Extraction of Arecanut Planting Distribution Based on the Feature Space Optimization of PlanetScope Imagery

The remote sensing extraction of large areas of arecanut (Areca catechu L.) planting plays an important role in investigating the distribution of arecanut planting area and the subsequent adjustment and optimization of regional planting structures. Satellite imagery has previously been used to investigate and monitor the agricultural and forestry vegetation in Hainan. However, the monitoring accuracy is affected by the cloudy and rainy climate of this region, as well as the high level of land fragmentation. In this paper, we used PlanetScope imagery at a 3 m spatial resolution over the Hainan arecanut planting area to investigate the high-precision extraction of the arecanut planting distribution based on feature space optimization. First, spectral and textural feature variables were selected to form the initial feature space, followed by the implementation of the random forest algorithm to optimize the feature space. Arecanut planting area extraction models based on the support vector machine (SVM), BP neural network (BPNN), and random forest (RF) classification algorithms were then constructed. The overall classification accuracies of the SVM, BPNN, and RF models optimized by the RF features were determined as 74.82%, 83.67%, and 88.30%, with Kappa coefficients of 0.680, 0.795, and 0.853, respectively. The RF model with optimized features exhibited the highest overall classification accuracy and kappa coefficient. The overall accuracy of the SVM, BPNN, and RF models following feature optimization was improved by 3.90%, 7.77%, and 7.45%, respectively, compared with the corresponding unoptimized classification model. The kappa coefficient also improved. The results demonstrate the ability of PlanetScope satellite imagery to extract the planting distribution of arecanut. Furthermore, the RF is proven to effectively optimize the initial feature space, composed of spectral and textural feature variables, further improving the extraction accuracy of the arecanut planting distribution. This work can act as a theoretical and technical reference for the agricultural and forestry industries.

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Network Intrusion Detection Method Based on RS-LSSVM

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.602-605.1634 ◽

2014 ◽

Vol 602-605 ◽

pp. 1634-1637

Author(s):

Fang Nian Wang ◽

Shen Shen Wang ◽

Wan Fang Che ◽

Yun Bai

Keyword(s):

Intrusion Detection ◽

Detection Method ◽

Attribute Reduction ◽

Feature Space ◽

Least Square ◽

Classification Model ◽

Network Intrusion Detection ◽

Support Vector ◽

Network Intrusion ◽

Sample Data

An intrusion detection method based on RS-LSSVM is studied in this paper. Firstly, attribute reduction algorithm based on the generalized decision table is proposed to remove the interference features and reduce the dimension of input feature space. Then the classification method based on least square support vector machine (LSSVM) is analyzed. The sample data after dimension reduction is used for LSSVM training, and the LSSVM classification model is obtained, which forms the ability of detecting unknown intrusion. Simulation results show that the proposed method can effectively remove the unnecessary features and improve the performance of network intrusion detection.

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Staging Melanocytic Skin Neoplasms Using High-Level Pixel-Based Features

Electronics ◽

10.3390/electronics9091443 ◽

2020 ◽

Vol 9 (9) ◽

pp. 1443

Author(s):

Mai Ramadan Ibraheem ◽

Shaker El-Sappagh ◽

Tamer Abuhmed ◽

Mohammed Elmogy

Keyword(s):

