Results 11 to 20 of about 5,977,377 (233)

Quantum inspired feature engineering for explainable EEG signal classification

Scientific Reports
In this research, our main objective is to extract more informative features by deploying a simple and effective framework. One of the cheapest data-gathering methods from the brain is electroencephalography signal collection.
Fahad A. Alotaibi, Mehmet Said Nur Yagmahan, Khalid A. Alobaid, Mousa Jari, Omer Faruk Goktas, Mehmet Baygin, Sengul Dogan, Turker Tuncer   +7 more
doaj   +2 more sources

Cardioish: Lead-Based Feature Extraction for ECG Signals

Diagnostics
Background: Electrocardiography (ECG) signals are commonly used to detect cardiac disorders, with 12-lead ECGs being the standard method for acquiring these signals.
Turker Tuncer, Abdul Hafeez Baig, Emrah Aydemir, Tarik Kivrak, Ilknur Tuncer, Gulay Tasci, Sengul Dogan   +6 more
doaj   +2 more sources

Feature-Oriented Variability Management in Product Line Engineering [PDF]

, 2006
Features are effective communication “media” among different stakeholders of products or product lines. Hence, feature orientation in analyzing com-monality and variability of product lines is appealing.
Muthig, Dirk, Lee, Jaejoon
core   +4 more sources

An Efficient Feature Selection and Explainable Classification Method for EEG-based Epileptic Seizure Detection [PDF]

, 2023
Epilepsy is a prevalent neurological disorder that poses life-threatening emergencies. Early electroencephalogram (EEG) seizure detection can mitigate the risks and aid in the treatment of patients with epilepsy.
Chen, Y., Samuel, O., Yao, C., Shabaz, M., Liu, Z., Chen, S., Li, G., Ullah, I., Wang, X., Huang, K., Ahmad, I., Li, L., Zhao, G.   +12 more
core   +1 more source

Feature interaction: a critical review and considered forecast [PDF]

, 2003
The state of the art of the field of feature interactions in telecommunications services is reviewed, concentrating on three major research trends: software engineering approaches, formal methods, and on line techniques.
Magill, Evan, Reiff-Marganiec, Stephan, Stephan Reiff-marganiec A, Calder, M., Stephan Reiff-Marganiec (753997), Magill, E.H., Evan H. Magill B, Calder, Muffy, Evan H. Magill, Reiff-Marganiec, S., Evan H. Magill (7588937), Mario Kolberg, Kolberg, Mario, Muffy Calder (7588931), Kolberg, M., Muffy Calder, Stephan Reiff-Marganiec, Mario Kolberg (7588934)   +17 more
core   +1 more source

Explainable Feature Engineering for Multi-class Money Laundering Classification [PDF]

Informatică economică
This paper provides insight into typical money laundering typologies used in the financial crime domain and provides a concrete set of methods through the use of which fraudulent transactions may be classified using traditional machine learning ...
Petre-Cornel GRIGORESCU, Antoaneta AMZA
doaj   +1 more source

Rapid and accurate reverse engineering of geometry based on a multi-sensor system [PDF]

, 2014
The reduction of the lead time in measurement and reverse engineering, and the increased requirements in terms of accuracy and flexibility, have resulted in a great deal of research effort aimed at developing and implementing multi-sensor systems.
Longstaff, Andrew P., Fletcher, Simon, Alan Myers, Li, Feng, Simon Fletcher, Andrew Peter Longstaff, Feng Li, Longstaff, Andrew Peter, Myers, Alan   +8 more
core   +1 more source

Feature importance versus feature influence and what it signifies for explainable AI

, 2023
When used in the context of decision theory, feature importance expresses how much changing the value of a feature can change the model outcome (or the utility of the outcome), compared to other features.
Främling, Kary,
core   +1 more source

Feature-based Virtual Engineering

, 2022
S.19-39In this paper the concept of Feature-based Virtual Engineering is presented as an approach to the integrated product development. The focus of the paper is on presenting the current state of our approach towards a feature-based parametric ...
Brunetti, G.
core   +3 more sources

Explainable AI for Software Engineering

, 2021
We are very proud to release the first version of this Explainable AI for Software Engineering book. This book consists of three parts: Part 1-Explainable AI: We first provide a concise yet essential introduction to the most important aspects of ...
Jirayus Jiarpakdee, Chakkrit (Kla) Tantithamthavorn   +1 more
core   +1 more source