Developing a new ensemble approach with multi-class SVMs for Manuka honey quality classification

Phillips, Tessa; Abdulla, Waleed H.

doi:10.1016/j.asoc.2021.107710

Cited by 35 publications

(16 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Support vector machine (SVM) is a small-sample machine learning method with a solid theoretical foundation [ 27 ]. SVM attempted to find an optimal decision edge that is farthest from the nearest samples of the two categories and transformed it into the solution of a convex quadratic programming problem.…”

Section: Methodsmentioning

confidence: 99%

Raman Spectroscopy and Improved Inception Network for Determination of FHB-Infected Wheat Kernels

Qiu

Zheng

Tang

et al. 2022

Foods

View full text Add to dashboard Cite

Detection of infected kernels is important for Fusarium head blight (FHB) prevention and product quality assurance in wheat. In this study, Raman spectroscopy (RS) and deep learning networks were used for the determination of FHB-infected wheat kernels. First, the RS spectra of healthy, mild, and severe infection kernels were measured and spectral changes and band attribution were analyzed. Then, the Inception network was improved by residual and channel attention modules to develop the recognition models of FHB infection. The Inception–attention network produced the best determination with accuracies in training set, validation set, and prediction set of 97.13%, 91.49%, and 93.62%, among all models. The average feature map of the channel clarified the important information in feature extraction, itself required to clarify the decision-making strategy. Overall, RS and the Inception–attention network provide a noninvasive, rapid, and accurate determination of FHB-infected wheat kernels and are expected to be applied to other pathogens or diseases in various crops.

show abstract

Section: Methodsmentioning

confidence: 99%

Raman Spectroscopy and Improved Inception Network for Determination of FHB-Infected Wheat Kernels

Qiu

Zheng

Tang

et al. 2022

Foods

View full text Add to dashboard Cite

show abstract

Section: Existing Quality Assurance Techniquesmentioning

confidence: 99%

“…A Hyperspectral imaging approach has been developed for detecting the botanical origins of honey [17,23,24,26,27,29]. In [27] the UMF grade of Manuka honey was predicted with 89% accuracy. The botanical origins of 21 different types of honey were predicted with 90% accuracy in [29].…”

Section: Existing Quality Assurance Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

A new honey adulteration detection approach using hyperspectral imaging and machine learning

Phillips

Abdulla

2022

Eur Food Res Technol

Self Cite

View full text Add to dashboard Cite

This paper develops a new approach to fraud detection in honey. Specifically, we examine adulterating honey with sugar and use hyperspectral imaging and machine learning techniques to detect adulteration. The main contributions of this paper are introducing a new feature smoothing technique to conform to the classification model used to detect the adulterated samples and the perpetration of an adulterated honey data set using hyperspectral imaging, which has been made available online for the first time. Above $$95\%$$ 95 % accuracy was achieved for binary adulteration detection and multi-class classification between different adulterant concentrations. The system developed in this paper can be used to prevent honey fraud as a reliable, low cost, data-driven solution.

show abstract

“…Several machine learning algorithms can be utilized for predictive analysis, such as SVM, NB, -K-NN, DT, and DL [30,31]. However, we used a support vector machine (SVM), which is recently used in the literature for multi-classification [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Infectious Outbreak COVID-19 and Prevalence of Anxiety: Global Evidence

Alghazzawi

Qazi

et al. 2021

Sustainability

View full text Add to dashboard Cite

Forecasting disease outbreaks in real-time using time-series data can help for the planning of public health interventions. We used a support vector machine (SVM) model using epidemiological data provided by Johns Hopkins University Centre for Systems Science and Engineering (JHU CCSE), World Health Organization (WHO), and the Centers for Disease Control and Prevention (CDC) to predict upcoming records before the WHO made an official declaration. Our study, conducted on the time series data available from 22 January till 10 March 2020, revealed that COVID-19 was spreading at an alarming rate and progressing towards a pandemic. The initial insight that confirmed COVID-19 cases were increasing was because these received the highest number of effects for our selected dataset from 22 January to 10 March 2020, i.e., 126,344 (64%). The recovered cases were 68289 (34%), and the death rate was around 2%. Moreover, we classified the tweets from 22 January to 15 April 2020 into positive and negative sentiments to identify the emotions (stress or relaxed) posted by Twitter users related to the COVID-19 pandemic. Our analysis identified that tweets mostly conveyed a negative sentiment with a high frequency of words for #coronavirus and #lockdown amid COVID-19. However, these anxiety tweets are an alarm for healthcare authorities to devise plans accordingly.

show abstract

Developing a new ensemble approach with multi-class SVMs for Manuka honey quality classification

Cited by 35 publications

References 25 publications

Raman Spectroscopy and Improved Inception Network for Determination of FHB-Infected Wheat Kernels

Raman Spectroscopy and Improved Inception Network for Determination of FHB-Infected Wheat Kernels

A new honey adulteration detection approach using hyperspectral imaging and machine learning

Prediction of the Infectious Outbreak COVID-19 and Prevalence of Anxiety: Global Evidence

Contact Info

Product

Resources

About