Craniometric Variation in a Regional Sample from Antioquia, Medellín, Colombia

2021 ◽  
Author(s):  
Kelly Kamnikar ◽  
Joseph Hefner ◽  
Timisay Monsalve ◽  
Liliana Bernal Florez
Keyword(s):  
Native American ◽  
Social Group ◽  
Discrete Groups ◽  
Classification Models ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Cranial Variation ◽  
American Black ◽  
Craniometric Variation ◽  
Population Affinity

Population affinity estimation is frequently assessed from measurements of the cranium. Traditional models place individuals into discrete groups―such as Hispanic―that often encompass very diverse populations. Current research, including this study, challenges these assumptions using more refined population affinity estimation analyses. We examine craniometric data for a sample of individuals from different regions in Antioquia, Colombia. We first assessed the sample to understand intraregional variation in cranial shape as a function of birthplace or a culturally constructed social group label. Then, pooling the Colombian data, we compare cranial variation with global contemporary and prehistoric groups. Results did not indicate significant intraregional variation in Antioquia; classification models performed poorly (28.6% for birthplace and 36.6% for social group). When compared to other groups (American Black, American White, Asian, modern Hispanic, and prehistoric Native American), our model correctly classified 75.5% of the samples. We further refined the model by separating the pooled Hispanic sample into Mexican and Guatemalan samples, which produced a correct classification rate of 74.4%. These results indicate significant differences in cranial form among groups commonly united under the classification “Hispanic” and bolster the addition of a refined approach to population affinity estimation using craniometric data.

Modeling approach for releasing a frankfurter production batch

2019 ◽  
Vol 121 (8) ◽  
pp. 1813-1824
Author(s):  
Ravipim Chaveesuk ◽  
Natthamon Konjanattham
Keyword(s):  
Neural Network ◽  
Physical Properties ◽  
Classification Model ◽  
Correct Classification ◽  
Classification Models ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Content Type ◽  
Release Probability ◽  
Batch Release

Purpose The purpose of this paper is to model the relationship between 11 frankfurter physical properties and their sensory scores to classify a release of frankfurter production batches to the market. Design/methodology/approach Data from 209 frankfurter batches were collected. Market batch release classifications were based on 11 physical properties via predictive and direct classification models. The predictive models under study included a regression, backpropagation neural network (BPN) and radial basis function neural network (RBFN) whereas the direct classification models were logistic regression, BPN and RBFN. Model performance was evaluated via correct classification rate. Findings The 11-7-4 RBFN predictive model proved superior with a 90 percent correct classification rate and 0 percent producer risk while the 11-5-1 RBFN, as a classification model, outperformed with the same level of accuracy, 90 and 0 percent, respectively. Producers prefer the less time-consuming direct classifiers for evaluation. Furthermore, the 11-5-1 RBFN direct classifier revealed that color measurement greatly influenced frankfurter batch release. Increases in redness, yellowness and brownness increased batch release probability. Originality/value This research attempts to establish a novel production batch release model for sausage manufacturing. Key factors can then be optimized for improving batch release probability for implementation throughout the sausage industry.

scholarly journals The Use of Artificial Neural Networks and a General Discriminant Analysis for Predicting Culling Reasons in Holstein-Friesian Cows Based on First-Lactation Performance Records

Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 721
Author(s):  
Krzysztof Adamczyk ◽  
Wilhelm Grzesiak ◽  
Daniel Zaborski
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Discriminant Analysis ◽  
Multilayer Perceptrons ◽  
Correct Classification ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Holstein Friesian ◽  
Artificial Neural ◽  
Friesian Cows

The aim of the present study was to verify whether artificial neural networks (ANN) may be an effective tool for predicting the culling reasons in cows based on routinely collected first-lactation records. Data on Holstein-Friesian cows culled in Poland between 2017 and 2018 were used in the present study. A general discriminant analysis (GDA) was applied as a reference method for ANN. Considering all predictive performance measures, ANN were the most effective in predicting the culling of cows due to old age (99.76–99.88% of correctly classified cases). In addition, a very high correct classification rate (99.24–99.98%) was obtained for culling the animals due to reproductive problems. It is significant because infertility is one of the conditions that are the most difficult to eliminate in dairy herds. The correct classification rate for individual culling reasons obtained with GDA (0.00–97.63%) was, in general, lower than that for multilayer perceptrons (MLP). The obtained results indicated that, in order to effectively predict the previously mentioned culling reasons, the following first-lactation parameters should be used: calving age, calving difficulty, and the characteristics of the lactation curve based on Wood’s model parameters.

