Pub Date : 2020-03-06DOI: 10.1504/ijenm.2020.10027440
K. Geetha, A. Kannan
Nowadays, data analytics in spatial database objects are associated with keywords. In the past decade, searching the keyword was a major focusing and active area to the researchers within the database server and information retrieval community in various applications. In recent years, the maximising the availability and ranking the most frequent keyword items evaluation in the spatial database are used to make the decision better. This motivates to carry out research towards of closest keyword cover search, which is also known as fine tuned keyword cover search methodology; it considers both inter object distance and keyword ranking of items in the spatial environment. Baseline algorithm derived in this area has its own drawbacks. While searching the keyword increases, the query result performance can be minimised gradually by generating the candidate keyword cover. To resolve this problem a new scalable methodology can be proposed in this paper.
{"title":"Maximising the efficiency of keyword analytics framework in wireless mobile network management","authors":"K. Geetha, A. Kannan","doi":"10.1504/ijenm.2020.10027440","DOIUrl":"https://doi.org/10.1504/ijenm.2020.10027440","url":null,"abstract":"Nowadays, data analytics in spatial database objects are associated with keywords. In the past decade, searching the keyword was a major focusing and active area to the researchers within the database server and information retrieval community in various applications. In recent years, the maximising the availability and ranking the most frequent keyword items evaluation in the spatial database are used to make the decision better. This motivates to carry out research towards of closest keyword cover search, which is also known as fine tuned keyword cover search methodology; it considers both inter object distance and keyword ranking of items in the spatial environment. Baseline algorithm derived in this area has its own drawbacks. While searching the keyword increases, the query result performance can be minimised gradually by generating the candidate keyword cover. To resolve this problem a new scalable methodology can be proposed in this paper.","PeriodicalId":39284,"journal":{"name":"International Journal of Enterprise Network Management","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48967633","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 : 2020-03-06DOI: 10.1504/ijenm.2020.10027438
S. Lavanya, B. Parvathavarthini
Contrastive opinion summarisation (COS) systems produce summary by selecting and aligning contrastive sentences from a set of positive and negative opinionated sentences. Most of the existing COS methods do not consider the implicit opinion present in a sentence while producing summary. Implicit opinion can be identified based on context terms present in a sentence. Therefore, a new COS approach called context-sensitive contrastive opinion summarisation is proposed. Initially linguistic rules are framed based on dependency relation to extract context-feature-opinion phrases. To automatically cluster the extracted context-feature-opinion phrases into contrastive arguments, a clustering algorithm is proposed. Context sensitive weight is calculated for each phrase based on their probability of occurrence in the concepts of ConceptNet. Clustering algorithm integrates context sensitivity with contrastive similarity for producing better arguments summary. Experimental conducted on car and product review datasets demonstrate that the context-sensitive clusters achieved good coverage and precision when compared to state-of-art approaches.
{"title":"Context-sensitive contrastive feature-based opinion summarisation of online reviews","authors":"S. Lavanya, B. Parvathavarthini","doi":"10.1504/ijenm.2020.10027438","DOIUrl":"https://doi.org/10.1504/ijenm.2020.10027438","url":null,"abstract":"Contrastive opinion summarisation (COS) systems produce summary by selecting and aligning contrastive sentences from a set of positive and negative opinionated sentences. Most of the existing COS methods do not consider the implicit opinion present in a sentence while producing summary. Implicit opinion can be identified based on context terms present in a sentence. Therefore, a new COS approach called context-sensitive contrastive opinion summarisation is proposed. Initially linguistic rules are framed based on dependency relation to extract context-feature-opinion phrases. To automatically cluster the extracted context-feature-opinion phrases into contrastive arguments, a clustering algorithm is proposed. Context sensitive weight is calculated for each phrase based on their probability of occurrence in the concepts of ConceptNet. Clustering algorithm integrates context sensitivity with contrastive similarity for producing better arguments summary. Experimental conducted on car and product review datasets demonstrate that the context-sensitive clusters achieved good coverage and precision when compared to state-of-art approaches.","PeriodicalId":39284,"journal":{"name":"International Journal of Enterprise Network Management","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47781819","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}
Productivity measures how efficiently productions inputs, such as labour and capital, are being used in an economy to produce a given level of output. In this article, Maynard operation sequence technique was used for time measurement study and minimisation of fatigue among the operators by using ergonomics in a stamping unit. The primary objective of the study reported was to reduce the motion of all tasks in order to reduce the effort and time to achieve higher production and better service level by the ergonomic approach. Scoring sheets approach was used in conducting ergonomics study to decide the fitness of any unit on the basis safety and posture analysis of the operator. The proposed hybrid approach (MOST-Ergo) can be used to improve the productivity of any organisation by reducing the time and fatigue consumed by the operator during the operation.
