A New Spinach Respiratory Prediction Method Using Particle Filtering Approach

Saenmuang, Soraya; Aunsri, Nattapol

doi:10.1109/access.2019.2941176

Cited by 13 publications

(3 citation statements)

References 33 publications

(27 reference statements)

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“…Banana Mosaic Virus (BMV): Banana Mosaic Virus (BMV) is another viral illness that affects bananas [24]. It results in mosaic-like patterns on the leaves and can impair plant growth, yield, and quality.…”

Section: Banana Fruit Diseasesmentioning

confidence: 99%

An Intelligent Grading System for Automated Identification and Classification of Banana Fruit Diseases Using Deep Neural Network

B. Patel,

J. Patil

2024

IJCDS

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This study proposes an intelligent system for automated diseases detect and categorization in banana fruit, as well as an integrated grading system. To accomplish accurate illness identification and grading, the suggested system combines computer vision methods, machine learning algorithms, and deep learning models. The system extracts key information from banana fruit images using image processing techniques, which are subsequently input into a trained classification model. The categorization model uses cutting-edge algorithms to categorize the banana fruit into several illness groups. Furthermore, the sophisticated grading system evaluates the severity and quality of the diseased fruit based on a variety of characteristics such as size, color, and texture. The experimental findings reveal that the proposed method is successful, with high accuracy rate of 99.8% in illness diagnosis and accurate banana grading. This automated technology provides a time-efficient and cost-effective approach for disease control in banana plantations, allowing producers and agricultural stakeholders to make more informed decisions.

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Section: Banana Fruit Diseasesmentioning

confidence: 99%

An Intelligent Grading System for Automated Identification and Classification of Banana Fruit Diseases Using Deep Neural Network

B. Patel,

J. Patil

2024

IJCDS

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“…The two quantities: variables (elements) in the random projection matrix, ϕ, and the number of predictors after the compression, m, these quantities are referred to Eqs. (7) and (8), respectively. Specifically, ϕ and m are generated from the uniform distributions, namely…”

Section: A Constant Coefficient Of Bayesian Compressed Varmentioning

confidence: 99%

A Time-Varying Bayesian Compressed Vector Autoregression for Macroeconomic Forecasting

Aunsri

Taveeapiradeecharoen

2020

IEEE Access

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This paper presents macroeconomic forecasting by using a time-varying Bayesian compressed vector autoregression approach. We apply a random compression by using projection matrix to randomly select predictive variables in vector autoregression (VAR), and then perform true out-of-sample forecast where the forecast values are averaged across all estimated models, containing different in both explanatory variables and number of those variables by using Bayesian model averaging (BMA). In addition to this, we allow the parameters in Bayesian compressed VAR to be time-varying by implementing dynamic model averaging (DMA) algorithm that is applicable with VAR using forgetting factor to control the degree of time-varying in the estimating parameters. We validate the performance of the proposed method via real macroeconomic data including up to 53 variables. The empirical results demonstrate that the predictive performance of time-varying Bayesian compressed VAR can beat traditional VAR types which are considered to have a potentiality to deal with large size variables. INDEX TERMS Bayesian econometrics, macroeconomics, Bayesian model averaging, mathematical models, time-varying parameters, dynamic model averaging, compression, Kalman filter

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“…Particle Filtering, a numerical technique to estimate or track the parameters of interest via a recursive computation, hence, comes into consideration. Recently, particle filter (PF) has been used in many science and engineering applications containing wireless networks, economic, multimedia, geography, and medicine [14] , [15] , [16] , [17] , [18] , [19] , [20] , for examples. The need of PF stems from the fact that it can handle the highly nonlinear/non-Gaussian systems, the restriction of Gassianity is now relaxed.…”

Section: Introductionmentioning

confidence: 99%

A novel adaptive resampling for sequential Bayesian filtering to improve frequency estimation of time-varying signals

Aunsri

Pipatphol

Thikeaw

et al. 2021

Heliyon

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This paper presents a new algorithm for adaptive resampling, called percentile-based resampling (PBR) in a sequential Bayesian filtering, i.e., particle filter (PF) in particular, to improve tracking quality of the frequency trajectories under noisy environments. Since the conventional resampling scheme used in the PF suffers from computational burden, resulting in less efficiency in terms of computation time and complexity as well as the real time applications of the PF. The strategy to remedy this issue is proposed in this work. After state updating, important high particle weights are used to formulate the pre-set percentile in each sequential iteration to create a new set of high quality particles for the next filtering stage. The number of particles after PBR remains the same as the original. To verify the effectiveness of the proposed method, we first evaluated the performance of the method via numerical examples to a complex and highly nonlinear benchmark system. Then, the proposed method was implemented for frequency estimation for two time-varying signals. From the experimental results, via three measurement metrics, our approach delivered better performance than the others. Frequency estimates obtained by our method were excellent as compared to the conventional resampling method when number of particles were identical. In addition, the computation time of the proposed work was faster than those recent adaptive resampling schemes in literature, emphasizing the superior performance to the existing ones.

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A New Spinach Respiratory Prediction Method Using Particle Filtering Approach

Cited by 13 publications

References 33 publications

An Intelligent Grading System for Automated Identification and Classification of Banana Fruit Diseases Using Deep Neural Network

An Intelligent Grading System for Automated Identification and Classification of Banana Fruit Diseases Using Deep Neural Network

A Time-Varying Bayesian Compressed Vector Autoregression for Macroeconomic Forecasting

A novel adaptive resampling for sequential Bayesian filtering to improve frequency estimation of time-varying signals

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