scholarly journals Fatty Acid Composition of Cosmetic Argan Oil: Provenience and Authenticity Criteria

Molecules ◽  
2020 ◽  
Vol 25 (18) ◽  
pp. 4080
Author(s):  
Milena Bučar Miklavčič ◽  
Fouad Taous ◽  
Vasilij Valenčič ◽  
Tibari Elghali ◽  
Maja Podgornik ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Fatty Acid ◽  
Discriminant Analysis ◽  
Trans Fatty Acids ◽  
Correct Classification ◽  
Fatty Acid Profiles ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Argan Oil ◽  
Geographical Regions

In this work, fatty-acid profiles, including trans fatty acids, in combination with chemometric tools, were applied as a determinant of purity (i.e., adulteration) and provenance (i.e., geographical origin) of cosmetic grade argan oil collected from different regions of Morocco in 2017. The fatty acid profiles obtained by gas chromatography (GC) showed that oleic acid (C18:1) is the most abundant fatty acid, followed by linoleic acid (C18:2) and palmitic acid (C16:0). The content of trans-oleic and trans-linoleic isomers was between 0.02% and 0.03%, while trans-linolenic isomers were between 0.06% and 0.09%. Discriminant analysis (DA) and orthogonal projection to latent structure—discriminant analysis (OPLS-DA) were performed to discriminate between argan oils from Essaouira, Taroudant, Tiznit, Chtouka-Aït Baha and Sidi Ifni. The correct classification rate was highest for argan oil from the Chtouka-Aït Baha province (90.0%) and the lowest for oils from the Sidi Ifni province (14.3%), with an overall correct classification rate of 51.6%. Pairwise comparison using OPLS-DA could predictably differentiate (≥0.92) between the geographical regions with the levels of stearic (C18:0) and arachidic (C20:0) fatty acids accounting for most of the variance. This study shows the feasibility of implementing authenticity criteria for argan oils by including limit values for trans-fatty acids and the ability to discern provenance using fatty acid profiling.

Alzheimer's Disease Classification Based on Multi-feature Fusion

2019 ◽  
Vol 15 (2) ◽  
pp. 161-169 ◽  
Author(s):  
Nuwan Madusanka ◽  
Heung-Kook Choi ◽  
Jae-Hong So ◽  
Boo-Kyeong Choi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Fusion ◽  
Biological Data ◽  
Correct Classification ◽  
Mr Images ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Morphometric Features

Background: In this study, we investigated the fusion of texture and morphometric features as a possible diagnostic biomarker for Alzheimer’s Disease (AD). Methods: In particular, we classified subjects with Alzheimer’s disease, Mild Cognitive Impairment (MCI) and Normal Control (NC) based on texture and morphometric features. Currently, neuropsychiatric categorization provides the ground truth for AD and MCI diagnosis. This can then be supported by biological data such as the results of imaging studies. Cerebral atrophy has been shown to correlate strongly with cognitive symptoms. Hence, Magnetic Resonance (MR) images of the brain are important resources for AD diagnosis. In the proposed method, we used three different types of features identified from structural MR images: Gabor, hippocampus morphometric, and Two Dimensional (2D) and Three Dimensional (3D) Gray Level Co-occurrence Matrix (GLCM). The experimental results, obtained using a 5-fold cross-validated Support Vector Machine (SVM) with 2DGLCM and 3DGLCM multi-feature fusion approaches, indicate that we achieved 81.05% ±1.34, 86.61% ±1.25 correct classification rate with 95% Confidence Interval (CI) falls between (80.75-81.35) and (86.33-86.89) respectively, 83.33%±2.15, 84.21%±1.42 sensitivity and 80.95%±1.52, 85.00%±1.24 specificity in our classification of AD against NC subjects, thus outperforming recent works found in the literature. For the classification of MCI against AD, the SVM achieved a 76.31% ± 2.18, 78.95% ±2.26 correct classification rate, 75.00% ±1.34, 76.19%±1.84 sensitivity and 77.78% ±1.14, 82.35% ±1.34 specificity. Results and Conclusion: The results of the third experiment, with MCI against NC, also showed that the multiclass SVM provided highly accurate classification results. These findings suggest that this approach is efficient and may be a promising strategy for obtaining better AD, MCI and NC classification performance.

