scholarly journals Feature-Level Fusion of Polarized SAR and Optical Images Based on Random Forest and Conditional Random Fields

2021 ◽  
Vol 13 (7) ◽  
pp. 1323
Author(s):  
Yingying Kong ◽  
Biyuan Yan ◽  
Yanjuan Liu ◽  
Henry Leung ◽  
Xiangyang Peng
Keyword(s):  
Random Forest ◽  
Random Fields ◽  
Conditional Random Fields ◽  
Classification Performance ◽  
Image Features ◽  
Classification Model ◽  
Feature Identification ◽  
Optical Images ◽  
Feature Level Fusion ◽  
Level Fusion

In terms of land cover classification, optical images have been proven to have good classification performance. Synthetic Aperture Radar (SAR) has the characteristics of working all-time and all-weather. It has more significant advantages over optical images for the recognition of some scenes, such as water bodies. One of the current challenges is how to fuse the benefits of both to obtain more powerful classification capabilities. This study proposes a classification model based on random forest with the conditional random fields (CRF) for feature-level fusion classification using features extracted from polarized SAR and optical images. In this paper, feature importance is introduced as a weight in the pairwise potential function of the CRF to improve the correction rate of misclassified points. The results show that the dataset combining the two provides significant improvements in feature identification when compared to the dataset using optical or polarized SAR image features alone. Among the four classification models used, the random forest-importance_ conditional random fields (RF-Im_CRF) model developed in this paper obtained the best overall accuracy (OA) and Kappa coefficient, validating the effectiveness of the method.

scholarly journals Transformer Oil Quality Assessment Using Random Forest with Feature Engineering

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1809
Author(s):  
Mohammed El Amine Senoussaoui ◽  
Mostefa Brahami ◽  
Issouf Fofana
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Oil Quality ◽  
Principal Component ◽  
Condition Assessment ◽  
Classification Performance ◽  
Transformer Oil ◽  
Classification Model ◽  
Insulation Degradation ◽  
Transformer Oils

Machine learning is widely used as a panacea in many engineering applications including the condition assessment of power transformers. Most statistics attribute the main cause of transformer failure to insulation degradation. Thus, a new, simple, and effective machine-learning approach was proposed to monitor the condition of transformer oils based on some aging indicators. The proposed approach was used to compare the performance of two machine-learning classifiers: J48 decision tree and random forest. The service-aged transformer oils were classified into four groups: the oils that can be maintained in service, the oils that should be reconditioned or filtered, the oils that should be reclaimed, and the oils that must be discarded. From the two algorithms, random forest exhibited a better performance and high accuracy with only a small amount of data. Good performance was achieved through not only the application of the proposed algorithm but also the approach of data preprocessing. Before feeding the classification model, the available data were transformed using the simple k-means method. Subsequently, the obtained data were filtered through correlation-based feature selection (CFsSubset). The resulting features were again retransformed by conducting the principal component analysis and were passed through the CFsSubset filter. The transformation and filtration of the data improved the classification performance of the adopted algorithms, especially random forest. Another advantage of the proposed method is the decrease in the number of the datasets required for the condition assessment of transformer oils, which is valuable for transformer condition monitoring.

scholarly journals PET/MR fusion texture analysis for the clinical outcome prediction in soft-tissue sarcoma

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Wenzhe Zhao ◽  
Xin Huang ◽  
Geliang Wang ◽  
Jianxin Guo
Keyword(s):  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Univariate Analysis ◽  
Multivariable Analysis ◽  
Classification Performance ◽  
Fusion Method ◽  
Mr Images ◽  
Feature Level Fusion ◽  
Optimal Classification ◽  
Level Fusion