Area Under The Curve ◽

Malignant Neoplasm ◽

Texture Features ◽

Feature Space ◽

Skin Neoplasms ◽

Support Vector ◽

Svm Classifier ◽

Abnormal Growth ◽

Malignant Skin ◽

High Level

The formation of malignant neoplasm can be seen as deterioration of a pre-malignant skin neoplasm in its functionality and structure. Distinguishing melanocytic skin neoplasms is a challenging task due to their high visual similarity with different types of lesions and the intra-structural variants of melanocytic neoplasms. Besides, there is a high visual likeliness level between different lesion types with inhomogeneous features and fuzzy boundaries. The abnormal growth of melanocytic neoplasms takes various forms from uniform typical pigment network to irregular atypical shape, which can be described by border irregularity of melanocyte lesion image. This work proposes analytical reasoning for the human-observable phenomenon as a high-level feature to determine the neoplasm growth phase using a novel pixel-based feature space. The pixel-based feature space, which is comprised of high-level features and other color and texture features, are fed into the classifier to classify different melanocyte neoplasm phases. The proposed system was evaluated on the PH2 dermoscopic images benchmark dataset. It achieved an average accuracy of 95.1% using a support vector machine (SVM) classifier with the radial basis function (RBF) kernel. Furthermore, it reached an average Disc similarity coefficient (DSC) of 95.1%, an area under the curve (AUC) of 96.9%, and a sensitivity of 99%. The results of the proposed system outperform the results of other state-of-the-art multiclass techniques.

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CPT Data Interpretation Employing Different Machine Learning Techniques

Geosciences ◽

10.3390/geosciences11070265 ◽

2021 ◽

Vol 11 (7) ◽

pp. 265

Author(s):

Stefan Rauter ◽

Franz Tschuchnigg

Keyword(s):

Machine Learning ◽

Grain Size ◽

Random Forest ◽

Classification Model ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Models ◽

Cone Penetration ◽

Tip Resistance ◽

Machine Learning Models

The classification of soils into categories with a similar range of properties is a fundamental geotechnical engineering procedure. At present, this classification is based on various types of cost- and time-intensive laboratory and/or in situ tests. These soil investigations are essential for each individual construction site and have to be performed prior to the design of a project. Since Machine Learning could play a key role in reducing the costs and time needed for a suitable site investigation program, the basic ability of Machine Learning models to classify soils from Cone Penetration Tests (CPT) is evaluated. To find an appropriate classification model, 24 different Machine Learning models, based on three different algorithms, are built and trained on a dataset consisting of 1339 CPT. The applied algorithms are a Support Vector Machine, an Artificial Neural Network and a Random Forest. As input features, different combinations of direct cone penetration test data (tip resistance qc, sleeve friction fs, friction ratio Rf, depth d), combined with “defined”, thus, not directly measured data (total vertical stresses σv, effective vertical stresses σ’v and hydrostatic pore pressure u0), are used. Standard soil classes based on grain size distributions and soil classes based on soil behavior types according to Robertson are applied as targets. The different models are compared with respect to their prediction performance and the required learning time. The best results for all targets were obtained with models using a Random Forest classifier. For the soil classes based on grain size distribution, an accuracy of about 75%, and for soil classes according to Robertson, an accuracy of about 97–99%, was reached.

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Evaluación de algoritmos de clasificación en la plataforma Google Earth Engine para la identificación y detección de cambios de construcciones rurales y periurbanas a partir de imágenes de alta resolución

Revista de Teledetección ◽

10.4995/raet.2021.15026 ◽

2021 ◽

pp. 71

Author(s):

Alejandro Coca-Castro ◽

Maycol A. Zaraza-Aguilera ◽

Yilsey T. Benavides-Miranda ◽

Yeimy M. Montilla-Montilla ◽

Heidy B. Posada-Fandiño ◽

...

Keyword(s):

Random Forest ◽

Rural Areas ◽

Disaster Response ◽

Google Earth ◽

Classification Model ◽

Support Vector ◽

Classification Algorithms ◽

Urban Rural ◽

High Resolution Images ◽

Google Earth Engine

<p>Building change detection based on remote sensing imagery is a key task for land management and planning e.g., detection of illegal settlements, updating land records and disaster response. Under the post- classification comparison approach, this research aimed to evaluate the feasibility of several classification algorithms to identify and capture buildings and their change between two time steps using very-high resolution images (<1 m/pixel) across rural areas and urban/rural perimeter boundaries. Through an App implemented on the Google Earth Engine (GEE) platform, we selected two study areas in Colombia with different images and input data. In total, eight traditional classification algorithms, three unsupervised (K-means, X-Means y Cascade K-Means) and five supervised (Random Forest, Support Vector Machine, Naive Bayes, GMO maximum Entropy and Minimum distance) available at GEE were trained. Additionally, a deep neural network named Feature Pyramid Networks (FPN) was added and trained using a pre-trained model, EfficientNetB3 model. Three evaluation zones per study area were proposed to quantify the performance of the algorithms through the Intersection over Union (IoU) metric. This metric, with a range between 0 and 1, represents the degree of overlapping between two regions, where the higher agreement the higher IoU values. The results indicate that the models configured with the FPN network have the best performance followed by the traditional supervised algorithms. The performance differences were specific to the study area. For the rural area, the best FPN configuration obtained an IoU averaged for both time steps of 0.4, being this four times higher than the best supervised model, Support Vector Machines using a linear kernel with an average IoU of 0.1. Regarding the setting of urban/rural perimeter boundaries, this difference was less marked, having an average IoU of 0.53 in comparison to 0.38 obtained by the best supervised classification model, in this case Random Forest. The results are relevant for institutions tracking the dynamics of building areas from cloud computing platfo future assessments of classifiers in likewise platforms in other contexts.</p>

