Publications

Wojtusiak, J., Bagais, W.H., Vang, J., Roess, A. and Alemi, F., “Order of Occurrence of COVID-19 Symptoms,”Quality Management in Healthcare, 32 (Supplement 1), S29-S34, 2023.

Wojtusiak, J., Bagais, W.H., Vang, J., Guralnik, E., Roess, A. and Alemi, F., “The role of symptom clusters in triage of COVID-19 patients,”Quality Management in Healthcare, 32 (Supplement 1), S21-S28, 2023.

Alemi, F., Vang, J., Bagais, W.H., Guralnik, E., Wojtusiak, J., Moeller, FG, Schilling, J, Peterson, R., Roess, A. and Jain, P., “Combined Symptom Screening and At-Home Tests for COVID-19,”Quality Management in Healthcare, 32 (Supplement 1), S11-S20, 2023.

Alemi, F., Guralnik, E., Vang, J., Wojtusiak, J., Peterson, R., Roess, A. and Jain, P., “Guidelines for Triage of COVID-19 Patients Presenting With Multisystemic Symptoms,”Quality Management in Healthcare, 32 (Supplement 1), S3-S10, 2023.

Wojtusiak, J., Alsadah, G., Anand, P., Dague, L. and Wagner, K., “Access to Paid Leave Among Those with Caregiving Needs,”Abstract in 2022 APPAM Fall Research Conference, 2022.

Wojtusiak, J. and Asadzadehzanjani, N., “Discussion on Comparing Machine Learning Models for Health Outcome Prediction,”HEALTHINF 2022, 5, 713-720, 2022.

Bagais, W.H. and Wojtusiak, J., “Dashboard for Machine Learning Models in Health Care,”HEALTHINF 2022, 5, 484-492, 2022.

Wang, Y., Tran, P. and Wojtusiak, J., “From Wearable Device to OpenEMR: 5G Edge Centered Telemedicine and Decision Support System,”HEALTHINF 2022, 5, 493-500, 2022.

Pierobon, M., Robert, NJ., Northfelt, DW., Jahanzeb, M., Wong, S., Hodge, KA., Baldelli, E., Aldrich, J., Craig, DW., Liotta, LA., Avramovic, S., Wojtusiak, J., Alemi, F., Wulfkuhle, JD., Bellos, A., Gallagher, RI., Arguello, D., Conrad, A., Kemkes, A., Loesch, DM., Vocila, L., Dunetz, B., Carpten, JD., Petricoin, E. and Anthony, SP., “Multi-omic molecular profiling guides efficacious treatment selection in refractory metastatic breast cancer: a prospective phase II clinical trial,”Molecular Oncology, 16(1), Jan. 2022.

Wojtusiak, J., Alemi, F., Asadzadehzanjani, N., Levy, C. and Williams, A., “Computational Barthel Index: an automated tool for assessing and predicting activities of daily living among nursing home patients,”BMC Medical Informatics and Decision Making, 21, 2021.

Wojtusiak, J., Bagchi, P., Durbha, S., Mobahi, H., Mogharab Nia, R. and Roess, A., “COVID-19 Symptom Monitoring and Social Distancing in a University Population,”Journal of Health Informatics Research, 2021.

Wojtusiak, J., Asadzadehzanjani, N., Alemi, F., Levy, C. and Williams, A., “Online Decision Support Tool that Explains Temporal Prediction of Activities of Daily Living (ADL),” HEALTHINF, 2021.

Wojtusiak, J., “Reproducibility, Transparency and Evaluation of Machine Learning in Health Applications,” HEALTHINF, 2021.

Wojtusiak, J., Vakkalagadda, V., Wang, Y., Durbha, S., Alemi, F. and Roess, A., “Towards Wi-Fi Contact Prediction: Methods and Initial Results,”Reports of the Machine Learning and Inference Laboratory, MLI 21-1, 2021.

Wojtusiak, J., Wang, Y., Vakkalagadda, V., Alemi, F. and Roess, A., “Using Wi-Fi Infrastructure to Predict Contacts During Pandemics,” IEEE International Conference on Healthcare Informatics, Victoria, Canada, 2021.

Asadzadehzanjani, N. and Wojtusiak, J., “Administrative Health Data Representation for Mortality and High Utilization Prediction,” VLDB Data Management and Analytics for Medicine and Healthcare Workshop, 2021.

