NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning

This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.

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Peringkasan dan Support Vector Machine pada Klasifikasi Dokumen

JURNAL INFOTEL ◽

10.20895/infotel.v9i4.312 ◽

2017 ◽

Vol 9 (4) ◽

pp. 416 ◽

Cited By ~ 1

Author(s):

Nelly Indriani Widiastuti ◽

Ednawati Rainarli ◽

Kania Evita Dewi

Keyword(s):

Support Vector Machine ◽

Naive Bayes ◽

Naïve Bayes ◽

Training Data ◽

Support Vector ◽

Good Reputation ◽

Multiclass Support Vector Machine ◽

Simple Logistic ◽

Better Than

Classification is the process of grouping objects that have the same features or characteristics into several classes. The automatic documents classification use words frequency that appears on training data as features. The large number of documents cause the number of words that appears as a feature will increase. Therefore, summaries are chosen to reduce the number of words that used in classification. The classification uses multiclass Support Vector Machine (SVM) method. SVM was considered to have a good reputation in the classification. This research tests the effect of summary as selection features into documents classification. The summaries reduce text into 50%. A result obtained that the summaries did not affect value accuracy of classification of documents that use SVM. But, summaries improve the accuracy of Simple Logistic Classifier. The classification testing shows that the accuracy of Naïve Bayes Multinomial (NBM) better than SVM

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A Comparison of Machine Learning Algorithms for the Segmentation and Classification of Snow Micro Penetrometer Profiles on Arctic Sea Ice

10.5194/egusphere-egu21-15637 ◽

2021 ◽

Author(s):

Julia Kaltenborn ◽

Viviane Clay ◽

Amy R. Macfarlane ◽

Joshua Michael Lloyd King ◽

Martin Schneebeli

Keyword(s):

Machine Learning ◽

Sea Ice ◽

Arctic Sea Ice ◽

Machine Learning Algorithms ◽

Training Data ◽

Support Vector ◽

Snow Layer ◽

Arctic Sea ◽

Execution Speed

Snow-layer classification is an essential diagnostic task for a wide variety of cryospheric science and climate research applications. Traditionally, these measurements are made in snow pits, requiring trained operators and a substantial time commitment. The SnowMicroPen (SMP), a portable high-resolution snow penetrometer, has been demonstrated as a capable tool for rapid snow grain classification and layer type segmentation through statistical inversion of its mechanical signal. The manual classification of the SMP profiles requires time and training and becomes infeasible for large datasets.Here, we introduce a novel set of SMP measurements collected during the MOSAiC expedition and apply Machine Learning (ML) algorithms to automatically classify and segment SMP profiles of snow on Arctic sea ice. To this end, different supervised and unsupervised ML methods, including Random Forests, Support Vector Machines, Artificial Neural Networks, and k-means Clustering, are compared. A subsequent segmentation of the classified data results in distinct layers and snow grain markers for the SMP profiles. The models are trained with the dataset by King et al. (2020) and the MOSAiC SMP dataset. The MOSAiC dataset is a unique and extensive dataset characterizing seasonal and spatial variation of snow on the central Arctic sea-ice.We will test and compare the different algorithms and evaluate the algorithms&#8217; effectiveness based on the need for initial dataset labeling, execution speed, and ease of implementation. In particular, we will compare supervised to unsupervised methods, which are distinguished by their need for labeled training data.The implementation of different ML algorithms for SMP profile classification could provide a fast and automatic grain type classification and snow layer segmentation. Based on the gained knowledge from the algorithms&#8217; comparison, a tool can be built to provide scientists from different fields with an immediate SMP profile classification and segmentation.&#160;&#160;King, J., Howell, S., Brady, M., Toose, P., Derksen, C., Haas, C., & Beckers, J. (2020). Local-scale variability of snow density on Arctic sea ice. The Cryosphere, 14(12), 4323-4339, https://doi.org/10.5194/tc-14-4323-2020.

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Extraction of Sea Ice Cover by Sentinel-1 SAR Based on SVM with Unsupervised Generation of Training Data

10.20944/preprints202005.0336.v1 ◽

2020 ◽

Author(s):

Xiaoming Li ◽

Yan Sun ◽

Qiang Zhang

Keyword(s):

Machine Learning ◽

Sea Ice ◽

Learning Algorithm ◽

Texture Features ◽

Open Water ◽

Ice Cover ◽

Training Data ◽

Support Vector ◽

Training Samples

In this paper, we focus on developing a novel method to extract sea ice cover (i.e., discrimination/classification of sea ice and open water) using Sentinel-1 (S1) cross-polarization (vertical-horizontal, VH or horizontal-vertical, HV) data in extra wide (EW) swath mode based on the machine learning algorithm support vector machine (SVM). The classification basis includes the S1 radar backscatter coefficients and texture features that are calculated from S1 data using the gray level co-occurrence matrix (GLCM). Different from previous methods where appropriate samples are manually selected to train the SVM to classify sea ice and open water, we proposed a method of unsupervised generation of the training samples based on two GLCM texture features, i.e. entropy and homogeneity, that have contrasting characteristics on sea ice and open water. We eliminate the most uncertainty of selecting training samples in machine learning and achieve automatic classification of sea ice and open water by using S1 EW data. The comparison shows good agreement between the SAR-derived sea ice cover using the proposed method and a visual inspection, of which the accuracy reaches approximately 90% - 95% based on a few cases. Besides this, compared with the analyzed sea ice cover data Ice Mapping System (IMS) based on 728 S1 EW images, the accuracy of extracted sea ice cover by using S1 data is more than 80%.