{"title":"Labour productivity improvement using hybrid Maynard operation sequence technique and ergonomic assessment","authors":"Medha, Sharath Kumar Reddy, K. Vimal, Aravind Raj Sakthivel, Jayakrishna Kandasamy","doi":"10.1504/ijenm.2020.10027437","DOIUrl":"https://doi.org/10.1504/ijenm.2020.10027437","url":null,"abstract":"Productivity measures how efficiently productions inputs, such as labour and capital, are being used in an economy to produce a given level of output. In this article, Maynard operation sequence technique was used for time measurement study and minimisation of fatigue among the operators by using ergonomics in a stamping unit. The primary objective of the study reported was to reduce the motion of all tasks in order to reduce the effort and time to achieve higher production and better service level by the ergonomic approach. Scoring sheets approach was used in conducting ergonomics study to decide the fitness of any unit on the basis safety and posture analysis of the operator. The proposed hybrid approach (MOST-Ergo) can be used to improve the productivity of any organisation by reducing the time and fatigue consumed by the operator during the operation.","PeriodicalId":39284,"journal":{"name":"International Journal of Enterprise Network Management","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44879413","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 : 2020-03-06DOI: 10.1504/ijenm.2020.10027439
Sujana Adapa, Josie Fisher
This article explores conceptualisations of corporate social responsibility (CSR); perceptions of its importance; and practices implemented by owner-managers of small and medium sized enterprises (SMEs) in Australia. Qualitative in-depth interview data was obtained from 17 owner-managers of small and medium-sized accounting firms operating in Sydney. Inductive content analysis was conducted by the researchers to identify the concepts and themes of importance by using Leximancer qualitative text analytical software. The results revealed that the owner-managers of these firms were aware of the basics of social responsibility and recognised that the adoption of responsible business practices contributes to business success. The owner-managers perceptions of the practices of CSR varied based on the firm size that resulted in the emergence of an additional category of family-owned firms. Micro-sized firms emerged on the basis of distinct CSR practices and unique orientations towards the concept of CSR as highlighted by the owner-managers.
{"title":"Owner-managers' perceptions of corporate social responsibility practices within small and medium-sized accounting firms – an Australian study","authors":"Sujana Adapa, Josie Fisher","doi":"10.1504/ijenm.2020.10027439","DOIUrl":"https://doi.org/10.1504/ijenm.2020.10027439","url":null,"abstract":"This article explores conceptualisations of corporate social responsibility (CSR); perceptions of its importance; and practices implemented by owner-managers of small and medium sized enterprises (SMEs) in Australia. Qualitative in-depth interview data was obtained from 17 owner-managers of small and medium-sized accounting firms operating in Sydney. Inductive content analysis was conducted by the researchers to identify the concepts and themes of importance by using Leximancer qualitative text analytical software. The results revealed that the owner-managers of these firms were aware of the basics of social responsibility and recognised that the adoption of responsible business practices contributes to business success. The owner-managers perceptions of the practices of CSR varied based on the firm size that resulted in the emergence of an additional category of family-owned firms. Micro-sized firms emerged on the basis of distinct CSR practices and unique orientations towards the concept of CSR as highlighted by the owner-managers.","PeriodicalId":39284,"journal":{"name":"International Journal of Enterprise Network Management","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46131613","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 : 2019-11-19DOI: 10.1504/ijenm.2019.10025349
S. Radha, C. K. Babu
The emergence of the digital era has led to growth in various types of data in a cloud. In fact, there may be three fourth of the total data will be treated as big data. In many organisations, massive volume of both structured and unstructured data sit idle. Various categories of data are complex for pre-processing, analysing, storing and visualising. Cloud computing provides suitable platform for big data analytics for the storage and for predicting customer behaviour to sell products. Unstructured data like emails, notes, messages, documents, notifications and Twitter comments (including from IoT devices) remains untapped and is not stored in a relational database. Valuable information on pricing, customer behaviour and competitors may be inhumed within unstructured data. This makes cloud-based analytics as an effective research field to address several issues and risks need to be reduced. So we propose a method to extract and cluster sentiment information from various types of unstructured text data from social networks by using SVM classifiers combined with lexicons and machine learning for sentiment analysis of customer behaviour feedback. The method has performed efficient data collection, data loading and efficiently performs sentiment analysis on deep and hidden web.
