UWB Waveform for Automotive Short Range Radar

Daghouj, Dounia; Fattah, Mohammed; Mazer, Saïd; Balboul, Younes; Bekkali, Moulhime El

doi:10.15866/irea.v8i4.18997

Cited by 13 publications

(4 citation statements)

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“…iv) Prediction: for a classification task: each tree "votes" for a class, and the class with the most votes is the final prediction of the forest. For a regression task: the average output of individual trees is the final prediction [23].…”

Section: Random Forest For Foetal Ecg Predictionmentioning

confidence: 99%

Fetal electrocardiogram prediction using machine learning: a random forest-based approach

Moutaib,

Fattah,

Farhaoui

et al. 2024

IJEECS

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<p class="IOPText">Monitoring fetal health during pregnancy ensures safe delivery and the newborn’s well-being. The fetal electrocardiogram (fetal ECG) is a valuable tool for assessing fetal cardiac health, but interpretation of ECG data can be challenging due to its complexity and variability. In this work, we explore the application of machine learning, particularly random forest, to predict and analyze fetal ECGs. With its ability to manage large datasets and provide precise insights, random forest is a promising solution for this challenge. By comparing our random forest-based approach with other standard machine learning techniques such as artificial neural network (ANN), support vector machines (SVM), and recurrent neural networks (RNN), we observed that our solution outperformed these methods in accuracy, robustness, and reliability. This article details the methodology used, the implementation of the algorithm, as well as the comparative results obtained. Emphasis is placed on the benefits of random forest in this specific medical context, highlighting its potential as a future tool for fetal ECG prediction. Ultimately, our research suggests a shift toward random forest-based solutions for more efficient and accurate analysis of fetal ECGs, with direct implications for clinical practice and fetal well-being.</p>

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Section: Random Forest For Foetal Ecg Predictionmentioning

confidence: 99%

Fetal electrocardiogram prediction using machine learning: a random forest-based approach

Moutaib,

Fattah,

Farhaoui

et al. 2024

IJEECS

View full text Add to dashboard Cite

show abstract

“…If the number of possible paths between the starting node and the destination node is small, it will suffice to calculate the distances of paths between the nodes by adding the length of the links, which compose it, and to directly compare the lengths obtained. However, a solution becomes impractical if the number of nodes is very large [22,23] .…”

Section: Proposed Routing Formulationmentioning

confidence: 99%

Application of Internet of Things in the Health Sector: Toward Minimizing Energy Consumption

Moutaib

Ahajjam

Fattah

et al. 2022

Big Data Min. Anal.

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“…Obstacle detection and localization is a subject that interests many researchers and has been the subject of much work. Several algorithms have been proposed for the detection and localization of obstacles in the road domain and each algorithm has its strengths and weaknesses [13]- [15]. Most of these methods have somewhat complex architectures and require the fusion of radar with another type of sensor.…”

Section: The Proposed Algorithm For Detecting and Locating Targetsmentioning

confidence: 99%

Automatic target detection and localization using ultra-wideband radar

Daghouj

Abdellaoui

Fattah

et al. 2022

IJECE

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<span>The pulse ultra-wide band (UWB) radar consists of switching of energy of very short duration in an ultra-broadband emission chain, and the UWB signal emitted is an ultrashort pulse, of the order of nanoseconds, without a carrier. These systems can indicate the presence and distances of a distant object, call a target, and determine its size, shape, speed, and trajectory. In this paper, we present a UWB radar system allowing the detection of the presence of a target and its localization in a road environment based on the principle of correlation of the reflected signal with the reference and the determination of its correlation peak.</span>

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UWB Waveform for Automotive Short Range Radar

Cited by 13 publications

References 0 publications

Fetal electrocardiogram prediction using machine learning: a random forest-based approach

Fetal electrocardiogram prediction using machine learning: a random forest-based approach

Application of Internet of Things in the Health Sector: Toward Minimizing Energy Consumption

Automatic target detection and localization using ultra-wideband radar

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