Pattern Recognition Using Artificial Neural Network: A Review

Kim, Tai-hoon

doi:10.1007/978-3-642-13365-7_14

Cited by 39 publications

(12 citation statements)

References 8 publications

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“…For classification with artificial neural network (Kim, 2010; Saravanana and Sasithra, 2014; Aboukarima et al, 2015), a feedforward neural network with 3–15 hidden layers is used. All leaf samples are mixed together at first.…”

Section: Resultsmentioning

confidence: 99%

LeafletAnalyzer, an Automated Software for Quantifying, Comparing and Classifying Blade and Serration Features of Compound Leaves during Development, and among Induced Mutants and Natural Variants in the Legume Medicago truncatula

2017

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Diverse leaf forms ranging from simple to compound leaves are found in plants. It is known that the final leaf size and shape vary greatly in response to developmental and environmental changes. However, changes in leaf size and shape have been quantitatively characterized only in a limited number of species. Here, we report development of LeafletAnalyzer, an automated image analysis and classification software to analyze and classify blade and serration characteristics of trifoliate leaves in Medicago truncatula. The software processes high quality leaf images in an automated or manual fashion to generate size and shape parameters for both blades and serrations. In addition, it generates spectral components for each leaflets using elliptic Fourier transformation. Reconstruction studies show that the spectral components can be reliably used to rebuild the original leaflet images, with low, and middle and high frequency spectral components corresponding to the outline and serration of leaflets, respectively. The software uses artificial neutral network or k-means classification method to classify leaflet groups that are developed either on successive nodes of stems within a genotype or among genotypes such as natural variants and developmental mutants. The automated feature of the software allows analysis of thousands of leaf samples within a short period of time, thus facilitating identification, comparison and classification of leaf groups based on leaflet size, shape and tooth features during leaf development, and among induced mutants and natural variants.

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Section: Resultsmentioning

confidence: 99%

LeafletAnalyzer, an Automated Software for Quantifying, Comparing and Classifying Blade and Serration Features of Compound Leaves during Development, and among Induced Mutants and Natural Variants in the Legume Medicago truncatula

2017

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“…Artificial neural network models are generic non-linear function approximation algorithms that are capable of computing, predicting, and classifying data (Ali et al, 2016). They have been widely used in applications including pattern recognition, classification, and regression in various fields (Hong et al, 2004; Kim, 2010; Zain et al, 2012; Neto et al, 2017). ANN refers to a multi-layer network structure that consists of an input layer, an output layer, and one or more hidden layers (Kimes et al, 1998).…”

Section: Regression Techniquesmentioning

confidence: 99%

Hyperspectral Leaf Reflectance as Proxy for Photosynthetic Capacities: An Ensemble Approach Based on Multiple Machine Learning Algorithms

Meacham-Hensold

Guan

et al. 2019

Front. Plant Sci.

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Global agriculture production is challenged by increasing demands from rising population and a changing climate, which may be alleviated through development of genetically improved crop cultivars. Research into increasing photosynthetic energy conversion efficiency has proposed many strategies to improve production but have yet to yield real-world solutions, largely because of a phenotyping bottleneck. Partial least squares regression (PLSR) is a statistical technique that is increasingly used to relate hyperspectral reflectance to key photosynthetic capacities associated with carbon uptake (maximum carboxylation rate of Rubisco, V c,max ) and conversion of light energy (maximum electron transport rate supporting RuBP regeneration, J max ) to alleviate this bottleneck. However, its performance varies significantly across different plant species, regions, and growth environments. Thus, to cope with the heterogeneous performances of PLSR, this study aims to develop a new approach to estimate photosynthetic capacities. A framework was developed that combines six machine learning algorithms, including artificial neural network (ANN), support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), random forest (RF), Gaussian process (GP), and PLSR to optimize high-throughput analysis of the two photosynthetic variables. Six tobacco genotypes, including both transgenic and wild-type lines, with a range of photosynthetic capacities were used to test the framework. Leaf reflectance spectra were measured from 400 to 2500 nm using a high-spectral-resolution spectroradiometer. Corresponding photosynthesis vs. intercellular CO 2 concentration response curves were measured for each leaf using a leaf gas-exchange system. Results suggested that the mean R 2 value of the six regression techniques for predicting V c,max ( J max ) ranged from 0.60 (0.45) to 0.65 (0.56) with the mean RMSE value varying from 47.1 (40.1) to 54.0 (44.7) μmol m -2 s -1 . Regression stacking for V c,max ( J max ) performed better than the individual regression techniques with increases in R 2 of 0.1 (0.08) and decreases in RMSE by 4.1 (6.6) μmol m -2 s -1 , equal to 8% (15%) reduction in RMSE . Better predictive performance of the regression stacking is likely attributed to the varying coefficients (or weights) in the level-2 model (the LASSO model) and the diverse ability of each individual regression technique to utilize spectral information for the best modeling performance. Further refinements can be made to apply this stacked regression t...

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“…Thus, the dependency on the GCS-UAV communication performance can be reduced. The artificial intelligence algorithms can deal with the basic flight control issues [88], [89], path planning problems [90], [91] of the UAVs themselves, as well as some mission-related problems, such as the machine vision [92], pattern recognition [93] especially when executing reconnaissance and tracking missions. What the artificial intelligence algorithms bring to the UAVs is the ability to learn and utilize the experiences and knowledge, which makes the UAVs think and behave like a human.…”

Section: (C) Intelligent Algorithmsmentioning

confidence: 99%

Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspective

Wang

Zhao

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

151

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Unmanned aerial vehicle (UAV) networks are playing an important role in various areas due to their agility and versatility, which have attracted significant attention from both the academia and industry in recent years. As an integration of the embedded systems with communication devices, computation capabilities and control modules, the UAV network could build a closed loop from data perceiving, information exchanging, decision making to the final execution, which tightly integrates the cyber processes into the physical devices. Therefore, the UAV network could be considered as a cyber physical system (CPS). Revealing the coupling effects among the three interacted components in this CPS system, i.e., communication, computation and control, is envisioned as the key to properly utilize all the available resources and hence improve the performance of the UAV networks. In this paper, we present a comprehensive survey on the UAV networks from a CPS perspective. Firstly, we respectively research the basics and advances with respect to the three CPS components in the UAV networks. Then we look inside to investigate how these components contribute to the system performance by classifying the UAV networks into three hierarchies, i.e., the cell level, the system level, and the system of system level. Further, the coupling effects among these CPS components are explicitly illustrated, which could be enlightening to deal with the challenges in each individual aspect. New research directions and open issues are discussed at the end of this survey. With this intensive literature review, we try to provide a novel insight into the state-of-the-art in the UAV networks.

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Pattern Recognition Using Artificial Neural Network: A Review

Cited by 39 publications

References 8 publications

LeafletAnalyzer, an Automated Software for Quantifying, Comparing and Classifying Blade and Serration Features of Compound Leaves during Development, and among Induced Mutants and Natural Variants in the Legume Medicago truncatula

LeafletAnalyzer, an Automated Software for Quantifying, Comparing and Classifying Blade and Serration Features of Compound Leaves during Development, and among Induced Mutants and Natural Variants in the Legume Medicago truncatula

Hyperspectral Leaf Reflectance as Proxy for Photosynthetic Capacities: An Ensemble Approach Based on Multiple Machine Learning Algorithms

Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspective

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