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Machine learning in the diagnosis of Myocardial Infarction with Non-Obstructive Coronary Arteries

European Heart Journal ◽

10.1093/eurheartj/ehab724.3067 ◽

2021 ◽

Vol 42 (Supplement_1) ◽

Author(s):

M J Espinosa Pascual ◽

P Vaquero Martinez ◽

V Vaquero Martinez ◽

J Lopez Pais ◽

B Izquierdo Coronel ◽

...

Keyword(s):

Machine Learning ◽

Myocardial Infarction ◽

Support Vector Machine ◽

Logistic Regression ◽

Random Forest ◽

Obstructive Coronary Artery Disease ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Classification Model ◽

Support Vector

Abstract Introduction Out of all patients admitted with Myocardial Infarction, 10 to 15% have Myocardial Infarction with Non-Obstructive Coronaries Arteries (MINOCA). Classification algorithms based on deep learning substantially exceed traditional diagnostic algorithms. Therefore, numerous machine learning models have been proposed as useful tools for the detection of various pathologies, but to date no study has proposed a diagnostic algorithm for MINOCA. Purpose The aim of this study was to estimate the diagnostic accuracy of several automated learning algorithms (Support-Vector Machine [SVM], Random Forest [RF] and Logistic Regression [LR]) to discriminate between people suffering from MINOCA from those with Myocardial Infarction with Obstructive Coronary Artery Disease (MICAD) at the time of admission and before performing a coronary angiography, whether invasive or not. Methods A Diagnostic Test Evaluation study was carried out applying the proposed algorithms to a database constituted by 553 consecutive patients admitted to our Hospital with Myocardial Infarction. According to the definitions of 2016 ESC Position Paper on MINOCA, patients were classified into two groups: MICAD and MINOCA. Out of the total 553 patients, 214 were discarded due to the lack of complete data. The set of machine learning algorithms was trained on 244 patients (training sample: 75%) and tested on 80 patients (test sample: 25%). A total of 64 variables were available for each patient, including demographic, clinical and laboratorial features before the angiographic procedure. Finally, the diagnostic precision of each architecture was taken. Results The most accurate classification model was the Random Forest algorithm (Specificity [Sp] 0.88, Sensitivity [Se] 0.57, Negative Predictive Value [NPV] 0.93, Area Under the Curve [AUC] 0.85 [CI 0.83–0.88]) followed by the standard Logistic Regression (Sp 0.76, Se 0.57, NPV 0.92 AUC 0.74 and Support-Vector Machine (Sp 0.84, Se 0.38, NPV 0.90, AUC 0.78) (see graph). The variables that contributed the most in order to discriminate a MINOCA from a MICAD were the traditional cardiovascular risk factors, biomarkers of myocardial injury, hemoglobin and gender. Results were similar when the 19 patients with Takotsubo syndrome were excluded from the analysis. Conclusion A prediction system for diagnosing MINOCA before performing coronary angiographies was developed using machine learning algorithms. Results show higher accuracy of diagnosing MINOCA than conventional statistical methods. This study supports the potential of machine learning algorithms in clinical cardiology. However, further studies are required in order to validate our results. FUNDunding Acknowledgement Type of funding sources: None. ROC curves of different algorithms

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Analysis of Wheat Samples Using the Calculation of Multifractal Spectrum

