2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)最新文献_第10页

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2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/icmla.2018.00001

引用次数: 0

Vision-Based Detection of Simultaneous Kicking for Identifying Movement Characteristics of Infants At-Risk for Neuro-Disorders 基于视觉的同时踢腿检测识别有神经障碍风险的婴儿的运动特征

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00230

Devleena Das, Katelyn E. Fry, A. Howard

{"title":"Vision-Based Detection of Simultaneous Kicking for Identifying Movement Characteristics of Infants At-Risk for Neuro-Disorders","authors":"Devleena Das, Katelyn E. Fry, A. Howard","doi":"10.1109/ICMLA.2018.00230","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00230","url":null,"abstract":"Neuro disorders such as Cerebral Palsy (CP) and Infantile Spasms (IS) in infants can cause a wide range of developmental coordination disorders (DCD). Simultaneous, non-complex, kicking patterns that persist in infants of 4-7 months of age is highly suggestive of a neuro-disorder. Early establishment of risk levels for infants at risk for neuro-disorders is beneficial for early intervention. To provide a method to track early infant kicking movements for determining risk-level, an automated method is established to track and classify periods of simultaneous (SM), non-simultaneous movements (NSM) and no movement (NM) during infant kicking actions. In this paper, a computer vision algorithm uses KAZE points to track infant kicking and collect kinematic data. Each movement type is classified by computing unique feature criterion and using a support vector machine (SVM) for learning a movement model. We discuss the significance of the classifier as well as analyze the percentage break down of movement types for typical infants and infants with IS.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"119 1","pages":"1413-1418"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85392968","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: 10

Improving Multi-modal Optimization Restart Strategy Through Multi-armed Bandit 基于多臂强盗的多模态优化重启策略改进

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00057

A. Dubois, J. Dehos, F. Teytaud

引用次数: 3

Annotating the Performance of Industrial Assets via Relevancy Estimation of Event Logs 通过事件日志的相关性评估来标注工业资产的绩效

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00205

Pierre Dagnely, T. Tourwé, E. Tsiporkova

引用次数: 3

Deep Convolution Neural Network Model to Predict Relapse in Breast Cancer 深度卷积神经网络模型预测乳腺癌复发

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00059

Alokkumar Jha, Ghanshyam Verma, Yasar Khan, Qaiser Mehmood, D. Rebholz-Schuhmann, Ratnesh Sahay

{"title":"Deep Convolution Neural Network Model to Predict Relapse in Breast Cancer","authors":"Alokkumar Jha, Ghanshyam Verma, Yasar Khan, Qaiser Mehmood, D. Rebholz-Schuhmann, Ratnesh Sahay","doi":"10.1109/ICMLA.2018.00059","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00059","url":null,"abstract":"A mishap in anti-cancer drug distribution is critical in breast cancer patients due to poor prediction model to identify the treatment regime in ER+ve and ER-ve (Estrogen Receptor (ER)) patients. The traditional method for the prediction depends on the change in expression across the normal-disease pair. However, it certainly misses the multidimensional aspect and underlying cause of relapse, such as various mutations, drug dosage side effects, methylation, etc. In this paper, we have developed a multi-layer neural network model to classify multidimensional genomics data into their similar annotation group. Further, we used this multi-layer cancer genomics perceptron for annotating differentially expressed genes (DEGs) to predict relapse based on ER status in breast cancer. This approach provides multivariate identification of genes, not just by differential expression, but, cause-effect of disease status due to drug overdosage and genomics-driven drug balancing method. The multi-layered neural network model, where each layer defines the relationship of similar databases with multidimensional knowledge. We illustrate that the use of multilayer knowledge graph with gene expression data for training the deep convolution neural network stratify the patient relapse and drug dosage along with underlying molecular properties.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"1051 1","pages":"351-358"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77241714","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: 6

Fast Proposals for Image and Video Annotation Using Modified Echo State Networks 基于改进回声状态网络的图像和视频标注快速方案

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00199

Sohini Roychowdhury, L. S. Muppirisetty

引用次数: 4

Interpretability and Reproducability in Production Machine Learning Applications 生产机器学习应用中的可解释性和可再现性

