Pub Date : 2019-06-27DOI: 10.1504/IJENM.2019.10022229
R. Rajesh, Chidambaranathan Bibin, P. Seeni Kannan, P. Devan
Biofuels, namely, biodiesel and ethanol produced from renewable energy sources are used as fuels in the blended form along with diesel to investigate the performance and emission characteristics of a DI diesel engine. Diestrol blend consists of diesel, biodiesel/methyl ester and ethanol. In diestrol blends, ethanol percentage is steadily elevated with an incremental factor of 5% culminating into three blends with a maximum percentage of 15% by volume and named as EB5, EB10 and EB15 respectively. A comprehensive analysis of engine performance characteristics such as brake thermal efficiency, brake specific fuel consumption, exhaust gas temperature and emission characteristics such as carbon monoxide, carbon dioxide, unburned hydrocarbon, oxides of nitrogen and smoke opacity were carried out. From the above investigation, it was found that brake thermal efficiency increased by 3%, 5% and oxides of nitrogen emission decreased by 23%, 24.5% when compared to diesel and B20 respectively.
{"title":"Performance and emission characteristics of a DI diesel engine using diestrol blends and diesel as fuel","authors":"R. Rajesh, Chidambaranathan Bibin, P. Seeni Kannan, P. Devan","doi":"10.1504/IJENM.2019.10022229","DOIUrl":"https://doi.org/10.1504/IJENM.2019.10022229","url":null,"abstract":"Biofuels, namely, biodiesel and ethanol produced from renewable energy sources are used as fuels in the blended form along with diesel to investigate the performance and emission characteristics of a DI diesel engine. Diestrol blend consists of diesel, biodiesel/methyl ester and ethanol. In diestrol blends, ethanol percentage is steadily elevated with an incremental factor of 5% culminating into three blends with a maximum percentage of 15% by volume and named as EB5, EB10 and EB15 respectively. A comprehensive analysis of engine performance characteristics such as brake thermal efficiency, brake specific fuel consumption, exhaust gas temperature and emission characteristics such as carbon monoxide, carbon dioxide, unburned hydrocarbon, oxides of nitrogen and smoke opacity were carried out. From the above investigation, it was found that brake thermal efficiency increased by 3%, 5% and oxides of nitrogen emission decreased by 23%, 24.5% when compared to diesel and B20 respectively.","PeriodicalId":39284,"journal":{"name":"International Journal of Enterprise Network Management","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44076565","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}
Pub Date : 2019-06-27DOI: 10.1504/IJENM.2019.10022247
A. Balakrishnan, J. Suresh
The importance of green has increased due to the environmental change. The burning of oil and other fossil fuels releases carbon dioxide, which rises, blankets the earth and traps heat. On environmental issues, there are intensive studies which have been dealt with extensively by practitioners and academicians. There is an increasing pressure on businesses to improve economic and environmental performance. Green supply chain management (GSCM) is an emerging approach for economic and ecological benefit to manufacturers. This paper presents the case study on how GSCM practiced in Ford India in the areas of logistics, packaging and manufacturing processes, how GSCM influence with firm performance and its gain by extending across firms in developing markets such as India.
{"title":"Do you gain by green supply chain management","authors":"A. Balakrishnan, J. Suresh","doi":"10.1504/IJENM.2019.10022247","DOIUrl":"https://doi.org/10.1504/IJENM.2019.10022247","url":null,"abstract":"The importance of green has increased due to the environmental change. The burning of oil and other fossil fuels releases carbon dioxide, which rises, blankets the earth and traps heat. On environmental issues, there are intensive studies which have been dealt with extensively by practitioners and academicians. There is an increasing pressure on businesses to improve economic and environmental performance. Green supply chain management (GSCM) is an emerging approach for economic and ecological benefit to manufacturers. This paper presents the case study on how GSCM practiced in Ford India in the areas of logistics, packaging and manufacturing processes, how GSCM influence with firm performance and its gain by extending across firms in developing markets such as India.","PeriodicalId":39284,"journal":{"name":"International Journal of Enterprise Network Management","volume":"10 1","pages":"118-132"},"PeriodicalIF":0.0,"publicationDate":"2019-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46642097","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}
Pub Date : 2019-03-05DOI: 10.1504/IJENM.2019.10019590
R. Kamalakannan, R. Pandian, P. Sivakumar
In this paper, the cell formation problem is considered with ratio level data with an objective of minimising the cell load variation. The attempt has been made to propose a simulated annealing (SA) based on the perturbation scheme as random insertion perturbation scheme (RIPS). The ratio level data is distinguished by utilising the workload information gathered from process times, production quantity of parts and also from the capacity of the machines. A modified grouping efficiency (MGE) is used to measure the performance of the system. From the results it is observed that the simulated annealing produces the solution does not differ significantly from the optimal solutions for the benchmark problems. The algorithms which we have chosen the benchmark problems are K-means, modified ART1 and genetic algorithm taken from the literature.
