Machine Learning-Based Facial Beauty Prediction and Analysis of Frontal Facial Images Using Facial Landmarks and Traditional Image Descriptors

The beauty industry has seen rapid growth in multiple countries and due to its applications in entertainment, the analysis and assessment of facial attractiveness have received attention from scientists, physicians, and artists because of digital media, plastic surgery, and cosmetics. An analysis of techniques is used in the assessment of facial beauty that considers facial ratios and facial qualities as elements to predict facial beauty. Here, the facial landmarks are extracted to calculate facial ratios according to Golden Ratios and Symmetry Ratios, and an ablation study is performed to find the best performing feature set from extracted ratios. Subsequently, Gray Level Covariance Matrix (GLCM), Hu’s Moments, and Color Histograms in the HSV space are extracted as texture, shape, and color features, respectively. Another ablation study is performed to find out which feature performs the best when concatenated with the facial landmarks. Experimental results show that the concatenation of primary facial characteristics with facial landmarks improved the prediction score of facial beauty. Four models are trained, K-Nearest Neighbors (KNN), Linear Regression (LR), Random Forest (RF), and Artificial Neural Network (ANN) on a dataset of 5500 frontal facial images, and amongst them, KNN performs the best for the concatenated features achieving a Pearson’s Correlation Coefficient of 0.7836 and a Mean Squared Error of 0.0963. Our analysis also provides us with insights into how different machine learning models can understand the concept of facial beauty.

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Glass Classification based on Machine Learning Algorithms

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.h6819.0991120 ◽

2020 ◽

Vol 9 (11) ◽

pp. 139-142

Keyword(s):

Machine Learning ◽

Random Forest ◽

Amorphous Solid ◽

Machine Learning Algorithms ◽

Support Vector ◽

K Nearest Neighbors ◽

X Ray ◽

Svm Algorithm ◽

Artificial Neural Network Ann ◽

Logistic Regression Algorithm

Glass Industry is considered one of the most important industries in the world. The Glass is used everywhere, from water bottles to X-Ray and Gamma Rays protection. This is a non-crystalline, amorphous solid that is most often transparent. There are lots of uses of glass, and during investigation in a crime scene, the investigators need to know what is type of glass in a scene. To find out the type of glass, we will use the online dataset and machine learning to solve the above problem. We will be using ML algorithms such as Artificial Neural Network (ANN), K-nearest neighbors (KNN) algorithm, Support Vector Machine (SVM) algorithm, Random Forest algorithm, and Logistic Regression algorithm. By comparing all the algorithm Random Forest did the best in glass classification.

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Facial Attractiveness: Beauty and the Machine

Neural Computation ◽

10.1162/089976606774841602 ◽

2006 ◽

Vol 18 (1) ◽

pp. 119-142 ◽

Cited By ~ 129

Author(s):

Yael Eisenthal ◽

Gideon Dror ◽

Eytan Ruppin

Keyword(s):

Machine Learning ◽

Facial Attractiveness ◽

Training Data ◽

Data Sets ◽

Learning Context ◽

Data Set ◽

Attractiveness Rating ◽

Facial Beauty ◽

Facial Images

This work presents a novel study of the notion of facial attractiveness in a machine learning context. To this end, we collected human beauty ratings for data sets of facial images and used various techniques for learning the attractiveness of a face. The trained predictor achieves a significant correlation of 0.65 with the average human ratings. The results clearly show that facial beauty is a universal concept that a machine can learn. Analysis of the accuracy of the beauty prediction machine as a function of the size of the training data indicates that a machine producing human-like attractiveness rating could be obtained given a moderately larger data set.

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Using Machine Learning to Predict the Fuel Peak Cladding Temperature for a Large Break Loss of Coolant Accident

Frontiers in Energy Research ◽

10.3389/fenrg.2021.755638 ◽

2021 ◽

Vol 9 ◽

Author(s):

Wazif Sallehhudin ◽

Aya Diab

Keyword(s):

Machine Learning ◽

Uncertainty Quantification ◽

Mean Squared Error ◽

Learning Algorithm ◽

Severe Accidents ◽

Loss Of Coolant Accident ◽

Lumped Parameter ◽

Loss Of Coolant ◽

Artificial Neural Network Ann ◽

Uncertainty Parameters

In this paper the use of machine learning (ML) is explored as an efficient tool for uncertainty quantification. A machine learning algorithm is developed to predict the peak cladding temperature (PCT) under the conditions of a large break loss of coolant accident given the various underlying uncertainties. The best estimate approach is used to simulate the thermal-hydraulic system of APR1400 large break loss of coolant accident (LBLOCA) scenario using the multidimensional reactor safety analysis code (MARS-KS) lumped parameter system code developed by Korea Atomic Energy Research Institute (KAERI). To generate the database necessary to train the ML model, a set of uncertainty parameters derived from the phenomena identification and ranking table (PIRT) is propagated through the thermal hydraulic model using the Dakota-MARS uncertainty quantification framework. The developed ML model uses the database created by the uncertainty quantification framework along with Keras library and Talos optimization to construct the artificial neural network (ANN). After learning and validation, the ML model can predict the peak cladding temperature (PCT) reasonably well with a mean squared error (MSE) of ∼0.002 and R2 of ∼0.9 with 9 to 11 key uncertain parameters. As a bounding accident scenario analysis of the LBLOCA case paves the way to using machine learning as a decision making tool for design extension conditions as well as severe accidents.

