William Eberle

William Eberle

Tennessee Tech University

H-index: 20

North America-United States

About William Eberle

William Eberle, With an exceptional h-index of 20 and a recent h-index of 15 (since 2020), a distinguished researcher at Tennessee Tech University, specializes in the field of Data Mining, Anomaly Detection.

His recent articles reflect a diverse array of research interests and contributions to the field:

Advances in Explainable, Fair, and Trustworthy AI

Domain Knowledge-Aided Explainable Artificial Intelligence

Instilling conscience about bias and fairness in automated decisions

Self-Organizing Map-Based Graph Clustering and Visualization on Streaming Graphs

Incorporating the concepts of fairness and bias into an undergraduate computer science course to promote fair automated decision systems

Network Intrusion Detection and Attack Type Classification using Machine Learning

A Survey of Scalable Reinforcement Learning

Online Guard: Identifying the misinformation in social media and its impact on COVID-19 vaccination progress in different countries

William Eberle Information

University

Position

___

Citations(all)

1977

Citations(since 2020)

1159

Cited By

1244

hIndex(all)

20

hIndex(since 2020)

15

i10Index(all)

37

i10Index(since 2020)

26

Email

University Profile Page

Tennessee Tech University

Google Scholar

View Google Scholar Profile

William Eberle Skills & Research Interests

Data Mining

Anomaly Detection

Top articles of William Eberle

Title

Journal

Author(s)

Publication Date

Advances in Explainable, Fair, and Trustworthy AI

INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS

Sheikh Rabiul Islam

Ingrid Russell

William Eberle

Douglas Talbert

Md Golam Moula Mehedi Hasan

2024/5

Domain Knowledge-Aided Explainable Artificial Intelligence

arXiv preprint arXiv:1911.09853

Sheikh Rabiul Islam

William Eberle

Sheikh K Ghafoor

Ambareen Siraj

Mike Rogers

2019/11/22

Instilling conscience about bias and fairness in automated decisions

Journal of Computing Sciences in Colleges

Sheikh Rabiul Islam

Ingrid Russell

William Eberle

Darina Dicheva

2022/4/1

Self-Organizing Map-Based Graph Clustering and Visualization on Streaming Graphs

Prabin B Lamichhane

William Eberle

2022/11/28

Incorporating the concepts of fairness and bias into an undergraduate computer science course to promote fair automated decision systems

Sheikh Rabiul Islam

Ingrid Russell

William Eberle

Darina Dicheva

2022/3/3

Network Intrusion Detection and Attack Type Classification using Machine Learning

Proceedings of Student Research and Creative Inquiry Day

Tanjila Mawla

William Eberle

2022/5/20

A Survey of Scalable Reinforcement Learning

George B Stone

Douglas A Talbert

William Eberle

2022

Online Guard: Identifying the misinformation in social media and its impact on COVID-19 vaccination progress in different countries

Proceedings of Student Research and Creative Inquiry Day

Sanjida Akter Sharna

William Eberle

2022/5/20

Utilizing Real-Time Strategy for Penetration Testing

Int. J. Chaotic Comput.

George B Stone

Douglas A Talbert

William Eberle

2022

Discovering breach patterns on the internet of health things: A graph and machine learning anomaly analysis

The International FLAIRS Conference Proceedings

Prabin B Lamichhane

Hannah Mannering

William Eberle

2022/5/4

Visualization of Anomalies using Graph-Based Anomaly Detection

The International FLAIRS Conference Proceedings

Ramesh Paudel

Lauren Tharp

Dulce Kaiser

William Eberle

Gerald Gannod

2021/4/18

Implications of Combining Domain Knowledge in Explainable Artificial Intelligence.

Sheikh Rabiul Islam

William Eberle

2021/3

Using ai/machine learning for reconnaissance activities during network penetration testing

International Conference on Cyber Warfare and Security

George Stone

Douglas Talbert

William Eberle

2021/2/1

Explainable artificial intelligence approaches: A survey

arXiv preprint arXiv:2101.09429

Sheikh Rabiul Islam

William Eberle

Sheikh Khaled Ghafoor

Mohiuddin Ahmed

2021/1/23

Anomaly detection in edge streams using term frequency-inverse graph frequency (tf-igf) concept

Prabin B Lamichhane

William Eberle

2021/12/15

An approach for concept drift detection in a graph stream using discriminative subgraphs

ACM Transactions on Knowledge Discovery from Data (TKDD)

Ramesh Paudel

William Eberle

2020/9/28

Snapsketch: Graph representation approach for intrusion detection in a streaming graph

Ramesh Paudel

William Eberle

2020/8

Node Similarity for Anomaly Detection in Attributed Graphs

Prajjwal Kandel

William Eberle

2020/5/5

Towards quantification of explainability in explainable artificial intelligence methods

Sheikh Rabiul Islam

William Eberle

Sheikh K Ghafoor

2020/5/18

Graph Filtering to Remove the" Middle Ground" for Anomaly Detection

William Eberle

Lawrence Holder

2020/12/10

See List of Professors in William Eberle University(Tennessee Tech University)