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Amos O Olagunju
St Cloud State University
St Cloud, Minnesota
 

Amos Olagunju is a professor in the Department of Computer Science and Information Technology at St. Cloud State University (SCSU) in Minnesota. He previously served as the interim dean of undergraduate studies for two years at SCSU. Prior to that position, he served as the dean of the School of Graduate Studies and chief research officer at Winston Salem State University in North Carolina. Amos served as the chair of the Mathematics and Computer Science Department, and later the Computing and Information Sciences Department, at Delaware State University (Dover, DE). Before that, he taught in the Asian Division at the University of Maryland University College, North Carolina A&T State University, and Michigan State University.

A faculty fellow and later a senior faculty fellow selected jointly by the American Society of Engineering Education and the Navy, Amos developed manpower mobilization and data-mining algorithms for monitoring the retention behaviors of personnel. As a member of the technical staff at Bell Communications Research (now Telcordia), he developed an architecture for a generalized C transaction environment, quantitative models for system workload projection and characterization, software metrics, and managerial decision support systems.

Amos developed statistical methods for the determination of content validity to obtain his doctorate in educational research and evaluation from the University of North Carolina at Greensboro. He investigated a distributed model as a basis for keyword detection to earn his master’s in computer and information sciences from Queen’s University (Canada). He received a bachelor’s degree in mathematics and computer science from Ahmadu Bello University in Nigeria. Amos was designated as an ACM senior member in 2007. His current research interests are in the areas of bioinformatics, quantitative security risk assessments, numerical computing, and artistic storytelling of breakthrough computing algorithms and technologies. He has been a reviewer for Computing Reviews since 2005, and has written over 100 reviews.


     

Healthcare agent: eliciting the power of large language models for medical consultation
Ren Z., Zhan Y., Yu B., Ding L., Xu P., Tao D. npj Artificial Intelligence 11-9, 2025.  Type: Article

The design of a reliable healthcare agent for solving diverse, intertwined healthcare issues is thought-provoking. How should an effective agent provide safe and quality responses to inquiries? Zhiyao et al. explore the potential of large language...

 

Breast cancer detection using a new parallel hybrid logistic regression model trained by particle swarm optimization and clonal selection algorithms
Etcil M., Dedeturk B., Kolukisa B., Bakir-Gungor B., Gungor V. Concurrency and Computation: Practice & Experience 37(12-14): 1-13, 2025.  Type: Article

Breast cancer research continues to look for new diagnostics and treatments. But how precise are the current artificial intelligence (AI) and machine learning (ML) models for diagnosing breast cancer? Etcil et al. investigate this timely issue via...

 

 Can you tell real from fake face images? Perception of computer-generated faces by humans
Bozkir E., Riedmiller C., Skodras A., Kasneci G., Kasneci E. ACM Transactions on Applied Perception 22(2): 1-23, 2025.  Type: Article

Perhaps the ever-increasing number of data images, machine learning algorithms, and technologies will continue to enhance life on Earth. But are most people able to distinguish between real and fake images? In experimental investigations, Bozkir e...

 

 Are disruptive papers more likely to impact technology and society?
Yang A., Yan X., Hu H., Hu H., Kong J., Deng S. The Journal of the Association for Information Science and Technology (JASIST) 76(3): 563-579, 2025.  Type: Article

The significance of science, technology, engineering, and mathematics (STEM)-related research on society is an interesting subject of debate. To what extent are the published research results from STEM areas truly impacting the advancements of tec...

 

Prevalence and prevention of large language model use in crowd work
Veselovsky V., Ribeiro M., Cozzolino P., Gordon A., Rothschild D., West R. Communications of the ACM 68(3): 42-47, 2025.  Type: Article

Large language model (LLM) and crowd-work platform tools offer both similar and different features for creating, annotating, administering, and summarizing survey data and experiments. But is there any robust, data-driven evidence of deficiencies ...

 
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