2019 International Conference on Applied Machine Learning (ICAML)最新文献_第2页

Optimal Distributed Energy Resources Mix for Distribution Network Planning with a Multiobjective Criterion 基于多目标准则的配电网最优分布式能源组合

2019 International Conference on Applied Machine Learning (ICAML) Pub Date : 2019-05-01 DOI: 10.1109/ICAML48257.2019.00056

Amaresh Gantayet, D. K. Dheer

引用次数: 2

Healthy Environment Using Cloud IoT Core 使用云物联网核心的健康环境

2019 International Conference on Applied Machine Learning (ICAML) Pub Date : 2019-05-01 DOI: 10.1109/ICAML48257.2019.00055

P. Das, B. Acharya

{"title":"Healthy Environment Using Cloud IoT Core","authors":"P. Das, B. Acharya","doi":"10.1109/ICAML48257.2019.00055","DOIUrl":"https://doi.org/10.1109/ICAML48257.2019.00055","url":null,"abstract":"Environment plays an important role for human being to lead a smooth and healthy life. There exist so many factors which affect the healthy environment accross our society. One of the reason for unhealthy environment is the incapability of collecting the garbage in proper time. Citizens use to place the garbage in and around the free space and also on the public road. Due to rapidly growing population, the volume of garbage also increases in the same manner. So the huge unmanaged garbage pollution spoils the environment. The unhealthy environment of smart city leads to health hazards also. To resolve this problem cloud IOT (Internet of Things) core with sensor devices (like: Raspberry pi) can be used effectively to manage the garbage and its collection in time. It detects the garbage on real time basis while being thrown around the free spaces and also on the public road by the citizens. It also alerts the citizens by playing the pre recorded audio message to put the garbage in the dustbin according to the categories of garbage. In addition to that it also detects the overloaded condition of dustbin and informs accordingly through message passing to the concerned staff for collecting the garbage.","PeriodicalId":369667,"journal":{"name":"2019 International Conference on Applied Machine Learning (ICAML)","volume":"42 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131549430","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

ICAML 2019 Organizing Committee ICAML 2019组委会

2019 International Conference on Applied Machine Learning (ICAML) Pub Date : 2019-05-01 DOI: 10.1109/icaml48257.2019.00008

引用次数: 0

Word Sense Disambiguation in Bengali Using Sense Induction 用语义归纳法消除孟加拉语词义歧义

2019 International Conference on Applied Machine Learning (ICAML) Pub Date : 2019-05-01 DOI: 10.1109/ICAML48257.2019.00040

Anindya Sau, Tarik Aziz Amin, Nabagata Barman, A. R. Pal

引用次数: 3

Semi-Automated Ontology Building Using Deep Learning to Provide Domain-Specific Knowledge Search in the Marathi Language 使用深度学习的半自动本体构建提供马拉地语领域特定知识搜索

2019 International Conference on Applied Machine Learning (ICAML) Pub Date : 2019-05-01 DOI: 10.1109/ICAML48257.2019.00029

N. Chandolikar, S. Shilaskar, Dipali Peddawad, Shivjeet Bhosale

引用次数: 0

Network Intrusion Detection and Monitoring in Cloud Based Systems 基于云系统的网络入侵检测与监控

2019 International Conference on Applied Machine Learning (ICAML) Pub Date : 2019-05-01 DOI: 10.1109/ICAML48257.2019.00045

M. Nanda, M. Patra

引用次数: 1

Blood Vessel Detection Using Modified Multiscale MF-FDOG Filters for Diabetic Retinopathy 应用改进的多尺度MF-FDOG滤波器检测糖尿病视网膜病变血管

2019 International Conference on Applied Machine Learning (ICAML) Pub Date : 2019-05-01 DOI: 10.1109/ICAML48257.2019.00024

Debojyoti Mallick, Kundan Kumar, Sumanshu Agarwal

引用次数: 2

Arrhythmia Classification Using Deep Neural Network 基于深度神经网络的心律失常分类

2019 International Conference on Applied Machine Learning (ICAML) Pub Date : 2019-05-01 DOI: 10.1109/ICAML48257.2019.00062

Saumendra Kumar Mohapatra, Geetika Srivastava, M. Mohanty

引用次数: 3

Automatic Power Quality Disturbances Detection and Recognition Using Empirical Wavelet Transform and Random Forest Method 基于经验小波变换和随机森林方法的电能质量干扰自动检测与识别

2019 International Conference on Applied Machine Learning (ICAML) Pub Date : 2019-05-01 DOI: 10.1109/ICAML48257.2019.00051

M. Sahani

引用次数: 2

A Bi-Level Approach for Hyper-Parameter Tuning of an Evolutionary Extreme Learning Machine 一种进化极限学习机超参数整定的双层方法

2019 International Conference on Applied Machine Learning (ICAML) Pub Date : 2019-05-01 DOI: 10.1109/ICAML48257.2019.00032

Krishanu Maity, Satyabrata Maity, Nimisha Ghosh

{"title":"A Bi-Level Approach for Hyper-Parameter Tuning of an Evolutionary Extreme Learning Machine","authors":"Krishanu Maity, Satyabrata Maity, Nimisha Ghosh","doi":"10.1109/ICAML48257.2019.00032","DOIUrl":"https://doi.org/10.1109/ICAML48257.2019.00032","url":null,"abstract":"One of the critical challenges in the implementation of machine learning algorithm is hyperparameter optimization as performance of any machine learning model is sensitive to the setting of their hyperparametersr. Evolutionary Algorithms (EA) is widely used for hyperparameter optimization due to its efficient intellectual tuning strategies. The time complexity is appreciably changed with respect to the size of dataset used for training. On the other hand, large dataset is required for pursuing the better prediction. In this paper, we have proposed a methodology namely Bi-Level Evolutionary Extreme Learning Machine(bL-EELM) based on bi-level programming approach for tuning hyperparameter of an Evolutionary Extreme Learning Machine(EELM). we divided our problem into two levels. We consider an E-ELM module as a lower level optimization problem. In our upper level we placed a evolutionary module whose task is to create a population of hyperparameters and feed to lower Level as an input of EELM. We have chosen ten benchmark classification problems for the experiment and analysis of our proposed approach. Experimental results proofs that our proposed approach has better prediction accuracy as well as generalization performances compare to Extreme learning machine(ELM) and EELM.","PeriodicalId":369667,"journal":{"name":"2019 International Conference on Applied Machine Learning (ICAML)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122570979","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