Abstract Background Various fusion strategies (feature-level fusion, matrix-level fusion, and image-level fusion) were used to fuse PET and MR images, which might lead to different feature values and classification performance. The purpose of this study was to measure the classification capability of features extracted using various PET/MR fusion methods in a dataset of soft-tissue sarcoma (STS). Methods The retrospective dataset included 51 patients with histologically proven STS. All patients had pre-treatment PET and MR images. The image-level fusion was conducted using discrete wavelet transformation (DWT). During the DWT process, the MR weight was set as 0.1, 0.2, 0.3, 0.4, …, 0.9. And the corresponding PET weight was set as 1- (MR weight). The fused PET/MR images was generated using the inverse DWT. The matrix-level fusion was conducted by fusing the feature calculation matrix during the feature extracting process. The feature-level fusion was conducted by concatenating and averaging the features. We measured the predictive performance of features using univariate analysis and multivariable analysis. The univariate analysis included the Mann-Whitney U test and receiver operating characteristic (ROC) analysis. The multivariable analysis was used to develop the signatures by jointing the maximum relevance minimum redundancy method and multivariable logistic regression. The area under the ROC curve (AUC) value was calculated to evaluate the classification performance. Results By using the univariate analysis, the features extracted using image-level fusion method showed the optimal classification performance. For the multivariable analysis, the signatures developed using the image-level fusion-based features showed the best performance. For the T1/PET image-level fusion, the signature developed using the MR weight of 0.1 showed the optimal performance (0.9524(95% confidence interval (CI), 0.8413–0.9999)). For the T2/PET image-level fusion, the signature developed using the MR weight of 0.3 showed the optimal performance (0.9048(95%CI, 0.7356–0.9999)). Conclusions For the fusion of PET/MR images in patients with STS, the signatures developed using the image-level fusion-based features showed the optimal classification performance than the signatures developed using the feature-level fusion and matrix-level fusion-based features, as well as the single modality features. The image-level fusion method was more recommended to fuse PET/MR images in future radiomics studies.

scholarly journals Development of a random forest cloud regime classification model based on surface radiation and cloud products

2021 ◽  
Author(s):  
Joseph Sedlar ◽  
Laura D. Riihimaki ◽  
Kathleen Lantz ◽  
David D. Turner
Keyword(s):  
Random Forest ◽  
Great Plains ◽  
Digital Processing ◽  
Classification Performance ◽  
Surface Radiation ◽  
Classification Model ◽  
Cloud Type ◽  
Model Classification ◽  
Advantages And Disadvantages ◽  
Cloud Regime

AbstractVarious methods have been developed to characterize cloud type, otherwise referred to as cloud regime. These include manual sky observations, combining radiative and cloud vertical properties observed from satellite, surface-based remote sensing, and digital processing of sky imagers. While each methodology has inherent advantages and disadvantages, none of these cloud typing methods actually include measurements of surface shortwave or longwave radiative fluxes. Here, a methodology that relies upon detailed, surface-based radiation and cloud measurements and derived data products to train a random forest machine learning cloud classification model is introduced. Measurements from five years of data from the ARM Southern Great Plains site were compiled to train and independently evaluate the model classification performance. A cloud type accuracy of approximately 80% using the random forest classifier reveals the model is well suited to predict climatological cloud properties. Furthermore, an analysis of the cloud type misclassifications is performed. While physical cloud types may be misreported, the shortwave radiative signatures are similar between misclassified cloud types. From this, we assert the cloud regime model has the capacity to successfully differentiate clouds with comparable cloud-radiative interactions. Therefore, we conclude the model can provide useful cloud property information for fundamental cloud studies, inform renewable energy studies, a tool for numerical model evaluation and parameterization improvement, among many other applications.

Detecting DDoS Attacks Using Conditional Random Fields

2014 ◽  
Vol 513-517 ◽  
pp. 522-526
Author(s):  
Yun Liu ◽  
Si Yu Jiang ◽  
Xiao Jie Yuan
Keyword(s):  
Random Fields ◽  
Conditional Random Fields ◽  
Contextual Information ◽  
Conditional Entropy ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Ddos Attacks ◽  
Detection Technology ◽  
Background Traffic ◽  
Great Progress

In recent years, the detection technology based on machine learning algorithms for distributed denialof-service (DDoS) attacks has made great progress. However, previous methods fail to make full use of contextual information and rely heavily on the probability distribution of the input data. To avoid those pitfalls, the Conditional Random Fields (CRF) model is introduced in this paper for DDoS attacks detection. Firstly, the CRF is trained to build the classification model for DDoS attacks based on three groups of statistical features including conditional entropy, flag ratios and protocol ratios. Then, the trained CRF models are used to identify the attacks with model inference. Experimental results demonstrate that, the proposed approach can accurately distinguish between attacks and normal network traffic, and is more robust to resist disturbance of background traffic than its counterparts.

scholarly journals Real-Time Heart Arrhythmia Detection Using Apache Spark Structured Streaming

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Sadegh Ilbeigipour ◽  
Amir Albadvi ◽  
Elham Akhondzadeh Noughabi
Keyword(s):  
Random Forest ◽  
Real Time ◽  
Cardiac Arrhythmias ◽  
Performance Metrics ◽  
Classification Performance ◽  
Apache Spark ◽  
Classification Model ◽  
Arrhythmia Detection ◽  
Class Labels ◽  
The Impact