{"title":"Enterprise big data analysis using SVM classifier and lexicon dictionary","authors":"S. Radha, C. K. Babu","doi":"10.1504/ijenm.2019.10025349","DOIUrl":"https://doi.org/10.1504/ijenm.2019.10025349","url":null,"abstract":"The emergence of the digital era has led to growth in various types of data in a cloud. In fact, there may be three fourth of the total data will be treated as big data. In many organisations, massive volume of both structured and unstructured data sit idle. Various categories of data are complex for pre-processing, analysing, storing and visualising. Cloud computing provides suitable platform for big data analytics for the storage and for predicting customer behaviour to sell products. Unstructured data like emails, notes, messages, documents, notifications and Twitter comments (including from IoT devices) remains untapped and is not stored in a relational database. Valuable information on pricing, customer behaviour and competitors may be inhumed within unstructured data. This makes cloud-based analytics as an effective research field to address several issues and risks need to be reduced. So we propose a method to extract and cluster sentiment information from various types of unstructured text data from social networks by using SVM classifiers combined with lexicons and machine learning for sentiment analysis of customer behaviour feedback. The method has performed efficient data collection, data loading and efficiently performs sentiment analysis on deep and hidden web.","PeriodicalId":39284,"journal":{"name":"International Journal of Enterprise Network Management","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41655743","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 : 2019-11-18DOI: 10.1504/ijenm.2019.10023700
B. Bhuvaneswari, T. Meeradevi
A cognitive radio (CR) technology enables all the users to utilise spectrum without interference. There will be a spectrum sensing for all the non-authorised users to perceive the other possibilities of getting a channel. The traffic feature will be unknown to be a priori to design the spectrum predictor with the back propagation (BP) neural network (NN) model and the multi-layer perceptron (MLP).This work proposed an optimised neural network to obtain improved results. The BP algorithm will not require prior knowledge of the real world problems that are trapped within the local minima. This is used widely to solve the problems and found in literature as an evolutionary algorithm like the bacterial foraging optimisation algorithm (BFOA) used for the MLP NN for enhancing the process of learning and improving the rate of convergence as well as accuracy of classification. Performing this spectrum predictor will be analysed using some extensive simulations.
{"title":"AN OPTIMIZED NEURAL NETWORK BASED SPECTRUM PREDICTION SCHEME FOR COGNITIVE RADIO","authors":"B. Bhuvaneswari, T. Meeradevi","doi":"10.1504/ijenm.2019.10023700","DOIUrl":"https://doi.org/10.1504/ijenm.2019.10023700","url":null,"abstract":"A cognitive radio (CR) technology enables all the users to utilise spectrum without interference. There will be a spectrum sensing for all the non-authorised users to perceive the other possibilities of getting a channel. The traffic feature will be unknown to be a priori to design the spectrum predictor with the back propagation (BP) neural network (NN) model and the multi-layer perceptron (MLP).This work proposed an optimised neural network to obtain improved results. The BP algorithm will not require prior knowledge of the real world problems that are trapped within the local minima. This is used widely to solve the problems and found in literature as an evolutionary algorithm like the bacterial foraging optimisation algorithm (BFOA) used for the MLP NN for enhancing the process of learning and improving the rate of convergence as well as accuracy of classification. Performing this spectrum predictor will be analysed using some extensive simulations.","PeriodicalId":39284,"journal":{"name":"International Journal of Enterprise Network Management","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49643717","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 : 2019-11-18DOI: 10.1504/ijenm.2019.10025350
S. Radhakrishnan, S. Neduncheliyan, K. Thyagharajan
The demand for increased data rate with improved QoS for real-time data traffic is ever increasing in the present day wireless environment. The scheduling schemes available in the literature incur lot of scheduling overhead at the eNodeB. Therefore, this work recommends an energy efficient, QoS-aware scheduler with reduced scheduling complexity at the eNodeB, for transmission of delay sensitive data. The scheduling problem is composed as a gain of weighted transmission rates of all possible combinations of various resources required by the channel for transmitting data. An improved greedy algorithm at the eNodeB, has been developed to allocate the resources dynamically to the user equipments (UEs) for the transmission of real-time data. The input video frames to the algorithm are compressed using discrete wavelet transform. The results of this research work show that the proposed scheduling algorithm greatly improves the coverage of the cell edge users. The performance of this greedy scheduler is compared with other two notable schedulers in the literature namely LOG rule and EXP-rule. This scheduling algorithm outperforms the other schemes in terms of QoS parameters for real-time data transmission.