scholarly journals A Randomized Study to Assess the Effect of Including the Graduate Record Examinations (GRE) Results on Reviewers Scores for Underrepresented Minorities

2021 ◽  
Author(s):  
Kristina V Dang ◽  
Francois Rerolle ◽  
Sarah F Ackley ◽  
Amanda M Irish ◽  
Kala M Mehta ◽  
...  
Keyword(s):  
Native American ◽  
San Francisco ◽  
Alaska Native ◽  
Fixed Effects ◽  
Doctoral Programs ◽  
Randomized Study ◽  
Underrepresented Minorities ◽  
American Black ◽  
Graduate Record Examinations ◽  
Phd Programs

Abstract Whether requiring Graduate Record Examinations (GRE) results for PhD applicants affects the diversity of admitted cohorts remains uncertain. This study randomized applications to two population health University of California San Francisco PhD programs to assess whether masking reviewers to applicant GRE results differentially affects reviewers’ scores for underrepresented minorities (URM) applicants from 2018-2020. Applications with GRE results and those without were randomly assigned to reviewers to designate scores for each copy (1-10, 1 being best). URM was defined as self-identification as African American/Black, Filipino, Hmong, Vietnamese, Hispanic/Latinx, Native American/Alaska Native, or Native Hawaiian/Other Pacific Islander. We used linear mixed models with random effects for applicant and fixed effects for each reviewer to evaluate the effect of masking the GRE results on the overall application score and whether this effect differed by URM status. Reviewer scores did not significantly differ for unmasked versus masked applications among non-URM applicants (b=0.15; 95% CI: [-0.03, 0.33]) or URM applicants (b=0.02, 95% CI: [-0.36, 0.40]). We did not find evidence that removing GREs differentially affected URM compared to non-URM students (b for interaction= -0.13, 95% CI: [-0.55, 0.29]). Within these doctoral programs, results indicate that GRE scores do not harm nor help URM applicants.

scholarly journals BatScope manages acoustic recordings, analyses calls, and classifies bat species automatically

2018 ◽  
Vol 96 (9) ◽  
pp. 939-954 ◽  
Author(s):  
M.K. Obrist ◽  
R. Boesch
Keyword(s):  
High Frequency ◽  
Reference Database ◽  
Correct Classification Rate ◽  
Statistical Parameters ◽  
Classification Rate ◽  
Echolocation Calls ◽  
Portable Software ◽  
Automated Processing ◽  
The Mean ◽  
Species Assignment

BatScope is a free application for processing acoustic high-frequency recordings of bats. It can import data, including meta-data information, from recorders such as Batlogger. The resulting content can be filtered visually as spectrograms or according to data fields and can be displayed. Automated processing includes detecting and extracting of echolocation calls, filtering noise, and measuring statistical parameters. Calls are classified to species by statistically matching to a reference database. A weighted list of classifiers helps to assign the most likely species per call. Classifiers were trained on 19 636 echolocation calls of 27 European bat species. When classifiers all agree on a species (76.4% of all cases), the mean correct classification rate reaches 95.7%. A sequence’s summary statistic indicates the most likely species occurring therein. Classifications can be verified visually, by filtering, and by acoustic comparison with reference calls. Procedures are available for, e.g., excluding dubious cutouts from the statistics and for accepting or overriding the proposed species assignment. Acoustic recordings can be exported and exchanged with other users. Finally, the verified results can be exported to spreadsheets for further analyses and reporting. We currently reprogram BatScope using Java, PostgreSQL, and R to reach a unified and portable software architecture.