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Physicochemical Habitat Traits Preferred by Small Indigenous Fish (Chanda Nama ) in Indian River Discerning through Machine Learning

10.21203/rs.3.rs-591781/v1 ◽

2021 ◽

Author(s):

Rohan Kumar Raman ◽

Archan Kanti Das ◽

Ranjan Kumar Manna ◽

Sanjeev Kumar Sahu ◽

Basanta Kumar Das

Keyword(s):

Machine Learning ◽

Random Forest ◽

River System ◽

Fish Distribution ◽

Support Vector ◽

Operating Characteristics ◽

K Nearest Neighbors ◽

Peninsular India ◽

Preferred Habitat ◽

Artificial Neural Network Ann

Abstract Physicochemical traits of river influence the habitat of fish species in aquatic ecosystems. Fish showed a complex relationship with aquatic factors in river. Machine learning (ML) modeling is a useful tool to established relationship between complex systems. This study identified the preferred habitat indicators of Chanda nama (a small indigenous fish), in the Krishna River, of peninsular India, using machine learning modeling. Data were observed on Chanda nama fish distribution (presence/absence) and associated ten physical and chemical parameters of water at 22 sampling sites on the river during year 2001-02. Machine learning models such as random forest (RF), artificial neural network (ANN), support vector machine (SVM), k-nearest neighbors (KNN) used for the classification of Chanda nama distribution in the river. The ML model efficiency was evaluated using classification accuracy (CCI), Cohen’s kappa coefficient (k), sensitivity, specificity and receiver-operating-characteristics (ROC). Results showed that random forest is the best model with 82% accuracy, CCI (0.82), k (0.55), sensitivity (0.57), specificity (0.76) and ROC (0.72) for Chanda nama distribution (presence/absence) in the Krishna River. Random Forest model identified three preferred physicochemical habitat traits like altitude, temperature and depth for Chanda nama distribution in the Krishna River, India. This study will be helpful for researcher and policy maker to understand the important habitat physicochemical traits for sustainable management of small indigenous fish (Chanda nama ) in the river system.

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A Real-Time Network Traffic Classifier for Online Applications Using Machine Learning

Algorithms ◽

10.3390/a14080250 ◽

2021 ◽

Vol 14 (8) ◽

pp. 250

Author(s):

Ahmed Abdelmoamen Ahmed ◽

Gbenga Agunsoye

Keyword(s):

Machine Learning ◽

Real Time ◽

Network Traffic ◽

Classification Accuracy ◽

Performance Metrics ◽

Ann Model ◽

K Nearest Neighbors ◽

Artificial Neural Network Ann ◽

User Friendly ◽

Network Administrators

The increasing ubiquity of network traffic and the new online applications’ deployment has increased traffic analysis complexity. Traditionally, network administrators rely on recognizing well-known static ports for classifying the traffic flowing their networks. However, modern network traffic uses dynamic ports and is transported over secure application-layer protocols (e.g., HTTPS, SSL, and SSH). This makes it a challenging task for network administrators to identify online applications using traditional port-based approaches. One way for classifying the modern network traffic is to use machine learning (ML) to distinguish between the different traffic attributes such as packet count and size, packet inter-arrival time, packet send–receive ratio, etc. This paper presents the design and implementation of NetScrapper, a flow-based network traffic classifier for online applications. NetScrapper uses three ML models, namely K-Nearest Neighbors (KNN), Random Forest (RF), and Artificial Neural Network (ANN), for classifying the most popular 53 online applications, including Amazon, Youtube, Google, Twitter, and many others. We collected a network traffic dataset containing 3,577,296 packet flows with different 87 features for training, validating, and testing the ML models. A web-based user-friendly interface is developed to enable users to either upload a snapshot of their network traffic to NetScrapper or sniff the network traffic directly from the network interface card in real time. Additionally, we created a middleware pipeline for interfacing the three models with the Flask GUI. Finally, we evaluated NetScrapper using various performance metrics such as classification accuracy and prediction time. Most notably, we found that our ANN model achieves an overall classification accuracy of 99.86% in recognizing the online applications in our dataset.