Cheng, X,, Lin, SY., Wang, K., Hong, YA., Wojtusiak, J., Gress, D., Cheskin, L.J. and Xue, H., “Healthfulness Assessment of Recipes Shared on Pinterest: Natural Language Processing and Content Analysis,”Journal of Medical Internet Research, 23, 4, e25757, 2021.

Guralnik, E., Alemi, F., Vang, J., Wojtusiak, J., Wilson, A. and Roess, A., “Modeling the Probability of COVID-19 Vs. Influenza or Influenza-like-Illnesses (ILI) Based on Symptom Screening,” 2021 AcademyHealth Annual Research Meeting, 2021.

Wojtusiak, J., “Machine Learning and Inference Reporting Criteria,” Reports of the Machine Learning and Inference Laboratory, MLI 20-1, 2020.

Wang, Y. and Wojtusiak, J., “Towards Active Learning for User-defined Chest X-Ray Diagnosis System,”Reports of the Machine Learning and Inference Laboratory, MLI 20-2, 2020.

Wojtusiak, J., Bagchi, P., Durbha, S., Mobahi, H., Mogharab Nia, R. and Roess, A., “COVID-19 Symptom Monitoring and Social Distancing in a University Population,”IEEE International Conference on Healthcare Informatics (ICHI), November, 2020.

Mogharab Nia, R. and Wojtusiak, J., “Using Landmark Information to Enhance Location Prediction for Missing People with Alzheimer’s Disease,” Poster Presentation at American Medical Informatics Annual Symposium, November. 2020.

Wojtusiak, J., Bagchi, P., B. Durai, U. N., Koester, R. J., Middle, B. , Mogharab Nia, R. and Tompkins, C., “Analysis of Wandering Patterns of Individuals with Alzheimers Disease,”Reports of the Machine Learning and Inference Laboratory, MLI 20-3, 2020.

Wojtusiak, J. and Mogharab Nia, R., “Location Prediction Using GPS Trackers: Can Machine Learning Help Locate the Missing People with Dementia?,” Internet of Things, Elsevier, 2019 (in press).

Kheirbek, R., Alemi, Y., Wojtusiak, J., Kheirbek, L., Madison, S., Fokar, A., Doukky, R. and Moore, H.J., “Impact of Hospice and Palliative Care Service Utilization on All-Cause 30-Day Readmission Rate for Older Adults Hospitalized with Heart Failure,”American Journal of Hospice and Palliative Medicine, 2019.

Mobahi, H., Asadzadehzanjani, N. and Wojtusiak, J., “Data-driven Categorization of Opioid Abuse Trajectories,” Poster at AMIA Informatics Summit, San Francisco, CA, March, 2019.

Asadzadehzanjani, N., Wojtusiak, J., Williams, A. and Levy, C., “Generic vs. Specialized Models for Assessing Disabilities among People with Dementia,” Poster at AMIA Annual Symposium, Washington D.C., November, 2019.

Wojtusiak, J., B. Durai, U. N., Koester, R. J., Middle, B. , Mogharab Nia, R. and Tompkins, C., “Using GPS to Locate the Missing and Track Wandering in People with Alzheimer’s Disease,” AMIA, 2019.

Mobahi, H., Min, H. and Wojtusiak, J., “Synthetic Data for Teaching Data Integration in Informatics Graduate Program,” Poster at AMIA Annual Symposium, Washington D.C., November, 2019.

Zare, M. and Wojtusiak, J., “Weighted Itemsets Error (WIE) Approach for Evaluating Generated Synthetic Patient Data,” 2018 17th IEEE International Conference on Machine Learning and Applications, 2018.

Zare, M., Wojtusiak, J. and Nilashi, M., “Prediction of Patients’ Mortality during Hospitalizations,” Journal of Soft Computing and Decision Support Systems, 5(4), 26-32, 2018.

Zare, M., Narayan, M., Lasway, A., Kitsantas, P., Wojtusiak, J. and Oetjen, C.A., “Influence of Adverse Childhood Experiences on Anxiety and Depression In Children Aged 6 to 11 Years,”Continuing Nursing Education, 44(6), 267-274, 2018.

