D
Daniel Berleant Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
faculty
20 h-index
125 pubs
1,746 cited
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Daniel Berleant's research primarily focuses on the application of computational methods to address complex problems in healthcare and technology forecasting. He has investigated the use of discrete-event simulation to optimize patient flow and staff allocation within clinical settings. His work also explores the automation of systematic literature reviews, employing natural language processing and text mining techniques to enhance efficiency and accuracy. Berleant has examined quantitative technology forecasting methods, including trend extrapolation.
His research in machine learning includes benchmarking the robustness of deep learning classifiers against perturbations and assessing the limitations of image quality assessments with noisy datasets. Berleant has a significant publication record, with 123 total publications and 1,731 citations, and an h-index of 19. He has collaborated with researchers such as Peng-Hung Tsai and Richard S. Segall, with whom he shares multiple publications, indicating a network of active scholarly engagement.
Metrics
- h-index: 20
- Publications: 125
- Citations: 1,746
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
Richard S. Segall
Arkansas State University
10 shared publications
In database
- 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 10 shared publications
Peng-Hung Tsai
10 shared publications
In database
- 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 10 shared publications
Hyacinthe Aboudja
Oklahoma City University (US)
9 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 9 shared publications
Venkata Jaipal Reddy Batthula
Southwestern College - Kansas (US)
6 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
- Forecasting of a Technology Using Quantitative Satellite Lifetime Data
Showing 5 of 6 shared publications
Michael Howell
5 shared publications
In database
- 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
- Forecasting of a Technology Using Quantitative Satellite Lifetime Data
- Moore's law, Wright's law and the Countdown to Exponential Space
Wei Dai
Purdue University Northwest (US)
5 shared publications
- Benchmarking Robustness of Deep Learning Classifiers Using Two-Factor Perturbation
- Discovering Limitations of Image Quality Assessments with Noised Deep Learning Image Sets
- Benchmarking Robustness of Deep Learning Classifiers Using Two-Factor Perturbation
- Discovering Limitations of Image Quality Assessments with Noised Deep Learning Image Sets
- Benchmarking Robustness of Deep Learning Classifiers Using Two-Factor Perturbation
V. P. Kodali
University of Arkansas at Little Rock (US)
4 shared publications
- Visual Question Answering (VQA) on Images with Superimposed Text
- Recent, rapid advancement in visual question answering architecture: a review
- Moore's law, Wright's law and the Countdown to Exponential Space
- Visual Question Answering (VQA) on Images with Superimposed Text
Michael Bauer
University of Arkansas for Medical Sciences
3 shared publications
In database
- 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
- Start Time End Time Integration (STETI): Analyzing Trends in Kidney Cancer Survival Time Data
Wei Dai
Purdue University Northwest (US)
2 shared publications
- Benchmarking Deep Learning Classifiers: Beyond Accuracy.
- ASI: Accuracy-Stability Index for Evaluating Deep Learning Models
John J. Forbus
University of Arkansas at Little Rock (US)
2 shared publications
- Discrete-Event Simulation in Healthcare Settings: A Review
- Using Discrete-Event Simulation to Balance Staff Allocation and Patient Flow between Clinic and Surgery
Girish Sundaram
University of Arkansas at Little Rock
2 shared publications
In database
- Automating Systematic Literature Reviews with Natural Language Processing and Text Mining: A Systematic Literature Review
- Automating Systematic Literature Reviews with Natural Language Processing and Text Mining: a Systematic Literature Review
Ty Ebsen
University of Arkansas at Little Rock
2 shared publications
In database
- A Customer Service Chatbot Using Python, Machine Learning, and Artificial Intelligence
- A Customer Service Chatbot Using Python, Machine Learning, and Artificial Intelligence
Thobani Chaduka
University of Arkansas at Little Rock
2 shared publications
In database
- Start Time End Time Integration (STETI): Method for Including Recent Data to Analyze Trends in Kidney Cancer Survival
- Start Time End Time Integration (STETI): Analyzing Trends in Kidney Cancer Survival Time Data
Shi‐Ming Tu
University of Arkansas for Medical Sciences (US)
2 shared publications
- Start Time End Time Integration (STETI): Method for Including Recent Data to Analyze Trends in Kidney Cancer Survival
- Start Time End Time Integration (STETI): Analyzing Trends in Kidney Cancer Survival Time Data
Michael O. Lawanson
University of Arkansas at Little Rock
2 shared publications
In database
- How Does Age at Diagnosis Influence Multiple Myeloma Survival? Empirical Evidence
- Epidemiology, risk factors, and prevention strategies of multiple myeloma cancer: a systematic review
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