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00105

Sindhu Ghanta, Sriram Ganapathi Subramanian, S. Sundararaman, L. Khermosh, Vinay Sridhar, D. Arteaga, Q. Luo, Dhananjoy Das, Nisha Talagala

引用次数: 6

Improving L-BFGS Initialization for Trust-Region Methods in Deep Learning 基于深度学习信任域方法的L-BFGS初始化改进

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00081

J. Rafati, Roummel F. Marcia

{"title":"Improving L-BFGS Initialization for Trust-Region Methods in Deep Learning","authors":"J. Rafati, Roummel F. Marcia","doi":"10.1109/ICMLA.2018.00081","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00081","url":null,"abstract":"Deep learning algorithms often require solving a highly non-linear and nonconvex unconstrained optimization problem. Generally, methods for solving the optimization problems in machine learning and in deep learning specifically are restricted to the class of first-order algorithms, like stochastic gradient descent (SGD). The major drawback of the SGD methods is that they have the undesirable effect of not escaping saddle-points. Furthermore, these methods require exhaustive trial-and-error to fine-tune many learning parameters. Using the second-order curvature information to find the search direction can help with more robust convergence for the non-convex optimization problem. However, computing the Hessian matrix for the large-scale problems is not computationally practical. Alternatively, quasi-Newton methods construct an approximate of Hessian matrix to build a quadratic model of the objective function. Quasi-Newton methods, like SGD, require only first-order gradient information, but they can result in superlinear convergence, which makes them attractive alternatives. The limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) approach is one of the most popular quasi-Newton methods that construct positive-definite Hessian approximations. Since the true Hessian matrix is not necessarily positive definite, an extra initialization condition is required to be introduced when constructing the L-BFGS matrices to avoid false negative curvature information. In this paper, we propose various choices for initialization methods of the L-BFGS matrices within a trust-region framework. We provide empirical results on the classification task of the MNIST digits dataset to compare the performance of the trust-region algorithm with different L-BFGS initialization methods.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"19 1","pages":"501-508"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78499674","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: 14

Frequent Chronicle Mining: Application on Predictive Maintenance 频繁时序挖掘:在预测性维护中的应用

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00226

Chayma Sellami, Ahmed Samet, Mohamed Anis Bach Tobji

引用次数: 9

An RNN-LSTM Based Flavor Recommender Framework in Hybrid Cloud 混合云中基于RNN-LSTM的风味推荐框架

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00047

E. G. Radhika, G. Sadhasivam

{"title":"An RNN-LSTM Based Flavor Recommender Framework in Hybrid Cloud","authors":"E. G. Radhika, G. Sadhasivam","doi":"10.1109/ICMLA.2018.00047","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00047","url":null,"abstract":"One of the key problem in hybrid cloud is to discover well matched cloud provider to scale up applications irrespective of their non-standardized naming technologies. No framework has been developed to monitor and recommend VM flavor for the period of autoscale in hybrid cloud based on utilization history. In the existing scenario, administrators manually consider heterogeneous sets of criteria and resource relationships to map cloud service providers for user's preferences in hybrid environment. Flavor selection remains constant irrespective of application's resource usage in hybrid cloud which results in under utilization. The proposed framework will fill the gap by monitoring applications and recommending flavor to adjust capacity of resources at low possible cost while maintaining stability and predictable performance. The framework a) Predicts future workload using deep learning technique Recurrent Neural Network (RNN) with Long Short Term Memory (LSTM) b) Recommend a flavor, aligned with Optimized Cost and Service Level Agreement (SLA) for autoscale group depending on CPU or RAM utilization in the current and history workloads. c) Operate recommended flavor on future workload and ensures zero application downtime in the current workload. The proposed flavor recommender framework has been implemented in hybrid cloud OpenStack and Amazon Web Services (AWS). The experimental results has shown significant cost difference of 17.65% per hour on autoscale group of instances with proposed flavor recommender framework over traditional flavor selection.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"77 1","pages":"270-277"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83183638","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: 2