Avramovic, S., Asadzadehzanjani, N., Mogharab Nia, R., Baldelli, E., Petricoin, E., Pierobon, M. and Wojtusiak, J., “The Side Out Foundation Metastatic Breast Cancer Database, an Open-access Portal for multi-omics Molecular Data and More,” Reports of the Machine Learning and Inference Laboratory, MLI 18-2, 2018.

Wojtusiak, J. and Mogharab Nia, R., “Location Prediction Using GPS Trackers: Towards Predicting Wandering in People with Dementia,” Reports of the Machine Learning and Inference Laboratory, MLI 18-1, 2018.

Wojtusiak, J., Elashkar, E. and Mogharab Nia, R., “C-LACE2: computational risk assessment tool for 30-day post hospital discharge mortality,”Health and Technology, 8, 5, Springer, 2018.

ElRafey, A., and Wojtusiak, J., Recent advances in scaling‐down sampling methods in machine learning. Wiley Interdisciplinary Reviews: Computational Statistics, 2017.

Min H, Avramovic S, Wojtusiak J, Khosla R, Fletcher RD, Alemi F, Elfadel Kheirbek R. A Comprehensive Multimorbidity Index for Predicting Mortality in Intensive Care Unit Patients, Journal of palliative medicine, 1;20(1):35-41, 2017.

Min H, Mobahi H, Irvin K, Avramovic S, Wojtusiak J. Predicting activities of daily living for cancer patients using an ontology-guided machine learning methodology. Journal of Biomedical Semantics, 16;8(1):39, 2017.

Wojtusiak, J., Elashkar, E. Mogharab Nia, R., “C-LACE: Computational Model to Predict 30-Day Post-Hospitalization Mortality” HealthInf, 2017.

Wojtusiak, J., “Towards Intelligent Patient Data Generator,” Reports of the Machine Learning and Inference Laboratory, MLI 16-2, 2016.

Min, H., Oz, T., Vukomanovic, S., Mobahi, H., Irvin, K., Krasniqi, I. and Wojtusiak, J., “Visualizing the Effects of Cancers on Relationships Between Comorbidities and Activities of Daily Living,” Poster at American Medical Informatics Association Annual Symposium, November, 2016.

Min, H., Oz, T., Vukomanovic, S., Mobahi, H., Irvin, K., Krasniqi, I. and Wojtusiak, J., “Applying Machine Learning Methods to Predict Activities of Daily Living for Cancer Patients,” Poster at American Medical Informatics Association Annual Symposium, November, 2016.

Aloudah, F.M. and Wojtusiak, J., “Towards Automated Selection of Patient-Specific Education Materials in Ambulatory Care Settings,” Poster at American Medical Informatics Association Annual Symposium, November, 2016.

Wojtusiak, J., Elashkar, Eman and Mogharab Nia, Reyhaneh, “Integrating Complex Health Data for Analytics,” Reports of The Machine Learning and Inference Laboratory, MLI-16-1, 2016.

Kheirbek, R,, Wojtusiak, J., Alemi, F. and Vlaicu, S., “Lack of Evidence for Racial Disparity in 30-Day All-Cause Readmission Rate for Older US Veterans Hospitalized with Heart Failure,” Quality Management in Health Care, 25(4), 191–196, 2016.

Min, H., Mobahi, H., Vukomanovic, S., Irvin, K., Krasniqi, I., Avramovic, S. and Wojtusiak, J., “Applying an Ontology-guided Machine Learning Methodology to SEER-MHOS Dataset,” , Orlando,FL, Bio-Ontologies SIG, 2016.

Levy, C.,, Zargoush, Manaf, Williams, A., Williams, A.R., Wojtusiak, J., Kheirbek, R,, Giang, P. and Alemi, F., “Sequence of Functional Loss and Recovery in Nursing Homes,” The Gerontologist, 56(1), 2016.

Wojtusiak, J., Levy, C.,, Williams, A. and Alemi, F., “Predicting Functional Decline and Recovery following Hospitalization of Residents in Veterans Affairs Nursing Homes,” The Gerontologist, 56(1), 2016.

Madison, S. and Wojtusiak, J., “Engaging Students in Health Informatics Research: Strategies for Success,” Presentation at HIMSS 2015 Annual Conference, AUPHA academic forum, Chicago, IL, 2015.

Helmchen, L., Burke, ME and Wojtusiak, J., “Designing highly reliable adverse-event detection systems to predict subsequent claims,” Journal of Healthcare Risk Management, 34(4), 7-17, 2015.

