scholarly journals An all-sky camera images classification method using cloud cover features

2021 ◽  
Author(s):  
Xiaotong Li ◽  
Baozhu Wang ◽  
Bo Qiu ◽  
Chao Wu
Keyword(s):  
Random Forest ◽  
Site Selection ◽  
Cloud Cover ◽  
Automatic Processing ◽  
Area Ratio ◽  
Astronomical Observatory ◽  
Classification Criteria ◽  
Large Dataset ◽  
Local Cloud

Abstract. The all-sky camera (ASC) images can reflect the local cloud cover information, and the cloud cover is one of the first factors considered for astronomical observatory site selection. Therefore, the realization of automatic classification of the ASC images plays an important role in astronomical observatory site selection. In this paper, three cloud cover features are proposed for the TMT (Thirty Meter Telescope) classification criteria, namely cloud weight, cloud area ratio and cloud dispersion. After the features are quantified, four classifiers are used to recognize the classes of the images. Four classes of ASC images are identified: “Clear”, “Inner”, “Outer” and “Covered”. The proposed method is evaluated on a large dataset, which contains 7328 ASC images taken by an all-sky camera located in Xinjiang (38.19° N, 74.53° E). In the end, the method achieves an accuracy of 97.28 % and F1_score of 96.97 % by a random forest (RF) classifier, which greatly improves the efficiency of automatic processing of the ASC images.

scholarly journals Classification of Genuinity in Job Posting Using Machine Learning

2021 ◽  
Vol 9 (12) ◽  
pp. 1569-1575
Author(s):  
Charan Lokku
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Language Processing ◽  
Large Dataset ◽  
Final Model ◽  
Inverse Document Frequency ◽  
Document Frequency ◽  
Machine Learning Approach ◽  
Job Postings

Abstract: To avoid fraudulent Job postings on the internet, we target to minimize the number of such frauds through the Machine Learning approach to predict the chances of a job being fake so that the candidate can stay alert and make informed decisions if required. The model will use NLP to analyze the sentiments and pattern in the job posting and TF-IDF vectorizer for feature extraction. In this model, we are going to use Synthetic Minority Oversampling Technique (SMOTE) to balance the data and for classification, we used Random Forest to predict output with high accuracy, even for the large dataset it runs efficiently, and it enhances the accuracy of the model and prevents the overfitting issue. The final model will take in any relevant job posting data and produce a result determining whether the job is real or fake. Keywords: Natural Language Processing (NLP), Term Frequency-Inverse Document Frequency (TF-IDF), Synthetic Minority Oversampling Technique (SMOTE), Random Forest.

DONG SON BRONZE DRUM - THE WORK OF THE LAC VIET PEOPLE

2018 ◽  
Author(s):  
Nguyen Van Hao
Keyword(s):  
Classification Criteria ◽  
Way Of Life ◽  
Tribal Identity ◽  
The Relationship

Bronze drums are widely distributed, broader than the range of a nation. Therefore, the identification of each kind of drum is a basic subject, should be concerned. In determining the tribal identity of the drum, the classification of drum is the key stage, the relationship between the objective of the classification and classification criteria is the relation as shape and shadow, if there is no right criteria then the result of division will be difficult to reach the desired goal. Likewise, the criterion of the pattern on the bronze drum brought to the affirmation is the Dong Son bronze drum of the Lac Viet people. And the parallel is the affirmation of the culture, way of life, residence of the nation created the drum.

scholarly journals Minimization of the Line Resistance Impact on Memdiode-Based Simulations of Multilayer Perceptron Arrays Applied to Pattern Recognition

2021 ◽  
Vol 11 (1) ◽  
pp. 9
Author(s):  
Fernando Leonel Aguirre ◽  
Nicolás M. Gomez ◽  
Sebastián Matías Pazos ◽  
Félix Palumbo ◽  
Jordi Suñé ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Ex Situ ◽  
Multilayer Perceptrons ◽  
Spice Simulation ◽  
Large Dataset ◽  
High Line ◽  
Calibration Algorithm ◽  
The Impact ◽  
Line Resistance

In this paper, we extend the application of the Quasi-Static Memdiode model to the realistic SPICE simulation of memristor-based single (SLPs) and multilayer perceptrons (MLPs) intended for large dataset pattern recognition. By considering ex-situ training and the classification of the hand-written characters of the MNIST database, we evaluate the degradation of the inference accuracy due to the interconnection resistances for MLPs involving up to three hidden neural layers. Two approaches to reduce the impact of the line resistance are considered and implemented in our simulations, they are the inclusion of an iterative calibration algorithm and the partitioning of the synaptic layers into smaller blocks. The obtained results indicate that MLPs are more sensitive to the line resistance effect than SLPs and that partitioning is the most effective way to minimize the impact of high line resistance values.

scholarly journals Classification of Germination Images of Pear Pollen Using Random Forest and Convolution Neural Network Models

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 45993-45999
Author(s):  
Ung Yang ◽  
Seungwon Oh ◽  
Seung Gon Wi ◽  
Bok-Rye Lee ◽  
Sang-Hyun Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Network Models ◽  
Convolution Neural Network ◽  
Neural Network Models

Modeling and multi-class classification of vibroarthographic signals via time domain curvilinear divergence random forest

2021 ◽  
Author(s):  
Balajee Alphonse ◽  
Venkatesan Rajagopal ◽  
Sudhakar Sengan ◽  
Kousalya Kittusamy ◽  
Amudha Kandasamy ◽  
...  
Keyword(s):  
Random Forest ◽  
Time Domain ◽  
Multi Class Classification

scholarly journals UV Spectral Classification of B Stars

1985 ◽  
Vol 111 ◽  
pp. 411-413
Author(s):  
Janet Rountree ◽  
George Sonneborn ◽  
Robert J. Panek
Keyword(s):  
Classification System ◽  
Classification Criteria ◽  
High Dispersion ◽  
Spectral Classification ◽  
Early Type ◽  
B Stars ◽  
Dimensional Classification ◽  
Early Type Stars ◽  
Comprehensive Classification

Previous studies of ultraviolet spectral classification have been insufficient to establish a comprehensive classification system for ultraviolet spectra of early-type stars because of inadequate spectral resolution. We have initiated a new study of ultraviolet spectral classification of B stars using high-dispersion IUE archival data. High-dispersion SWP spectra of MK standards and other B stars are retrieved from the IUE archives and numerically degraded to a uniform resolution of 0.25 or 0.50 Å. The spectra (in the form of plots or photowrites) are then visually examined with the aim of setting up a two-dimensional classification matrix. We follow the method used to create the MK classification system for visual spectra. The purpose of this work is to examine the applicability of the MK system (and in particular, the set of standard stars) in the ultraviolet, and to establish classification criteria in this spectral region.

scholarly journals Classification of Compact Objects: QSS, QSOs, N-Type and Compact Galaxies, Seyfert and Galactic Nuclei

1972 ◽  
Vol 44 ◽  
pp. 97-103
Author(s):  
W. W. Morgan
Keyword(s):  
Galactic Nuclei ◽  
Seyfert Galaxy ◽  
Classification Criteria ◽  
Compact Objects ◽  
Stellar Objects ◽  
Quasi Stellar Objects ◽  
Compact Galaxies

Some methods currently in use for the classification of the optical forms of the ‘compact’ galaxies and quasi-stellar objects are reviewed. It is shown that the category ‘Seyfert Galaxy’ is basically a spectroscopic (rather than a form) classification.An optical form-classification is described which is, in principle, identical with published classification criteria for QSO, N-type, and compact objects. The importance of maintaining rigid form-standards is emphasized.

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