Brain–computer interfaces and their applications

Sarraf, Jay; Pattnaik, Prasant Kumar

doi:10.1016/b978-0-12-821326-1.00002-4

Cited by 23 publications

(11 citation statements)

References 53 publications

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“…The hyperplane is created according to the two criteria considered simultaneously: (1) maximizing the distance between the means of two classes and (2) minimizing the variation in each category. 32–34…”

Section: Resultsmentioning

confidence: 99%

Laser-induced breakdown spectroscopy combined with multivariate linear discriminant analysis for discriminating different growth phases of bacteria

Naseri,

Khalilzadeh,

Reza Darbani

et al. 2024

J. Anal. At. Spectrom.

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Section: Resultsmentioning

confidence: 99%

Laser-induced breakdown spectroscopy combined with multivariate linear discriminant analysis for discriminating different growth phases of bacteria

Naseri,

Khalilzadeh,

Reza Darbani

et al. 2024

J. Anal. At. Spectrom.

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“…This common technique provides class separability by drawing a decision region between the different classes. This advanced method focuses on pattern recognition and machine learning to find a linear combination of characteristics that separates observations into classes, which is used for dimensionality reduction and better classification (maximizing the distance between the means of two classes, minimizing the variation between each category) [24,25].…”

Section: Discussionmentioning

confidence: 99%

Comparative Analysis of Selected Chemical Parameters of Coffea arabica, from Cascara to Silverskin

Bobková

Poláková

Demianová

et al. 2022

Foods

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Nowadays, there is an increased interest in coffee derivatives (green beans, roasted beans, and coffee by-products (Cascara and Silverskin)) due to their particular chemical composition. This study aimed to compare the content of dry matter, total fat, fatty acids, and fiber (ADF, NDF) of coffee by-products (Cascara and Silverskin) and coffee beans (green and roasted under different conditions). Coffee beans and their by-products were obtained from 100% C. arabica coffee cherries from Panama by dry process. The lowest concentrations of fat corresponded to Cascara 4.24 g·kg−1 and Silverskin 23.70 g·kg−1, respectively. The major fatty acids detected in all samples were palmitic, stearic, oleic, and linoleic acids, the latter two being essential fatty acids. LDA showed that 89.01% of the variability between beans and by-products was explained by lignoceric, myristic, behenic, tricosanoic, arachidic, and heneicosanoic acids. Silverskin appeared to be a good source of lignoceric, myristic, and behenic acids and had a higher concentration of dietary fiber (314.95 g·kg−1) than Cascara (160.03 g·kg−1). Coffee by-products (Silverskin and Cascara) are low-fat products enriched in dietary fiber. Their incorporation, after adjustment, into the global diet may contribute to nutrition security, the sustainability of the coffee sector, and human health.

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“…Linear Discriminant Analysis (LDA) is a technique used for both dimensionality reduction and classification [ 27 ]. It aims to determine linear combinations of features that effectively differentiate classes within a dataset [ 28 ]. LDA projects data onto a lower-dimensional space, maximizing the distance between class means and minimizing the variance within each class [ 28 ].…”

Section: Methodsmentioning

confidence: 99%

“…It aims to determine linear combinations of features that effectively differentiate classes within a dataset [ 28 ]. LDA projects data onto a lower-dimensional space, maximizing the distance between class means and minimizing the variance within each class [ 28 ]. It functions as a dimensionality reduction method and effectively handles multi-class problems.…”

Section: Methodsmentioning

confidence: 99%

Classification of the Crosslink Density Level of Para Rubber Thick Film of Medical Glove by Using Near-Infrared Spectral Data

Jongyingcharoen,

Howimanporn,

Sitorus

et al. 2024

Polymers

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Classification of the crosslink density level of para rubber medical gloves by using near-infrared spectral data combined with machine learning is the first time reported in this paper. The spectra of medical glove samples with different crosslink densities acquired by an ultra-compact portable MicroNIR spectrometer were correlated with their crosslink density levels, which were referencely evaluated by the toluene swell index (TSI). The machine learning protocols used to classify the 3 groups of TSI were specified as less than 80% TSI, 80–88% TSI, and more than 88% TSI. The 80–88% TSI group was the group in which the compounded latex was suitable for medical glove production, which made the glove specification comply with the requirements of customers as indicated by the tensile test. The results show that when comparing the algorithms used for modeling, the linear discriminant analysis (LDA) developed by 2nd derivative spectra with 15 k-best selected wavelengths fairly accurately predicted the class but was most reliable among other algorithms, i.e., artificial neural networks (ANN), support vector machines (SVM), and k-nearest neighbors (kNN), due to higher prediction accuracy, precision, recall, and F1-score of the same value of 0.76 and no overfitting or underfitting prediction. This developed model can be implemented in the glove factory for screening purposes in the production line. However, deep learning modeling should be explored with a larger sample number required for better model performance.

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Brain–computer interfaces and their applications

Cited by 23 publications

References 53 publications

Laser-induced breakdown spectroscopy combined with multivariate linear discriminant analysis for discriminating different growth phases of bacteria

Laser-induced breakdown spectroscopy combined with multivariate linear discriminant analysis for discriminating different growth phases of bacteria

Comparative Analysis of Selected Chemical Parameters of Coffea arabica, from Cascara to Silverskin

Classification of the Crosslink Density Level of Para Rubber Thick Film of Medical Glove by Using Near-Infrared Spectral Data

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