The Classification of Skateboarding Tricks : A Transfer Learning and Machine Learning Approach

The skateboarding scene has arrived at new statures, particularly with its first appearance at the now delayed Tokyo Summer Olympic Games. Hence, attributable to the size of the game in such competitive games, progressed creative appraisal approaches have progressively increased due consideration by pertinent partners, particularly with the enthusiasm of a more goal-based assessment. This study purposes for classifying skateboarding tricks, specifically Frontside 180, Kickflip, Ollie, Nollie Front Shove-it, and Pop Shove-it over the integration of image processing, Trasnfer Learning (TL) to feature extraction enhanced with tradisional Machine Learning (ML) classifier. A male skateboarder performed five tricks every sort of trick consistently and the YI Action camera captured the movement by a range of 1.26 m. Then, the image dataset were features built and extricated by means of three TL models, and afterward in this manner arranged to utilize by k-Nearest Neighbor (k-NN) classifier. The perception via the initial experiments showed, the MobileNet, NASNetMobile, and NASNetLarge coupled with optimized k-NN classifiers attain a classification accuracy (CA) of 95%, 92% and 90%, respectively on the test dataset. Besides, the result evident from the robustness evaluation showed the MobileNet+k-NN pipeline is more robust as it could provide a decent average CA than other pipelines. It would be demonstrated that the suggested study could characterize the skateboard tricks sufficiently and could, over the long haul, uphold judges decided for giving progressively objective-based decision.

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Sentiment Analysis for Iraqis Dialect in Social Media

Iraqi Journal of Information & Communications Technology ◽

10.31987/ijict.1.2.17 ◽

2018 ◽

Vol 1 (2) ◽

pp. 24-32

Author(s):

Lamiaa Abd Habeeb

Keyword(s):

Machine Learning ◽

Social Media ◽

Nearest Neighbor ◽

Learning Algorithm ◽

Learning Approach ◽

Machine Learning Algorithm ◽

K Nearest Neighbor ◽

Multinomial Models ◽

Ensemble Machine Learning ◽

Machine Learning Approach

In this paper, we designed a system that extract citizens opinion about Iraqis government and Iraqis politicians through analyze their comments from Facebook (social media network). Since the data is random and contains noise, we cleaned the text and builds a stemmer to stem the words as much as possible, cleaning and stemming reduced the number of vocabulary from 28968 to 17083, these reductions caused reduction in memory size from 382858 bytes to 197102 bytes. Generally, there are two approaches to extract users opinion; namely, lexicon-based approach and machine learning approach. In our work, machine learning approach is applied with three machine learning algorithm which are; Naïve base, K-Nearest neighbor and AdaBoost ensemble machine learning algorithm. For Naïve base, we apply two models; Bernoulli and Multinomial models. We found that, Naïve base with Multinomial models give highest accuracy.

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A Machine Learning Approach to Predict Customer Usage of a Home Workout Platform

Applied Sciences ◽

10.3390/app11219927 ◽

2021 ◽

Vol 11 (21) ◽

pp. 9927

Author(s):

Qiuying Chen ◽

SangJoon Lee

Keyword(s):

Machine Learning ◽

Random Forest ◽

Nearest Neighbor ◽

Important Variable ◽

Receiver Operating Curve ◽

Support Vector ◽

Learning Approach ◽

K Nearest Neighbor ◽

Machine Learning Approach ◽

Supervised Learning Algorithms

Health authorities have recommended the use of digital tools for home workouts to stay active and healthy during the COVID-19 pandemic. In this paper, a machine learning approach is proposed to assess the activity of users on a home workout platform. Keep is a home workout application dedicated to providing one-stop exercise solutions such as fitness teaching, cycling, running, yoga, and fitness diet guidance. We used a data crawler to collect the total training set data of 7734 Keep users and compared four supervised learning algorithms: support vector machine, k-nearest neighbor, random forest, and logistic regression. The receiver operating curve analysis indicated that the overall discrimination verification power of random forest was better than that of the other three models. The random forest model was used to classify 850 test samples, and a correct rate of 88% was obtained. This approach can predict the continuous usage of users after installing the home workout application. We considered 18 variables on Keep that were expected to affect the determination of continuous participation. Keep certification is the most important variable that affected the results of this study. Keep certification refers to someone who has verified their identity information and can, therefore, obtain the Keep certification logo. The results show that the platform still needs to be improved in terms of real identity privacy information and other aspects.

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Machine learning approach for prediction of hepatic enhancement during the hepatobiliary phase of Gd-EOB DTPA-enhanced MRI.