Levy C, Kheirbek R, Alemi F, Wojtusiak J, Sutton B, Williams AR, Williams A., “Predictors of 6-Month Mortality among Nursing Home Residents: Diagnoses Maybe More Predictive Than Functional Disability.” Journal of Palliative Medicine, 18(2), 100-6, 2015.

Ngufor, C. and Wojtusiak, J., “Learning from Large Distributed Data: A Scaling Down Sampling Scheme for Efficient Data Processing,” International Journal of Machine Learning and Computing (IJMLC), Vol.4(3), 216-224, 2014.

Ngufor, C., Wojtusiak, J., Hooker, A., Oz, T. and Hadley, J., “Extreme Logistic Regression: A Large Scale Learning Algorithm with Application to Prostate Cancer Mortality Prediction,” Proceedings of the The 27th International Florida Artificial Intelligence Research Society Conference, 2014.

Ngufor, C. and Wojtusiak, J., “Extreme logistic regression,” Advances in Data Analysis and Classification (ADAC), Springer, 2014

Oz, T., and Wojtusiak, J., “Specialty and Physician Referral Network,” International Sunbelt Social Network Conference XXXIV (INSNA), St. Pete Beach, FL, Feb 2014

Oz, T., and Wojtusiak, J., “Turkish News Audience and Their Political Leanings on Twitter,” 7th Political Networks Conference (PolNet), Montreal, QC, Canada , May 2014.

Domanski, PA, Brown, JS, Heo, J, Wojtusiak, J, McLinden, MO., “A THERMODYNAMIC ANALYSIS OF REFRIGERANTS: PerFormance limits OF the vapor compression cycle,” International Journal of Refrigeration, 2013.

Kolaceveki, A., Wojtusiak J., “Machine Learning-based Detection of Health Data Elements,” American Medical Informatics Annual Symposium, 2013.

Oz, T., Ngufor, C., Wojtusiak, J., “Mining Progress Notes for Prediction of Activities of Daily Living,” American Medical Informatics Annual Symposium, 2013.

Ngufor, C., Wojtusiak J., “Learning from Large-Scale Distributed Health Data: An Approximate Logistic Regression Approach,” International Conference on Machine Learning, Workshop on Role of Machine Learning in Transforming Healthcare, 2013.

Ngufor, C. and Wojtusiak, J., “Unsupervised Labeling of Data for Supervised Learning and Its Application to Medical Claims Prediction,” Computer Science Journal, AGH Press, 14, 2, 191-214, 2013.

Wojtusiak, J., “Semantic Data Types in Machine Learning from Healthcare Data,” Proceedings of the International Conference on Machine Learning and Applications (ICMLA), Florida, December, 2012.

Wojtusiak, J., Warden, T., Herzog, O., “Machine Learning in Agent-based Stochastic Simulation: Inferential Theory and Evaluation in Transportation Logistics” Computers & Mathematics with Applications, 64, 12, 3658-3665, 2012.

Wojtusiak, J., “Semantic Data Types in Machine Learning from Healthcare Data,” Proceedings of the International Conference on Machine Learning and Applications, Boca Raton, Florida, December 12-15, 2012.

Irvin, K., Ngufor C., Wojtusiak, J., “Comparison of Classification Learning Methods for Medical Claims Payments,” American Medical Informatics Annual Symposium, November, 2012.

Wojtusiak, J., “Recent Advances in AQ21 Rule Learning System for Healthcare Data,” American Medical Informatics Annual Symposium, November, 2012.

Yashar, D., Wojtusiak, J., Kaufman, K., and Domanski, P.A., “A Dual Mode Evolutionary Algorithm for Designing Optimized Refrigerant Circuitries for Finned-Tube Heat Exchangers,” HVAC&R Research, 18,5, 834-844, Taylor & Francis, 2012.

Warden, T., Wojtusiak, J., Herzog, O., “Intelligent Modeling and Control for Autonomous Logistics,” In: Advances in Intelligent Modelling and Simulation: Artificial Intelligence-based Models and Techniques in Scalable Computing, J. Kolodziej, S.U. Khan and T. Burczynski (EDS), 295-325, Springer, 1012.

Wojtusiak, J., Warden, T., Herzog, O., “The Learnable Evolution Model in Agent-based Delivery Optimization,” Memetic Computing, 4, 3, 165-181, 2012.

