Evaluation of Different Models for Global Solar Radiation Components Assessment

Rabehi, Abdelhalim; Rabehi, Abdelaziz; Guermoui, Mawloud

doi:10.3103/s0003701x21010060

Cited by 4 publications

(1 citation statement)

References 32 publications

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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%

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

Rabehi,

Helal,

Zappa

et al. 2024

Applied Sciences

Self Cite

View full text Add to dashboard Cite

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Prediction of power conversion efficiency parameter of inverted organic solar cells using artificial intelligence techniques

Marzouglal,

Souahlia,

Bessissa

et al. 2024

Sci Rep

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Organic photovoltaic (OPV) cells are at the forefront of sustainable energy generation due to their lightness, flexibility, and low production costs. These characteristics make OPVs a promising solution for achieving sustainable development goals. However, predicting their lifetime remains challenging task due to complex interactions between internal factors such as material degradation, interface stability, and morphological changes, and external factors like environmental conditions, mechanical stress, and encapsulation quality. In this study, we propose a machine learning-based technique to predict the degradation over time of OPVs. Specifically, we employ multi-layer perceptron (MLP) and long short-term memory (LSTM) neural networks to predict the power conversion efficiency (PCE) of inverted organic solar cells (iOSCs) made from the blend PTB7-Th:PC 70 BM, with PFN as the electron transport layer (ETL), fabricated under an N2 environment. We evaluate the performance of the proposed technique using several statistical metrics, including mean squared error (MSE), root mean squared error (rMSE), relative squared error (RSE), relative absolute error (RAE), and the correlation coefficient (R). The results demonstrate the high accuracy of our proposed technique, evidenced by the minimal error between predicted and experimentally measured PCE values: 0.0325 for RSE, 0.0729 for RAE, 0.2223 for rMSE, and 0.0541 for MSE using the LSTM model. These findings highlight the potential of proposed models in accurately predicting the performance of OPVs, thus contributing to the advancement of sustainable energy technologies.

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