Implementation of multilayer perceptron (MLP) and radial basis function (RBF) neural networks for predicting Shatavarin IV content in Asparagus racemosus accessions.

Padhiari, Bhuban Mohan; Ray, Asit; Jena, Sudipta; Sahoo, Ambika; Mohanty, Sujata; Patnaik, Jeetendranath; Naik, Pradeep Kumar; Panda, Pratap Chandra; Champati, Bibhuti Bhusan

doi:10.1016/j.indcrop.2022.115968

Cited by 10 publications

(3 citation statements)

References 37 publications

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“…The intricate story of shatavari emerges as we navigate the pages that follow, providing insights into its ongoing relevance and great possibilities in the pursuit of well-rounded health. 3…”

Section: Aspects Of Tradition Vs Modernitymentioning

confidence: 99%

Asparagus racemosus: A Holistic Review of Its Traditional Uses and Modern Research

Sinha,

Tare

2024

IJPQA

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TThis comprehensive review digs into the multiple significance of Asparagus racemosus, also known as shatavari, as both a beloved traditional cure and a topic of ongoing scientific research. The review weaves together the story of shatavari, from its deep roots in traditional medical systems to its incorporation into current healthcare procedures. The chapter discusses shatavari’s historical applications, particularly in women’s health and reproductive wellbeing, emphasizing its adaptogenic, hormone-balancing, and caring qualities. The investigation also extends to the herb’s phytochemical makeup, revealing the functions of saponins, steroidal glycosides, and flavonoids in the herb’s medicinal actions. The chapter details recent scientific research highlighting shatavari’s potential anti-inflammatory, antioxidant, and immunomodulatory properties. Its function in treating illnesses such as polycystic ovarian syndrome (PCOS) and menopausal symptoms has received special attention. Safety issues, dosage recommendations, and potential contraindications are addressed to ensure responsible use. The paper concludes with observations on the synergistic interaction between traditional knowledge and current research, highlighting the significance of combining both to fully comprehend shatavari’s holistic potential. Shatavari’s dual significance as a valued embodiment of cultural history and a subject of rigorous scientific investigation emphasizes its ageless relevance in promoting wellbeing..

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Section: Aspects Of Tradition Vs Modernitymentioning

confidence: 99%

Asparagus racemosus: A Holistic Review of Its Traditional Uses and Modern Research

Sinha,

Tare

2024

IJPQA

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“…By emulating the operating mechanism of biological neural networks and connecting a large number of neuron nodes to construct a complex network that processes signals in parallel, it is possible to simulate biological neural networks. The RBF neural network of the feedforward neural network is employed in this paper [41][42][43][44][45][46]. It possesses the benefits of simple network topology, rapid training speed, robust local approximation ability, and high approximation precision.…”

Section: Neural Network Modelmentioning

confidence: 99%

Comparison and Analysis of Several Quantitative Identification Models of Pesticide Residues Based on Quick Detection Paperboard

et al. 2023

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Pesticide residues have long been a significant aspect of food safety, which has always been a major social concern. This study presents research and analysis on the identification of pesticide residue fast detection cards based on the enzyme inhibition approach. In this study, image recognition technology is used to extract the color information RGB eigenvalues from the detection results of the quick detection card, and four regression models are established to quantitatively predict the pesticide residue concentration indicated by the quick detection card using RGB eigenvalues. The four regression models are linear regression model, quadratic polynomial regression model, exponential regression model and RBF neural network model. Through study and comparison, it has been shown that the exponential regression model is superior at predicting the pesticide residue concentration indicated by the rapid detection card. The correlation value is 0.900, and the root mean square error is 0.106. There will be no negative prediction value when the expected concentration is near to 0. This gives a novel concept and data support for the development of image recognition equipment for pesticide residue fast detection cards based on the enzyme inhibition approach.

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“…MLP neural networks are composed of three distinct categories of neurons, specifically input neurons, hidden neurons, and output neurons. These components work together to enable the network to process and analyze complex data patterns (Fath, Madanifar et al 2020, Champati, Padhiari et al 2023.…”

Section: -3-mlp Neural Networkmentioning

confidence: 99%

Using Artificial Neural Network for Forecasting PM2.5 Levels in a Prominent Industrial and Historically Significant Urban Center in Iran: Evaluating Health Implications

bardshahi,

Jaafarzadeh,

Tabatabaie

et al. 2023

Preprint

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There has been a scarcity of comprehensive studies that aim to predict both the levels of PM2.5 particles and their corresponding impacts on human health. To fill this gap, we developed an artificial neural network model and AirQ+ software to predict PM2.5 concentrations and assess their associated health effects. The ANN model utilized five distinct input parameters, specifically wind velocity, wind orientation, atmospheric temperature, relative moisture content, and PM2.5 levels, observed within the time frame of 2018 to 2019. The concealed stratum comprised a total of ten neurons, in addition to the presence of an output layer. The MLP neural network demonstrated strong correlations at each stage: 0.908 (training), 0.910 (validation), 0.914 (testing), and 0.907 overall. The RMSE was determined as 6.52 µg/m3 when evaluating the neural network, thus indicating the notable predictive precision exhibited by the multilayer perceptron (MLP) neural network when forecasting the concentration of PM2.5 particles. The AirQ+ software, created by the World Health Organization (WHO), was employed to assess the magnitude and consequences of PM2.5 concentrations. The average concentration of PM2.5 particles throughout the duration of the study was recorded as 26.5 μg/m3, which exceeds the recommended limit provided by the WHO by a factor of 5.3. The study estimated the proportions and numbers of deaths attributed to various conditions. Specifically, chronic obstructive pulmonary disease (COPD), ischemic heart disease (IHD), lung cancer (LC), stroke, and all-cause mortality accounted for approximately 11.67%, 15.02%, 13.25%, 15.225%, and 9.45% respectively. The estimated number of deaths were 19,195 for all causes, 10,063 for COPD, 564 for IHD, and 1,063 for lung cancer. The findings of this investigation demonstrate a high degree of reliability in the methodologies utilized. Through the utilization of these approaches, individuals in positions of authority and responsibility can aptly evaluate the cost-benefit analysis, leading to a reduction in human casualties and mitigating the economic burdens on society.

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Implementation of multilayer perceptron (MLP) and radial basis function (RBF) neural networks for predicting Shatavarin IV content in Asparagus racemosus accessions.

Cited by 10 publications

References 37 publications

Asparagus racemosus: A Holistic Review of Its Traditional Uses and Modern Research

Asparagus racemosus: A Holistic Review of Its Traditional Uses and Modern Research

Comparison and Analysis of Several Quantitative Identification Models of Pesticide Residues Based on Quick Detection Paperboard

Using Artificial Neural Network for Forecasting PM2.5 Levels in a Prominent Industrial and Historically Significant Urban Center in Iran: Evaluating Health Implications

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