algorithms, held in conjunction with National Conference on Artificial Intelligence AAAI, Portland, OR, pp. 93-94. (PS)
  • Obradovic, Z. and Chenoweth, T. (1996) “Selection of Learning Algorithms for Trading Systems Based on Biased Estimators,” Proc. Adaptive Distributed Parallel Computing Conference, Dayton, OH, pp. 458-467.
  • Milenkovic, S., Obradovic, Z. and Litovski, V. (1996) “Annealing Based Dynamic Learning in Second-Order Neural Networks,” Proc. IEEE Int. Conf. on Neural Networks, Washington D.C., pp. 458-463. (PDF)
  • Drossu, R., Obradovic, Z. and Fletcher, J. (1996) “A Flexible Graphical User Interface to Heterogeneous Neural Network Simulators,” Proc. 10th European Simulation Multiconference, Int. Society for Computer Simulation, Budapest, Hungary, pp. 273-278.
  • Venkateswaran, R., Obradovic, Z., and Raghavendra, C.V. (1996) “Cooperative Genetic Algorithm for Optimization Problems in Distributed Computer Systems,” Proc. 2nd Online Workshop on Evolutionary Computation, March 11-22, 1996, pp. 49-52. (PDF)
  • Milenkovic, S., Litovski V. and Obradovic Z. (1996) “Nondeterminism in Artificial Neural Networks,” Proc. Int’l. Memorial Conference “D.S.Mitrinovic”, Nis, Yugoslavia.
  • Romero, P. and Obradovic, Z. (1995) “Comparison of Symbolic and Connectionist Approaches to Local Experts Integration,” Proc. the IEEE Technical Applications Conference at Northcon 95, Portland, OR, pp. 105-110. (PDF)
  • Chenoweth, T., Obradovic, Z., and Lee, S. (1995) “Technical Trading Rules as a Prior Knowledge to a Neural Networks Prediction System for the S&P 500 Index,” Proc. The IEEE Technical Applications Conference at Northcon 95, Portland, OR, pp. 111-115. (PDF)
  • Chenoweth, T. and Obradovic, Z., (1995) “A Multi-Component Approach to Stock Market Prediction,” Proc. 3rd Int. Conf. on Artificial Intelligence on Wall Street, New York, N.Y., pp. 74-79.
  • Drossu, R. and Obradovic, Z., (1995) “Novel Results on Stochastic Modelling Hints for Neural Network Prediction,” Proc. World Congress on Neural Networks, Washington, D.C., vol. 3, pp. 230-233.
  • Drossu, R. and Obradovic, Z., (1995) “Stochastic Modeling Hints for Neural Network Prediction,” Proc. World Congress on Neural Networks, Washington, D.C., vol. 2, pp. 16-19 and 88-91. (PDF)
  • Drossu, R. and Obradovic, Z., (1995) “Prediction Horizon Effects on Stochastic Modeling Hints for Neural Networks,” Proc. the Workshop on Environmental and Energy Applications of Neural Networks, Pacific Northwest Laboratory, Richland, WA, World Scientific Publishing.
  • Venkateswaran, R. and Obradovic, Z., (1994) “Efficient Learning through Cooperation,” Proc. World Congress on Neural Networks, San Diego, CA, vol. 3, pp. 390-395. (PDF)
  • Mehr, I., and Obradovic, Z., (1994) “Parallel Neural Network Learning Through Repetitive Bounded Depth Trajectory Branching,” Proc. 8th IEEE Int. Parallel Processing Symposium, Cancun, Mexico, pp. 784-791. (PDF)
  • Chenoweth, T. and Obradovic, Z., (1994) “Feature Selection for Predictive Models of the Stock Market,” Proc. 2nd Int. Workshop Neural Networks in the Capital Market, Pasadena, CA.
  • Drossu, R., Lakshman, T.V., Obradovic, Z. and Raghavendra C.S. (1994) “Neural Network Techniques for Video Traffic Prediction,” Proc. 6th Int’l. Workshop on Packed Video, Portland, pp. D.9.1-D.9.4.
  • Fletcher, J. and Obradovic, Z. (1994) “Constructively Learning a Near-Minimal Neural Network Architecture,” Proc. IEEE Int’l. Conf. on Neural Networks, Orlando, FL, pp. 204-208. (PDF)
  • Obradovic, Z. and Srikumar, R. (1994) “Evolutionary Design of Application Tailored Neural Networks,” Proc. IEEE Int’l. Symp. Evolutionary Computation, Orlando, FL, pp. 284-289. (PDF)
  • Perez, L.G., Flechsig, A.J., Meador, J.L. and Obradovic, Z. (1993) “Training an Artificial Neural Network to Discriminate Between Magnetizing Inrush and Internal Faults,” IEEE Power Engineering Society 1993 Winter Meeting.
  • Milenkovic, S., Litovski, V. and Obradovic, Z., (1993) “A New Adaptive Move Selection in Simulating Annealing,” Proc. 15-16 Int. Annual School on Semiconductor and Hybrid Technologies, pp. 22-31, Sozopol, Bulgaria, 13-17 May, 1992-1993.
  • Obradovic, Z. and Fletcher, J., (1993) “Integration of Knowledge-Based and Constructive Learning Neural Networks,” Proc. 1993 World Congress on Neural Networks, Portland, OR, vol. 1, pp. 589-592.
