Land Subsidence Hazard Assessment Based on Novel Hybrid Approach: BWM, Weighted Overlay Index (WOI), and Support Vector Machine (SVM)
In: Natural Hazards, 2022; DOI: 10.1007/s11069-022-05624-0
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In: Natural Hazards, 2022; DOI: 10.1007/s11069-022-05624-0
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This paper has been presented at : 9th International Conference on Pattern Recognition Systems (ICPRS 2018) ; Computer vision systems have become increasingly popular, being used to solve a wide range of problems. In this paper, a computer vision algorithm with a support vector machine (SVM) classifier is presented. The work focuses on the recognition of human actions through computer vision, using a multi-camera dataset of human actions called MuHAVi. The algorithm uses a method to extract features, based on silhouettes. The challenge is that in MuHAVi these silhouettes are noisy and in many cases include shadows. As there are many actions that need to be recognised, we take a multiclass classification ap-proach that combines binary SVM classifiers. The results are compared with previous results on the same dataset and show a significant improvement, especially for recognising actions on a different view, obtaining overall accuracy of 85.5% and of 93.5% for leave-one-camera-out and leave-one-actor-out tests respectively. ; Sergio A Velastin has received funding from the Universidad Carlos III de Madrid, the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 600371, el Ministerio de Economía, Industria y Competitividad (COFUND2013-51509) el Ministerio de Educación, cultura y Deporte (CEI-15-17) and Banco Santander.
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In: Computers and electronics in agriculture: COMPAG online ; an international journal, Band 206, S. 107694
In: Lecture Notes in Economics and Mathematical Systems; Forecasting and Hedging in the Foreign Exchange Markets, S. 183-184
This study was conducted to text-based data mining or often called text mining, classification methods commonly used method Naïve bayes classifier (NBC) and support vector machine (SVM). This classification is emphasized for Indonesian language documents, while the relationship between documents is measured by the probability that can be proven with other classification algorithms. This evident from the conclusion that the probability result Naïve Bayes Classifier (NBC) word "party" at least in the economic document and political. Then the result of the algorithm support vector machine (svm) with the word "price" and "kpk" contains in both economic and politic document.
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Functional Data Analysis (FDA) has become a very important eld in recent years due to its wide range of applications. However, there are several real-life applications in which hybrid functional data appear, i.e., data with functional and static covariates. The classi cation of such hybrid functional data is a challenging problem that can be handled with the Support Vector Machine (SVM). Moreover, the selection of the most informative features may yield to drastic improvements in the classi cation rates. In this paper, an embedded feature selection approach for SVM classi cation is proposed, in which the isotropic Gaussian kernel is modi ed by associating a bandwidth to each feature. The bandwidths are jointly optimized with the SVM parameters, yielding an alternating optimization approach. The e ectiveness of our methodology was tested on benchmark data sets. Indeed, the proposed method achieved the best average performance when compared to 17 other feature selection and SVM classi cation approaches. A comprehensive sensitivity analysis of the parameters related to our proposal was also included, con rming its robustness. ; Spanish Government MTM2015-65915-R Junta de Andalucia P11-FQM-7603 P18-FR-2369 FQM329 German Research Foundation (DFG) VI PPITUS (Universidad de Sevilla) EU ERDF funds FBBVA-COSECLA ANID, FONDECYT project 1200221 Complex Engineering Systems Institute (ANID, PIA) FB0816
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Online news is a digital information media currently has a very easy and flexible updating process. The News Document grouping process is implemented in several stages, including Text Mining which includes Text Pre-processing which includes Tokenizing, Stopword removal, Stemming, Word Merging, TF-IDF and Confusion Matrix. Of the several techniques in Text Mining, the most frequently used for News Document classification is the Support Vector Machine (SVM). SVM has the advantage of being able to identify separate hyperplane that maximizes the margin between two or more different classes. The selection of features in SVM significantly affects the classification accuracy results. Therefore, in this study a combination of feature selection methods is used, namely Singular Value Decomposition in order to increase accuracy and reduce the Classifier Time Support Vector Machine. This research resulted in text classification in the form of categories Entertainment, Health, Politics and Technology. Based on the Support Vector Machines Algorithm, an accuracy rate of 81% was obtained with 360 Data Training and 120 Data Testing, after adding the Singular Value Decomposition feature with