One of the major causes of death in the world is cardiac arrhythmias. In the field of healthcare, physicians use the patient’s electrocardiogram (ECG) records to detect arrhythmias, which indicate the electrical activity of the patient’s heart. The problem is that the symptoms do not always appear and the physician may be mistaken in the diagnosis. Therefore, patients need continuous monitoring through real-time ECG analysis to detect arrhythmias in a timely manner and prevent an eventual incident that threatens the patient’s life. In this research, we used the Structured Streaming module built top on the open-source Apache Spark platform for the first time to implement a machine learning pipeline for real-time cardiac arrhythmias detection and evaluate the impact of using this new module on classification performance metrics and the rate of delay in arrhythmia detection. The ECG data collected from the MIT/BIH database for the detection of three class labels: normal beats, RBBB, and atrial fibrillation arrhythmias. We also developed three decision trees, random forest, and logistic regression multiclass classifiers for data classification where the random forest classifier showed better performance in classification than the other two classifiers. The results show previous results in performance metrics of the classification model and a significant decrease in pipeline runtime by using more class labels compared to previous studies.

scholarly journals Machine Learning-Based Hourly Frost-Prediction System Optimized for Orchards Using Automatic Weather Station and Digital Camera Image Data

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 846
Author(s):  
Ilseok Noh ◽  
Hae-Won Doh ◽  
Soo-Ock Kim ◽  
Su-Hyun Kim ◽  
Seoleun Shin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Digital Camera ◽  
Image Data ◽  
Classification Performance ◽  
Classification Model ◽  
Support Vector ◽  
Observation Data ◽  
Freezing Resistance

Spring frosts damage crops that have weakened freezing resistance after germination. We developed a machine learning (ML)-based frost-classification model and optimized it for orchard farming environments. First, logistic regression, decision tree, random forest, and support vector machine models were trained using balanced Korea Meteorological Administration (KMA) Automated Synoptic Observing System (ASOS) frost observation data for March from the last 10 years (2008–2017). Random forest and support vector machine models showed good classification performance and were selected as the main techniques, which were optimized for orchard fields based on initial frost occurrence times. The training period was then extended to March–April for 20 years (2000–2019). Finally, the model was applied to the KMA ASOS frost observation data from March to April 2020, which were not used in the previous steps, and RGB data were extracted by digital cameras installed in an orchard in Gyeonggi-do. The developed model successfully classified 117 of 139 frost observation cases from the domestic ASOS data and 35 of 37 orchard camera observations. The assumption of the initial frost occurrence time for training helped the most in improving the frost-classification model. These results clearly indicate that the frost-classification model using ML has applicable accuracy in orchard farming.

scholarly journals MMPC-RF: A Deep Multimodal Feature-Level Fusion Architecture for Hybrid Spam E-mail Detection

2021 ◽  
Vol 11 (24) ◽  
pp. 11968
Author(s):  
Ghizlane Hnini ◽  
Jamal Riffi ◽  
Mohamed Adnane Mahraz ◽  
Ali Yahyaouy ◽  
Hamid Tairi
Keyword(s):  
Electronic Mail ◽  
Feature Space ◽  
Classification Model ◽  
Final Decision ◽  
Proposed Model ◽  
Feature Level Fusion ◽  
Strong Representation ◽  
Intelligent Approach ◽  
E Mail ◽  
Level Fusion

Hybrid spam is an undesirable e-mail (electronic mail) that contains both image and text parts. It is more harmful and complex as compared to image-based and text-based spam e-mail. Thus, an efficient and intelligent approach is required to distinguish between spam and ham. To our knowledge, a small number of studies have been aimed at detecting hybrid spam e-mails. Most of these multimodal architectures adopted the decision-level fusion method, whereby the classification scores of each modality were concatenated and fed to another classification model to make a final decision. Unfortunately, this method not only demands many learning steps, but it also loses correlation in mixed feature space. In this paper, we propose a deep multimodal feature-level fusion architecture that concatenates two embedding vectors to have a strong representation of e-mails and increase the performance of the classification. The paragraph vector distributed bag of words (PV-DBOW) and the convolutional neural network (CNN) were used as feature extraction techniques for text and image parts, respectively, of the same e-mail. The extracted feature vectors were concatenated and fed to the random forest (RF) model to classify a hybrid e-mail as either spam or ham. The experiments were conducted on three hybrid datasets made using three publicly available corpora: Enron, Dredze, and TREC 2007. According to the obtained results, the proposed model provides a higher accuracy of 99.16% compared to recent state-of-the-art methods.

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