Daniel Berleant
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Researcher
Also affiliated: Jet Propulsion Laboratory (1989); Iowa State University (2000–2020); Virtual Reality Medical Center (2005); The University of Texas at Austin (1988–2002)
Faculty Researcher
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Daniel Berleant's research focuses on the application of computational methods, particularly natural language processing (NLP) and text mining, to address challenges in information retrieval and scientific literature analysis. His work includes developing and evaluating techniques for automating systematic literature reviews, a process crucial for synthesizing research findings across various disciplines. Berleant has investigated the robustness of machine learning classifiers, including deep learning models, by examining their performance under different perturbation scenarios.
His research also extends to the use of discrete-event simulation for optimizing healthcare operations, such as balancing staff allocation and patient flow between clinic and surgery settings. Berleant has published extensively on these topics, with a notable body of work reviewing advancements in areas like visual question answering and quantitative technology forecasting. His scholarly output is reflected in an h-index of 20, with over 1,700 citations across more than 125 publications.
Berleant collaborates with researchers at the University of Arkansas at Little Rock, including Peng-Hung Tsai and Michael Howell, as well as with Richard S. Segall from Arkansas State University and Michael Bauer from the University of Arkansas for Medical Sciences. He maintains an active lab website to share his research.
Metrics
- h-index: 20
- Publications: 125
- Citations: 1,761
Selected Publications
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Epidemiology, risk factors, and prevention strategies of multiple myeloma cancer: a systematic review (2025)
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How Does Age at Diagnosis Influence Multiple Myeloma Survival? Empirical Evidence (2025)
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Start Time End Time Integration (STETI): Method for Including Recent Data to Analyze Trends in Kidney Cancer Survival (2025)
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REVIEW AND RECOMMENDATIONS FOR HEALTH INFORMATICS IN SUB-SAHARAN AFRICAN COUNTRIES: BETWEEN OPPORTUNITIES AND CHALLENGES (2025)
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An Information Quality Framework for Managed Health Care (2024)
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Predicting Future Participation of Women in Space by Analyzing Past Trends (2024)
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A Customer Service Chatbot Using Python, Machine Learning, and Artificial Intelligence (2024)
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A Customer Service Chatbot Using Python, Machine Learning, and Artificial Intelligence (2024)
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ASI: Accuracy-Stability Index for Evaluating Deep Learning Models (2023)
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ASI: Accuracy-Stability Index for Evaluating Deep Learning Models (2023)
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Using Discrete-Event Simulation to Balance Staff Allocation and Patient Flow between Clinic and Surgery (2023)
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Visual Question Answering (VQA) on Images with Superimposed Text (2023)
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Automating Systematic Literature Reviews with Natural Language Processing and Text Mining: A Systematic Literature Review (2023)
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Data Science Knowledge and Skills That Reliability Engineers Need: A Survey (2023)
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Quantitative Technology Forecasting: A Review of Trend Extrapolation Methods (2023)
Collaboration Network
Top Collaborators
- Quantitative Technology Forecasting: A Review of Trend Extrapolation Methods
- Spacecraft for Deep Space Exploration: Combining Time and Budget to Model the Trend in Lifespan
- A Customer Service Chatbot Using Python, Machine Learning, and Artificial Intelligence
- Is technological progress a random walk? Examining data from space travel
- Future Satellite Lifetime Prediction From the Historical Trend in Satellite Half-Lives
Showing 5 of 8 shared publications
- Quantitative Technology Forecasting: A Review of Trend Extrapolation Methods
- Spacecraft for Deep Space Exploration: Combining Time and Budget to Model the Trend in Lifespan
- Is technological progress a random walk? Examining data from space travel
- Future Satellite Lifetime Prediction From the Historical Trend in Satellite Half-Lives
- Future Satellite Lifetime Prediction From the Historical Trend in Satellite Half-Lives
Showing 5 of 7 shared publications
- Quantitative Technology Forecasting: A Review of Trend Extrapolation Methods
- Spacecraft for Deep Space Exploration: Combining Time and Budget to Model the Trend in Lifespan
- A Customer Service Chatbot Using Python, Machine Learning, and Artificial Intelligence
- Is technological progress a random walk? Examining data from space travel
- Future Satellite Lifetime Prediction From the Historical Trend in Satellite Half-Lives
Showing 5 of 7 shared publications
- Quantitative Technology Forecasting: A Review of Trend Extrapolation Methods
- Spacecraft for Deep Space Exploration: Combining Time and Budget to Model the Trend in Lifespan
- Future Satellite Lifetime Prediction From the Historical Trend in Satellite Half-Lives
- Future Satellite Lifetime Prediction From the Historical Trend in Satellite Half-Lives
- Epidemiology, risk factors, and prevention strategies of multiple myeloma cancer: a systematic review
- Quantitative Technology Forecasting: A Review of Trend Extrapolation Methods
- Spacecraft for Deep Space Exploration: Combining Time and Budget to Model the Trend in Lifespan
- Is technological progress a random walk? Examining data from space travel
- Benchmarking Robustness of Deep Learning Classifiers Using Two-Factor Perturbation
- Discovering Limitations of Image Quality Assessments with Noised Deep Learning Image Sets
- Discrete-Event Simulation in Healthcare Settings: A Review
- Using Discrete-Event Simulation to Balance Staff Allocation and Patient Flow between Clinic and Surgery
- A Customer Service Chatbot Using Python, Machine Learning, and Artificial Intelligence
- A Customer Service Chatbot Using Python, Machine Learning, and Artificial Intelligence
- REVIEW AND RECOMMENDATIONS FOR HEALTH INFORMATICS IN SUB-SAHARAN AFRICAN COUNTRIES: BETWEEN OPPORTUNITIES AND CHALLENGES
- Epidemiology, risk factors, and prevention strategies of multiple myeloma cancer: a systematic review
- Start Time End Time Integration (STETI): Method for Including Recent Data to Analyze Trends in Kidney Cancer Survival
- How Does Age at Diagnosis Influence Multiple Myeloma Survival? Empirical Evidence
- How Does Age at Diagnosis Influence Multiple Myeloma Survival? Empirical Evidence
- Epidemiology, risk factors, and prevention strategies of multiple myeloma cancer: a systematic review
- An Information Quality Framework for College and University Websites
- An Automated Data Validation Approach to Enterprise Asset Management for Power and Utilities Organizations
- Recent, Rapid Advancement in Visual Question Answering: a Review
- Quantitative Technology Forecasting: A Review of Trend Extrapolation Methods
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