A fuzzy expert system for automatic seismic signal classification

Laasri, El Hassan Ait; Akhouayri, Es-Saïd; Agliz, Dris; Zonta, Daniele; Atmani, Abderrahman

doi:10.1016/j.eswa.2014.08.023

Cited by 29 publications

(15 citation statements)

References 59 publications

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“…Regarding feature selection, we have addressed the problem in this paper from the machine learning theory point of view, and most of the main features found by the algorithms were consistent with common features used in other works for event classification in other volcanoes [24], [28], [30]. Although the volcanologist significance of the most important features related to Cotopaxi volcano is still unclear at this moment, and it requires deeper and further investigation, feature selection techniques were used in this paper to simplify the problem of classification.…”

Section: B Results Using Linear and Nonlinear Svm Classifiersmentioning

confidence: 87%

“…Similarly, in [23], a hidden Markov model (HMM) classifier was applied to the data from the San Cristóbal Volcano (Nicaragua), to identify LP from two types of explosions, and BN in raw seismic data, yielding an 80% accuracy rating. Another system was proposed in [24], in which a fuzzy algorithm was used for automatic classification of local and regional earthquakes, and two nonvolcanic originated events, i.e., QB and machinery noise. Six main features were considered in the time and frequency domains, yielding a 96% classification accuracy.…”

Section: Related Workmentioning

confidence: 99%

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Automatic Recognition of Long Period Events From Volcano Tectonic Earthquakes at Cotopaxi Volcano

Lara-Cueva

Benítez

Ruiz

et al. 2016

IEEE Trans. Geosci. Remote Sensing

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Geophysics experts are interested in understanding the behavior of volcanoes and forecasting possible eruptions by monitoring and detecting the increment on volcano-seismic activity, with the aim of safeguarding human lives and material losses. This paper presents an automatic volcanic event detection and classification system, which considers feature extraction and feature selection stages, to reduce the processing time toward a reliable real-time volcano early warning system (RT-VEWS). We built the proposed approach in terms of the seismicity presented in 2009 and 2010 at the Cotopaxi Volcano located in Ecuador. In the detection stage, the recordings were time segmented by using a nonoverlapping 15-s window, and in the classification stage, the detected seismic signals were 1-min long. For each detected signal conveying seismic events, a comprehensive set of statistical, temporal, spectral, and scale-domain features were compiled and extracted, aiming to separate long-period (LP) events from volcano-tectonic (VT) earthquakes. We benchmarked two commonly used types of feature selection techniques, namely, wrapper (recursive feature extraction) and embedded (cross-validation and pruning). Each technique was used within a suitable and appropriate classification algorithm, either the support vector machine (SVM) or the decision trees. The best result was obtained by using the SVM classifier, yielding up to 99% accuracy in the detection stage and 97% accuracy and sensitivity in the event classification stage. Selected features and their interpretation were consistent among different input spaces in simple terms of the spectral content of the frequency bands at 3.1 and 6.8 Hz. A comparative analysis showed that the most relevant features for automatic discrimination between LP and VT events were one in the time domain, five in the frequency domain, and nine in the scale domain. Our study provides the framework for an event classification system with high accuracy and reduced computational requirements, according to the orientation toward a future RT-VEWS.

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Section: B Results Using Linear and Nonlinear Svm Classifiersmentioning

confidence: 87%

Section: Related Workmentioning

confidence: 99%

Automatic Recognition of Long Period Events From Volcano Tectonic Earthquakes at Cotopaxi Volcano

Lara-Cueva

Benítez

Ruiz

et al. 2016

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Laasri et al [25] used a fuzzy rule-based expert system for seismic signal classification. Their purpose was to develop a rule-based expert classification system that can work in cooperation with the analysis information of seismic case classification and imitate human evaluation and deduction processes.…”

Section: Related Workmentioning

confidence: 99%

Designing a Rule Based Expert Systems for Contact Lenses Patients

Aydilek¹,

Gümüşçü²

2018

IJITCS

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Abstract-Expert systems that bring facts and valuable experiences together and make some deductions possible. Expression of relevant knowledge and experience in these structures may be in a set of rules. Learning problems are valid with expert systems. Therefore, they cannot add new rules and information automatically by themselves. Rules are created by human experts on the way and added upon the system. Classification datasets are collections of data commonly used in machine learning that contain and classify the previously obtained experiences. In this study, rules were obtained by using Part, NNge, Prism rule classifier algorithms, and a knowledge base of expert systems was systematically created to achieve enrichment. Enrichment and rule deduction process needs careful and sensitive attention. A combined methodology and study was revealed during this sensitive process. In this context, studies were conducted on five widely used datasets. It was aimed to reduce the redundant, conflicting, subsumed and circular rules in order to create a consistent and complete knowledge base. In this way, a methodology was developed to establish more powerful and richer contents of knowledge base that have higher quality.

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“…Rule-based reasoning is mainly based on the characteristics of the video content, and combine with professional theory to develop the relevant rules, according to which to derive the semantic feature extraction video [9,6] . Currently, these methods focus on applying the fuzzy theory into the developing of the rules [5] . Through all above research [4] for video semantic feature extraction, this paper proposes a video semantic feature extraction framework, aiming the actual needs and the target of video semantic retrieval.…”

Section: Related Researchmentioning

confidence: 99%

Multilayer Video Semantic Feature Extraction Method

Zhong¹,

Yu²,

Lu³

2017

Proceedings of the 2017 2nd Joint International Information Technology, Mechanical and Electronic Engineering Conference (JIMEC

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To solve video semantic retrieval, the key problem is the difficulty of analyzing, searching and processing the massive video data. In this paper, we propose an innovative multilayer video semantic feature extraction method. The whole method can achieve automatic extraction, label of the concept ontology of video semantic and provide support for the semantic retrieval. The main idea of the method includes the MPE (Model Parameter Estimation) algorithm, MR(Model Replace) algorithm and MVSS(Multilayer Video Semantic Symbolic representation) algorithm. Express the semantic information of the whole video key-frame from three levels. Furthermore, we examine the validity of those algorithms by labelling the video object ontology. The experimental results show that the method proposed in this paper can improve the accuracy rate of the video semantic feature extraction.

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A fuzzy expert system for automatic seismic signal classification

Cited by 29 publications

References 59 publications

Automatic Recognition of Long Period Events From Volcano Tectonic Earthquakes at Cotopaxi Volcano

Automatic Recognition of Long Period Events From Volcano Tectonic Earthquakes at Cotopaxi Volcano

Designing a Rule Based Expert Systems for Contact Lenses Patients

Multilayer Video Semantic Feature Extraction Method

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