In recent years, the modeling industry has attracted many people, causing a drastic increase in the number of modeling training classes. Modeling takes practice, and without professional training, few beginners know if they are doing it right or not. In this paper, we present a real-time 2D model walk grading app based on Mediapipe, a library for real-time, multi-person keypoint detection. After capturing 2D positions of a person's joints and skeletal wireframe from an uploaded video, our app uses a scoring formula to provide accurate scores and tailored feedback to each user for their modeling skills.
{"title":"Catwalkgrader: A Catwalk Analysis and Correction System using Machine Learning and Computer Vision","authors":"Tianjiao Dong, Yu Sun","doi":"10.5121/mlaij.2021.8303","DOIUrl":"https://doi.org/10.5121/mlaij.2021.8303","url":null,"abstract":"In recent years, the modeling industry has attracted many people, causing a drastic increase in the number of modeling training classes. Modeling takes practice, and without professional training, few beginners know if they are doing it right or not. In this paper, we present a real-time 2D model walk grading app based on Mediapipe, a library for real-time, multi-person keypoint detection. After capturing 2D positions of a person's joints and skeletal wireframe from an uploaded video, our app uses a scoring formula to provide accurate scores and tailored feedback to each user for their modeling skills.","PeriodicalId":347528,"journal":{"name":"Machine Learning and Applications: An International Journal","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116193654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the spread of smartphones incorporating various sensors, accelerometers and gyro sensors have become familiar to us. Based on such situations, sensor-based human activity recognition (HAR) that uses human sensor data to identify human activity has come to use smartphones as data acquisition sources. In the early studies of HAR using smartphones, handcrafted methods were used if various statistical values were required as feature quantities and high accuracy was realized. Meanwhile, the popularization of deep learning in recent years has not been discussed, and its application has been made to HAR. Although deep learning has the advantage of being able to automatically extract feature quantities from data, it has not reached a step beyond precision in handcrafted methods. Furthermore, in the previous research, to divide data by time window of a fixed interval, except for some part, inference could not be performed unless the data for the time window was secured. We attempted to overcome these limitations using recurrent neural network. Our method records higher accuracy than previous studies using convolutional neural network and long short term memory, which are typical methods in deep learning and display results comparable to handcrafted methods. We also succeeded in pre-calculating many feature quantities, whose calculation was a problem in the previous research, and eliminating the time window.
{"title":"Human Activity Recognition Using Recurrent Neural Network","authors":"Yoshihiro Ando","doi":"10.5121/mlaij.2019.6301","DOIUrl":"https://doi.org/10.5121/mlaij.2019.6301","url":null,"abstract":"With the spread of smartphones incorporating various sensors, accelerometers and gyro sensors have become familiar to us. Based on such situations, sensor-based human activity recognition (HAR) that uses human sensor data to identify human activity has come to use smartphones as data acquisition sources. In the early studies of HAR using smartphones, handcrafted methods were used if various statistical values were required as feature quantities and high accuracy was realized. Meanwhile, the popularization of deep learning in recent years has not been discussed, and its application has been made to HAR. Although deep learning has the advantage of being able to automatically extract feature quantities from data, it has not reached a step beyond precision in handcrafted methods. Furthermore, in the previous research, to divide data by time window of a fixed interval, except for some part, inference could not be performed unless the data for the time window was secured. We attempted to overcome these limitations using recurrent neural network. Our method records higher accuracy than previous studies using convolutional neural network and long short term memory, which are typical methods in deep learning and display results comparable to handcrafted methods. We also succeeded in pre-calculating many feature quantities, whose calculation was a problem in the previous research, and eliminating the time window.","PeriodicalId":347528,"journal":{"name":"Machine Learning and Applications: An International Journal","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114827453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The world has witnessed mass forced population displacement across the globe. Population displacement has various indications, with different social and policy consequences. Mitigation of the humanitarian crisis requires tracking and predicting the population movements to allocate the necessary resources and inform the policymakers. The set of events that triggers population movements can be traced in the news articles. In this paper, we propose the Population Displacement-Signal Extraction Framework (PD-SEF) to explore a large news corpus and extract the signals of forced population displacement. PD-SEF measures and evaluates violence signals, which is a critical factor of forced displacement from it. Following signal extraction, we propose a displacement prediction model based on extracted violence scores. Experimental results indicate the effectiveness of our framework in extracting high quality violence scores and building accurate prediction models.
