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Optimizing event selection with the random grid search [PDF]

Computer Physics Communications, 2018
The random grid search (RGS) is a simple, but efficient, stochastic algorithm to find optimal cuts that was developed in the context of the search for the top quark at Fermilab in the mid-1990s.
Sezen Sekmen, Chip Stewart
exaly   +4 more sources

An Optimal Approach for Heart Sound Classification Using Grid Search in Hyperparameter Optimization of Machine Learning [PDF]

Bioengineering, 2022
Heart-sound auscultation is one of the most widely used approaches for detecting cardiovascular disorders. Diagnosing abnormalities of heart sound using a stethoscope depends on the physician’s skill and judgment.
Yunendah Nur Fuadah, Muhammad Adnan Pramudito, Ki Moo Lim   +2 more
doaj   +2 more sources

PPG Signals-Based Blood-Pressure Estimation Using Grid Search in Hyperparameter Optimization of CNN–LSTM [PDF]

Diagnostics, 2023
Researchers commonly use continuous noninvasive blood-pressure measurement (cNIBP) based on photoplethysmography (PPG) signals to monitor blood pressure conveniently. However, the performance of the system still needs to be improved.
Nurul Qashri Mahardika T, Yunendah Nur Fuadah, Da Un Jeong, Ki Moo Lim   +3 more
doaj   +2 more sources

Grid Search and Genetic Algorithm Optimization of Neural Networks for Automotive Radar Object Classification [PDF]

Sensors
This paper proposes and evaluates two neural network-based approaches for object classification in automotive radar systems, comparing the performance impact of grid search and genetic algorithm (GA) hyperparameter optimization strategies.
Atila Gabriel Ham, Corina Nafornita, Vladimir Cristian Vesa, George Copacean, Voislava Denisa Davidovici, Ioan Nafornita   +5 more
doaj   +2 more sources

Deep Learning for Predicting Late-Onset Breast Cancer Metastasis: The Single-Hyperparameter Grid Search (SHGS) Strategy for Meta-Tuning a Deep Feed-Forward Neural Network [PDF]

Bioengineering
Background: While machine learning has advanced in medicine, its widespread use in clinical applications, especially in predicting breast cancer metastasis, is still limited. We have been dedicated to constructing a deep feed-forward neural network (DFNN)
Yijun Zhou, Om Arora-Jain, Xia Jiang
doaj   +2 more sources

Detection of Obstructive Sleep Apnea from ECG Signal Using SVM Based Grid Search [PDF]

International Journal of Electronics and Telecommunications, 2021
Obstructive Sleep Apnea is one common form of sleep apnea and is now tested by means of a process called Polysomnography which is time-consuming, expensive and also requires a human observer throughout the study of the subject which makes it inconvenient
K.K. Valavan, S. Manoj, S. Abishek, T.G. Gokull Vijay, A.P. Vojaswwin, J. Rolant Gini, K.I. Ramachandran   +6 more
doaj   +1 more source

Machine learning approach to evaluate TdP risk of drugs using cardiac electrophysiological model including inter-individual variability

Frontiers in Physiology, 2023
Introduction: Predicting ventricular arrhythmia Torsade de Pointes (TdP) caused by drug-induced cardiotoxicity is essential in drug development. Several studies used single biomarkers such as qNet and Repolarization Abnormality (RA) in a single cardiac ...
Yunendah Nur Fuadah, Yunendah Nur Fuadah, Ali Ikhsanul Qauli, Ali Ikhsanul Qauli, Aroli Marcellinus, Muhammad Adnan Pramudito, Ki Moo Lim, Ki Moo Lim, Ki Moo Lim   +8 more
doaj   +1 more source

Grid Search Based Tire-Road Friction Estimation

IEEE Access, 2020
The tire-road friction coefficient (μmax) is an important input for vehicle dynamics control system and automated driving modules. However, reliable and accurate measurement of this parameter is difficult and costly in mass-produced vehicles and ...
Liang Shao, Chi Jin, Arno Eichberger, Cornelia Lex   +3 more
doaj   +1 more source

Financial Fraud Detection and Prediction in Listed Companies Using SMOTE and Machine Learning Algorithms

Entropy, 2022
This paper proposes a new method that can identify and predict financial fraud among listed companies based on machine learning. We collected 18,060 transactions and 363 indicators of finance, including 362 financial variables and a class variable. Then,
Zhihong Zhao, Tongyuan Bai
doaj   +1 more source

Parameter estimation of support vector machine with radial basis function kernel using grid search with leave-p-out cross validation for classification of motion patterns of subviral particles

Current Directions in Biomedical Engineering, 2021
The classification of subviral particle motion in fluorescence microscopy video sequences is relevant to drug development. This work introduces a method for estimating parameters for support vector machines (SVMs) with radial basis function (RBF) kernels
Schuhmann Ricardo M., Rausch Andreas, Schanze Thomas   +2 more
doaj   +1 more source