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Phenomic Data-Facilitated Rust and Senescence Prediction in Maize Using Machine Learning Algorithms

10.21203/rs.3.rs-1108535/v1 ◽

2021 ◽

Author(s):

Aaron J. DeSalvio ◽

Alper Adak ◽

Seth C. Murray ◽

Scott C. Wilde ◽

Thomas Isakeit

Keyword(s):

Machine Learning ◽

Mean Squared Error ◽

Vegetation Indices ◽

Agricultural Practices ◽

Grain Filling ◽

Machine Learning Algorithms ◽

Aerial Images ◽

Support Vector ◽

K Nearest Neighbors ◽

Filling Time

Abstract Current methods in measuring maize (Zea mays L.) southern rust (Puccinia polyspora Underw.) and subsequent crop senescence require expert observation which are resource-intensive and prone to subjectivity. In this study, unoccupied aerial system (UAS) field-based high-throughput phenotyping (HTP) was employed to collect high-resolution aerial imagery of elite maize hybrids planted in the 2020 and 2021 growing seasons, with 13 UAS flights obtained from 2020 and 17 from 2021. Vegetation indices (VIs) were extracted from mosaicked aerial images that served as temporal phenomic predictors for southern rust scored in the field and senescence as scored using UAS-acquired mosaic images. Temporal best linear unbiased predictors (TBLUPs) were calculated using a nested model that treated the pedigree performances as nested within flights in terms of rust and senescence. All eight machine learning regressions tested (ridge, lasso, elastic net, random forest, support vector machine with radial and linear kernels, partial least squares, and k-nearest neighbors) outperformed a general linear model with both higher prediction accuracies (92-98%) and lower root mean squared error (RMSE) for rust and senescence scores. UAS-acquired VIs enabled the discovery of novel early quantitative phenotypic indicators of maize senescence and southern rust before being detectable by expert annotation and revealed positive correlations between grain filling time and yield (0.22 and 0.44 in 2020 and 2021), with practical implications for precision agricultural practices.

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Fake News Detection Using Machine Learning Technique

10.52458/978-93-91842-08-6-5 ◽

2021 ◽

Author(s):

Dammavalam Srinivasa Rao ◽

N. Rajasekhar ◽

D. Sowmya ◽

D. Archana ◽

T. Hareesha ◽

...

Keyword(s):

Machine Learning ◽

Digital Media ◽

Excellent Result ◽

Computation Time ◽

Ensemble Methods ◽

Fake News ◽

K Nearest Neighbors ◽

Directional Model ◽

Digital Devices ◽

Left And Right

People got to know about the world from newspapers to today’s digital media.From 1605 to 2021 the topography of news has evolved at an immense. People forgotten about newspapers and habituated to digital devices so that they can view it at anytime and anywhere soon it became a crucial asset for people. From the past few years fake news also evolved and people always being believed by the available fake news who are being shared by fake profiles in digital media. Currently numerous approaches for detecting fake news by neural networks in one-directional model. We proposed BERT- Bidirectional Encoder Representations from Transformers is the bidirectional model where it uses left and right content in each word so that it is used for pre-train the words into two-way representations from unlabeled words it shown an excellent result when dealt with fake news it attained 99% of accuracy and outperform logistic regression and K-Nearest Neighbors. This method became a crucial in dealing with fake news so that it improves categorization easily and reduces computation time. Through this proposal, we are aiming to build a model to spot fake news present across various sites. The motivation behind this work to help people improve the consumption of legitimate news while discarding misleading information relationship in social media. Classification accuracy of fake news may be improved from the utilization of machine learning ensemble methods.

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Genome-wide prediction for complex traits under the presence of dominance effects in simulated populations using GBLUP and machine learning methods

Journal of Animal Science ◽

10.1093/jas/skaa179 ◽

2020 ◽

Vol 98 (6) ◽

Author(s):

Anderson Antonio Carvalho Alves ◽

Rebeka Magalhães da Costa ◽

Tiago Bresolin ◽

Gerardo Alves Fernandes Júnior ◽

Rafael Espigolan ◽

...