Michalski, R. S. and Wojtusiak, J., “Reasoning with Missing, Not-applicable and Irrelevant Meta-values in Concept Learning and Pattern Discovery,” Journal of Intelligent Information Systems, 39, 1, 141-166, Springer, 2012.

Wojtusiak, J., “Rule Learning,” Encyclopedia of the Sciences of Learning, N. Seal (Ed), Springer, 2012.

Wojtusiak, J., “Machine Learning,” Encyclopedia of the Sciences of Learning, N. Seal (Ed), Springer, 2012.

Wojtusiak, J., “AQ Learning,” Encyclopedia of the Sciences of Learning, N. Seal (Ed), Springer, 2012.

Wojtusiak, J., Gewa, C.A., Pawloski, L.A., “Dietary assessment in Africa: integration with innovative technology,” African Journal of Food, Agriculture, Nutrition, and Development, 11, 7, 2011.

Wojtusiak. J., Irvin, K., Birerdinc, A., Baranova, A., “Using Published Medical Results and Non-homogenous Data in Rule Learning,” Proceedings of the International Conference on Machine Learning and Applications, Honolulu, HI, December 2011.

Wojtusiak, J., Ngufor, C., Shiver, J., Ewald, R., “Rule-based Prediction of Medical Claims’ Payments: A Method and Initial Application to Medicaid Data” Proceedings of the International Conference on Machine Learning and Applications, Honolulu, HI, December 2011.

Wojtusiak, J., Warden, T. and Herzog, O., “Agent-based Pickup and Delivery Planning: The Learnable Evolution Model Approach,” Proceedings of The Fifth International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2011), Seoul, Korea, June 30 – July 2, 2011.

Wojtusiak, J. and Baranova, A. V., “Model Learning from Published Aggregated Data,” Learning Structure and Schemas from Documents, Studies in Computational Intelligence, 375, 369-384, 2011.

Wojtusiak, J., Shiver, J., Ngufor, C. and Ewald, R., “Machine Learning in Hospital Billing Management,” Presentation at HIMSS 2011 Acedemic Forum (Hosted by AUPHA), Orlando, FL, Feb. 20, 2011.

Wojtusiak, J. and Alemi, F., “Analyzing Decisions Using Datasets with Multiple Attributes: A Machine Learning Approach,” Handbook of Healthcare Delivery Systems, CRC Press, 2010.

Wojtusiak, J. and Herzog, O., “Distributed Decision Support in Disruptive Environments,” Reports of the Machine Learning and Inference Laboratory, MLI 10-1, December 2010.

Sniezynski, B., Wojcik, W., Gehrke J. D. and Wojtusiak, J., “Combining Rule Induction and Reinforcement Learning: An Agent-based Vehicle Routing,” Proceedings of the Ninth International Conference on Machine Learning and Applications (ICMLA 2010), Washington D.C., pp. 851-856, 12-14 Dec. 2010.

Wojtusiak, J. and Zurada, J. M., “Automated Computational Intelligence and Machine Learning Advising in Healthcare,” Proceedings of the AMIA 2010 Annual Symposium, Washington D.C., 1310, 2010.

Warden, T. and Wojtusiak, J., “Learnable Evolutionary Optimization in Autonomous Pickup & Delivery Planning: A Scenario, System Architecture and Initial Results,” TZI-Bericht Nr. 55, TZI, Universitat Bremen, 2010.

Yashar, D., Domanski, P. A., Wojtusiak, J. and Kaufman, K., “Evolutionary Computation Approach to Heat Exchanger Optimization,” Proceedings of the American Society of Heating, Refrigerating and Air-Conditioning Engineers Annual Conference, Albuquerque, NM, June 26-30, 2010.

Wojtusiak, J., Prior, S. and Thompson, D., “Adaptive Infrastructure Protection: Machine Learning Approach to Data Collection,” Workshop on Grand Challenges in Modeling, Simulation, and Analysis for Homeland Security (MSAHS-2010), Arlington, VA, March 17-18, 2010.

Wojtusiak, J. and Kaufman, K., “Ryszard S. Michalski: The Vision and Evolution of Machine Learning,” Advances in Machine Learning I, Koronacki, J., Z. W. Ras, Wierzchon, S.T. and Kacprzyk, J (Eds.), 3-22, Springer-Verlag, 2010.

