2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)最新文献

An ensemble based missing value estimation in DNA microarray using artificial neural network 基于集成的人工神经网络DNA芯片缺失值估计

2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) Pub Date : 2016-09-01 DOI: 10.1109/ICRCICN.2016.7813671

Sujay Saha, Saikat Bandopadhyay, A. Ghosh, K. Dey

{"title":"An ensemble based missing value estimation in DNA microarray using artificial neural network","authors":"Sujay Saha, Saikat Bandopadhyay, A. Ghosh, K. Dey","doi":"10.1109/ICRCICN.2016.7813671","DOIUrl":"https://doi.org/10.1109/ICRCICN.2016.7813671","url":null,"abstract":"DNA microarrays are normally used to measure the expression values of thousands of several genes simultaneously in the form of large matrices. This raw gene expression data may contain some missing cells. These missing values may affect the analysis performed subsequently on these gene expression data. Several imputation methods, like K-Nearest Neighbor Imputation (KNNImpute), Singular Value Decomposition Imputation (SVDImpute), Local Least Square Imputation (LLSImpute), Bayesian Principal Component Analysis (BPCAImpute) etc. have already been proposed to impute those missing values. In this work we have proposed an ensemble classifier based Artificial Neural Network implementation, ANNImpute, to enhance the accuracy of the missing value imputation technique by applying Two Layer Perceptron Learning algorithm. Ensemble classification is done on the parameters such as learning rate a, weight vector & bias. We have applied our algorithm on two benchmark datasets like SPELLMAN and Tumour (GDS2932) and the results show that this approach performs well compared to the other existing methods as far as RMSE measures are concerned.","PeriodicalId":254393,"journal":{"name":"2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115391805","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: 3

An exploratory analysis for predicting passenger satisfaction at global hub airports using logistic model trees 利用logistic模型树预测全球枢纽机场旅客满意度的探索性分析

2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) Pub Date : 2016-09-01 DOI: 10.1109/ICRCICN.2016.7813672

Hari Bhaskar Sankaranarayanan, B. V. Vishwanath, V. Rathod

{"title":"An exploratory analysis for predicting passenger satisfaction at global hub airports using logistic model trees","authors":"Hari Bhaskar Sankaranarayanan, B. V. Vishwanath, V. Rathod","doi":"10.1109/ICRCICN.2016.7813672","DOIUrl":"https://doi.org/10.1109/ICRCICN.2016.7813672","url":null,"abstract":"On-time performance during travel is a key factor in determining passenger convenience and satisfaction. On time flight arrivals and departures are dependent on various factors including airport characteristics like the number of flights handled, ground handler's efficiency, disruptions caused by weather conditions, security alerts, queue times at immigration and air traffic congestion. There is a direct causation based relationship on delays, cancellations, re-schedule at the airport with passenger complaints and churn. Airports and airlines can infer signals of passenger satisfaction with the relevant events associated with on-time performance and delays. Hub airports deal with high passenger traffic, connections and operational complexities hence they are good candidates for passenger service studies. In this paper, we had collected datasets for on-time performance, flights for 48 global hub airports and passenger reviews about queue time. Further, we applied Logistic Model Trees (LMT) machine learning method for predicting the level of passenger satisfaction based on factors like an airport on time performance, the number of flights, on-time ranking, average delays and queue time. The results are presented and discussed for further insights and studies.","PeriodicalId":254393,"journal":{"name":"2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127018007","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: 8

Performance estimation of N-channel ZnO based thin film transistor using simulation 基于仿真的n沟道ZnO薄膜晶体管性能估计

2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) Pub Date : 2016-09-01 DOI: 10.1109/ICRCICN.2016.7813667

S. K. Dargar, J. K. Srivastava, Santosh Bharti, Abha Nyati

引用次数: 2

Classification of wearable computing: A survey of electronic assistive technology and future design 可穿戴计算的分类:电子辅助技术与未来设计综述