  • Fletcher, J. and Obradovic, Z., (1993) “Parallel and Distributed Systems for Constructive Neural Network Learning,” Proc. 2nd IEEE Int. Symp. on High-Performance Distributed Computing, Spokane, WA, pp. 174-178. (PDF)
  • Obradovic, Z. and Fletcher, J. (1992) “Integration of Knowledge-Based and Constructive Learning Neural Networks,” Notes of the 1992 AAAI Workshop on Integrating Neural and Symbolic Processes, held in conjunction with National Conference on Artificial Intelligence AAAI, San Jose, CA.
  • Fletcher, J. and Obradovic, Z. (1992) “Creation of Neural Networks by Hyperplane Generation from Examples Alone,” Notes of the Neural Networks for Learning, Recognition, and Control Research Conference, G. A. Carpenter and S. Grossberg (eds.), the Wang Institute of Boston University.
  • Meador, J. and Obradovic, Z., (1992), “A Connectionist AI Approach to Automatic Test,” IEEE Pacific Test Workshop, Whistler, BC, Canada.
  • Obradovic, Z. and Srikumar, R., (1992) “Dynamic Evaluation of a Backup Hypothesis,” Notes of the Neural Networks for Learning, Recognition, and Control Research Conference, G. A. Carpenter and S. Grossberg (eds.), the Wang Institute of Boston University.
  • Palmer, D., Obradovic, Z. and Allison, C. (1992) “Determining the Cause for Poor Performance of a Classification Learning System,” Notes of the Neural Networks for Learning, Recognition, and Control Research Conference, G. A. Carpenter and S. Grossberg (eds.), the Wang Institute of Boston University.
  • Obradovic, Z. and Parberry, I. (1990) “Analog Neural Networks of Limited Precision I:Computing with Multilinear Threshold Functions,” in Advances in Neural Information Processing Systems 2, ed. D.S. Touretzky, San Mateo, CA: Morgan-Kaufmann, pp. 702- 709.
  • Obradovic, Z. and Parberry, I. (1990) “Learning with Discrete Multi-Valued Neurons,” Machine Learning: Proc. 7th Int’l. Conf., ed. B. W. Porter and R.J. Mooney, Austin, TX, Morgan-Kaufmann, pp. 392-399.
  • Obradovic, Z. and Obradovic, M., (1989) “Design of a New Parallel Language and Compiler Development,” Proc. 11-th Int’l Symposium Computer at the University, Cavtat, pp. 3.6.1- 3.6.6.
  • Obradovic, Z. and Potkonjak, M., (1987) “Software Speed-up of VLSI Systolics with Idle Cells,” Proc. 9th Int’l. Symposium Computer at the University, Cavtat, pp. 2S.01.1-2S.01.4.
  • Obradovic, Z. and Potkonjak, M., (1986) “A New Heuristic Algorithm for Solving Travelling Salesman Problem and Similar Problems,” Proc. 8th Int’l. Symposium Computer at the University, Cavtat, vol. I, pp. 37.1-37.7.
  • Protic, V., Mladenovic, B. and Obradovic, Z., (1986) “An Environment for Microcomputer Development, Testing and Installation,” Proc. 10-th BIH Symposium on Informatics, Jahorina, pp. 187.1-187.8.
  • Obradovic, Z., Das, D., Radosavljevic, V., Ristovski, K., Vucetic, S. (2010) “Spatio-Temporal Characterization of Aerosols through Active Use of Data from Multiple Sensors,” Proc. International Society for Photogrammetry and Remote Sensing (ISPRS) Technical Commission VII Symposium, Vienna, Austria, ISPRS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. (PDF)
  • Obradovic, Z. and Liu, H. (2009) “Editorial: Special Issue on the Best of SDM’09,” Statistical Analysis and Data Mining, vol. 2, no. 5-6, 291-293. (PDF)
  • Lazarevic, A., Pokrajac, D. and Obradovic, Z. (2000) “An E-commerce System for Mining Distributed Spatial Databases,” Int’l Conf. on Advances in Infrastructure for Electronic Business, Science, and Education on the Internet, L’Aquila, Italy, August 2000 (by invitation conference). (PDF)
  • Drossu, R., Fiez, T., Lazarevic, A., Pokrajac, D., Vucetic, S., and Obradovic, Z. (1998) “Use of Terrain Analysis in Yield Map Interpretation,” Geographical Information Systems in Agriculture Conference, Orlando, Florida. Parallel and Distributed Data Mining
  • Obradovic, Z. (1998) “Embedding Prior Knowledge to Statistical Learning Systems for Efficient Knowledge Discovery in Large Databases,” Symposium on Contemporary Mathematics, Belgrade, Yugoslavia.
  • Obradovic, Z. (1997) “Guest Editorial: Hybrid Intelligence for Financial Forecasting,” Journal of Computational Intelligence in Finance, vol. 5, no. 1, pp. 4-5.
  • Fletcher, J. and Obradovic, Z., (1994) “Integrating a Parallel Constructive Neural Network Algorithm with an Expert System,” Notes of the Neural Networks Workshop for the Hanford Community, Pacific Northwest Laboratory, Richland, WA, pp. 58-66.
  • Mehr, I., Obradovic, Z. and Venkateshwaran, R., (1994) “Parallel and Distributed Gradient Descent Learning,” Notes of the Neural Networks Workshop for the Hanford Community, Pacific Northwest Laboratory, Richland, WA, pp. 31-38.

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