scholarly journals Users' Emotions Analysis based on Hybrid Feature Extraction Techniques

2020 ◽  
pp. 291-296
Author(s):  
Sulis Sandiwarno
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Learning Systems ◽  
Experimental Results ◽  
Extraction Techniques ◽  
Machine Learning Classifiers ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
E Learning ◽  
Hybrid Feature Extraction

In order to solve some problems of importance of words and missing relations of semantic between words in the emotional analysis of e-learning systems, the TF-IWF algorithm weighted Word2vec algorithm model was proposed as a feature extraction algorithm. Moreover, to support this study, we employ Multinomial Naïve Bayes (MNB) to obtain more accurate results. There are three mainly steps, firstly, TF-IWF is employed used to compute the weight of word. Second, Word2vec algorithm is adopted to compute the vector of words, Third, we concatenate first and second steps. Finally, the users' opinions data is trained and classified through several machine learning classifiers especially MNB classifier. The experimental results indicate that the proposed method outperformed against previous approaches in terms of precision, recall, F-Score, and accuracy.

Hybrid Feature Extraction for Machining Applications

1993 ◽  
Author(s):  
Malcolm C. Fields ◽  
D. C. Anderson
Keyword(s):  
Feature Extraction ◽  
Product Information ◽  
Design Feature ◽  
Boundary Representation ◽  
Feature Model ◽  
Solid Model ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Feature Based ◽  
Hybrid Feature Extraction

Abstract A hybrid feature extraction algorithm for extracting cavity features for machining applications is presented. The algorithm operates on both a feature-based solid model of a part and its corresponding boundary representation solid model. Information available from both part representations is used, offering a more robust and efficient solution for some of the critical limitations of current feature extraction algorithms, such as verification of completeness, computation of cavity volumes, and maintenance of design information. The hybrid feature extraction algorithm combines the strengths of feature-based design and feature extraction approaches to linking design and manufacturing. Starting with a feature-based model of a part consisting of volumetric design features combined with a stock shape using set operations, the algorithm transforms this model into a feature model containing only machinable cavity features. The transformation involves computations on both the set theoretic feature model and its corresponding boundary representation solid model, and deals with the complexities of feature-feature intersections and protrusions. By combining the higher-level product information contained in the design feature model with the topological and geometric information in the boundary representation model, the algorithm supplements traditional boundary representation extraction with non-geometric product information, enabling the verification of completeness, and significantly aiding the computation of the appropriate feature volumes.

scholarly journals Paddy Seed Classification and Identifying Varieties using Random Assessment Classification

2019 ◽  
Vol 9 (2) ◽  
pp. 2682-2685
Keyword(s):  
Image Analysis ◽  
Feature Extraction ◽  
Classification Accuracy ◽  
Research Work ◽  
Classification Algorithm ◽  
Experimental Results ◽  
Classification Algorithms ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Knn Classification

The current research work focuses in developing an accurate and efficient classification and feature extraction algorithm for paddy seed image analysis. The paddy images that are preprocessed by applying hybrid mediangaustransform algorithms were segmented using Paddysegmatch algorithm. The resultant image’s features are extracted by applying the proposed enhanced rapid SURF feature extraction including various features of image. Later, the paddy seeds are classified to form different categories by applying the proposed Random Assessment Classification algorithm. Experimental results on Paddy seed realtime image analysis database show that the proposed method performs better classification accuracy compared with SVM and KNN classification algorithms.

Platform for Analysing and Encouraging Student Activity on Contest and E-learning Systems

2018 ◽  
Vol 12 ◽  
pp. 85-98
Author(s):  
Bojan Kostadinov ◽  
Mile Jovanov ◽  
Emil STANKOV
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Educational Policy ◽  
Learning Systems ◽  
Data Sources ◽  
Or Education ◽  
Student Activity ◽  
The World ◽  
E Learning ◽  
Analyse Data

Data collection and machine learning are changing the world. Whether it is medicine, sports or education, companies and institutions are investing a lot of time and money in systems that gather, process and analyse data. Likewise, to improve competitiveness, a lot of countries are making changes to their educational policy by supporting STEM disciplines. Therefore, it’s important to put effort into using various data sources to help students succeed in STEM. In this paper, we present a platform that can analyse student’s activity on various contest and e-learning systems, combine and process the data, and then present it in various ways that are easy to understand. This in turn enables teachers and organizers to recognize talented and hardworking students, identify issues, and/or motivate students to practice and work on areas where they’re weaker.

Corresponding Feature Extraction Algorithm between Infrared and Visible Images Using MSER

2011 ◽  
Vol 33 (7) ◽  
pp. 1625-1631 ◽  
Author(s):  
Lin Lian ◽  
Guo-hui Li ◽  
Hai-tao Wang ◽  
hao Tian ◽  
Shu-kui Xu
Keyword(s):  
Feature Extraction ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
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