Computer Tools in Education ◽

10.32603/2071-2340-2021-1-5-20 ◽

2021 ◽

pp. 5-20

Author(s):

Ivan Murenin ◽

◽

Natalia Ampilova ◽

Keyword(s):

Random Forest ◽

Local Density ◽

Principal Component ◽

Feature Space ◽

Multifractal Spectrum ◽

Support Vector ◽

Clustering Methods ◽

Wheat Varieties ◽

Crystallization With Additives ◽

Classi Fication

The computational analysis of wheat images to identify wheat varieties and quality has wide applications in agriculture and production. This paper presents an approach to the analysis and classiﬁcation of images of wheat samples obtained by the method of crystallization with additives. In tests 3 concentration and 4 times for each concentration were used, such that each type of wheat was characterized by 12 images. We used the images obtained for 5 classes. All the images have similar visual characteristics, that makes it diﬃcult to use statistical methods of analysis. The multifractal spectrum obtained by calculating the local density function was used as a classifying feature. The classiﬁcation was performed on a set of 60 wheat images corresponding to 5 different samples (classes) by various machine learning methods such as linear regression, naive Bayesian classiﬁer, support vector machine, and random forest. In some cases, to reduce the dimension of the feature space the method of principal components was applied. To identify the relationships between wheat samples obtained at different concentrations, 3 different clustering methods were used. The classiﬁcation results showed that the multifractal spectrum as classifying sign and using the random forest method in combination with the principal component analysis allow identifying wheat samples obtained by crystallization with additives, being the highest average classi- ﬁcation accuracy is 74 %.

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crops planting area identification and analysis based on multi-source high resolution remote sensing data

10.5194/egusphere-egu2020-13227 ◽

2020 ◽

Author(s):

Lei Wang ◽

Haoran Sun ◽

Wenjun Li ◽

Liang Zhou

Keyword(s):

Remote Sensing ◽

Random Forest ◽

Classification Accuracy ◽

Kappa Coefficient ◽

Optical Data ◽

Accurate Estimation ◽

Support Vector ◽

Random Forest Method ◽

Multi Temporal ◽

Sar Data

<p>Crop planting structure is of great significance to the quantitative management of agricultural water and the accurate estimation of crop yield. With the increasing spatial and temporal resolution of remote sensing optical and SAR(Synthetic Aperture Radar) images, &#160;efficient crop mapping in large area becomes possible and the accuracy is improved. In this study, Qingyijiang Irrigation District in southwest of China is selected for crop identification methods comparison, which has heterogeneous terrain and complex crop structure . Multi-temporal optical (Sentinel-2) and SAR (Sentinel-1) data were used to calculate NDVI and backscattering coefficient as the main classification indexes. The multi-spectral and SAR data showed significant change in different stages of the whole crop growth period and varied with different crop types. Spatial distribution and texture analysis was also made. Classification using different combinations of indexes were performed using neural network, support vector machine and random forest method. The results showed that, the use of multi-temporal optical data and SAR data in the key growing periods of main crops can both provide satisfactory classification accuracy. The overall classification accuracy was greater than 82% and Kappa coefficient was greater than 0.8. SAR data has high accuracy and much potential in rice identification. However optical data had more accuracy in upland crops classification. In addition, the classification accuracy can be effectively improved by combination of classification indexes from optical and SAR data, the overall accuracy was up to 91.47%. The random forest method was superior to the other two methods in terms of the overall accuracy and the kappa coefficient.</p>

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Machine Learning Model to Map Tribocorrosion Regimes in Feature Space

Coatings ◽

10.3390/coatings11040450 ◽

2021 ◽

Vol 11 (4) ◽

pp. 450

Author(s):

Rahul Ramachandran

Keyword(s):