Landon, B.E. , Reschovsky, J.D. , Pham, H.H., Kitsantas, P., Wojtusiak, J. and Hadley, J., “Creating a parsimonious typology of physician financial incentives,” Health Services and Outcomes Research Methodology, 9, 219-233, 2009.

Wojtusiak, J., Chorowski, J., Pietrzykowski, J. and Zurada, J. M., “Searching and Reasoning with Distributed Resources in Computational Intelligence and Machine Learning,” Journal of Applied Computer Science Methods, 1, 2, 2009.

Wojtusiak, J., Michalski, R. S., Simanivanh, T. and Baranova, A. V., “Towards application of rule learning to the meta-analysis of clinical data: An example of the metabolic syndrome,” International Journal of Medical Informatics, 4, 1, pp. 43-54, 2009.

Wojtusiak, J., Zurada, J. M., Malof, J.M., Mehta, D. and Moidu, K., “Toward VO-based Collaboration between Computational Intelligence – Machine Learning and Healthcare Communities,” Recent Advances in Intelligent Information Systems, Klopotek, M.A., Przepiorkowski, A, Wierzchon, S.T. and Trojanowski, K. (Eds.), 507-518, Academic Publishing House EXIT, 2009.

Mehta, D., Perchansky, K., Wojtusiak, J., Moidu, K. and He, Z., “Microaspiration from Gastroesophageal Reflux Is Common in Children with Asthma,” Poster at Pediatric Academic Societies Annual Meeting, Baltimore, MD, May 2-5, 2009.

Wojtusiak, J., “The LEM3 System for Multitype Evolutionary Optimization,” Computing and Informatics, 28, pp. 225-236, 2009.

Zurada, J. M., Mazurowski, M.A., Abdullin, A., Ragade, R., Wojtusiak, J. and Gentle, J. E., “Building Virtual Community in Computational Intelligence and Machine Learning,” Computational Intelligence Magazine, 4, 1, pp. 43-54, 2009.

Mazurowski, M.A., Zurada, J. M., Wojtusiak, J., Ragade, R., Gentle, J. E. and Abdullin, A., Workshop on Building Computational Intelligence and Machine Learning Virtual Organizations, George Mason University, Fairfax, VA, October 24, 2008.

Zurada, J. M., Wojtusiak, J., Mazurowski, M.A., Mehta, D., Moidu, K. and Margolis, S., “Toward Multidisciplinary Collaboration in the CIML Virtual Community,” Workshop on Building Computational Intelligence and Machine Learning Virtual Organizations, George Mason University, Fairfax, VA, pp. 62-66, October 24, 2008.

Boyle, C., Abdullin, A., Ragade, R., Mazurowski, M.A., Wojtusiak, J. and Zurada, J. M., “Workflow considerations in the emerging CI-ML virtual organization,” Workshop on Building Computational Intelligence and Machine Learning Virtual Organizations, George Mason University, Fairfax, VA, pp. 67-70, October 24, 2008.

Gehrke, J.D. and Wojtusiak, J., “Traffic Prediction for Agent Route Planning,” Proceedings of the International Conference on Computational Science, Krakow, Poland, Lecture Notes in Computer Science, Springer, 2008.

Wojtusiak, J., “Data-driven Constructive Induction in the Learnable Evolution Model,” Proceedings of the 16th International Conference Intelligent Information Systems, Zakopane, Poland, June 16-18, 2008.

Pietrzykowski, J. and Wojtusiak, J., “Learning Attributional Ruletrees,” Proceedings of the 16th International Conference Intelligent Information Systems, Zakopane, Poland, June 16-18, 2008.

Zurada, J. M., Wojtusiak, J., Chowdhury, F., Gentle, J. E., Jeannot, C. and Mazurowski, M.A., “Computational Intelligence Virtual Community: Framework and Implementation Issues,”Proceedings of the IEEE World Congress on Computational Intelligence, Hong Kong, June 1-6, 2008.

Michalski, R. S. and Wojtusiak, J., “The Distribution Approximation Approach to Learning from Aggregated Data,” Reports of the Machine Learning and Inference Laboratory, MLI 08-2, George Mason University, Fairfax, VA, 2008.