2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) Pub Date : 2016-09-01 DOI: 10.1109/ICRCICN.2016.7813545

A. Chatterjee, A. Aceves, R. Dungca, Hugo Flores, K. Giddens

引用次数: 26

DWT based sonoelastography prostate cancer image classification using back propagation neural network 基于DWT的超声弹性成像前列腺癌图像分类

2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) Pub Date : 2016-09-01 DOI: 10.1109/ICRCICN.2016.7813633

Koushik Layek, Susobhan Das, Sourav Samanta

{"title":"DWT based sonoelastography prostate cancer image classification using back propagation neural network","authors":"Koushik Layek, Susobhan Das, Sourav Samanta","doi":"10.1109/ICRCICN.2016.7813633","DOIUrl":"https://doi.org/10.1109/ICRCICN.2016.7813633","url":null,"abstract":"In modern days, Cancer is spreading rapidly which requires a significant attention along with its proper detection and identification, which is even more crucial. Attempt should be made to detect it an early stage so that it may be controlled and sometimes cured. But this requires proper diagnosing methods so that the demerits and pains of being diagnosed are minimized among patients. With respect to these recent day diagnosis that are leaning towards the non-invasiveness associating the Computer-aided technologies, bringing many benefits to this specific area of Medical field, we have tried to propose a methodology which will remove the manual interpretation of detecting the affected regions from an image generated from a modern and new diagnosing modality named Sonoelastography(SE). SE images marks regions in the form of color coded patches depending on the Elasticity scores in a particular Region of Interest (RoI). Here we have tried to analyze such images and classify an image that whether it is malignant or not. We have taken color SE images as our principle input to the proposed system. Discrete Wavelet Transform (DWT) is used to identify the most relevant sections of the image after the preprocessing step(s), where generation of two separate images containing the affected regions and the unaffected regions are carried out. Multilevel thresholding has been used to generate the affected and unaffected images from the original image. The images after thresholding are channeled to red and blue components and are applied to DWT from where the Low Low (LL) component is subjected to obtain features which are used finally to classify the images using Back Propagation Neural Network.","PeriodicalId":254393,"journal":{"name":"2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116003149","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: 4

Application of wireless technology for a vision based rehabilitation system 无线技术在视觉康复系统中的应用

2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) Pub Date : 2016-09-01 DOI: 10.1109/ICRCICN.2016.7813640

Arpita Sarkar, G. Sanyal, S. Majumder

{"title":"Application of wireless technology for a vision based rehabilitation system","authors":"Arpita Sarkar, G. Sanyal, S. Majumder","doi":"10.1109/ICRCICN.2016.7813640","DOIUrl":"https://doi.org/10.1109/ICRCICN.2016.7813640","url":null,"abstract":"Though several assistive technologies are available for home-based rehabilitation of stroke patients, people are not using the same for several reasons. Lack of technical competence, chance of electrocution is some of such issues. The cables/ wires of different instruments, measuring devises, rehabilitation systems are often the source of infection; causes of electrocution, discomfort and immobility. This has led to implement wireless technology/ system for medical applications. Accordingly researches are going on to use wireless technologies for different purposes of rehabilitation. The present work uses a wireless night vision camera for gaze tracking of the patient for 24×7 support eliminating the use of cables/ wires and thus reducing the chances of infections and other hazardous. The camera along with a power supply (battery) serves as a standalone unit for ready deployment. The control computer can be set up at a distance even out of the cabin and helps the physiotherapists and healthcare staffs to monitor and assist several patients remotely at a time. The experiments have been carried out in both day and night with users wearing spectacles or no spectacles. Promising results have been obtained in spite of several issues of data loss, IR glaring etc. Several applications of wireless technologies have been attempted, but use of such approach has never been tried earlier. This system has huge potential for application for day and night support and rehabilitation of stroke patients.","PeriodicalId":254393,"journal":{"name":"2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"319 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124515510","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