{"title":"A simulated annealing for the cell formation problem with ratio level data","authors":"R. Kamalakannan, R. Pandian, P. Sivakumar","doi":"10.1504/IJENM.2019.10019590","DOIUrl":"https://doi.org/10.1504/IJENM.2019.10019590","url":null,"abstract":"In this paper, the cell formation problem is considered with ratio level data with an objective of minimising the cell load variation. The attempt has been made to propose a simulated annealing (SA) based on the perturbation scheme as random insertion perturbation scheme (RIPS). The ratio level data is distinguished by utilising the workload information gathered from process times, production quantity of parts and also from the capacity of the machines. A modified grouping efficiency (MGE) is used to measure the performance of the system. From the results it is observed that the simulated annealing produces the solution does not differ significantly from the optimal solutions for the benchmark problems. The algorithms which we have chosen the benchmark problems are K-means, modified ART1 and genetic algorithm taken from the literature.","PeriodicalId":39284,"journal":{"name":"International Journal of Enterprise Network Management","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44492112","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}
Pub Date : 2019-03-05DOI: 10.1504/IJENM.2019.10019586
G. Ganapathy, N. Sivakumaran, M. Punniyamoorthy, R. Surendheran, Srijan Thokala
The number of new cases of female breast cancer was 124.9 per 100,000 women per year. Similarly, deaths were 21.2 per 100,000 women per year. It calls for an urge to increase the awareness of breast cancer and very accurately analyse the causes which may differ in minute variations. This is why the application of computation techniques are widely increasing to support the diagnostic results. In this paper, we present the application of several machine learning techniques and models like neural network, SVM is used to quantify the classifications. The techniques that are most reliable, accurate and robust are emphasised. It gives a plethora of explorations into the research field for developing predictive models. To achieve higher reliability on the data, we present the comparison of various Machine Learning techniques on a dataset that is available on the website Kaggle.
{"title":"Comparative study of machine learning techniques for breast cancer identification/diagnosis","authors":"G. Ganapathy, N. Sivakumaran, M. Punniyamoorthy, R. Surendheran, Srijan Thokala","doi":"10.1504/IJENM.2019.10019586","DOIUrl":"https://doi.org/10.1504/IJENM.2019.10019586","url":null,"abstract":"The number of new cases of female breast cancer was 124.9 per 100,000 women per year. Similarly, deaths were 21.2 per 100,000 women per year. It calls for an urge to increase the awareness of breast cancer and very accurately analyse the causes which may differ in minute variations. This is why the application of computation techniques are widely increasing to support the diagnostic results. In this paper, we present the application of several machine learning techniques and models like neural network, SVM is used to quantify the classifications. The techniques that are most reliable, accurate and robust are emphasised. It gives a plethora of explorations into the research field for developing predictive models. To achieve higher reliability on the data, we present the comparison of various Machine Learning techniques on a dataset that is available on the website Kaggle.","PeriodicalId":39284,"journal":{"name":"International Journal of Enterprise Network Management","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48419939","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}
Pub Date : 2019-03-05DOI: 10.1504/IJENM.2019.10019570
S. A. Kumar, R. Rajesh
Composite applications require presence of multiple holes for mechanical fasteners or cutouts in laminates. Unlike isotropic materials, composite materials experience change in stress values due to different parameters such as geometric, material and loading parameters. The present study is devoted to primarily determine whether geometric or material parameters have dominant influence on strength of composite laminates. Computational study using ABAQUS CAE software is employed for the analyses. Results reveal that geometric parameters have much significant influence on stress concentration factor and thereby the strength of composite laminates, when compared to material parameters. An elliptical cutout is seen to have comparatively more adverse effect on strength of laminate, when compared with other cutout shapes. Further, effect of load parameters - in-plane tension, compression and shear, is also studied. However, no significant effect was evidenced in stress concentration factor due to load parameters.
