scholarly journals Automatic detection of fish sounds based on multi-stage classification including logistic regression via adaptive feature weighting

2018 ◽  
Vol 144 (5) ◽  
pp. 2709-2718 ◽  
Author(s):  
Ryosuke Harakawa ◽  
Takahiro Ogawa ◽  
Miki Haseyama ◽  
Tomonari Akamatsu
Keyword(s):  
Logistic Regression ◽  
Automatic Detection ◽  
Feature Weighting ◽  
Fish Sounds ◽  
Multi Stage ◽  
Stage Classification

scholarly journals Analysis of Consumers’ Preference for Local Rice among Households in Ekiti State, Nigeria

2020 ◽  
pp. 18-23
Author(s):  
G. T. Ajayi ◽  
A. Ajiboye
Keyword(s):  
Logistic Regression ◽  
Control Policy ◽  
Structured Interview ◽  
Sampling Procedure ◽  
Primary Data ◽  
Multi Stage ◽  
Good Taste ◽  
The Mean ◽  
The Government

Consumers’ preference for local rice determines its demand. Therefore, the study was carried out to analyze consumers’ preference for local rice among households in Ekiti State. A multi-stage sampling procedure was used to select respondents for this study. A total of 240 women were randomly selected from three Local Government Areas (LGAs) in the State. Primary data were obtained with the use of a well-structured interview schedule. Data collected were analyzed using descriptive statistics as well as inferential statistic like logistic regression. The mean age of the respondents was 38 years and more than half (53.3%) of the respondents were females. Most (70.0%) of the respondents had a mean family size of 7 persons. Most (87.0%) preferred local rice and factors influencing consumers’ preference for local rice include good nutritional value, quality of rice and good taste. Local rice was very much preferred by the respondents. Logistic regression shows that significant influence exists between price, taste, availability of rice and presence of particles and preferred choice of rice. Therefore, efforts should be made by the government to formulate price control policy on local rice for its affordability by the consumers and there should be improvement on processing technology of local rice to eliminate presence of particles for improved quality and good taste to enhance the consumers’ preference for choice of rice. Also, the government should support farmers through provision of incentives and credit facilities so as to produce more local rice for its availability all year round.

A Multi-stage Classification Framework for Disaster-Specific Tweets

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Harshit Mehrotra ◽  
Akanksha Mishra ◽  
Sukomal Pal
Keyword(s):  
Classification Framework ◽  
Multi Stage ◽  
Stage Classification

scholarly journals Multi-Stage Recognition of Speech Emotion Using Sequential Forward Feature Selection

2016 ◽  
Vol 10 (1) ◽  
pp. 35-41 ◽  
Author(s):  
Tatjana Liogienė ◽  
Gintautas Tamulevičius
Keyword(s):  
Feature Selection ◽  
Emotion Recognition ◽  
Classification Scheme ◽  
Recognition Rate ◽  
Single Stage ◽  
Speech Emotion Recognition ◽  
Forward Selection ◽  
Multi Stage ◽  
Stage Scheme ◽  
Stage Classification

Abstract The intensive research of speech emotion recognition introduced a huge collection of speech emotion features. Large feature sets complicate the speech emotion recognition task. Among various feature selection and transformation techniques for one-stage classification, multiple classifier systems were proposed. The main idea of multiple classifiers is to arrange the emotion classification process in stages. Besides parallel and serial cases, the hierarchical arrangement of multi-stage classification is most widely used for speech emotion recognition. In this paper, we present a sequential-forward-feature-selection-based multi-stage classification scheme. The Sequential Forward Selection (SFS) and Sequential Floating Forward Selection (SFFS) techniques were employed for every stage of the multi-stage classification scheme. Experimental testing of the proposed scheme was performed using the German and Lithuanian emotional speech datasets. Sequential-feature-selection-based multi-stage classification outperformed the single-stage scheme by 12–42 % for different emotion sets. The multi-stage scheme has shown higher robustness to the growth of emotion set. The decrease in recognition rate with the increase in emotion set for multi-stage scheme was lower by 10–20 % in comparison with the single-stage case. Differences in SFS and SFFS employment for feature selection were negligible.

scholarly journals Automatic Detection of Concrete Spalling Using Piecewise Linear Stochastic Gradient Descent Logistic Regression and Image Texture Analysis

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Nhat-Duc Hoang ◽  
Quoc-Lam Nguyen ◽  
Xuan-Linh Tran
Keyword(s):  
Logistic Regression ◽  
Gradient Descent ◽  
Piecewise Linear ◽  
Automatic Detection ◽  
Stochastic Gradient ◽  
Image Feature ◽  
Stochastic Gradient Descent ◽  
Image Texture ◽  
Gray Level ◽  
Concrete Wall

Recognition of spalling on surface of concrete wall is crucial in building condition survey. Early detection of this form of defect can help to develop cost-effective rehabilitation methods for maintenance agencies. This study develops a method for automatic detection of spalled areas. The proposed approach includes image texture computation for image feature extraction and a piecewise linear stochastic gradient descent logistic regression (PL-SGDLR) used for pattern recognition. Image texture obtained from statistical properties of color channels, gray-level cooccurrence matrix, and gray-level run lengths is used as features to characterize surface condition of concrete wall. Based on these extracted features, PL-SGDLR is employed to categorize image samples into two classes of “nonspall” (negative class) and “spall” (positive class). Notably, PL-SGDLR is an extension of the standard logistic regression within which a linear decision surface is replaced by a piecewise linear one. This improvement can enhance the capability of logistic regression in dealing with spall detection as a complex pattern classification problem. Experiments with 1240 collected image samples show that PL-SGDLR can help to deliver a good detection accuracy (classification accuracy rate = 90.24%). To ease the model implementation, the PL-SGDLR program has been developed and compiled in MATLAB and Visual C# .NET. Thus, the proposed PL-SGDLR can be an effective tool for maintenance agencies during periodic survey of buildings.

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