Evaluation of Different Models for Global Solar Radiation Components Assessment

Rabehi, Abdelhalim; Rabehi, Abdelaziz; Guermoui, Mawloud

doi:10.3103/s0003701x21010060

Cited by 3 publications

(1 citation statement)

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“…In regression scenarios, SVM applies comparable principles to those used in classification, though with some nuances. The central objective remains minimizing errors by defining a hyperplane (a subspace of N − 1 dimensions) that maximizes the margin while accounting for potential error tolerance [89][90][91][92]. By harnessing SVM's capabilities both as a classifier and a regressor (with independent regressors for each class), exceptional outcomes are attainable for classification and quantification tasks alike.…”

Section: Support Vector Machinesmentioning

confidence: 99%

Advancements and Prospects of Electronic Nose in Various Applications: A Comprehensive Review

Rabehi,

Helal,

Zappa

et al. 2024

Applied Sciences

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An electronic nose, designed to replicate human olfaction, captures distinctive ‘fingerprint’ data from mixed gases or odors. Comprising a gas sensing system and an information processing unit, electronic noses have evolved significantly since their inception in the 1980s. They have transitioned from bulky, costly, and energy-intensive devices to today’s streamlined, economical models with minimal power requirements. This paper presents a comprehensive and systematic review of the electronic nose technology domain, with a special focus on advancements over the last five years. It highlights emerging applications, innovative methodologies, and potential future directions that have not been extensively covered in previous reviews. The review explores the application of electronic noses across diverse fields such as food analysis, environmental monitoring, and medical diagnostics, including new domains like veterinary pathology and pest detection. This work aims to underline the adaptability of electronic noses and contribute to their continued development and application in various industries, thereby addressing gaps in current literature and suggesting avenues for future research.

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Section: Support Vector Machinesmentioning

confidence: 99%