10.21203/rs.3.rs-274189/v1 ◽

2021 ◽

Author(s):

Ji Su Ko ◽

Jieun Byun ◽

Seongkeun Park ◽

Ji Young Woo

Keyword(s):

Machine Learning ◽

Liver Disease ◽

Predictive Model ◽

Nearest Neighbor ◽

Area Under The Curve ◽

Learning Approach ◽

Hepatobiliary Phase ◽

K Nearest Neighbor ◽

Enhanced Mri ◽

Machine Learning Approach

Abstract We retrospectively assessed 214 patients with chronic liver disease or liver cirrhosis who underwent magnetic resonance imaging (MRI) enhanced with gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) from August 2016 to May 2020 to evaluate the relationship between biochemical results that reflect liver function and hepatic enhancement. With the information gained we employed a machine learning approach with the K-Nearest Neighbor (KNN) algorithm to develop a predictive model for determining insufficient hepatic enhancement during the hepatobiliary phase (HBP) in Gd-EOB-DTPA-enhanced MRI. Using both quantitative and qualitative assessments, the total bilirubin (TB), albumin (Alb), prothrombin time-international normalized ratio, platelet, Child-Pugh score (CPS), and Model for End-stage Liver Disease Sodium (MELD-Na) score were related to decreased hepatic enhancement. In a multivariate analysis, TB and Alb were associated with insufficient enhancement (p < 0.001). The predictive model showed that a combination of a variety of biochemical parameters had better performance (accuracy = 82.8%, area under the curve (AUC) = 0.861) in predicting insufficient enhancement than either the CPS (accuracy = 79.5%, AUC = 0.845) or the MELD-Na score (accuracy = 80.8%, AUC = 0.821). By using a machine-learning-based predictive model with the KNN algorithm, radiologists can predict insufficient hepatic enhancement during HBP in advance and adjust each patient's individually optimized MRI protocol.

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A hybrid machine learning approach of fuzzy-rough-k-nearest neighbor, latent semantic analysis, and ranker search for efficient disease diagnosis

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-211820 ◽

2021 ◽

pp. 1-16

Author(s):

Sunil Kumar Jha ◽

Ninoslav Marina ◽

Jinwei Wang ◽

Zulfiqar Ahmad

Keyword(s):

Machine Learning ◽

Latent Semantic Analysis ◽

Semantic Analysis ◽

Nearest Neighbor ◽

Disease Diagnosis ◽

Learning Approach ◽

Learning Approaches ◽

K Nearest Neighbor ◽

Machine Learning Approach ◽

Hybrid Machine

Machine learning approaches have a valuable contribution in improving competency in automated decision systems. Several machine learning approaches have been developed in the past studies in individual disease diagnosis prediction. The present study aims to develop a hybrid machine learning approach for diagnosis predictions of multiple diseases based on the combination of efficient feature generation, selection, and classification methods. Specifically, the combination of latent semantic analysis, ranker search, and fuzzy-rough-k-nearest neighbor has been proposed and validated in the diagnosis prediction of the primary tumor, post-operative, breast cancer, lymphography, audiology, fertility, immunotherapy, and COVID-19, etc. The performance of the proposed approach is compared with single and other hybrid machine learning approaches in terms of accuracy, analysis time, precision, recall, F-measure, the area under ROC, and the Kappa coefficient. The proposed hybrid approach performs better than single and other hybrid approaches in the diagnosis prediction of each of the selected diseases. Precisely, the suggested approach achieved the maximum recognition accuracy of 99.12%of the primary tumor, 96.45%of breast cancer Wisconsin, 94.44%of cryotherapy, 93.81%of audiology, and significant improvement in the classification accuracy and other evaluation metrics in the recognition of the rest of the selected diseases. Besides, it handles the missing values in the dataset effectively.

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A Machine Learning Approach to Study Glycosidase Activities from Bifidobacterium

Microorganisms ◽

10.3390/microorganisms9051034 ◽

2021 ◽

Vol 9 (5) ◽

pp. 1034

Author(s):

Carlos Sabater ◽

Lorena Ruiz ◽

Abelardo Margolles

Keyword(s):

Machine Learning ◽

Supervised Classification ◽

Machine Learning Algorithms ◽

Learning Approach ◽

Human Milk Oligosaccharides ◽

Future Studies ◽

High Fiber ◽

Machine Learning Approach ◽

Prebiotic Oligosaccharides

This study aimed to recover metagenome-assembled genomes (MAGs) from human fecal samples to characterize the glycosidase profiles of Bifidobacterium species exposed to different prebiotic oligosaccharides (galacto-oligosaccharides, fructo-oligosaccharides and human milk oligosaccharides, HMOs) as well as high-fiber diets. A total of 1806 MAGs were recovered from 487 infant and adult metagenomes. Unsupervised and supervised classification of glycosidases codified in MAGs using machine-learning algorithms allowed establishing characteristic hydrolytic profiles for B. adolescentis, B. bifidum, B. breve, B. longum and B. pseudocatenulatum, yielding classification rates above 90%. Glycosidase families GH5 44, GH32, and GH110 were characteristic of B. bifidum. The presence or absence of GH1, GH2, GH5 and GH20 was characteristic of B. adolescentis, B. breve and B. pseudocatenulatum, while families GH1 and GH30 were relevant in MAGs from B. longum. These characteristic profiles allowed discriminating bifidobacteria regardless of prebiotic exposure. Correlation analysis of glycosidase activities suggests strong associations between glycosidase families comprising HMOs-degrading enzymes, which are often found in MAGs from the same species. Mathematical models here proposed may contribute to a better understanding of the carbohydrate metabolism of some common bifidobacteria species and could be extrapolated to other microorganisms of interest in future studies.