Keyword(s):

Machine Learning ◽

Complex Traits ◽

Mean Squared Error ◽

Genetic Effect ◽

Predictive Ability ◽

Predictive Performance ◽

Support Vector ◽

Learning Methods ◽

Machine Learning Methods ◽

Artificial Neural Network Ann

Abstract The aim of this study was to compare the predictive performance of the Genomic Best Linear Unbiased Predictor (GBLUP) and machine learning methods (Random Forest, RF; Support Vector Machine, SVM; Artificial Neural Network, ANN) in simulated populations presenting different levels of dominance effects. Simulated genome comprised 50k SNP and 300 QTL, both biallelic and randomly distributed across 29 autosomes. A total of six traits were simulated considering different values for the narrow and broad-sense heritability. In the purely additive scenario with low heritability (h2 = 0.10), the predictive ability obtained using GBLUP was slightly higher than the other methods whereas ANN provided the highest accuracies for scenarios with moderate heritability (h2 = 0.30). The accuracies of dominance deviations predictions varied from 0.180 to 0.350 in GBLUP extended for dominance effects (GBLUP-D), from 0.06 to 0.185 in RF and they were null using the ANN and SVM methods. Although RF has presented higher accuracies for total genetic effect predictions, the mean-squared error values in such a model were worse than those observed for GBLUP-D in scenarios with large additive and dominance variances. When applied to prescreen important regions, the RF approach detected QTL with high additive and/or dominance effects. Among machine learning methods, only the RF was capable to cover implicitly dominance effects without increasing the number of covariates in the model, resulting in higher accuracies for the total genetic and phenotypic values as the dominance ratio increases. Nevertheless, whether the interest is to infer directly on dominance effects, GBLUP-D could be a more suitable method.

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Machine Learning Models of Breast Cancer Risk Prediction

10.1101/723304 ◽

2019 ◽

Author(s):

Md. Mohaimenul Islam ◽

Tahmina Narin Poly

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Clinical Features ◽

Machine Learning Algorithms ◽

Receiver Operating Curve ◽

Support Vector ◽

Breast Cancer Patients ◽

K Nearest Neighbors ◽

Cancer Prediction ◽

Artificial Neural Network Ann

AbstractBreast cancer is the most common cancer in women both in the developed and less developed world. Early detection based on clinical features can greatly increase the chances for successful treatment. Our goal was to construct a breast cancer prediction model based on machine learning algorithms. A total of 10 potential clinical features like age, BMI, glucose, insulin, HOMA, leptin, adiponectin, resistin, and MCP-1 were collected from 116 patients. In this report, most commonly used machine learning model such as decision tree (DT), random forest (RF), K-nearest neighbors (KNN), support vector machine (SVM), logistic regression (LR), and artificial neural network (ANN) models were tested for breast cancer prediction. A repeated 10-fold cross-validation model was used to rank variables on the randomly split dataset. The accuracy of DT, RF, SVM, LR, ANN, and KNN was 0.71, 0.71, 0.77, 0.80, 0.81, and 0.86 respectively. However, The KNN model showed most higher accuracy with area under receiver operating curve, sensitivity, and specificity of 0.95, 0.80, 0.91. Therefore, identification of breast cancer patients correctly would create care opportunities such as monitoring and adopting intervention plans may benefit the quality of care in long-term.

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Use of Machine Learning to Investigate the Quantitative Checklist for Autism in Toddlers (Q-CHAT) towards Early Autism Screening

Diagnostics ◽

10.3390/diagnostics11030574 ◽

2021 ◽

Vol 11 (3) ◽

pp. 574

Author(s):

Gennaro Tartarisco ◽

Giovanni Cicceri ◽

Davide Di Pietro ◽

Elisa Leonardi ◽

Stefania Aiello ◽

...

Keyword(s):

Machine Learning ◽

High Performance ◽

Behavioral Science ◽

Autistic Traits ◽

Classification Performance ◽

Recursive Feature Elimination ◽

Diagnostic Tools ◽

Support Vector ◽

K Nearest Neighbors ◽

Autism Screening

In the past two decades, several screening instruments were developed to detect toddlers who may be autistic both in clinical and unselected samples. Among others, the Quantitative CHecklist for Autism in Toddlers (Q-CHAT) is a quantitative and normally distributed measure of autistic traits that demonstrates good psychometric properties in different settings and cultures. Recently, machine learning (ML) has been applied to behavioral science to improve the classification performance of autism screening and diagnostic tools, but mainly in children, adolescents, and adults. In this study, we used ML to investigate the accuracy and reliability of the Q-CHAT in discriminating young autistic children from those without. Five different ML algorithms (random forest (RF), naïve Bayes (NB), support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN)) were applied to investigate the complete set of Q-CHAT items. Our results showed that ML achieved an overall accuracy of 90%, and the SVM was the most effective, being able to classify autism with 95% accuracy. Furthermore, using the SVM–recursive feature elimination (RFE) approach, we selected a subset of 14 items ensuring 91% accuracy, while 83% accuracy was obtained from the 3 best discriminating items in common to ours and the previously reported Q-CHAT-10. This evidence confirms the high performance and cross-cultural validity of the Q-CHAT, and supports the application of ML to create shorter and faster versions of the instrument, maintaining high classification accuracy, to be used as a quick, easy, and high-performance tool in primary-care settings.

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