{"title":"Predicting Forced Population Displacement Using News Articles","authors":"Sadra Abrishamkar, Forouq Khonsari","doi":"10.5121/MLAIJ.2019.6101","DOIUrl":"https://doi.org/10.5121/MLAIJ.2019.6101","url":null,"abstract":"The world has witnessed mass forced population displacement across the globe. Population displacement has various indications, with different social and policy consequences. Mitigation of the humanitarian crisis requires tracking and predicting the population movements to allocate the necessary resources and inform the policymakers. The set of events that triggers population movements can be traced in the news articles. In this paper, we propose the Population Displacement-Signal Extraction Framework (PD-SEF) to explore a large news corpus and extract the signals of forced population displacement. PD-SEF measures and evaluates violence signals, which is a critical factor of forced displacement from it. Following signal extraction, we propose a displacement prediction model based on extracted violence scores. Experimental results indicate the effectiveness of our framework in extracting high quality violence scores and building accurate prediction models.","PeriodicalId":347528,"journal":{"name":"Machine Learning and Applications: An International Journal","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116594978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Predictive maintenance and condition-based monitoring systems have seen significant prominence in recent years to minimize the impact of machine downtime on production and its costs. Predictive maintenance involves using concepts of data mining, statistics, and machine learning to build models that are capable of performing early fault detection, diagnosing the faults and predicting the time to failure. Fault diagnosis has been one of the core areas where the actual failure mode of the machine is identified. In fluctuating environments such as manufacturing, clustering techniques have proved to be more reliable compared to supervised learning methods. One of the fundamental challenges of clustering is developing a test hypothesis and choosing an appropriate statistical test for hypothesis testing. Most statistical analyses use some underlying assumptions of the data which most real-world data is incapable of satisfying those assumptions. This paper is dedicated to overcoming the following challenge by developing a test hypothesis for fault diagnosis application using clustering technique and performing PERMANOVA test for hypothesis testing.
{"title":"Fault Diagnosis Using Clustering. What Statistical Test to use for Hypothesis Testing?","authors":"Nagdev Amruthnath, Tarun Gupta","doi":"10.5121/mlaij.2019.6102","DOIUrl":"https://doi.org/10.5121/mlaij.2019.6102","url":null,"abstract":"Predictive maintenance and condition-based monitoring systems have seen significant prominence in recent years to minimize the impact of machine downtime on production and its costs. Predictive maintenance involves using concepts of data mining, statistics, and machine learning to build models that are capable of performing early fault detection, diagnosing the faults and predicting the time to failure. Fault diagnosis has been one of the core areas where the actual failure mode of the machine is identified. In fluctuating environments such as manufacturing, clustering techniques have proved to be more reliable compared to supervised learning methods. One of the fundamental challenges of clustering is developing a test hypothesis and choosing an appropriate statistical test for hypothesis testing. Most statistical analyses use some underlying assumptions of the data which most real-world data is incapable of satisfying those assumptions. This paper is dedicated to overcoming the following challenge by developing a test hypothesis for fault diagnosis application using clustering technique and performing PERMANOVA test for hypothesis testing.","PeriodicalId":347528,"journal":{"name":"Machine Learning and Applications: An International Journal","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124445200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Businesses typically have assets such as machinery, electronics or their customers. These assets share a common trait in that at some stage they will fail or, in the case of customers, they will churn. Knowing when and where to focus limited resources is a key area of concern for businesses. A prediction model called the WTTE-RNN was shown to be effective for predicting the time to event for topics such as machine failure. The purpose of this research is to identify neural network architecture variants of the WTTE-RNN model that have improved performance. The research results on these WTTE-RNN model variant would be useful in the application of the model.
