Developments on Support Vector Machines for Multiple-Expert Learning

Umaquinga-Criollo, Ana C.; Tamayo-Quintero, J. D.; García, María N. Moreno; Aalaila, Yahya; Peluffo-Ordóñez, Diego H.

doi:10.1007/978-3-030-91608-4_57

Cited by 2 publications

(1 citation statement)

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“…This application is different than conventional classification frameworks in the sense that labels of contaminated or not contaminated are generated based on multiple threshold values; thus, a single sample could have a vector of labels corresponding to each threshold value. This is referred to as multiexpert learning [ 117 ]. In this context, support vector machines (SVM) were used with a majority vote.…”

Section: Data Description and Analysismentioning

confidence: 99%

Combining AI Tools with Non-Destructive Technologies for Crop-Based Food Safety: A Comprehensive Review

Raki,

Aalaila,

Taktour

et al. 2023

Foods

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On a global scale, food safety and security aspects entail consideration throughout the farm-to-fork continuum, considering food’s supply chain. Generally, the agrifood system is a multiplex network of interconnected features and processes, with a hard predictive rate, where maintaining the food’s safety is an indispensable element and is part of the Sustainable Development Goals (SDGs). It has led the scientific community to develop advanced applied analytical methods, such as machine learning (ML) and deep learning (DL) techniques applied for assessing foodborne diseases. The main objective of this paper is to contribute to the development of the consensus version of ongoing research about the application of Artificial Intelligence (AI) tools in the domain of food-crop safety from an analytical point of view. Writing a comprehensive review for a more specific topic can also be challenging, especially when searching within the literature. To our knowledge, this review is the first to address this issue. This work consisted of conducting a unique and exhaustive study of the literature, using our TriScope Keywords-based Synthesis methodology. All available literature related to our topic was investigated according to our criteria of inclusion and exclusion. The final count of data papers was subject to deep reading and analysis to extract the necessary information to answer our research questions. Although many studies have been conducted, limited attention has been paid to outlining the applications of AI tools combined with analytical strategies for crop-based food safety specifically.

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Section: Data Description and Analysismentioning

confidence: 99%