Outlier Detection in Time-Series Receive Signal Strength Observation Using Z-Score Method with Sn Scale Estimator for Indoor Localization

Yaro, Abdulmalık Shehu; Maly, Filip; Pražák, Pavel

doi:10.3390/app13063900

Cited by 20 publications

(18 citation statements)

References 31 publications

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“…Typically, a Z-score threshold of 3 or −3 is employed to identify outliers. This implies that any data point with a Z-score exceeding 3 or falling below −3 is considered an outlier (Yaro et al 2023). Frequently utilized in diverse fields, basic statistical tools like the Z-score play a routine role in outlier identification within datasets.…”

Section: Z-score Methodsmentioning

confidence: 99%

A Generalized Linear Model and Machine Learning Approach for Predicting the Frequency and Severity of Cargo Insurance in Thailand’s Border Trade Context

Panjee,

Amornsawadwatana

2024

Risks

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The study compares model approaches in predictive modeling for claim frequency and severity within the cross-border cargo insurance domain. The aim is to identify the optimal model approach between generalized linear models (GLMs) and advanced machine learning techniques. Evaluations focus on mean absolute error (MAE) and root mean squared error (RMSE) metrics to comprehensively assess predictive performance. For frequency prediction, extreme gradient boosting (XGBoost) demonstrates the lowest MAE, indicating higher accuracy compared to gradient boosting machines (GBMs) and a generalized linear model (Poisson). Despite XGBoost’s lower MAE, it shows higher RMSE values, suggesting a broader error spread and larger magnitudes compared to gradient boosting machines (GBMs) and a generalized linear model (Poisson). Conversely, the generalized linear model (Poisson) showcases the best RMSE values, indicating tighter clustering and smaller error magnitudes, despite a slightly higher MAE. For severity prediction, extreme gradient boosting (XGBoost) displays the lowest MAE, implying better accuracy. However, it exhibits a higher RMSE, indicating wider error dispersion compared to a generalized linear model (Gamma). In contrast, a generalized linear model (Gamma) demonstrates the lowest RMSE, portraying tighter clustering and smaller error magnitudes despite a higher MAE. In conclusion, extreme gradient boosting (XGBoost) stands out in mean absolute error (MAE) for both frequency and severity prediction, showcasing superior accuracy. However, a generalized linear model (Gamma) offers a balance between accuracy and error magnitude, and its performance outperforms extreme gradient boosting (XGBoost) and gradient boosting machines (GBMs) in terms of RMSE metrics, with a slightly higher MAE. These findings empower insurance companies to enhance risk assessment processes, set suitable premiums, manage reserves, and accurately forecast claim occurrences, contributing to competitive pricing for clients while ensuring profitability. For cross-border trade entities, such as trucking companies and cargo owners, these insights aid in improved risk management and potential cost savings by enabling more reasonable insurance premiums based on accurate predictive claims from insurance companies.

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Section: Z-score Methodsmentioning

confidence: 99%

A Generalized Linear Model and Machine Learning Approach for Predicting the Frequency and Severity of Cargo Insurance in Thailand’s Border Trade Context

Panjee,

Amornsawadwatana

2024

Risks

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“…The median is robust to outliers and represents the center of the data without being heavily influenced by extreme values. The MAD is also robust to outliers and characterizes the variability in the data without being distorted by extreme values [55].…”

Section: Outliers Removalmentioning

confidence: 99%

Four-Wheeled Vehicle Sideslip Angle Estimation: A Machine Learning-Based Technique for Real-Time Virtual Sensor Development

Napolitano Dell’Annunziata,

Ruffini,

Stefanelli

et al. 2024

Applied Sciences

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In the last few decades, the role of vehicle dynamics control systems has become crucial. In this complex scenario, the correct real-time estimation of the vehicle’s sideslip angle is decisive. Indeed, this quantity is deeply linked to several aspects, such as traction and stability optimization, and its correct understanding leads to the possibility of reaching greater road safety, increased efficiency, and a better driving experience for both autonomous and human-controlled vehicles. This paper aims to estimate accurately the sideslip angle of the vehicle using different neural network configurations. Then, the proposed approach involves using two separate neural networks in a dual-network architecture. The first network is dedicated to estimating the longitudinal velocity, while the second network predicts the sideslip angle and takes the longitudinal velocity estimate from the first network as input. This enables the creation of a virtual sensor to replace the real one. To obtain a reliable training dataset, several test sessions were conducted on different tracks with various layouts and characteristics, using the same reference instrumented vehicle. Starting from the acquired channels, such as lateral and longitudinal acceleration, steering angle, yaw rate, and angular wheel speeds, it has been possible to estimate the sideslip angle through different neural network architectures and training strategies. The goodness of the approach was assessed by comparing the estimations with the measurements obtained from an optical sensor able to provide accurate values of the target variable. The obtained results show a robust alignment with the reference values in a great number of tested conditions. This confirms that the adoption of artificial neural networks represents a reliable strategy to develop real-time virtual sensors for onboard solutions, expanding the information available for controls.

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“…Outliers, which can potentially skew model performance, are detected using techniques such as scatter plots and histograms. Various outlier detection methods, including linear regression, Z-score method, interquartile range (IQR) method, local deviation factor method combined outliers, are used to identify and process deviations from standard values in a dataset [25,26]. Computational approach employed to identify influential parameters on the Young's modulus of biocompatible Ti alloys and to predict their composition.…”

Section: Data Preprocessingmentioning

confidence: 99%

Predicting Low-Modulus Biocompatible Titanium Alloys Using Machine Learning

Marković,

Manojlović,

Ružić

et al. 2023

Materials

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Titanium alloys have been present for decades as the main components for the production of various orthopedic and dental elements. However, modern times require titanium alloys with a low Young’s modulus, and without the presence of cytotoxic alloying elements. Machine learning was used with aim to analyze biocompatible titanium alloys and predict the composition of Ti alloys with a low Young’s modulus. A database was created using experimental data for alloy composition, Young’s modulus, and mechanical and thermal properties of biocompatible titanium alloys. The Extra Tree Regression model was built to predict the Young’s modulus of titanium alloys. By processing data of 246 alloys, the specific heat was discovered to be the most influential parameter that contributes to the lowering of the Young’s modulus of titanium alloys. Further, the Monte Carlo method was used to predict the composition of future alloys with the desired properties. Simulation results of ten million samples, with predefined conditions for obtaining titanium alloys with a Young’s modulus lower than 70 GPa, show that it is possible to obtain several multicomponent alloys, consisting of five main elements: titanium, zirconium, tin, manganese and niobium.

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Outlier Detection in Time-Series Receive Signal Strength Observation Using Z-Score Method with Sn Scale Estimator for Indoor Localization

Cited by 20 publications

References 31 publications

A Generalized Linear Model and Machine Learning Approach for Predicting the Frequency and Severity of Cargo Insurance in Thailand’s Border Trade Context

A Generalized Linear Model and Machine Learning Approach for Predicting the Frequency and Severity of Cargo Insurance in Thailand’s Border Trade Context

Four-Wheeled Vehicle Sideslip Angle Estimation: A Machine Learning-Based Technique for Real-Time Virtual Sensor Development

Predicting Low-Modulus Biocompatible Titanium Alloys Using Machine Learning

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