{"title":"Analysis of WTTE-RNN Variants that Improve Performance","authors":"Rory Cawley, John Burns","doi":"10.5121/MLAIJ.2019.6103","DOIUrl":"https://doi.org/10.5121/MLAIJ.2019.6103","url":null,"abstract":"Businesses typically have assets such as machinery, electronics or their customers. These assets share a common trait in that at some stage they will fail or, in the case of customers, they will churn. Knowing when and where to focus limited resources is a key area of concern for businesses. A prediction model called the WTTE-RNN was shown to be effective for predicting the time to event for topics such as machine failure. The purpose of this research is to identify neural network architecture variants of the WTTE-RNN model that have improved performance. The research results on these WTTE-RNN model variant would be useful in the application of the model.","PeriodicalId":347528,"journal":{"name":"Machine Learning and Applications: An International Journal","volume":"7 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130594401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We modeled an SVM radial classification machine learning algorithm to determine the ruptured and unruptured risk of saccular cerebral aneurysms using 60 samples with 6 predictors as the gender, the age, the Womersley number, the Time-Averaged Wall Shear Stress (TAWSS), the Aspect Ratio (AR) and the bottleneck of the aneurysms, considering real cases of patients. We reconstructed computationally each geometry from an angiography image to realize a CFD simulations, where the TAWSS was computed by CFD analysis. A cross validation method was used in the training sample to validate the classification model, getting an accuracy of 92.86% in the test sample. This result may be used to help in medical decisions to avoid a complicated operation when the probability of rupture is low.
{"title":"STUDY ON CEREBRAL ANEURYSMS: RUPTURE RISK PREDICTION USING GEOMETRICAL PARAMETERS AND WALL SHEAR STRESS WITH CFD AND MACHINE LEARNING TOOLS","authors":"A. Aranda, A. Valencia","doi":"10.5121/MLAIJ.2018.5401","DOIUrl":"https://doi.org/10.5121/MLAIJ.2018.5401","url":null,"abstract":"We modeled an SVM radial classification machine learning algorithm to determine the ruptured and unruptured risk of saccular cerebral aneurysms using 60 samples with 6 predictors as the gender, the age, the Womersley number, the Time-Averaged Wall Shear Stress (TAWSS), the Aspect Ratio (AR) and the bottleneck of the aneurysms, considering real cases of patients. We reconstructed computationally each geometry from an angiography image to realize a CFD simulations, where the TAWSS was computed by CFD analysis. A cross validation method was used in the training sample to validate the classification model, getting an accuracy of 92.86% in the test sample. This result may be used to help in medical decisions to avoid a complicated operation when the probability of rupture is low.","PeriodicalId":347528,"journal":{"name":"Machine Learning and Applications: An International Journal","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133494365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Islam, Mohammad Farhad Bulbul, Md. Sirajul Islam
This paper proposes a framework for human action recognition (HAR) by using skeletal features from depth video sequences. HAR has become a basis for applications such as health care, fall detection, human position tracking, video analysis, security applications, etc. Wehave used joint angle quaternion and absolute joint position to recognitionhuman action. We also mapped joint position on Lie algebra and fuse it with other features. This approach comprised of three steps namely (i) an automatic skeletal feature (absolute joint position and joint angle) extraction (ii) HAR by using multi-class Support Vector Machine and (iii) HAR by features fusion and decision fusion classification outcomes. The HAR methodsare evaluated on two publicly available challenging datasets UTKinect-Action and Florence3D-Action datasets. The experimental results show that the absolute joint positionfeature is the best than other features and the proposed framework being highly promising compared to others existing methods.
{"title":"A Comparative Study on Human Action Recognition Using Multiple Skeletal Features and Multiclass Support Vector Machine","authors":"S. Islam, Mohammad Farhad Bulbul, Md. Sirajul Islam","doi":"10.5121/MLAIJ.2018.5201","DOIUrl":"https://doi.org/10.5121/MLAIJ.2018.5201","url":null,"abstract":"This paper proposes a framework for human action recognition (HAR) by using skeletal features from depth video sequences. HAR has become a basis for applications such as health care, fall detection, human position tracking, video analysis, security applications, etc. Wehave used joint angle quaternion and absolute joint position to recognitionhuman action. We also mapped joint position on Lie algebra and fuse it with other features. This approach comprised of three steps namely (i) an automatic skeletal feature (absolute joint position and joint angle) extraction (ii) HAR by using multi-class Support Vector Machine and (iii) HAR by features fusion and decision fusion classification outcomes. The HAR methodsare evaluated on two publicly available challenging datasets UTKinect-Action and Florence3D-Action datasets. The experimental results show that the absolute joint positionfeature is the best than other features and the proposed framework being highly promising compared to others existing methods.","PeriodicalId":347528,"journal":{"name":"Machine Learning and Applications: An International Journal","volume":"168 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134152496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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