scholarly journals V2X Wireless Technology Identification Using Time–Frequency Analysis and Random Forest Classifier

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4286
Author(s):  
Camelia Skiribou ◽  
Fouzia Elbahhar
Keyword(s):  
Random Forest ◽  
Noise Level ◽  
Spectrum Sharing ◽  
Feature Vector ◽  
Learning Algorithm ◽  
Random Forest Classifier ◽  
Identification System ◽  
Signal Identification ◽  
Communication Signals ◽  
Time Frequency

Signal identification is of great interest for various applications such as spectrum sharing and interference management. A typical signal identification system can be divided into two steps. A feature vector is first extracted from the received signal, then a decision is made by a classification algorithm according to its observed values. Some existing techniques show good performance but they are either sensitive to noise level or have high computational complexity. In this paper, a machine learning algorithm is proposed for the identification of vehicular communication signals. The feature vector is made up of Instantaneous Frequency (IF) resulting from time–frequency (TF) analysis. Its dimension is then reduced using the Singular Value Decomposition (SVD) technique, before being fed into a Random Forest classifier. Simulation results show the relevance and the low complexity of IF features compared to existing cyclostationarity-based ones. Furthermore, we found that the same accuracy can be maintained regardless of the noise level. The proposed framework thus provides a more accurate, robust and less complex V2X signal identification system.

Lung Cancer Prediction Using Random Forest

2019 ◽  
Vol 13 ◽  
Author(s):  
A. Rajini ◽  
M.A. Jabbar
Keyword(s):  
Lung Cancer ◽  
Random Forest ◽  
Predictive Model ◽  
Learning Algorithm ◽  
Early Stage ◽  
Random Forest Classifier ◽  
Cancer Dataset ◽  
Medical Practitioners ◽  
Cancer Data ◽  
Cancer Prediction

Background: In recent days, lung cancer is a familiar cancer across the globe. For the early prediction of lung cancer, medical practitioners and researchers require an efficient predictive model, which will reduce the number of deaths. In this paper, proposed a lung cancer prediction model by using random forest classifier, which aims at analyzing symptoms (gender, age, air pollution, weight loss, etc.). Objective: In this work, we address the problem of classification of lung cancer data using Random Forest Algorithm. Random Forest is the most accurate learning algorithm and many researchers in the health care domain use it. Method: This paper deals with the prediction of lung cancer by using Random Forest Classifier. Results: Proposed method (Random Forest Classifier) applied on two lung cancer datasets, achieved an accuracy of 100% for the lung cancer dataset-1 and 96.31 on dataset-2. In the prediction of lung cancer, the random forest has shown improved accuracy compared with other methods. Conclusion : This predictive model will help health professionals in predicting lung cancer at an early stage.

scholarly journals Arabic tweeps dialect prediction based on machine learning approach

2021 ◽  
Vol 11 (2) ◽  
pp. 1627
Author(s):  
Khaled Alrifai ◽  
Ghaida Rebdawi ◽  
Nada Ghneim
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Prediction Model ◽  
Feature Vector ◽  
Random Forest Classifier ◽  
Learning Approach ◽  
Feature Vectors ◽  
Machine Learning Approach ◽  
Important Trait

In this paper, we present our approach for profiling Arabic authors on twitter, based on their tweets. We consider here the dialect of an Arabic author as an important trait to be predicted. For this purpose, many indicators, feature vectors and machine learning-based classifiers were implemented. The results of these classifiers were compared to find out the best dialect prediction model. The best dialect prediction model was obtained using random forest classifier with full forms and their stems as feature vector.

scholarly journals Classification of Sea Ice Types in Sentinel-1 SAR Data Using Convolutional Neural Networks

2020 ◽  
Vol 12 (13) ◽  
pp. 2165 ◽  
Author(s):  
Hugo Boulze ◽  
Anton Korosov ◽  
Julien Brajard
Keyword(s):  
Random Forest ◽  
Sea Ice ◽  
Learning Algorithm ◽  
Computing Time ◽  
Texture Features ◽  
Random Forest Classifier ◽  
Synthetic Aperture ◽  
First Year ◽  
Sar Data

A new algorithm for classification of sea ice types on Sentinel-1 Synthetic Aperture Radar (SAR) data using a convolutional neural network (CNN) is presented. The CNN is trained on reference ice charts produced by human experts and compared with an existing machine learning algorithm based on texture features and random forest classifier. The CNN is trained on two datasets in 2018 and 2020 for retrieval of four classes: ice free, young ice, first-year ice and old ice. The accuracy of our classification is 90.5% for the 2018-dataset and 91.6% for the 2020-dataset. The uncertainty is a bit higher for young ice (85%/76% accuracy in 2018/2020) and first-year ice (86%/84% accuracy in 2018/2020). Our algorithm outperforms the existing random forest product for each ice type. It has also proved to be more efficient in computing time and less sensitive to the noise in SAR data. The code is publicly available.

Identification of species based on DNA barcode using k-mer feature vector and Random forest classifier

Gene ◽  
2016 ◽  
Vol 592 (2) ◽  
pp. 316-324 ◽  
Author(s):  
Prabina Kumar Meher ◽  
Tanmaya Kumar Sahu ◽  
A.R. Rao
Keyword(s):  
Random Forest ◽  
Feature Vector ◽  
Dna Barcode ◽  
Random Forest Classifier ◽  
Identification Of Species

The Performance Improvement on 4-FB Face using Random Forest Classifier

2018 ◽  
Vol 10 (5) ◽  
pp. 1-12
Author(s):  
B. Nassih ◽  
A. Amine ◽  
M. Ngadi ◽  
D. Naji ◽  
N. Hmina
Keyword(s):  
Random Forest ◽  
Performance Improvement ◽  
Random Forest Classifier

Methodology for Malware Classification using a Random Forest Classifier

2018 ◽  
Author(s):  
Carlos Domenick Morales-Molina ◽  
Diego Santamaria-Guerrero ◽  
Gabriel Sanchez-Perez ◽  
Hector Perez-Meana ◽  
Aldo Hernandez-Suarez
Keyword(s):  
Random Forest ◽  
Random Forest Classifier ◽  
Malware Classification
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