Open Access. Powered by Scholars. Published by Universities.®
Artificial Intelligence and Robotics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Social and Behavioral Sciences (29)
- Medicine and Health Sciences (26)
- Engineering (21)
- Library and Information Science (16)
- Theory and Algorithms (14)
-
- Data Science (13)
- Analytical, Diagnostic and Therapeutic Techniques and Equipment (10)
- Databases and Information Systems (10)
- Life Sciences (10)
- Scholarly Publishing (10)
- Graphics and Human Computer Interfaces (9)
- Information Security (9)
- Medical Specialties (9)
- Physics (9)
- Electrical and Computer Engineering (8)
- Genetics and Genomics (8)
- Chemicals and Drugs (7)
- Computational Biology (7)
- Numerical Analysis and Scientific Computing (7)
- Public Affairs, Public Policy and Public Administration (6)
- Anatomy (5)
- Biomedical Engineering and Bioengineering (5)
- Business (5)
- Education (5)
- Engineering Physics (5)
- Other Computer Sciences (5)
- Scholarly Communication (5)
- Institution
- Keyword
-
- Deep learning (23)
- Machine learning (20)
- Artificial intelligence (19)
- Neural networks (12)
- Humans (7)
-
- Natural language processing (7)
- Artificial Intelligence (5)
- Classification (5)
- Data Mining, Software Engineering (5)
- Large language models (5)
- Algorithms (4)
- Artificial neural networks (4)
- Computing methodologies (4)
- Contrastive learning (4)
- Education (4)
- Feature extraction (4)
- Graph neural networks (4)
- Secondary structure (4)
- Semantics (4)
- Sentiment analysis (4)
- Adaptation models (3)
- Adversarial machine learning (3)
- Blockchain (3)
- Computational linguistics (3)
- Computer (3)
- Computer science (3)
- Computer vision (3)
- Data models (3)
- Datasets (3)
- Decision making (3)
Articles 121 - 125 of 125
Full-Text Articles in Artificial Intelligence and Robotics
High-Dimensional Software Engineering Data And Feature Selection, Huanjing Wang, Taghi M. Khoshgoftaar, Kehan Gao
High-Dimensional Software Engineering Data And Feature Selection, Huanjing Wang, Taghi M. Khoshgoftaar, Kehan Gao
Computer Science Faculty Publications
Software metrics collected during project development play a critical role in software quality assurance. A software practitioner is very keen on learning which software metrics to focus on for software quality prediction. While a concise set of software metrics is often desired, a typical project collects a very large number of metrics. Minimal attention has been devoted to finding the minimum set of software metrics that have the same predictive capability as a larger set of metrics – we strive to answer that question in this paper. We present a comprehensive comparison between seven commonly-used filter-based feature ranking techniques (FRT) …
An Empirical Investigation Of Filter Attribute Selection Techniques For Software Quality Classification, Kehan Gao, Taghi M. Khoshgoftaar, Huanjing Wang
An Empirical Investigation Of Filter Attribute Selection Techniques For Software Quality Classification, Kehan Gao, Taghi M. Khoshgoftaar, Huanjing Wang
Computer Science Faculty Publications
Attribute selection is an important activity in data preprocessing for software quality modeling and other data mining problems. The software quality models have been used to improve the fault detection process. Finding faulty components in a software system during early stages of software development process can lead to a more reliable final product and can reduce development and maintenance costs. It has been shown in some studies that prediction accuracy of the models improves when irrelevant and redundant features are removed from the original data set. In this study, we investigated four filter attribute selection techniques, Automatic Hybrid Search (AHS), …
Pedagogical Possibilities For The N-Puzzle Problem, Zdravko Markov, Ingrid Russell, Todd W. Neller, Neli Zlatareva
Pedagogical Possibilities For The N-Puzzle Problem, Zdravko Markov, Ingrid Russell, Todd W. Neller, Neli Zlatareva
Computer Science Faculty Publications
In this paper we present work on a project funded by the National Science Foundation with a goal of unifying the Artificial Intelligence (AI) course around the theme of machine learning. Our work involves the development and testing of an adaptable framework for the presentation of core AI topics that emphasizes the relationship between AI and computer science. Several hands-on laboratory projects that can be closely integrated into an introductory AI course have been developed. We present an overview of one of the projects and describe the associated curricular materials that have been developed. The project uses machine learning as …
Enhancing Undergraduate Ai Courses Through Machine Learning Projects, Ingrid Russell, Zdravko Markov, Todd W. Neller, Susan Coleman
Enhancing Undergraduate Ai Courses Through Machine Learning Projects, Ingrid Russell, Zdravko Markov, Todd W. Neller, Susan Coleman
Computer Science Faculty Publications
It is generally recognized that an undergraduate introductory Artificial Intelligence course is challenging to teach. This is, in part, due to the diverse and seemingly disconnected core topics that are typically covered. The paper presents work funded by the National Science Foundation to address this problem and to enhance the student learning experience in the course. Our work involves the development of an adaptable framework for the presentation of core AI topics through a unifying theme of machine learning. A suite of hands-on semester-long projects are developed, each involving the design and implementation of a learning system that enhances a …
Unifying An Introduction To Artificial Intelligence Course Through Machine Learning Laboratory Experiences, Ingrid Russell, Zdravko Markov, Todd W. Neller, Michael Georgiopoulos, Susan Coleman
Unifying An Introduction To Artificial Intelligence Course Through Machine Learning Laboratory Experiences, Ingrid Russell, Zdravko Markov, Todd W. Neller, Michael Georgiopoulos, Susan Coleman
Computer Science Faculty Publications
This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application …