Hyperspectral inversion of nitrogen content in maize leaves based on different dimensionality reduction algorithms

Animal manures have been used as natural crop fertilizers for centuries. Because of poultry manure’s high nitrogen content, it has long been recognized as one of the most desirable manures. Besides fertilizing crops, manures also supply other essential plant nutrients and serve as a soil amendment by adding organic matter, which helps improve the soil’s moisture and nutrient retention. Organic matter persistence will vary with temperature, drainage, rainfall, and other environmental factors. This 2-page fact sheet was written by Michael A. Davis, D.R. Sloan, Gerald Kidder, and R.D. Jacobs, and published by the UF Department of Animal Science, November 2013. http://edis.ifas.ufl.edu/aa205

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Analysis on Research Paper Publication Recommendation System with Composition of Papers and Conferences Matrices

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset207330 ◽

2020 ◽

pp. 120-127

Author(s):

Htay Htay Win ◽

Aye Thida Myint ◽

Mi Cho Cho

Keyword(s):

Social Network ◽

Dimensionality Reduction ◽

Correspondence Analysis ◽

Recommendation System ◽

Research Paper ◽

Topic Modelling ◽

Research Papers ◽

The Right ◽

Publication Recommendation

For years, achievements and discoveries made by researcher are made aware through research papers published in appropriate journals or conferences. Many a time, established s researcher and mainly new user are caught up in the predicament of choosing an appropriate conference to get their work all the time. Every scienti?c conference and journal is inclined towards a particular ?eld of research and there is a extensive group of them for any particular ?eld. Choosing an appropriate venue is needed as it helps in reaching out to the right listener and also to further one’s chance of getting their paper published. In this work, we address the problem of recommending appropriate conferences to the authors to increase their chances of receipt. We present three di?erent approaches for the same involving the use of social network of the authors and the content of the paper in the settings of dimensionality reduction and topic modelling. In all these approaches, we apply Correspondence Analysis (CA) to obtain appropriate relationships between the entities in question, such as conferences and papers. Our models show hopeful results when compared with existing methods such as content-based ?ltering, collaborative ?ltering and hybrid ?ltering.

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STANDING CROP NITROGEN CONTENT (FIFE)

ORNL Distributed Active Archive Center Datasets ◽

10.3334/ornldaac/118 ◽

1994 ◽

Author(s):

T. G. F. KITTEL ◽

W. J. PARTON ◽

D. S. SCHIMEL

Keyword(s):

Nitrogen Content ◽

Standing Crop

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Speech Emotion Recognition Based on Sparse Representation

Archives of Acoustics ◽

10.2478/aoa-2013-0055 ◽

2013 ◽

Vol 38 (4) ◽

pp. 465-470 ◽

Cited By ~ 11

Author(s):

Jingjie Yan ◽

Xiaolan Wang ◽

Weiyi Gu ◽

LiLi Ma

Keyword(s):

Dimensionality Reduction ◽

Emotion Recognition ◽

Least Squares ◽

Partial Least Squares ◽

Partial Least Squares Regression ◽

Speech Emotion Recognition ◽

Least Squares Regression ◽

Computer Science Pedagogy ◽

Reduction Methods ◽

Analysis Computer

Abstract Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do- mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth. We make use of the sparse partial least squares regression method to implement the feature selection and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting the SPLSR method, the component parts of those redundant and meaningless speech emotion features are lessened to zero while those serviceable and informative speech emotion features are maintained and selected to the following classification step. A number of tests on Berlin database reveal that the recogni- tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality reduction methods.

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