Machine Learning ◽

Feature Space ◽

Operating Conditions ◽

Classification Model ◽

Support Vector ◽

Interfacial Conditions ◽

Machine Learning Model ◽

Wear And Corrosion ◽

Wide Availability ◽

Material Forming

Degradation by wear and corrosion are frequently encountered in a variety of tribosystems, including materials and tools in forming operations. The combined effect of wear and corrosion, known as tribocorrosion, can result in accelerated material degradation. Interfacial conditions can affect this degradation. Tribocorrosion maps serve the purpose of identifying operating conditions at the interface for an acceptable rate of degradation. This paper proposes a machine learning-based approach to generate tribocorrosion maps, which can be used to predict tribosystem performance. Two tribocorrosion datasets from the published literature are used. The materials have been chosen based on the wide availability of their tribocorrosion data in the literature. First, unsupervised machine learning is used to identify and label clusters from tribocorrosion data. The identified clusters are then used to train a support vector classification model. The trained support vector machine is used to generate tribocorrosion maps. The generated maps are compared with those from the literature. The general approach can be applied to create tribocorrosion maps of materials widely used in material forming.

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DDOS Botnets Attacks Detection in Anomaly Traffic: A Comparative Study.

Journal of Information Security and Cybercrimes Research ◽

10.26735/zrxn1433 ◽

2020 ◽

Vol 3 (1) ◽

pp. 64-74

Author(s):

Ahmed A. Elsherif ◽

Arwa A. Aldaej

Keyword(s):

Random Forest ◽

Decision Tree ◽

Numerical Data ◽

Classification Model ◽

Support Vector ◽

Classification Algorithms ◽

Traffic Classification ◽

Significant Drop ◽

Random Forest Classification ◽

Ddos Attack

One of the major challenges that faces the acceptance and growth rate of business and governmental sites is a Botnet-based DDoS attack. A flooding DDoS strikes a victim machine by means of sending a vast amount of malicious traffic, causing a significant drop in the service quality (QoS) in IoT devices. Nonetheless, it is not that easy to detect and tackle flooding DDoS attacks, owing to the significant number of attacking machines, the usage of source-address spoofing, and the common areas shared between legitimate and malicious traffic. New kinds of attacks are identified daily, and some remain undiscovered, accordingly, this paper aims to improve the traffic classification algorithm of network traffic, that hackers use to try to be ambiguous or misleading. A recorded simulated traffic was used for both samples; normal and DDoS attack traffic, approximately 104.000 cases of each, where both datasets -which were created for this study- represent the input data in order to create a classification model, to be used as a tool to mitigate the risk of being attacked. The next step is putting datasets in a format suitable for classification. This process is done through preprocessing techniques, to convert categorical data into numerical data. A classification process is applied to capture datasets, to create a classification model, by using five classification algorithms which are; Decision Tree, Support Vector Machine, Naive Bayes, K-Neighbours and Random Forest. The core code used for classification is the python code, which is controlled by a user interface. The highest prediction, precision and accuracy are obtained using the Decision Tree and Random Forest classification algorithms, which also have the lowest processing time.

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Reliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning

Agriculture ◽

10.3390/agriculture11111106 ◽

2021 ◽

Vol 11 (11) ◽

pp. 1106

Author(s):

Yan Hu ◽

Lijia Xu ◽

Peng Huang ◽

Xiong Luo ◽

Peng Wang ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Principal Component ◽

Classification Model ◽

Recursive Feature Elimination ◽

Support Vector ◽

K Nearest Neighbor ◽

Oolong Tea ◽

The Impact ◽

T Distribution

A rapid and nondestructive tea classification method is of great significance in today’s research. This study uses fluorescence hyperspectral technology and machine learning to distinguish Oolong tea by analyzing the spectral features of tea in the wavelength ranging from 475 to 1100 nm. The spectral data are preprocessed by multivariate scattering correction (MSC) and standard normal variable (SNV), which can effectively reduce the impact of baseline drift and tilt. Then principal component analysis (PCA) and t-distribution random neighborhood embedding (t-SNE) are adopted for feature dimensionality reduction and visual display. Random Forest-Recursive Feature Elimination (RF-RFE) is used for feature selection. Decision Tree (DT), Random Forest Classification (RFC), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are used to establish the classification model. The results show that MSC-RF-RFE-SVM is the best model for the classification of Oolong tea in which the accuracy of the training set and test set is 100% and 98.73%, respectively. It can be concluded that fluorescence hyperspectral technology and machine learning are feasible to classify Oolong tea.

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