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Wyszukujesz frazę "feature detection" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
Presenting a technique for registering images and range data using a topological representation of a path within an environment
Autorzy:
Ferreira, F.
Davim, L.
Rocha, R.
Dias, J.
Santos, V.
Powiązania:
https://bibliotekanauki.pl/articles/385035.pdf
Data publikacji:
2007
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
sensor feature integration
binary data
Bernoulli mixture model
dimensionality reduction
robot localisation
change detection
Opis:
This article presents a novel method to the utilize topological representation of a path, thatpath that is created from sequences of images from digital cameras and sensor data from range sensors. A topological representation of the environment is created by leading the robot around the environment during a familiarisation phaseLeading the robot around the environment during a familiarisation phase creates a topological representation of the environment. While moving down the same path, the robot is able to localise itself within the topological representation that is has been previously created. The principal contribution to the state of the art is that, by using a topological representation of the environment, individual 3D data sets acquired from a set of range sensors need not be registered in a single, [Global] Coordinate Reference System. Instead, 3D point clouds for small sections of the environment are indexed to a sequence of multi-sensor views, of images and range data. Such a registration procedure can be useful in the construction of 3D representations of large environments and in the detection of changes that might occur within these environments.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2007, 1, 3; 47-56
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A contemporary multi-objective feature selection model for depression detection using a hybrid pBGSK optimization algorithm
Autorzy:
Kavi Priya, Santhosam
Pon Karthika, Kasirajan
Powiązania:
https://bibliotekanauki.pl/articles/2201021.pdf
Data publikacji:
2023
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
depression detection
text classification
dimensionality reduction
hybrid feature selection
wykrywanie depresji
klasyfikacja tekstu
redukcja wymiarowości
wybór funkcji
Opis:
Depression is one of the primary causes of global mental illnesses and an underlying reason for suicide. The user generated text content available in social media forums offers an opportunity to build automatic and reliable depression detection models. The core objective of this work is to select an optimal set of features that may help in classifying depressive contents posted on social media. To this end, a novel multi-objective feature selection technique (EFS-pBGSK) and machine learning algorithms are employed to train the proposed model. The novel feature selection technique incorporates a binary gaining-sharing knowledge-based optimization algorithm with population reduction (pBGSK) to obtain the optimized features from the original feature space. The extensive feature selector (EFS) is used to filter out the excessive features based on their ranking. Two text depression datasets collected from Twitter and Reddit forums are used for the evaluation of the proposed feature selection model. The experimentation is carried out using naive Bayes (NB) and support vector machine (SVM) classifiers for five different feature subset sizes (10, 50, 100, 300 and 500). The experimental outcome indicates that the proposed model can achieve superior performance scores. The top results are obtained using the SVM classifier for the SDD dataset with 0.962 accuracy, 0.929 F1 score, 0.0809 log-loss and 0.0717 mean absolute error (MAE). As a result, the optimal combination of features selected by the proposed hybrid model significantly improves the performance of the depression detection system.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2023, 33, 1; 117--131
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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