{"title":"Investigation on effect of geometric, material and load parameters on strength of composites with cutouts","authors":"S. A. Kumar, R. Rajesh","doi":"10.1504/IJENM.2019.10019570","DOIUrl":"https://doi.org/10.1504/IJENM.2019.10019570","url":null,"abstract":"Composite applications require presence of multiple holes for mechanical fasteners or cutouts in laminates. Unlike isotropic materials, composite materials experience change in stress values due to different parameters such as geometric, material and loading parameters. The present study is devoted to primarily determine whether geometric or material parameters have dominant influence on strength of composite laminates. Computational study using ABAQUS CAE software is employed for the analyses. Results reveal that geometric parameters have much significant influence on stress concentration factor and thereby the strength of composite laminates, when compared to material parameters. An elliptical cutout is seen to have comparatively more adverse effect on strength of laminate, when compared with other cutout shapes. Further, effect of load parameters - in-plane tension, compression and shear, is also studied. However, no significant effect was evidenced in stress concentration factor due to load parameters.","PeriodicalId":39284,"journal":{"name":"International Journal of Enterprise Network Management","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41880498","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}
Pub Date : 2019-03-05DOI: 10.1504/IJENM.2019.10019585
C. K. Faseela, H. Vennila
This paper highlight the use of latest whale optimisation meta heuristic algorithm for solving economic dispatch problem efficiently. This is used to solve the combined economic and emission dispatch problems for standard three generators system and 30 bus IEEE system. The whale optimisation algorithm was found to provide optimum results with easy convergence in comparison with other algorithms like PSO algorithm. Fuel cost and emission costs are combined to derive better result for economic dispatch. For checking the effectiveness of the algorithm, the results obtained using the same are compared with the results of particle swarm optimisation (PSO) and analysed the same against minimum generation cost and easy convergence. The results are found to be excellent for the systems considered.
{"title":"Combined economic and emission dispatch using whale optimisation algorithm","authors":"C. K. Faseela, H. Vennila","doi":"10.1504/IJENM.2019.10019585","DOIUrl":"https://doi.org/10.1504/IJENM.2019.10019585","url":null,"abstract":"This paper highlight the use of latest whale optimisation meta heuristic algorithm for solving economic dispatch problem efficiently. This is used to solve the combined economic and emission dispatch problems for standard three generators system and 30 bus IEEE system. The whale optimisation algorithm was found to provide optimum results with easy convergence in comparison with other algorithms like PSO algorithm. Fuel cost and emission costs are combined to derive better result for economic dispatch. For checking the effectiveness of the algorithm, the results obtained using the same are compared with the results of particle swarm optimisation (PSO) and analysed the same against minimum generation cost and easy convergence. The results are found to be excellent for the systems considered.","PeriodicalId":39284,"journal":{"name":"International Journal of Enterprise Network Management","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48575916","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}
Pub Date : 2019-03-05DOI: 10.1504/IJENM.2019.10019583
R. Gopakumar, R. Rajesh
In the present environment scenario, toxic wastes and their disposal is a major issue. Use of natural materials is the viable solution for this problem. This work aims to design and develop an elastomer composite using natural materials - natural rubber composite reinforced with sisal fibres. Since rubber components are essentials in industrial products, the developed material has lot significance. Six composites made with sisal fibres with various surface modifications and a 10%w/w coconut shell filler powder in natural rubber matrix. Sisal fibres used are raw fibre, alkalised fibres, rubber pre-impregnated raw fibres and rubber pre-impregnated alkalised fibre. The specimens tested for wear resistance, compressive strength and hardness. Maximum wear resistance exhibited by alkalised pre-impregnated sisal-rubber composite, followed by raw pre-impregnated-coconut shell powder-rubber composite. The hardness of raw sisal-rubber and raw pre-impregnated fibre composites improved by 228% than pure rubber (25 Shore A). Compressive strengths also showed improvements.
