Early Prediction of Wheat Diseases Using SVM Multiclass

Reda, Ahmed; Fakharany, Essam El; Hazman, Maryam

doi:10.1007/978-3-319-64861-3_24

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…In the current research situation, the multi-classification of SVM has been applied in many fields [1,4,10,18], and there are two kinds of SVM multi-classification ideas [2]. One is to compute all the classification decision functions and solve multiple classification problems at the same time; however, the optimization process of such method is very complex, the computation is huge, and the implementation is difficult, so it has not been widely applied.…”

Section: Analysis Of the Existing Algorithmsmentioning

confidence: 99%

SVM Multi-Classification Optimization Research based on Multi-Chromosome Genetic Algorithm

Ren¹

2018

IJPE

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Regarding SVM multi-classification problem, optimizing the parameters of SVM has become the key problem to improve the performance of the SVM multi-classification algorithm. In order to solve this problem, multi-chromosome genetic algorithm is proposed in this paper and used to optimize these parameters. In the SVM multi-classification decision tree, the algorithm constructs a chromosome for SVM parameter of each node and improves the corresponding rules of crossover and mutation in the genetic algorithm. The improved genetic algorithm optimizes the parameters of SVM in all nodes in the SVM multi-classification decision tree. The experimental results show that the SVM multi-classification decision tree algorithm using the multi-chromosome genetic algorithm has higher classification quality, compared with the traditional multi-SVM multi-classification algorithm.

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Section: Analysis Of the Existing Algorithmsmentioning

confidence: 99%

SVM Multi-Classification Optimization Research based on Multi-Chromosome Genetic Algorithm

Ren¹

2018

IJPE

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“…In recent years, several studies have developed machine-learning (ML) tools to predict the taste of specific compounds starting from their chemical structure 13 . In literature, there is a net prevalence of ML tools for predicting sweet and bitter tastes e.g., BitterX 14 , BitterPredict 15 , e-Bitter 16 , iBitter-SCM applications for classifying compounds by taste and predicting the relative taste intensity [36][37][38][39] . Recently, a multi-class classification method based on learning vector quantization NN to classify tea samples of five commercial brands has been proposed 40 .…”

Section: Introductionmentioning

confidence: 99%

VirtuousMultiTaste: Predicting Multiple Taste Sensations with a Multi- objective ML-driven Method

Androutsos,

Pallante,

Bompotas

et al. 2023

Preprint

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Taste perception plays a pivotal role in guiding nutrient intake and aiding in the avoidance of potentially harmful substances through five basic tastes - sweet, bitter, umami, salty, and sour. Taste perception originates from molecular interactions in the oral cavity between taste receptors and chemical tastants. Hence, the recognition of taste receptors and the subsequent perception of taste heavily rely on the physicochemical properties of food ingredients. In recent years, several advances have been made towards the development of machine learning-based algorithms to classify chemical compounds' tastes using their molecular structures. Despite the great efforts, there remains significant room for improvement by developing multi-class models to predict the entire spectrum of basic tastes. Here, we present a multi-class predictor aimed at distinguishing three different tastes, i.e., bitter, sweet, and umami, from other taste sensations. The developed model has been integrated into a publicly accessible web platform. This work lays the groundwork for a comprehensive understanding of the molecular features that drive the perception of tastes, paving the way towards new methodologies in the rational design of foods, such as the pre-determination of specific tastes, the engineering of complementary diets to traditional pharmacological treatments, and many others.

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Predicting multiple taste sensations with a multiobjective machine learning method

Androutsos,

Pallante,

Bompotas

et al. 2024

npj Sci Food

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Taste perception plays a pivotal role in guiding nutrient intake and aiding in the avoidance of potentially harmful substances through five basic tastes - sweet, bitter, umami, salty, and sour. Taste perception originates from molecular interactions in the oral cavity between taste receptors and chemical tastants. Hence, the recognition of taste receptors and the subsequent perception of taste heavily rely on the physicochemical properties of food ingredients. In recent years, several advances have been made towards the development of machine learning-based algorithms to classify chemical compounds’ tastes using their molecular structures. Despite the great efforts, there remains significant room for improvement in developing multi-class models to predict the entire spectrum of basic tastes. Here, we present a multi-class predictor aimed at distinguishing bitter, sweet, and umami, from other taste sensations. The development of a multi-class taste predictor paves the way for a comprehensive understanding of the chemical attributes associated with each fundamental taste. It also opens the potential for integration into the evolving realm of multi-sensory perception, which encompasses visual, tactile, and olfactory sensations to holistically characterize flavour perception. This concept holds promise for introducing innovative methodologies in the rational design of foods, including pre-determining specific tastes and engineering complementary diets to augment traditional pharmacological treatments.

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Early Prediction of Wheat Diseases Using SVM Multiclass

Cited by 3 publications

References 11 publications

SVM Multi-Classification Optimization Research based on Multi-Chromosome Genetic Algorithm

SVM Multi-Classification Optimization Research based on Multi-Chromosome Genetic Algorithm

VirtuousMultiTaste: Predicting Multiple Taste Sensations with a Multi- objective ML-driven Method

Predicting multiple taste sensations with a multiobjective machine learning method

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