scholarly journals Near infrared spectroscopy in the supply chain monitoring of Annurca apple

2019 ◽  
Vol 27 (1) ◽  
pp. 86-92 ◽  
Author(s):  
Marina Buccheri ◽  
Maurizio Grassi ◽  
Fabio Lovati ◽  
Milena Petriccione ◽  
Pietro Rega ◽  
...  
Keyword(s):  
Supply Chain ◽  
Infrared Spectroscopy ◽  
Shelf Life ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Correct Classification ◽  
Geographical Indication ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Campania Region

Annurca is the most cultivated apple variety in the Campania region (Italy). It is an Italian protected geographical indication product and its management must follow a strict product specification which requires a typical postharvest treatment: the fruit must be subjected to a reddening process in air (‘melaio’) that improves the red colour and the flavour of the fruit but is very expensive and time consuming. For this reason there is sometimes a tendency to skip the ‘melaio’ process, but in this case the fruit cannot be labelled as ‘Melannurca Campana PGI’. The purpose of this work was to discriminate ‘melaio’ treated fruit from untreated fruit using near infrared spectroscopy. A further objective of the work was the non-destructive evaluation of the apple storage conditions which can affect the product quality. Fruit of Annurca ‘Rossa del Sud’ subjected or not subjected to the reddening treatment in ‘melaio’ were stored at 0.5℃ in air (Air) or in controlled atmosphere (1%O2, 0.7% CO2) for eight-month duration. Following storage, fruit were analysed for standard maturity indices (flesh firmness, soluble solids, acidity) and the near infrared spectrum of each fruit was collected. The spectral data, subjected to various pre-treatments, were used to calculate a calibration model by applying partial least squares-discriminant analysis. The best model allowed discrimination of fruit immediately after storage under different conditions, but with 0 days of shelf life, to be classified with a 93.3% correct classification rate for the prediction set. However, after seven days of shelf life at 20℃, post-storage, correct classification rate dropped to 70%, but it was always possible to discriminate the two treatments (96.6% correct classification rate). The results of this preliminary work suggest a possible use of the portable near infrared instrument in the monitoring of the Annurca (protected geographical indication) supply chain.

A Fuzzy–Expert System For Classification Of Short Duration Voltage Disturbances

2012 ◽  
Author(s):  
Ghafour Amouzad Mahdiraji ◽  
Azah Mohamed
Keyword(s):  
Expert System ◽  
Power Quality ◽  
Short Duration ◽  
Automated Detection ◽  
Voltage Sag ◽  
Fuzzy Expert System ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Fast Fourier Transform Analysis

Satu aspek penting dalam penilaian kualiti kuasa adalah pengesanan dan pengkelasan gangguan kualiti kuasa secara automatik yang memerlukan penggunaan teknik kepintaran buatan. Kertas kerja ini membentangkan penggunaan sistem pakar-kabur untuk pengkelasan gangguan voltan jangka masa pendek yang termasuk lendut voltan, ampul dan sampukan. Untuk memperolehi sifat unik bagi gangguan voltan, analisis jelmaan Fourier pantas dan teknik purataan punca min kuasa dua digunakan untuk menentukan parameter gangguan seperti tempoh masa, magnitud voltan pmk maksimum dan minimum. Berasaskan pada parameter ini, sebuah sistem pakar–kabur telah dibangunkan dengan mengset aturan kabur yang menimbangkan lima masukan dan tiga keluaran. Sistem ini direka bentuk untuk mengesan dan mengkelaskan tiga jenis gangguan voltan tempoh masa pendek dengan menentukan sama ada gangguan adalah gangguan ketika, gangguan seketika dan bukan gangguan lendut, ampul dan sampukan. Untuk mengesahkan kejituan sistem yang dicadangkan, ia telah diuji dengan gangguan voltan yang diperolehi dari pengawasan. Keputusan ujian menunjukkan bahawa sistem pakar–kabur yang dibangunkan telah memberikan kadar pengkelasan yang betul sebanyak 98.4 %. Kata kunci: Kualiti kuasa, sistem pakar–kabur, lendut, ampul dan sampukan One of the important aspects in power quality assessment is automated detection and classification of power quality disturbances which requires the use of artificial intelligent techniques. This paper presents the application of fuzzy–expert system for classification of short duration voltage disturbances which include voltage sag, swell and interruption. To obtain unique features of the voltage disturbances, fast Fourier transform analysis and root mean square averaging technique are utilized so as to determine the disturbance parameters such as duration, maximum and minimum rms voltage magnitudes. Based on these parameters, a fuzzy-expert system has been developed to set the fuzzy rules incorporating five inputs and three outputs. The system is designed for detecting and classifying the three types of short duration voltage disturbances, so as to determine whether the disturbance is instantaneous, momentary and non sag, swell and interruption. To verify the accuracy of the proposed system, it has been tested with recorded voltage disturbances obtained from monitoring. Tests results showed that the developed fuzzy–expert system gives a correct classification rate of 98.4 %. Key words: Power quality, fuzzy–expert system, sag, swell and interruption.