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Supervised Learning Based Classification of Cardiovascular Diseases

Proceedings of Engineering and Technology Innovation ◽

10.46604/peti.2021.7217 ◽

2021 ◽

Vol 20 ◽

pp. 24-34

Author(s):

Arif Hussain ◽

Hassaan Malik ◽

Muhammad Umar Chaudhry

Keyword(s):

Machine Learning ◽

Model Building ◽

Matthews Correlation Coefficient ◽

Early Stage ◽

Support Vector ◽

Logistics Regression ◽

Efficiency And Effectiveness ◽

Sensitivity Specificity ◽

Better Than

Detecting cardiovascular disease (CVD) in the early stage is a difficult and crucial process. The objective of this study is to test the capability of machine learning (ML) methods for accurately diagnosing the CVD outcomes. For this study, the efficiency and effectiveness of four well renowned ML classifiers, i.e., support vector machine (SVM), logistics regression (LR), naive Bayes (NB), and decision tree (J48), are measured in terms of precision, sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC), correctly and incorrectly classified instances, and model building time. These ML classifiers are applied on publically available CVD dataset. In accordance with the measured result, J48 performs better than its competitor classifiers, providing significant assistance to the cardiologists.

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Website-Based Application for Classification of Diabetes Using Logistic Regression Method

Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) ◽

10.24843/jim.2021.v09.i01.p03 ◽

2021 ◽

pp. 23

Author(s):

Muhamad Soleh ◽

Naufal Ammar ◽

Indrati Sukmadi

Keyword(s):

Machine Learning ◽

Regression Method ◽

Training Data ◽

Support Vector ◽

Application Development ◽

Science Field ◽

Logistics Regression ◽

Vector Machines ◽

Logistic Regression Method

Machine learning is a one of computer science field, machine-learning studies how computers are able to learn from data to improve their intelligence. Machine learning consists of many classification methods, including Neural Networks, Support Vector Machines, Logistics Regression, and others. In this study, a classification process carried out using the Logistics Regression method for cases of Diabetes. Diabetes is an increase in glucose in the bloodstream due to a lack of insulin, which is responsible for the transfer of glucose from the blood to tissues or cells. This study created with the aim of improving previous paper. The data used in this study are the same data as previous studies published by the Pima Indian Diabetes Dataset. In this study, several stages used, those are pre-processing, processing, evaluation, and website-based application development. The data in this study divided into two, 75% for training data, and 25% for testing data. This study produces an evaluation with an accuracy 80%, which means it is better than the previous paper, which is 75, 97%.

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Machine Learning-Based CYGNSS Soil Moisture Estimates over ISMN sites in CONUS

Remote Sensing ◽

10.3390/rs12071168 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1168 ◽

Cited By ~ 5

Author(s):

Volkan Senyurek ◽

Fangni Lei ◽

Dylan Boyd ◽

Mehmet Kurum ◽

Ali Cafer Gurbuz ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Soil Moisture ◽

Microwave Remote Sensing ◽

Satellite System ◽

Vital Role ◽

Support Vector ◽

Near Surface ◽

Data Products ◽

Global Navigation Satellite

Soil moisture (SM) derived from satellite-based remote sensing measurements plays a vital role for understanding Earth’s land and near-surface atmosphere interactions. Bistatic Global Navigation Satellite System (GNSS) Reflectometry (GNSS-R) has emerged in recent years as a new domain of microwave remote sensing with great potential for SM retrievals, particularly at high spatio-temporal resolutions. In this work, a machine learning (ML)-based framework is presented for obtaining SM data products over the International Soil Moisture Network (ISMN) sites in the Continental United States (CONUS) by leveraging spaceborne GNSS-R observations provided by NASA’s Cyclone GNSS (CYGNSS) constellation alongside remotely sensed geophysical data products. Three widely-used ML approaches—artificial neural network (ANN), random forest (RF), and support vector machine (SVM)—are compared and analyzed for the SM retrieval through utilizing multiple validation strategies. Specifically, using a 5-fold cross-validation method, overall RMSE values of 0.052, 0.061, and 0.065 cm3/cm3 are achieved for the RF, ANN, and SVM techniques, respectively. In addition, both a site-independent and a year-based validation techniques demonstrate satisfactory accuracy of the proposed ML model, suggesting that this SM approach can be generalized in space and time domains. Moreover, the achieved accuracy can be further improved when the model is trained and tested over individual SM networks as opposed to combining all available SM networks. Additionally, factors including soil type and land cover are analyzed with respect to their impacts on the accuracy of SM retrievals. Overall, the results demonstrated here indicate that the proposed technique can confidently provide SM estimates over lightly-vegetated areas with vegetation water content (VWC) less than 5 kg/m2 and relatively low spatial heterogeneity.