{"title":"Experimental study on the influence of fibre surface treatments and coconut shell powder addition on the compressive strength, hardness and tribological properties of sisal fibre-natural rubber composites","authors":"R. Gopakumar, R. Rajesh","doi":"10.1504/IJENM.2019.10019583","DOIUrl":"https://doi.org/10.1504/IJENM.2019.10019583","url":null,"abstract":"In the present environment scenario, toxic wastes and their disposal is a major issue. Use of natural materials is the viable solution for this problem. This work aims to design and develop an elastomer composite using natural materials - natural rubber composite reinforced with sisal fibres. Since rubber components are essentials in industrial products, the developed material has lot significance. Six composites made with sisal fibres with various surface modifications and a 10%w/w coconut shell filler powder in natural rubber matrix. Sisal fibres used are raw fibre, alkalised fibres, rubber pre-impregnated raw fibres and rubber pre-impregnated alkalised fibre. The specimens tested for wear resistance, compressive strength and hardness. Maximum wear resistance exhibited by alkalised pre-impregnated sisal-rubber composite, followed by raw pre-impregnated-coconut shell powder-rubber composite. The hardness of raw sisal-rubber and raw pre-impregnated fibre composites improved by 228% than pure rubber (25 Shore A). Compressive strengths also showed improvements.","PeriodicalId":39284,"journal":{"name":"International Journal of Enterprise Network Management","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45902845","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}
Pub Date : 2019-03-05DOI: 10.1504/IJENM.2019.10019587
N. Rajini, R. Bhavani
A brain tumour detection and classification system has been designed and developed. This work presents a new approach to the automated detection and classification of astrocytoma, medulloblastoma, glioma, glioblastoma multiforme and craniopharyngioma type of brain tumours based on k-means clustering and texture features, which separate brain tumour from healthy tissues in magnetic resonance images. The magnetic resonance feature image used for the tumour detection consists of T2-weighted magnetic resonance images for each axial slice through the head. The application of the proposed method for tracking tumour is demonstrated to help pathologists distinguish exactly tumour region and its type of tumour. The results are quantitatively evaluated by a human expert. The average overlap metric, average precision and the average recall between the results obtained using the proposed approach and ground truth are 0.92, 0.97 and 0.92, respectively. A classification with accuracy of 100%, 99% and 98% has been obtained by SVM, ANN and decision tree.
{"title":"Automatic detection and classification of brain tumours using k-means clustering with classifiers","authors":"N. Rajini, R. Bhavani","doi":"10.1504/IJENM.2019.10019587","DOIUrl":"https://doi.org/10.1504/IJENM.2019.10019587","url":null,"abstract":"A brain tumour detection and classification system has been designed and developed. This work presents a new approach to the automated detection and classification of astrocytoma, medulloblastoma, glioma, glioblastoma multiforme and craniopharyngioma type of brain tumours based on k-means clustering and texture features, which separate brain tumour from healthy tissues in magnetic resonance images. The magnetic resonance feature image used for the tumour detection consists of T2-weighted magnetic resonance images for each axial slice through the head. The application of the proposed method for tracking tumour is demonstrated to help pathologists distinguish exactly tumour region and its type of tumour. The results are quantitatively evaluated by a human expert. The average overlap metric, average precision and the average recall between the results obtained using the proposed approach and ground truth are 0.92, 0.97 and 0.92, respectively. A classification with accuracy of 100%, 99% and 98% has been obtained by SVM, ANN and decision tree.","PeriodicalId":39284,"journal":{"name":"International Journal of Enterprise Network Management","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42679973","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}
Pub Date : 2018-09-13DOI: 10.1504/IJENM.2018.10015842
D. Arivudainambi, D. Dhanya
Cloud computing services enable the users to use required computing resources for the required period which thereby permit the users to build a flexible information system. Choosing genuine and secured virtual machines (VM) among thousands of virtual machines is one of the main challenges in resource allocation to achieve higher accuracy. In this paper, VM optimisation techniques namely match making algorithm, memory aware VM and cuckoo technique is incorporated to select the best and secured virtual machine from cloud. In the proposed technique the first two are combined to select the best VM, where cuckoo is used to provide optimised resources to the finest VM. During the selection of finest VM from above techniques, it is necessary to select secured VM's to achieve an optimum result during resource allocation. To select secured VM, signature based detection and secure counters are employed to analyse network traffic and to monitor the data transactions.
