Maria Seale

493 total citations
26 papers, 258 citations indexed

About

Maria Seale is a scholar working on Artificial Intelligence, Control and Systems Engineering and Signal Processing. According to data from OpenAlex, Maria Seale has authored 26 papers receiving a total of 258 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 7 papers in Control and Systems Engineering and 5 papers in Signal Processing. Recurrent topics in Maria Seale's work include Machine Fault Diagnosis Techniques (6 papers), Anomaly Detection Techniques and Applications (6 papers) and Time Series Analysis and Forecasting (5 papers). Maria Seale is often cited by papers focused on Machine Fault Diagnosis Techniques (6 papers), Anomaly Detection Techniques and Applications (6 papers) and Time Series Analysis and Forecasting (5 papers). Maria Seale collaborates with scholars based in United States and Puerto Rico. Maria Seale's co-authors include Shahram Rahimi, Sudip Mittal, William Anderson, Ioana Banicescu, Somayeh Bakhtiari Ramezani, Amol V. Janorkar, Amin Amirlatifi, Linkan Bian, Andrew Thompson and Yi Zhang and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and IEEE Access.

In The Last Decade

Maria Seale

22 papers receiving 247 citations

Peers

Maria Seale

CNC

CSE

Tran Viet Khoa Australia

Mohammed Alkhathami Saudi Arabia

Malek Alrashidi Saudi Arabia

Jinwei Liu United States

Hongjun Zhu China

Davide Frey France

Γεώργιος Καρόπουλος Greece

Heba Mamdouh Farghaly Egypt

Fatma Helmy Ismail Egypt

Tran Viet Khoa Australia

Maria Seale

103 ×0.9

101 ×1.4

CNC

13 ×0.3

CSE

34 ×1.2

22 ×1.0

Citations per year, relative to Maria Seale Maria Seale (= 1×) peers Tran Viet Khoa

Countries citing papers authored by Maria Seale

Since Specialization

Citations

This map shows the geographic impact of Maria Seale's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Maria Seale with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maria Seale more than expected).

Fields of papers citing papers by Maria Seale

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Maria Seale. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Maria Seale. The network helps show where Maria Seale may publish in the future.

Co-authorship network of co-authors of Maria Seale

This figure shows the co-authorship network connecting the top 25 collaborators of Maria Seale. A scholar is included among the top collaborators of Maria Seale based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Maria Seale. Maria Seale is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Mun, Sungkwang, et al.. (2024). Frequency domain tensor-based 1D-convolutional neural network and multilinear principal component analysis for machinery fault detection. Annual Conference of the PHM Society. 16(1).

Ramezani, Somayeh Bakhtiari, et al.. (2024). Generating Synthetic Time Series Data for Cyber-Physical Systems. 1–7. 1 indexed citations

Mittal, Sudip, et al.. (2024). Explainable Anomaly Detection: Counterfactual driven What-If Analysis. 144–151. 3 indexed citations

Ramezani, Somayeh Bakhtiari, et al.. (2024). Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities. IEEE Access. 12. 57574–57602. 29 indexed citations

Anderson, William, Sudip Mittal, Shahram Rahimi, et al.. (2024). Enhancing Explainability and Trustworthiness of Intrusion Detection Systems Using Competitive Learning. 90–99. 1 indexed citations

Thompson, Andrew, et al.. (2024). AAD-LLM: Adaptive Anomaly Detection Using Large Language Models. 4194–4203. 9 indexed citations

Ramezani, Somayeh Bakhtiari, et al.. (2023). A Comparative Study of Continual, Lifelong, and Online Supervised Learning Libraries. SHILAP Revista de lepidopterología. 36. 1 indexed citations

Seale, Maria, et al.. (2022). An Ontology for an Epigenetics Approach to Prognostics and Health Management. 808–812.

Anderson, William, et al.. (2022). Creating an Explainable Intrusion Detection System Using Self Organizing Maps. 404–412. 17 indexed citations

10.

Seale, Maria, et al.. (2021). An Epigenetic Modeling Approach for Adaptive Prognostics of Engineered Systems. Procedia Computer Science. 185. 311–319. 1 indexed citations

11.

Seale, Maria, et al.. (2021). A Dynamic Hyperbolic Surface Model for Responsive Data Mining. Procedia Computer Science. 185. 170–176. 1 indexed citations

12.

Seale, Maria, et al.. (2021). Machine learning to determine optimal conditions for controlling the size of elastin-based particles. Scientific Reports. 11(1). 6343–6343. 7 indexed citations

13.

Amirlatifi, Amin, et al.. (2021). Machinery Faults Prediction Using Ensemble Tree Classifiers: Bagging or Boosting?. Annual Conference of the PHM Society. 13(1). 2 indexed citations

14.

Ramezani, Somayeh Bakhtiari, et al.. (2021). Similarity Based Methods for Faulty Pattern Detection in Predictive Maintenance. 2021 International Conference on Computational Science and Computational Intelligence (CSCI). 207–213. 4 indexed citations

15.

Ramezani, Somayeh Bakhtiari, et al.. (2021). A Survey of HMM-based Algorithms in Machinery Fault Prediction. 2021 IEEE Symposium Series on Computational Intelligence (SSCI). 1–9. 8 indexed citations

16.

Coleman, Cody, et al.. (2020). Text Classification and Tagging of United States Army Ground Vehicle Fault Descriptions in Support of Data-Driven Prognostics. Annual Conference of the PHM Society. 12(1). 8–8. 2 indexed citations

17.

Seale, Maria, et al.. (2020). Examining the effect of social distancing on the compound growth rate of COVID-19 at the county level (United States) using statistical analyses and a random forest machine learning model. Public Health. 185. 27–29. 43 indexed citations

18.

Seale, Maria, et al.. (2020). Evaluation of machine learning algorithms to predict the hydrodynamic radii and transition temperatures of chemo-biologically synthesized copolymers. Computers in Biology and Medicine. 128. 104134–104134. 9 indexed citations

19.

O’Neill, Eduardo, et al.. (2020). An Integrated Blockchain Approach for Provenance of Rotorcraft Maintenance Data. 1–8.

20.

Seale, Maria, et al.. (2019). Approaches for Using Machine Learning Algorithms with Large Label Sets for Rotorcraft Maintenance. 1–8. 6 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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