Evaluation of four machine learning models for signal detection

Evaluation of four machine learning models for signal detection

Background: Logistic regression-based signal detection algorithms have benefits over disproportionality analysis due to their ability to handle potential confounders and masking factors. Feature exploration and developing alternative machine learning algorithms can further strengthen signal detectio...

Full description

Bibliographic Details
Main Authors:	Daniel G. Dauner, Eleazar Leal, Terrence J. Adam, Rui Zhang, Joel F. Farley
Format:	Article
Language:	English
Published:	SAGE Publishing 2023-12-01
Series:	Therapeutic Advances in Drug Safety
Online Access:	https://doi.org/10.1177/20420986231219472

Similar Items

Evaluation of different time domain peak models using extreme learning machine-based peak detection for EEG signal
by: Adam, A., et al.
Published: (2016)

Evaluation of Different Time Domain Peak Models using Extreme Learning Machine‐Based Peak Detection for EEG Signal
by: Asrul, Adam, et al.
Published: (2016)

Improvement of signal detection based on using machine learning
by: Bassam Abd
Published: (2025-02-01)

Detection and Evaluation of Machine Learning Bias
by: Salem Alelyani
Published: (2021-07-01)

Machine-Learning Methods for Speech and Handwriting Detection Using Neural Signals: A Review
by: Ovishake Sen, et al.
Published: (2023-06-01)

Evaluation of Machine Learning Algorithms for Classification of EEG Signals
by: Francisco Javier Ramírez-Arias, et al.
Published: (2022-06-01)

Further studies of extreme learning machine and compressed signal detection
by: Cao, Jiuwen
Published: (2013)

Drowsiness Detection using EEG signals and Machine Learning Algorithms
by: Joshi Adinath, et al.
Published: (2022-01-01)

Evaluating machine learning models for supernova gravitational wave signal classification
by: Y Sultan Abylkairov, et al.
Published: (2025-01-01)

Machine learning Calabi-Yau four-folds
by: He, Y-H, et al.
Published: (2021)

Emotion Detection from EEG Signals Using Machine Deep Learning Models
by: João Vitor Marques Rabelo Fernandes, et al.
Published: (2024-08-01)

Optimized machine learning model for Alzheimer and epilepsy detection from EEG signals
by: P. Jasphin Jeni Sharmila, et al.
Published: (2024-04-01)

Evaluation of Machine Learning Algorithms for Malware Detection
by: Muhammad Shoaib Akhtar, et al.
Published: (2023-01-01)

Artefact Detection in Impedance Pneumography Signals: A Machine Learning Approach
by: Jonathan Moeyersons, et al.
Published: (2021-04-01)

Machine Learning for Detection of Muscular Activity from Surface EMG Signals
by: Francesco Di Nardo, et al.
Published: (2022-04-01)

Variational Quantum Circuits for Machine Learning. An Application for the Detection of Weak Signals
by: Israel Griol-Barres, et al.
Published: (2021-07-01)

Hypertension Detection Based on Photoplethysmography Signal Morphology and Machine Learning Techniques
by: Lucian Evdochim, et al.
Published: (2022-08-01)

Machine learning detection of dust impact signals observed by the Solar Orbiter
by: A. Kvammen, et al.
Published: (2023-01-01)

Machine Learning models for the Cognitive Stress Detection Using Heart Rate Variability Signals
by: Nailul Izzah, et al.
Published: (2022-11-01)

Epileptic Seizure Detection in EEG Signals Using Machine Learning and Deep Learning Techniques
by: Hepseeba Kode, et al.
Published: (2024-01-01)

Detection of epileptic seizure in EEG signals using machine learning and deep learning techniques
by: Pankaj Kunekar, et al.
Published: (2024-01-01)

Machine learning for microalgae detection and utilization
by: Hongwei Ning, et al.
Published: (2022-07-01)

Four Machine Learning Algorithms for Biometrics Fusion: A Comparative Study
by: I. G. Damousis, et al.
Published: (2012-01-01)

Machine Learning Applied to Reference Signal-Less Detection of Motion Artifacts in Photoplethysmographic Signals: A Review
by: Erick Javier Argüello-Prada, et al.
Published: (2024-11-01)

Evaluation of Machine Learning based Network Attack Detection
by: Muhammad Awais Rajput, et al.
Published: (2023-02-01)

Robust machine-learning based algorithm for detection of signal under noise floor
by: Wang, Wenbo
Published: (2022)

Towards fully automated machine learning and processing of ballistocardiograph signal for heartbeat detection
by: Zhang, Shiyu
Published: (2020)

Detection of single channel signal under noise floor using machine learning
by: Tay, Shaun
Published: (2021)

Machine learning based technique for detection of communication signal under noise floor
by: Zhong, Hongrui
Published: (2022)

Detection of Olfactory Stimulus in Electroencephalogram Signals Using Machine and Deep Learning Methods
by: Burak Akbugday, et al.
Published: (2024-01-01)

Ensemble Machine Learning Approach for Parkinson’s Disease Detection Using Speech Signals
by: Syed Nisar Hussain Bukhari, et al.
Published: (2024-05-01)

Early Life Stress Detection Using Physiological Signals and Machine Learning Pipelines
by: Zeinab Shahbazi, et al.
Published: (2023-01-01)

Machine Learning Approaches for MDD Detection and Emotion Decoding Using EEG Signals
by: Lijuan Duan, et al.
Published: (2020-09-01)

Predictive modeling for eosinophilic chronic rhinosinusitis: Nomogram and four machine learning approaches
by: Panhui Xiong, et al.
Published: (2024-02-01)

Evaluation of Four Strategies for SARS-CoV-2 Detection: Characteristics and Prospects
by: Yuqing Chen, et al.
Published: (2022-12-01)

Variations in treatment need using four screening methods
by: Freer Elissa, et al.
Published: (1999-04-01)

Multi-Rate Vibration Signal Analysis for Bearing Fault Detection in Induction Machines Using Supervised Learning Classifiers
by: Nada El Bouharrouti, et al.
Published: (2023-12-01)

Architecture for pavement pothole evaluation using deep learning, machine vision, and fuzzy logic
by: Mario Roman-Garay, et al.
Published: (2025-07-01)

An Evaluation of the Factors Affecting ‘Poacher’ Detection with Drones and the Efficacy of Machine-Learning for Detection
by: Katie E. Doull, et al.
Published: (2021-06-01)

Ensemble-based machine learning models for vehicle drivers’ fatigue state detection utilizing EEG signals
by: Hasan, Md Mahmudul, et al.
Published: (2024)