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Assessing the Relation between Mud Components and Rheology for Loss Circulation Prevention Using Polymeric Gels: A Machine Learning Approach

Energies ◽

10.3390/en14051377 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1377

Author(s):

Musaab I. Magzoub ◽

Raj Kiran ◽

Saeed Salehi ◽

Ibnelwaleed A. Hussein ◽

Mustafa S. Nasser

Keyword(s):

Machine Learning ◽

Rheological Properties ◽

Nearest Neighbor ◽

Drilling Fluid ◽

Gradient Boosting ◽

K Nearest Neighbor ◽

Wide Range ◽

Machine Learning Approach ◽

Drilling Operations

The traditional way to mitigate loss circulation in drilling operations is to use preventative and curative materials. However, it is difficult to quantify the amount of materials from every possible combination to produce customized rheological properties. In this study, machine learning (ML) is used to develop a framework to identify material composition for loss circulation applications based on the desired rheological characteristics. The relation between the rheological properties and the mud components for polyacrylamide/polyethyleneimine (PAM/PEI)-based mud is assessed experimentally. Four different ML algorithms were implemented to model the rheological data for various mud components at different concentrations and testing conditions. These four algorithms include (a) k-Nearest Neighbor, (b) Random Forest, (c) Gradient Boosting, and (d) AdaBoosting. The Gradient Boosting model showed the highest accuracy (91 and 74% for plastic and apparent viscosity, respectively), which can be further used for hydraulic calculations. Overall, the experimental study presented in this paper, together with the proposed ML-based framework, adds valuable information to the design of PAM/PEI-based mud. The ML models allowed a wide range of rheology assessments for various drilling fluid formulations with a mean accuracy of up to 91%. The case study has shown that with the appropriate combination of materials, reasonable rheological properties could be achieved to prevent loss circulation by managing the equivalent circulating density (ECD).

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A machine-learning approach for automatic classification of volcanic seismicity at La Soufrière Volcano, Guadeloupe

Journal of Volcanology and Geothermal Research ◽

10.1016/j.jvolgeores.2020.107151 ◽

2020 ◽

pp. 107151

Author(s):

Alexis Falcin ◽

Jean-Philippe Métaxian ◽

Jérôme Mars ◽

Éléonore Stutzmann ◽

Jean-Christophe Komorowski ◽

...

Keyword(s):

Machine Learning ◽

Automatic Classification ◽

Learning Approach ◽

Machine Learning Approach ◽

Volcanic Seismicity

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MACHINE LEARNING APPROACH FOR THE CLASSIFICATION OF VIOLENT ATTACK BASED ON FABRIC SENSORS

Mechatronic Systems and Control (formerly Control and Intelligent Systems) ◽

10.2316/j.2021.201-0107 ◽

2021 ◽

Vol 49 (0) ◽

Author(s):

Princy Randhawa ◽

Vijay C. Shanthagiri ◽

Ajay Kumar

Keyword(s):

Machine Learning ◽

Learning Approach ◽

Machine Learning Approach ◽

Violent Attack

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Machine learning for psychiatric patient triaging: an investigation of cascading classifiers

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocy109 ◽

2018 ◽

Vol 25 (11) ◽

pp. 1481-1487 ◽

Cited By ~ 1

Author(s):

Vivek Kumar Singh ◽

Utkarsh Shrivastava ◽

Lina Bouayad ◽

Balaji Padmanabhan ◽

Anna Ialynytchev ◽

...

Keyword(s):

Machine Learning ◽

Psychiatric Patient ◽

Classification Accuracy ◽

Psychiatric Patients ◽

Patient Records ◽

Classification Technique ◽

Machine Learning Approach ◽

The One ◽

Unique Dataset

Abstract Objective Develop an approach, One-class-at-a-time, for triaging psychiatric patients using machine learning on textual patient records. Our approach aims to automate the triaging process and reduce expert effort while providing high classification reliability. Materials and Methods The One-class-at-a-time approach is a multistage cascading classification technique that achieves higher triage classification accuracy compared to traditional multiclass classifiers through 1) classifying one class at a time (or stage), and 2) identification and application of the highest accuracy classifier at each stage. The approach was evaluated using a unique dataset of 433 psychiatric patient records with a triage class label provided by “I2B2 challenge,” a recent competition in the medical informatics community. Results The One-class-at-a-time cascading classifier outperformed state-of-the-art classification techniques with overall classification accuracy of 77% among 4 classes, exceeding accuracies of existing multiclass classifiers. The approach also enabled highly accurate classification of individual classes—the severe and mild with 85% accuracy, moderate with 64% accuracy, and absent with 60% accuracy. Discussion The triaging of psychiatric cases is a challenging problem due to the lack of clear guidelines and protocols. Our work presents a machine learning approach using psychiatric records for triaging patients based on their severity condition. Conclusion The One-class-at-a-time cascading classifier can be used as a decision aid to reduce triaging effort of physicians and nurses, while providing a unique opportunity to involve experts at each stage to reduce false positive and further improve the system’s accuracy.

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