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Classification of Spam Text using SVM

Journal of University of Shanghai for Science and Technology ◽

10.51201/jusst/21/08437 ◽

2021 ◽

Vol 23 (08) ◽

pp. 616-624

Author(s):

Gaddam Akhil Reddy ◽

◽

Dr. B. Indira Reddy ◽

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Text Messages ◽

Training Data ◽

Support Vector ◽

Spam Detection ◽

Advantages And Disadvantages ◽

Machine Learning Model ◽

Model Support

The necessity for spam detection is particularly pertinent nowadays, as there is no quality control over social media, and users have the ability to distribute unverified material, therefore facilitating fraud and deceit. Spam detection can aid in the prevention of such fraud. This scenario has developed mostly as a result of the distribution of disparate, unconfirmed information via shopping websites, emails, and text messages (SMS). There are several ways of categorising and identifying spam. Each of them has certain advantages and disadvantages. The machine learning model “Support Vector Machine” is employed to detect spam in this case. SVM is a basic concept: the method proposes a line or hyperplane to classify the data. The model can categorise any type of text into a given category after being fed a set of labelled training data for each category.

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Jammer Classification in GNSS Bands Via Machine Learning Algorithms

Sensors ◽

10.3390/s19224841 ◽

2019 ◽

Vol 19 (22) ◽

pp. 4841 ◽

Cited By ~ 3

Author(s):

Ruben Morales Ferre ◽

Alberto de la Fuente ◽

Elena Simona Lohan

Keyword(s):

Machine Learning ◽

Classification Problem ◽

Satellite System ◽

Machine Learning Algorithms ◽

Support Vector ◽

Learning Approaches ◽

Time Frequency ◽

Black And White ◽

Image Mapping ◽

Global Navigation Satellite

This paper proposes to treat the jammer classification problem in the Global Navigation Satellite System bands as a black-and-white image classification problem, based on a time-frequency analysis and image mapping of a jammed signal. The paper also proposes to apply machine learning approaches in order to sort the received signal into six classes, namely five classes when the jammer is present with different jammer types and one class where the jammer is absent. The algorithms based on support vector machines show up to 94 . 90 % accuracy in classification, and the algorithms based on convolutional neural networks show up to 91 . 36 % accuracy in classification. The training and test databases generated for these tests are also provided in open access.

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Delineating Smallholder Maize Farms from Sentinel-1 Coupled with Sentinel-2 Data Using Machine Learning

Sustainability ◽

10.3390/su13094728 ◽

2021 ◽

Vol 13 (9) ◽

pp. 4728

Author(s):

Zinhle Mashaba-Munghemezulu ◽

George Johannes Chirima ◽

Cilence Munghemezulu

Keyword(s):

Machine Learning ◽

Food Security ◽

Rural Communities ◽

Machine Learning Algorithms ◽

Support Vector ◽

Subsistence Agriculture ◽

Smallholder Farms ◽

Main Driver ◽

Sentinel 2

Rural communities rely on smallholder maize farms for subsistence agriculture, the main driver of local economic activity and food security. However, their planted area estimates are unknown in most developing countries. This study explores the use of Sentinel-1 and Sentinel-2 data to map smallholder maize farms. The random forest (RF), support vector (SVM) machine learning algorithms and model stacking (ST) were applied. Results show that the classification of combined Sentinel-1 and Sentinel-2 data improved the RF, SVM and ST algorithms by 24.2%, 8.7%, and 9.1%, respectively, compared to the classification of Sentinel-1 data individually. Similarities in the estimated areas (7001.35 ± 1.2 ha for RF, 7926.03 ± 0.7 ha for SVM and 7099.59 ± 0.8 ha for ST) show that machine learning can estimate smallholder maize areas with high accuracies. The study concludes that the single-date Sentinel-1 data were insufficient to map smallholder maize farms. However, single-date Sentinel-1 combined with Sentinel-2 data were sufficient in mapping smallholder farms. These results can be used to support the generation and validation of national crop statistics, thus contributing to food security.

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Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Electronics ◽

10.3390/electronics10040495 ◽

2021 ◽

Vol 10 (4) ◽

pp. 495

Author(s):

Imayanmosha Wahlang ◽

Arnab Kumar Maji ◽

Goutam Saha ◽

Prasun Chakrabarti ◽

Michal Jasinski ◽

...

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Support Vector ◽

Variational Autoencoder ◽

Different Types ◽

Static Images ◽

Long Short Term Memory ◽

2D And 3D ◽

Better Than

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

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