scholarly journals Detection of Malignancy Associated Changes in Cervical Cell Nuclei Using Feed-Forward Neural Networks

1997 ◽  
Vol 14 (1) ◽  
pp. 31-40 ◽  
Author(s):  
Roger A. Kemp ◽  
Calum MacAulay ◽  
David Garner ◽  
Branko Palcic
Keyword(s):  
Neural Networks ◽  
Discriminant Function ◽  
Severe Dysplasia ◽  
Correct Classification ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Normal Cells ◽  
Feed Forward ◽  
Linear Discriminant ◽  
Feed Forward Neural Networks

Normal cells in the presence of a precancerous lesion undergo subtle changes of their DNA distribution when observed by visible microscopy. These changes have been termed Malignancy Associated Changes (MACs). Using statistical models such as neural networks and discriminant functions it is possible to design classifiers that can separate these objects from truly normal cells. The correct classification rate using feed‐forward neural networks is compared to linear discriminant analysis when applied to detecting MACs. Classifiers were designed using 53 nuclear features calculated from images for each of 25,360 normal appearing cells taken from 344 slides diagnosed as normal or containing severe dysplasia. A linear discriminant function achieved a correct classification rate of 61.6% on the test data while neural networks scored as high as 72.5% on a cell‐by‐cell basis. The cell classifiers were applied to a library of 93,494 cells from 395 slides, and the results were jackknifed using a single slide feature. The discriminant function achieved a correct classification rate of 67.6% while the neural networks managed as high as 76.2%.

The Recognition Memory Test, Digit Span, and Knox Cube Test as Markers of Malingered Memory Impairment

Assessment ◽  
1994 ◽  
Vol 1 (4) ◽  
pp. 323-334 ◽  
Author(s):  
Grant L. Iverson ◽  
Michael D. Franzen
Keyword(s):  
Recognition Memory ◽  
Memory Impairment ◽  
Digit Span ◽  
Head Injuries ◽  
Memory Test ◽  
Correct Classification ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Recognition Memory Test ◽  
General Populations

The purpose of this investigation was to examine the efficacy of using the Recognition Memory Test (RMT), Digit Span subtest (WAIS-R), and Knox Cube Test as markers for malingered memory deficits. Participants were 100 subjects from three general populations: university students, federal inmates, and patients with head injuries. Twenty students, 20 inmates, and 20 patients with head injuries resulting in memory impairment were instructed to try their best on the assessment procedures. The remaining 20 students and 20 inmates were instructed to malinger memory impairment on the procedures. The experimental-malingerers (i.e., students and inmates) performed more poorly than the patients with head injuries on nearly every score derived from the three tests. Discriminant function analyses using the age-corrected Digit Span scale score, the Knox Cube Test total score, and the RMT raw scores for words and faces as predictors of group membership resulted in an overall 98% correct classification rate and 100% correct on cross-validation. Simultaneously applying two empirically-derived RMT cutoff scores resulted in an overall correct classification rate of 100%. The extraordinarily high classification rates in this study were likely influenced by the experimental design and procedures.

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