Gehrke J. D. and Wojtusiak, J., “A Natural Induction Approach to Traffic Prediction for Autonomous Agent-based Vehicle Route Planning,” Reports of the Machine Learning and Inference Laboratory, MLI 08-1, George Mason University, Fairfax, VA, February 17, 2008.

Zurada, J. M., Wojtusiak, J., Gentle, J. E., Chowdhury, F., Ragade, R. and Jeannot, C., “Computational Intelligence and Machine Learning Virtual Infrastructure Network (CIMLVIN),”Poster at the National Science Foundation Workshop on Building Effective Virtual Organizations, Washington, D.C., January 14-16, 2008.

Wojtusiak, J. and Michalski, R. S., “Analyzing Diaries for Analytical Relapse Prevention Using Natural Induction: A Method and Preliminary Results,” Quality Management in Health Care, 17, 2008.

Wojtusiak, J., “Handling Constrained Optimization Problems and Using Constructive Induction to Improve Representation Spaces in Learnable Evolution Model,” SIGEVOlution, Dissertation Corner, 2(3), 24-25, Autumn, 2007.

Wojtusiak, J. and Michalski, R. S., Simanivanh, M., and Baranova, A.V. “The Natural Induction System AQ21 and Its Application to Data Describing Patients with Metabolic Syndrome: Initial Results,” International Conference on Machine Learning and Applications, ICMLA, Cincinnati, OH, 2007.

Wojtusiak, J., “Handling Constrained Optimization Problems and Using Constructive Induction to Improve Representation Spaces in Learnable Evolution Model,” Ph.D. Dissertation, College of Science, Reports of the Machine Learning and Inference Laboratory, MLI 0-3, George Mason University, Fairfax, VA, November, 2007.

Kaufman, K., Michalski, R. S., Pietrzykowski, J. and Wojtusiak, J., “An Integrated Multi-task Inductive Database and Decision Support System VINLEN: Initial Implementation and Early Results,” In Knowledge Discovery in Inductive Databases, Lecture Notes in Computer Science, 4747, Springer, 2007.

Michalski R.S. and Wojtusiak J., “Generalizing Data in Natural Language,” Proceedings of the International Conference Rough Sets and Emerging Intelligent Systems Paradigms, RSEISP’07, Lecture Notes in Computer Science, 4585, Springer, 2007.

Michalski, R. S., Wojtusiak, J. and Kaufman, K., “Progress Report on the Learnable Evolution Model,” Reports of the Machine Learning and Inference Laboratory, MLI 07-2, George Mason University, Fairfax, VA, 2007.

Michalski, R. S. and Wojtusiak, J., “Semantic and Syntactic Attribute Types in AQ Learning,”Reports of the Machine Learning and Inference Laboratory, MLI 07-1, George Mason University, Fairfax, VA, 2007.

Michalski, R. S., Wojtusiak, J. and Kaufman, K., “Intelligent Optimization via Learnable Evolution Model,” Proceedings of The 18th IEEE International Conference on Tools with Artificial Intelligence, Washington D.C., November 13-15, 2006.

Wojtusiak, J., Michalski, R. S., Kaufman, K. and Pietrzykowski, J., “The AQ21 Natural Induction Program for Pattern Discovery: Initial Version and its Novel Features,” Proceedings of The 18th IEEE International Conference on Tools with Artificial Intelligence, Washington D.C., November 13-15, 2006 .

Kaufman, K., Michalski, R. S., Pietrzykowski, J. and Wojtusiak, J., “An Integrated Multi-task Inductive Database and Decision Support System VINLEN: An Initial Implementation and First Results,” Proceedings of The 5th International Workshop on Knowledge Discovery in Inductive Databases, KDID’06, in conjunction with ECML/PKDD, Berlin, Germany, September 18, 2006.

Wojtusiak, J. and Michalski, R.S., “The LEM3 Implementation of Learnable Evolution Model and Its Testing on Complex Function Optimization Problems,” Proceedings of Genetic and Evolutionary Computation Conference, GECCO-2006, 2006.

Wojtusiak, J., “Initial Study on Handling Constrained Optimization Problems in Learnable Evolution Model,” Proceedings of Graduate Student Workshop at Genetic and Evolutionary Computation Conference, GECCO-2006, 2006.

