Machine Learning and Applications: An International Journal最新文献_第2页

Catwalkgrader: A Catwalk Analysis and Correction System using Machine Learning and Computer Vision 猫步分级器:使用机器学习和计算机视觉的猫步分析和校正系统

Machine Learning and Applications: An International Journal Pub Date : 2021-09-30 DOI: 10.5121/mlaij.2021.8303

Tianjiao Dong, Yu Sun

引用次数: 0

Human Activity Recognition Using Recurrent Neural Network 基于递归神经网络的人类活动识别

Machine Learning and Applications: An International Journal Pub Date : 2019-09-30 DOI: 10.5121/mlaij.2019.6301

Yoshihiro Ando

{"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}

引用次数: 1

Predicting Forced Population Displacement Using News Articles 利用新闻文章预测被迫的人口迁移

Machine Learning and Applications: An International Journal Pub Date : 2019-03-31 DOI: 10.5121/MLAIJ.2019.6101

Sadra Abrishamkar, Forouq Khonsari

引用次数: 0

Fault Diagnosis Using Clustering. What Statistical Test to use for Hypothesis Testing? 基于聚类的故障诊断。假设检验用什么统计检验?

Machine Learning and Applications: An International Journal Pub Date : 2019-03-31 DOI: 10.5121/mlaij.2019.6102

Nagdev Amruthnath, Tarun Gupta

{"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}

引用次数: 9

Analysis of WTTE-RNN Variants that Improve Performance 改进性能的WTTE-RNN变体分析

Machine Learning and Applications: An International Journal Pub Date : 2019-03-31 DOI: 10.5121/MLAIJ.2019.6103

Rory Cawley, John Burns

引用次数: 3

STUDY ON CEREBRAL ANEURYSMS: RUPTURE RISK PREDICTION USING GEOMETRICAL PARAMETERS AND WALL SHEAR STRESS WITH CFD AND MACHINE LEARNING TOOLS 基于CFD和机器学习工具的几何参数和壁面剪应力的脑动脉瘤破裂风险预测研究

Machine Learning and Applications: An International Journal Pub Date : 2018-12-31 DOI: 10.5121/MLAIJ.2018.5401

A. Aranda, A. Valencia

引用次数: 8

A Comparative Study on Human Action Recognition Using Multiple Skeletal Features and Multiclass Support Vector Machine 基于多骨骼特征和多类支持向量机的人体动作识别比较研究

Machine Learning and Applications: An International Journal Pub Date : 2018-06-30 DOI: 10.5121/MLAIJ.2018.5201

S. Islam, Mohammad Farhad Bulbul, Md. Sirajul Islam

引用次数: 2