{"title":"An improved downlink packet scheduling algorithm for delay sensitive devices in both H2H and M2M communications in LTE-advanced networks","authors":"S. Radhakrishnan, S. Neduncheliyan, K. Thyagharajan","doi":"10.1504/ijenm.2019.10025350","DOIUrl":"https://doi.org/10.1504/ijenm.2019.10025350","url":null,"abstract":"The demand for increased data rate with improved QoS for real-time data traffic is ever increasing in the present day wireless environment. The scheduling schemes available in the literature incur lot of scheduling overhead at the eNodeB. Therefore, this work recommends an energy efficient, QoS-aware scheduler with reduced scheduling complexity at the eNodeB, for transmission of delay sensitive data. The scheduling problem is composed as a gain of weighted transmission rates of all possible combinations of various resources required by the channel for transmitting data. An improved greedy algorithm at the eNodeB, has been developed to allocate the resources dynamically to the user equipments (UEs) for the transmission of real-time data. The input video frames to the algorithm are compressed using discrete wavelet transform. The results of this research work show that the proposed scheduling algorithm greatly improves the coverage of the cell edge users. The performance of this greedy scheduler is compared with other two notable schedulers in the literature namely LOG rule and EXP-rule. This scheduling algorithm outperforms the other schemes in terms of QoS parameters for real-time data transmission.","PeriodicalId":39284,"journal":{"name":"International Journal of Enterprise Network Management","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41595987","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 : 2019-10-18DOI: 10.1504/ijenm.2019.10024742
P. Prakash, R. Suresh, D. Pn
Conventional video surveillance systems require infrastructure including expensive servers with capability to process images and store video recordings. These surveillance systems produce and need to store a huge amount of data and to execute in real time to detect safety events. The problems of the anti-social activities which gradually increasing across the country especially in the urban areas in recent times which lead to the need for technological innovations in the security and surveillance system. The proposed system is based on cloud computing. In this paper the application has been modelled and simulated using iFogSim. The results predicts that the fog-based model is more secured and efficient compared to cloud computing parameter energy consumption. The proposed system helps to increase the effectiveness of the intelligent agencies and thereby increase crime safety at public places.
{"title":"Smart city video surveillance using fog computing","authors":"P. Prakash, R. Suresh, D. Pn","doi":"10.1504/ijenm.2019.10024742","DOIUrl":"https://doi.org/10.1504/ijenm.2019.10024742","url":null,"abstract":"Conventional video surveillance systems require infrastructure including expensive servers with capability to process images and store video recordings. These surveillance systems produce and need to store a huge amount of data and to execute in real time to detect safety events. The problems of the anti-social activities which gradually increasing across the country especially in the urban areas in recent times which lead to the need for technological innovations in the security and surveillance system. The proposed system is based on cloud computing. In this paper the application has been modelled and simulated using iFogSim. The results predicts that the fog-based model is more secured and efficient compared to cloud computing parameter energy consumption. The proposed system helps to increase the effectiveness of the intelligent agencies and thereby increase crime safety at public places.","PeriodicalId":39284,"journal":{"name":"International Journal of Enterprise Network Management","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46052534","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 : 2019-10-18DOI: 10.1504/ijenm.2019.10024734
T. Vairam, S. Sarathambekai
Most of the metropolitan cities facing the problem of collecting garbage on time. Due to the inadequacy of garbage collection, the trash bin gets overflow and causes various risk such as spreading diseases, unpleasant aroma, ugliness, etc. To evade all these circumstances, this paper addresses the efficient collection of garbage's by implementing IoT based smart trash can system. This system monitor the overall status of all trash cans around the city. Whenever the trash level of bin reaches the threshold level it sends the alert message to the truck driver. It also helps the garbage truck driver by providing him with the shortest path to attend all trash cans in city. IoT based smart trashcan is implemented using Raspberry Pi board with HC SR04 ultrasonic sensor for measuring trash level. Amazon Web Services (AWS) helps in storage of data and sending notifications to the concerned people who are involved in the process of collecting garbage. Managing data traffic in IoT network is difficult task, we also addressed this issue by designing the software defined networking (SDN) for smart trash can system. SDN will further help to improve the performance of our system.