Michalski, R. S., Kaufman, K. A., Pietrzykowski, J., Sniezynski, B., Wojtusiak, J., “Learning Symbolic User Models for Intrusion Detection: A Method and Initial Results,” Proccedings of New Trends in Intelligent Information Processing and Web Mining Conference, Ustron, Poland, June 19-22, Advances in Soft Computing, 35, pp. 273-285, Springer, 2006.

Wojtusiak, J. and Michalski, R. S., “The Use of Compound Attributes in AQ Learning,”Proccedings of New Trends in Intelligent Information Processing and Web Mining Conference, Ustron, Poland, June 19-22, Advances in Soft Computing, 35, pp. 189-198, Springer, 2006.

Michalski, R. S., Kaufman, K., Pietrzykowski, J., Wojtusiak, J., Mitchell, S. and Seeman, W.D., “Natural Induction and Conceptual Clustering: A Review of Applications,” Reports of the Machine Learning and Inference Laboratory, MLI 06-3, George Mason University, Fairfax, VA, June, 2006.

Wojtusiak, J., Michalski, R. S., Kaufman, K. and Pietrzykowski, J., “Multitype Pattern Discovery Via AQ21: A Brief Description of the Method and Its Novel Features,” Reports of the Machine Learning and Inference Laboratory, MLI 06-2, George Mason University, Fairfax, VA, 2006.

Michalski, R. S., Kaufman, K., Pietrzykowski, J., Sniezynski, B. and Wojtusiak, J., “Learning User Models for Computer Intrusion Detection: Preliminary Results from Natural Induction Approach,”Reports of the Machine Learning and Inference Laboratory, MLI 05-3, George Mason University, Fairfax, VA, November, 2005.

Wojtusiak, J. and Michalski, R. S., “The LEM3 System for Non-Darwinian Evolutionary Computation and Its Application to Complex Function Optimization,” Reports of the Machine Learning and Inference Laboratory, MLI 05-2, George Mason University, Fairfax, VA, October, 2005.

Michalski, R. S. and Wojtusiak, J., “Reasoning with Missing, Not-applicable and Irrelevant Meta-values in Concept Learning and Pattern Discovery,” Technical Report 2005-02, Collaborative Research Center 637, University of Bremen, Germany, July 2005.

Michalski, R. S. and Wojtusiak, J., “Reasoning with Meta-values in AQ Learning,” Reports of the Machine Learning and Inference Laboratory, MLI 05-1, George Mason University, Fairfax, VA, June, 2005.

Wojtusiak, J., “The LEM3 Implementation of Learnable Evolution Model: User’s Guide,” Reports of the Machine Learning and Inference Laboratory, MLI 04-5, George Mason University, Fairfax, VA, November, 2004.

Wojtusiak, J., “AQ21 User’s Guide,” Reports of the Machine Learning and Inference Laboratory, MLI 04-3, George Mason University, Fairfax, VA, September, 2004.

Wojtusiak, J., “Use of Artificial Intelligence Methods for Analyzing Results of Simulation of Life Strategies’ Evolution,” (in Polish: Zastosowanie Metod Sztucznej Inteligencji do Analizy Wynikow Symulacji Ewolucji Strategii Zyciowych), M.S. Thesis, Institute of Computer Science, Jagiellonian University, 2001.

Kolodziej J., Gwizdala R., Wojtusiak J., “Hierarchical Genetic Strategy as a Method of Improving Search Efficiency,” Advances in Multi-Agent Systems, R. Schaefer and S. Sedziwy (Eds.), Chapter 9, pp. 149-161, Jagiellonian University Press, 2001.

Schaefer R., Kolodziej J., Gwizdala R., Wojtusiak J., “How Simpletons Can Increase the Community Development – An Attempt to Hierarchical Genetic Computation,” Proc. of the 4-th Polish Conf. on Evolutionary Algorithms, pp. 187-199, Ladek Zdroj, 2000.

Reports of the Machine Learning and Inference Laboratory, a publication of our laboratory that has been continuously published since 1972 (before our group moved from the University of Illinois at Champaign-Urbana to GMU, they were published under the name Reports of the Intelligent Systems Group). The reports are peer-reviewed, and are made downloadable from MLI website (this website lists all publications, not only reports). Since our field is developing very rapidly and publishing a paper in a journal may take two or even three years when it becomes de facto obsolete, communicating quickly novel results via the mechanism of technical reports (now instantaneously available from our website) is an important service to the scientific community and very beneficial to students. Such practice is well-established at major universities.