a K- Rank value of 50%, a significant increase in accuracy was obtained with an accuracy value of 94% and The time of Algorithm process is faster. ; Online news is a digital information media currently has a very easy and flexible updating process. The News Document grouping process is implemented in several stages, including Text Mining which includes Text Pre-processing which includes Tokenizing, Stopword removal, Stemming, Word Merging, TF-IDF and Confusion Matrix. Of the several techniques in Text Mining, the most frequently used for News Document classification is the Support Vector Machine (SVM). SVM has the advantage of being able to identify separate hyperplane that maximizes the margin between two or more different classes. The selection of features in SVM significantly affects the classification accuracy results. Therefore, in this study a combination of feature selection methods is used, namely Singular Value Decomposition in order to increase accuracy and reduce the Classifier Time Support Vector Machine. This research resulted in text classification in the form of categories Entertainment, Health, Politics and Technology. Based on the Support Vector Machines Algorithm, an accuracy rate of 81% was obtained with 360 Data Training and 120 Data Testing, after adding the Singular Value Decomposition feature with a K- Rank value of 50%, a significant increase in accuracy was obtained with an accuracy value of 94% and The time of Algorithm process is faster.
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In: International Review of Finance, Band 19, Heft 3, S. 483-504
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In: Advances in Multimedia Information Processing — PCM 2002; Lecture Notes in Computer Science, S. 928-935
In: Bundesbank Series 2 Discussion Paper No. 2007,18
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This paper presents a Support Vector Machine (SVM) based approach for energy consumption forecasting. The proposed approach includes the combination of both the historic log of past consumption data and the history of contextual information. By combining variables that influence the electrical energy consumption, such as the temperature, luminosity, seasonality, with the log of consumption data, it is possible for the proposed method by find patterns and correlations between the different sources of data and therefore improves the forecasting performance. A case study based on real data from a pilot microgrid located at the GECAD campus in the Polytechnic of Porto is presented. Data from the pilot buildings are used, and the results are compared to those achieved by several states of the art forecasting approaches. Results show that the proposed method can reach lower forecasting errors than the other considered methods. ; This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO) and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013 ; info:eu-repo/semantics/publishedVersion
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In: SFB 649 Discussion Paper 2006-077
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Working paper
In: Computers and electronics in agriculture: COMPAG online ; an international journal, Band 191, S. 106546
News is a source of information disseminated in various types of media. In order to make it easier for news readers to obtain the desired news, the news needs to be classified. The large number of scattered news creates difficulties in classifying the news based on the topic. Therefore the author conducted a study to classify news into 12 classes (culture, economy, entertainment, law, health, life, automotive, education, politics, sports, technology, and tourism) automatically against 360 Indonesian news data. In this study several test scenarios were conducted to see the effect of stopword removal and stemming methods on data preprocessing, the effect of mutual information in selecting features, and performance of Support Vector Machine in classifying news data. The test results showed that the data using only stemming without stopword removal, using the MI selection feature and SVM classification method produced the best results of 94.24%, compared to the other methods.
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In: Iraqi journal of science, S. 944-957
ISSN: 0067-2904
The most common artifacts in ultrasound (US) imaging are reverberation and comet-tail. These are multiple reflection echoing the interface that causing them, and result in ghost echoes in the ultrasound image. A method to reduce these unwanted artifacts using a Otsu thresholding to find region of interest (reflection echoes) and output applied to median filter to remove noise. The developed method significantly reduced the magnitude of the reverberation and comet-tail artifacts. Support Vector Machine (SVM) algorithm is most suitable for hyperplane differentiate. For that, we use image enhancement, extraction of feature, region of interest, Otsu thresholding, and finally classification image datasets to normal or abnormal image. Because of the machine's training for both types of images, the machine can now predict whether a new image is an abnormal image or a normal image. As a result, it reduced medical work for many checkups and other things. Our proposed method shows the correct classification result by more than 89%.