Feature Reduction from Correlation Matrix for Classification of Two Basil Species in Common Genus

Chouvatut, Varin; Wattanapairotrat, Supawit

doi:10.1109/jcsse.2019.8864221

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…In this work, a combination of dimensionality reduction techniques was applied to identify a minimum number of critical sensors that are independent from each other and the combination of which contains most of the original data information. These techniques include PLS and correlation matrices . The correlation matrices were first used to remove redundant sensor information by removing sensors that are highly correlated with others (i.e., generating an initial set of independent sensors).…”

Section: Methodsmentioning

confidence: 99%

“…These techniques include PLS 18 and correlation matrices. 19 The correlation matrices were first used to remove redundant sensor information by removing sensors that are highly correlated with others (i.e., generating an initial set of independent sensors). Then, PLS was applied to estimate the correlation between each selected independent sensor with the KPI through a data visualization strategy developed in our previous research.…”

Section: Methodsmentioning

confidence: 99%

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Constructing a Symbolic Regression-Based Interpretable Soft Sensor for Industrial Data Analytics and Product Quality Control

Kay,

Mowbray

et al. 2024

Ind. Eng. Chem. Res.

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Deriving physical models for key performance indicators (KPIs) has been a challenge for industries developing accurate control and optimization schemes. As a result, data-driven models have seen a rise in application within recent literature; however, commonly used "black-box" data-driven models suffer from a lack of interpretability, limiting their uptake within industrial settings. To address this challenge, we developed an interpretable soft sensor by integrating symbolic regression among dimensionality reduction, statistical feature engineering, and latent variable modeling. Through this approach, symbolic regression can effectively extract important statistical relations between the KPI and key process variables. To investigate their performance and compare against existing machine learning models, two industrial case studies were presented focusing on formulation product quality control. It is concluded that symbolic regression-generated soft sensors are of high accuracy, and their expressions can provide meaningful physical insights into the underlying processes, highlighting their great potential for future applications.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Constructing a Symbolic Regression-Based Interpretable Soft Sensor for Industrial Data Analytics and Product Quality Control

Kay,

Mowbray

et al. 2024

Ind. Eng. Chem. Res.

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“…Basil 's comprehensive range antimicrobial commotion, which comprises action counter to an assortment of anthropological and visceral pathogens, proposes it can be castoff as a hand disinfectant, rinse and H2O sterilizer as well as in visceral nurturing, wound therapeutic, the conservation of nutriment objects and herbal rare ingredients and itinerant's wellbeing. Farming of Basil plants [10][11][12][13][14][15] has equally divine and real-world importance that joins the cultivator to the ingenious influences of environment, and biological farming deals elucidations for nutriment safety, bucolic scarcity, starvation, ecological dilapidation and weather transformation. The usage of Basil in everyday sacraments is a testimony to Ayurveda knowledge and delivers an illustration of antique acquaintance proposing elucidations to contemporary glitches.…”

Section: Enriched Basil Seed Algorithmmentioning

confidence: 99%

Novel Enriched Basil Seed Optimization, Little Child Imagination and Learning Inspired, Malignant Neoplasm of Uterine Algorithm

Kanagasabai

2023

Int. J. Autom. Smart

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In this paper Enriched Basil Seed Optimization (EBS), Little Child Imagination and Learning Inspired (LISPO), Malignant Neoplasm of Uterine Algorithm (MNUO) designed. Key objectives of the paper are Actual Power Loss Reduction and Voltage stability Enhancement in Electrical Power System. Basil seed algorithm based on the connotation amongst Basil and seeds. In pursuit of the optimal standards of dispersal of Basil seeds are significant and it is not conceivable to produce the seeds in capricious manner. In order to augment the process, actions of the Valonia ventricosa been integrated into the procedure. LISPO procedure utilizes the Golden proportion and imitates the intellectual knowledge and kid's sketch phases by means of finger compression girth, span and golden proportion of the kid's sketch. By experimental and slip, the kids' sketches progress from scrawl to an attractive sketch. Malignant Neoplasm of Uterine mathematically designed to formulate the MNUO algorithm. Preliminary Malignant Neoplasm magnitude in the uterus computed mathematically and it emphasizes the location in the procedure. Proposed EBS, LISPO and MNUO validated in 23 Benchmarking functions, IEEE 30 and 354 bus systems.

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Feature Reduction from Correlation Matrix for Classification of Two Basil Species in Common Genus

Cited by 2 publications

References 7 publications

Constructing a Symbolic Regression-Based Interpretable Soft Sensor for Industrial Data Analytics and Product Quality Control

Constructing a Symbolic Regression-Based Interpretable Soft Sensor for Industrial Data Analytics and Product Quality Control

Novel Enriched Basil Seed Optimization, Little Child Imagination and Learning Inspired, Malignant Neoplasm of Uterine Algorithm

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