A comparative study of gene selection methods for cancer classification using microarray data 利用微阵列数据进行癌症分类的基因选择方法比较研究

2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) Pub Date : 2016-09-01 DOI: 10.1109/ICRCICN.2016.7813657

M. Babu, K. Sarkar

引用次数: 11

Decision template fusion for classifying Indian edible oils using singular value decomposition on NIR spectrometry data 基于近红外光谱数据奇异值分解的印度食用油分类决策模板融合

2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) Pub Date : 2016-09-01 DOI: 10.1109/ICRCICN.2016.7813661

Shiladitya Saha, S. Saha

引用次数: 1

Machine learning for predictive modeling in management of operations of EDM equipment product 机器学习在电火花加工设备产品运行管理中的预测建模

2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) Pub Date : 2016-09-01 DOI: 10.1109/ICRCICN.2016.7813651

I. Ghosh, M. Sanyal, R. K. Jana, P. Dan

{"title":"Machine learning for predictive modeling in management of operations of EDM equipment product","authors":"I. Ghosh, M. Sanyal, R. K. Jana, P. Dan","doi":"10.1109/ICRCICN.2016.7813651","DOIUrl":"https://doi.org/10.1109/ICRCICN.2016.7813651","url":null,"abstract":"To sustain and excel in competitive global market, organizations often bank on high productivity and world class quality. Endeavor of this research is to comprehend and model the manufacturing process of Electrical Discharge Machining (EDM) equipment product in order to increase productivity. Outcome of EDM operation is strongly influenced by various process parameters. The paper presents a framework based on machine learning algorithms to analyze the relationship between input process parameters and EDM response to build a predictive model of EDM operations. Physical experimentations have conducted considering Discharge Current, Pulse Duration, Duty Cycle and Discharge Voltage as independent variables while Material Removal Rate has been used as target variable. Four different machine learning algorithms namely Random Forest, Support Vector Regression, Elastic Net and Bagging have been adopted as applied predictive modeling tools. Results justify the usage of machine learning methods to deal with the research problem. Statistical analysis has been conducted as well for comparative performance analysis. Further correlation based supervised feature selection methodology has been applied to identify the key predictors.","PeriodicalId":254393,"journal":{"name":"2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127668331","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: 5

Improving energy efficiency of computing servers and communication fabric in cloud data centers 提高云数据中心计算服务器和通信结构的能源效率

2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) Pub Date : 2016-09-01 DOI: 10.1109/ICRCICN.2016.7813544

S. Prathibha, B. Latha, G. Sumathi

{"title":"Improving energy efficiency of computing servers and communication fabric in cloud data centers","authors":"S. Prathibha, B. Latha, G. Sumathi","doi":"10.1109/ICRCICN.2016.7813544","DOIUrl":"https://doi.org/10.1109/ICRCICN.2016.7813544","url":null,"abstract":"Cloud Computing has started to dominate the computing environment in recent days. Despite its various advantages, it has a threat of high energy consumption from both computing servers and communication fabric. Most of the existing work for reducing Data Center energy consumption is focused at computing servers only. The goal of the proposed work is to minimize the energy consumption at both computing servers and communication devices. Enhanced weighted Dynamic Voltage Frequency Scheduling Algorithm(DVFS) for assigning tasks to virtual machine is implemented for minimizing energy consumption of the computing servers. Also availability of renewable energy powered Data Centers is checked for scheduling jobs. Networking devices such as switches, routers which are part of the communication fabric also contribute to the major energy consumption in the cloud Data Centers. In this work for reducing energy consumption from networking components of the Data Center is addressed by extending Energy-Efficient Network Aware Scheduling (DENS) with stochastic hybrid load balancer. The proposed system is evaluated for set of independent tasks and also on scientific workflow applications which contain set of interdependent tasks.","PeriodicalId":254393,"journal":{"name":"2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132345492","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: 4