Introduction to Supervised Learning

Verma, Ram U.; Nagar, Vishal; Mahapatra, Satyasundara

doi:10.1002/9781119785620.ch1

Cited by 13 publications

(3 citation statements)

References 59 publications

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“…Some of the underlying algorithms of supervised learning include linear regression, support vector machine (SVM), logistic regression, ANN, gradient-boosted regression/classification, random forest, etc. [34][35][36][37].…”

Section: Machine Learning (Ml)mentioning

confidence: 99%

Enhancing Food Integrity through Artificial Intelligence and Machine Learning: A Comprehensive Review

Gbashi,

Njobeh

2024

Applied Sciences

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Herein, we examined the transformative potential of artificial intelligence (AI) and machine learning (ML) as new fronts in addressing some of the pertinent challenges posed by food integrity to human and animal health. In recent times, AI and ML, along with other Industry 4.0 technologies such as big data, blockchain, virtual reality, and the internet of things (IoT), have found profound applications within nearly all dimensions of the food industry with a key focus on enhancing food safety and quality and improving the resilience of the food supply chain. This paper provides an accessible scrutiny of these technologies (in particular, AI and ML) in relation to food integrity and gives a summary of their current advancements and applications within the field. Key areas of emphasis include the application of AI and ML in quality control and inspection, food fraud detection, process control, risk assessments, prediction, and management, and supply chain traceability, amongst other critical issues addressed. Based on the literature reviewed herein, the utilization of AI and ML in the food industry has unequivocally led to improved standards of food integrity and consequently enhanced public health and consumer trust, as well as boosting the resilience of the food supply chain. While these applications demonstrate significant promise, the paper also acknowledges some of the challenges associated with the domain-specific implementation of AI in the field of food integrity. The paper further examines the prospects and orientations, underscoring the significance of overcoming the obstacles in order to fully harness the capabilities of AI and ML in safeguarding the integrity of the food system.

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Section: Machine Learning (Ml)mentioning

confidence: 99%

Enhancing Food Integrity through Artificial Intelligence and Machine Learning: A Comprehensive Review

Gbashi,

Njobeh

2024

Applied Sciences

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“…Each tree, individually, is a weak learner; however, all the decision trees together can build a strong learner. It is random because (a) when building trees, a random sampling of training data sets is followed; and (b) when splitting nodes, a random subset of features is considered [59].…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Supervised Machine Learning Methods for Seasonal Influenza Diagnosis

Marquez,

Barrón-Palma,

Rodríguez

et al. 2023

Diagnostics

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Influenza has been a stationary disease in Mexico since 2009, and this causes a high cost for the national public health system, including its detection using RT-qPCR tests, treatments, and absenteeism in the workplace. Despite influenza’s relevance, the main clinical features to detect the disease defined by international institutions like the World Health Organization (WHO) and the United States Centers for Disease Control and Prevention (CDC) do not follow the same pattern in all populations. The aim of this work is to find a machine learning method to facilitate decision making in the clinical differentiation between positive and negative influenza patients, based on their symptoms and demographic features. The research sample consisted of 15480 records, including clinical and demographic data of patients with a positive/negative RT-qPCR influenza tests, from 2010 to 2020 in the public healthcare institutions of Mexico City. The performance of the methods for classifying influenza cases were evaluated with indices like accuracy, specificity, sensitivity, precision, the f1-measure and the area under the curve (AUC). Results indicate that random forest and bagging classifiers were the best supervised methods; they showed promise in supporting clinical diagnosis, especially in places where performing molecular tests might be challenging or not feasible.

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“…Machine learning is a data analysis technique currently applied in many scientific disciplines. An example study by Shahi et al [16] could be mentioned, where the authors comparatively studied the development of stock prices through deep learning. Another example is the contribution by Guefrechi et al [17], where the authors proposed a classification model for detecting the COVID-19 viral disease, which achieved an accuracy of ~ 98%.…”

Section: Introductionmentioning

confidence: 99%

Applying the Machine Learning Method to Improve Calibration Quality of TDR Measuring Technique

Mikušová,

Suchorab,

Paśnikowska-Łukaszuk

et al. 2024

Adv. Sci. Technol. Res. J.

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The article presents the application of the time domain reflectometry (TDR) technique for measuring the moisture of porous building materials used in construction. The work is focused on using the potential of artificial intelligence to improve the quality of TDR measurements through a new approach to the interpretation of data obtained from the TDR readings. Machine learning is a data analysis technique, used nowadays in many scientific disciplines. The authors performed a measurement data analysis using the artificial intelligence algorithms to assess moisture of aerated concrete samples tested with a TDR multimeter using two non-invasive sensors which differ in thickness. Data analysis was carried out using supervised machine learning to analyse a series of reflectograms obtained during the measurement. For the data achieved by the classical and machine learning method interpretation, correlation analysis was conducted to confirm the potential of artificial intelligence to improve the quality of TDR measurement. The summary of the work discusses the obtained analytical results and highlights the effectiveness of moisture assessment using the Gaussian process regression method, which allowed achieving a level of 0.2-0.3% of the RMSE errors value, which is about 10 times lower than the traditional approach.

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Introduction to Supervised Learning

Cited by 13 publications

References 59 publications

Enhancing Food Integrity through Artificial Intelligence and Machine Learning: A Comprehensive Review

Enhancing Food Integrity through Artificial Intelligence and Machine Learning: A Comprehensive Review

Supervised Machine Learning Methods for Seasonal Influenza Diagnosis

Applying the Machine Learning Method to Improve Calibration Quality of TDR Measuring Technique

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