{"title":"Three phase optimisation for qualified and secured VMs for resource allocation","authors":"D. Arivudainambi, D. Dhanya","doi":"10.1504/IJENM.2018.10015842","DOIUrl":"https://doi.org/10.1504/IJENM.2018.10015842","url":null,"abstract":"Cloud computing services enable the users to use required computing resources for the required period which thereby permit the users to build a flexible information system. Choosing genuine and secured virtual machines (VM) among thousands of virtual machines is one of the main challenges in resource allocation to achieve higher accuracy. In this paper, VM optimisation techniques namely match making algorithm, memory aware VM and cuckoo technique is incorporated to select the best and secured virtual machine from cloud. In the proposed technique the first two are combined to select the best VM, where cuckoo is used to provide optimised resources to the finest VM. During the selection of finest VM from above techniques, it is necessary to select secured VM's to achieve an optimum result during resource allocation. To select secured VM, signature based detection and secure counters are employed to analyse network traffic and to monitor the data transactions.","PeriodicalId":39284,"journal":{"name":"International Journal of Enterprise Network Management","volume":"9 1","pages":"273-293"},"PeriodicalIF":0.0,"publicationDate":"2018-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43224833","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}
Pub Date : 2018-09-13DOI: 10.1504/IJENM.2018.10015770
S. Lakshmi, B. Sathiyabhama, K. Batri
This article tries to analyse, and measure the uncertainty associated with the relevant document selection in web-search engines. The number of index terms, and their occurrence frequency influences the relevance calculation. The variation in term frequency either in processed web documents or in users' query influences the relevance calculation. This leads to an uncertainty associated with the document selection, and it is relevance calculation. In this article, we proposed a new measure called entropy. The entropy can be measured by varying the documents' term frequency or user's query term frequency. As the web documents can't be changed, we used variation in user's query term frequency to measure the uncertainty associated with the document selection in web-search engines. The query's term frequency is varied and given to the search engines. namely 'Google', and 'Bing' The high uncertainty gives scope for search engine optimisation. From the high uncertainty search engines, we can extract more relevant documents.
{"title":"Entropy a new measure to gauge search engine optimisation","authors":"S. Lakshmi, B. Sathiyabhama, K. Batri","doi":"10.1504/IJENM.2018.10015770","DOIUrl":"https://doi.org/10.1504/IJENM.2018.10015770","url":null,"abstract":"This article tries to analyse, and measure the uncertainty associated with the relevant document selection in web-search engines. The number of index terms, and their occurrence frequency influences the relevance calculation. The variation in term frequency either in processed web documents or in users' query influences the relevance calculation. This leads to an uncertainty associated with the document selection, and it is relevance calculation. In this article, we proposed a new measure called entropy. The entropy can be measured by varying the documents' term frequency or user's query term frequency. As the web documents can't be changed, we used variation in user's query term frequency to measure the uncertainty associated with the document selection in web-search engines. The query's term frequency is varied and given to the search engines. namely 'Google', and 'Bing' The high uncertainty gives scope for search engine optimisation. From the high uncertainty search engines, we can extract more relevant documents.","PeriodicalId":39284,"journal":{"name":"International Journal of Enterprise Network Management","volume":"9 1","pages":"189"},"PeriodicalIF":0.0,"publicationDate":"2018-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42573423","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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