{"title":"Proficient smart trash can management using internet of things and SDN architecture approach","authors":"T. Vairam, S. Sarathambekai","doi":"10.1504/ijenm.2019.10024734","DOIUrl":"https://doi.org/10.1504/ijenm.2019.10024734","url":null,"abstract":"Most of the metropolitan cities facing the problem of collecting garbage on time. Due to the inadequacy of garbage collection, the trash bin gets overflow and causes various risk such as spreading diseases, unpleasant aroma, ugliness, etc. To evade all these circumstances, this paper addresses the efficient collection of garbage's by implementing IoT based smart trash can system. This system monitor the overall status of all trash cans around the city. Whenever the trash level of bin reaches the threshold level it sends the alert message to the truck driver. It also helps the garbage truck driver by providing him with the shortest path to attend all trash cans in city. IoT based smart trashcan is implemented using Raspberry Pi board with HC SR04 ultrasonic sensor for measuring trash level. Amazon Web Services (AWS) helps in storage of data and sending notifications to the concerned people who are involved in the process of collecting garbage. Managing data traffic in IoT network is difficult task, we also addressed this issue by designing the software defined networking (SDN) for smart trash can system. SDN will further help to improve the performance of our system.","PeriodicalId":39284,"journal":{"name":"International Journal of Enterprise Network Management","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42463916","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 : 2019-10-17DOI: 10.1504/ijenm.2019.10024720
P. S. Devi, R. Viswanathan
Discovering solution for partial differential equations (PDEs) is considered to be difficult task. Exact solution is said to be identified only in certain specified cases. In this paper, convergence of partial differential equation using fuzzy linear parabolic (PDE-FLP) method on a finite domain is designed. The method is based on PDE where coefficients are obtained as fuzzy numbers and solved by linear parabolic derivatives. Firstly, PDE form and fuzzy representation of two independent variables are derived. Secondly, fuzzy linear parabolic (FLP) derivative is provided for numerical convergence. FLP derivatives are employed to describe time dependent aspects. Parabolic derivatives are also due to similar coefficient condition for the analytic solution. Finally, numerical results are given, which demonstrates the effectiveness and convergence of PDE-FLP method. A detailed comparison between approximate solutions obtained is discussed. Also, figurative representation to compare between approximate solutions is also presented.
{"title":"Convergence of partial differential equation using fuzzy linear parabolic derivatives","authors":"P. S. Devi, R. Viswanathan","doi":"10.1504/ijenm.2019.10024720","DOIUrl":"https://doi.org/10.1504/ijenm.2019.10024720","url":null,"abstract":"Discovering solution for partial differential equations (PDEs) is considered to be difficult task. Exact solution is said to be identified only in certain specified cases. In this paper, convergence of partial differential equation using fuzzy linear parabolic (PDE-FLP) method on a finite domain is designed. The method is based on PDE where coefficients are obtained as fuzzy numbers and solved by linear parabolic derivatives. Firstly, PDE form and fuzzy representation of two independent variables are derived. Secondly, fuzzy linear parabolic (FLP) derivative is provided for numerical convergence. FLP derivatives are employed to describe time dependent aspects. Parabolic derivatives are also due to similar coefficient condition for the analytic solution. Finally, numerical results are given, which demonstrates the effectiveness and convergence of PDE-FLP method. A detailed comparison between approximate solutions obtained is discussed. Also, figurative representation to compare between approximate solutions is also presented.","PeriodicalId":39284,"journal":{"name":"International Journal of Enterprise Network Management","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41501518","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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