Analysis and Automatic Classification of Some Discourse Particles on a Large Set of French Spoken Corpora

Automatic Classification of Text Complexity

Applied Sciences ◽

10.3390/app10207285 ◽

2020 ◽

Vol 10 (20) ◽

pp. 7285

Author(s):

Valentino Santucci ◽

Filippo Santarelli ◽

Luciana Forti ◽

Stefania Spina

Keyword(s):

Italian Text ◽

Classification Problem ◽

Automatic Classification ◽

Point Of View ◽

Experimental Comparison ◽

Good Prediction ◽

Large Set ◽

Linguistic Features ◽

Good Prediction Accuracy

This work introduces an automatic classification system for measuring the complexity level of a given Italian text under a linguistic point-of-view. The task of measuring the complexity of a text is cast to a supervised classification problem by exploiting a dataset of texts purposely produced by linguistic experts for second language teaching and assessment purposes. The commonly adopted Common European Framework of Reference for Languages (CEFR) levels were used as target classification classes, texts were elaborated by considering a large set of numeric linguistic features, and an experimental comparison among ten widely used machine learning models was conducted. The results show that the proposed approach is able to obtain a good prediction accuracy, while a further analysis was conducted in order to identify the categories of features that influenced the predictions.

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Automatic classification of seismo-volcanic signals at La Soufrière of Guadeloupe

10.5194/egusphere-egu2020-10234 ◽

2020 ◽

Author(s):

Alexis Falcin ◽

Jean-Philippe Metaxian ◽

Jérôme Mars ◽

Eléonore Stutzmann ◽

Roberto Moretti ◽

...

Keyword(s):

Physical Process ◽

Average Rate ◽

Spectral Characteristics ◽

Automatic Classification ◽

Full Description ◽

Large Set ◽

Classification Rate ◽

Time Frequency ◽

Accuracy And Precision

Seismic activity at La Soufri&#232;re volcano of Guadeloupe is composed of various transient signals, which are classified manually by the Observatoire Volcanologique et Sismologique de Guadeloupe (OVSG-IPGP) considering waveforms recorded at several stations. Although five main types of signals are recognized in the data analysis by the observatory (Moretti et al., 2020), only three main classes readily distinguishable on seismic traces during the daily analytical protocol have been catalogued: Volcano-Tectonic events, Long-Period events and Nested events, each related to a distinct physical process.Automatic classification of seismo-volcanic signals of La Soufri&#232;re was performed by using an architecture based on supervised learning, available at github.com/malfante/AAA. Seismic waveforms are transformed into a large set of features (34 features for each representation domain) computed from three representation domain of the signal (time, frequency, quefrency). The resulting vectors of features are then used for the modeling. We are using the Random Forest Classifier algorithm from the scikit-learn library.At first, we trained the model with the dataset given by the OVSG consisting of 845 available labeled events (542 VT, 217 nested and 86 LP) recorded in the period 2013-2018. We obtained an average classification rate of 72 %. We determined that the VT class includes a variety of signals covering the LP, Nested and VT classes. Reviewing in details the waveforms and the spectral characteristics of the signals belonging to the 3 classes we then introduced Hybrid events and also defined a monochromatic class (so-called Tornillo) of LP signals, thus matching the full description of signals provided in Moretti et al. (2020).Then, using the new information, a new model was trained with 5 classes and tested. We obtained a much better classification average rate of 84 %. The classification is excellent for Nested events (93 % of accuracy and precision) and Tornillo events (93% of accuracy and precision). The classification of VT events (90% accuracy, 89% precision) and LP events (86% accuracy, 82% precision) were also very good. The most difficult class to recognize is the Hybrid class (64 % accuracy, 69 % precision). Hybrid events are often mixed with VT and LP events. This may be explained by the nature of this class and the physical process that includes both a fracturing and a resonating component with different modal frequencies.Machine learning is a powerful tool to handle large datasets. From a dataset built manually, the processing we applied allowed to obtain a reliable automatic classification by refining class definitions. This has important implications for observatory data processing during unrest and eruptive activity.

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Interpreting HVEM of muscle-cell impulse networks

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010012103x ◽

1992 ◽

Vol 50 (1) ◽

pp. 126-127

Author(s):

Paul DeCosta ◽

Kyugon Cho ◽

Stephen Shemlon ◽

Heesung Jun ◽

Stanley M. Dunn

Keyword(s):

Structural Analysis ◽

Muscle Cell ◽

Electron Micrographs ◽

Relative Size ◽

Automatic Classification ◽

Nerve Fibers ◽

Analysis Algorithm ◽

Structural Networks

Introduction: The analysis and interpretation of electron micrographs of cells and tissues, often requires the accurate extraction of structural networks, which either provide immediate 2D or 3D information, or from which the desired information can be inferred. The images of these structures contain lines and/or curves whose orientation, lengths, and intersections characterize the overall network.Some examples exist of studies that have been done in the analysis of networks of natural structures. In, Sebok and Roemer determine the complexity of nerve structures in an EM formed slide. Here the number of nodes that exist in the image describes how dense nerve fibers are in a particular region of the skin. Hildith proposes a network structural analysis algorithm for the automatic classification of chromosome spreads (type, relative size and orientation).

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Automatic classification of sleep stages

Electroencephalography and Clinical Neurophysiology ◽

10.1016/s0013-4694(97)88102-7 ◽

1997 ◽

Vol 103 (1) ◽

pp. 44 ◽

Cited By ~ 2

Author(s):

B Kemp

Keyword(s):

Automatic Classification ◽

Sleep Stages

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An Enhanced Technique for Classification of Fruits using Shape Color and Texture Features

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse/v7i7/0107 ◽

2017 ◽

Vol 7 (7) ◽

pp. 408

Author(s):

Yashpal Jitarwal ◽

Tabrej Ahamad Khan ◽

Pawan Mangal

Keyword(s):

Image Processing ◽

Texture Features ◽

Automatic Classification ◽

Processing Technique ◽

Image Processing Technique ◽

Advance Technique ◽

Time Required ◽

Fruit Sorting ◽

Human Inspection

In earlier times fruits were sorted manually and it was very time consuming and laborious task. Human sorted the fruits of the basis of shape, size and color. Time taken by human to sort the fruits is very large therefore to reduce the time and to increase the accuracy, an automatic classification of fruits comes into existence.To improve this human inspection and reduce time required for fruit sorting an advance technique is developed that accepts information about fruits from their images, and is called as Image Processing Technique.

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