Pub Date : 2019-11-01DOI: 10.1109/SMART46866.2019.9117484
Gona Ashwini Rao, R. Nagaswetha, D. Singh
We propose an innovative Voice Based Virtual Agri Farming Analyzer (v2) which creates a virtual environment where the climate will be monitored and controlled by the sensors connected to a microcontroller. These sensors accept the real time data of temperature, humidity, soil moisture and soil temperature from the agriculture fields. On comparing values with the standard one, necessary actions will be taken by the actuators like fans, cool mist humidifier, water motors. In the background, the data will be continuously visualized using cloud platform and will be displayed on the screen, the data will be applied with prediction algorithms in a web tool. Final predicted value will be generated in the format of a tree model with a range of optimum values. These optimum values can be used inside the system to maintain the effective growth. An inlet for fertilizers is present at the top of the system. This inlet drops the fertilizer along with the water whenever required for that specific plants. A touch-display will be available for the user to obtain any of the internal conditions. A voice enabled device is attached which provides information about the internal processes taking place. A push notification will be sent to the farmers through the cloud service whenever any action is being taken internally.
{"title":"Voice Based Virtual Agri Farming Analyzer with BigML Algorithms","authors":"Gona Ashwini Rao, R. Nagaswetha, D. Singh","doi":"10.1109/SMART46866.2019.9117484","DOIUrl":"https://doi.org/10.1109/SMART46866.2019.9117484","url":null,"abstract":"We propose an innovative Voice Based Virtual Agri Farming Analyzer (v2) which creates a virtual environment where the climate will be monitored and controlled by the sensors connected to a microcontroller. These sensors accept the real time data of temperature, humidity, soil moisture and soil temperature from the agriculture fields. On comparing values with the standard one, necessary actions will be taken by the actuators like fans, cool mist humidifier, water motors. In the background, the data will be continuously visualized using cloud platform and will be displayed on the screen, the data will be applied with prediction algorithms in a web tool. Final predicted value will be generated in the format of a tree model with a range of optimum values. These optimum values can be used inside the system to maintain the effective growth. An inlet for fertilizers is present at the top of the system. This inlet drops the fertilizer along with the water whenever required for that specific plants. A touch-display will be available for the user to obtain any of the internal conditions. A voice enabled device is attached which provides information about the internal processes taking place. A push notification will be sent to the farmers through the cloud service whenever any action is being taken internally.","PeriodicalId":328124,"journal":{"name":"2019 8th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123879139","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 : 2019-11-01DOI: 10.1109/SMART46866.2019.9117286
A.N.M. Jubaer, A. Sayem, Md. Ashikur Rahman
Toxic comment classification problem is a popular classification problem nowadays. There are many attempts in English but it's rare in Bangla language. We tried to build a classifier for Bangla language. We tried different approach to find the optimized classifier with better accuracy and optimized for log-loss, hamming-loss. As this is a multilevel problem, we used binary relevance methods for binary classifiers.
{"title":"Bangla Toxic Comment Classification (Machine Learning and Deep Learning Approach)","authors":"A.N.M. Jubaer, A. Sayem, Md. Ashikur Rahman","doi":"10.1109/SMART46866.2019.9117286","DOIUrl":"https://doi.org/10.1109/SMART46866.2019.9117286","url":null,"abstract":"Toxic comment classification problem is a popular classification problem nowadays. There are many attempts in English but it's rare in Bangla language. We tried to build a classifier for Bangla language. We tried different approach to find the optimized classifier with better accuracy and optimized for log-loss, hamming-loss. As this is a multilevel problem, we used binary relevance methods for binary classifiers.","PeriodicalId":328124,"journal":{"name":"2019 8th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116170443","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 : 2019-11-01DOI: 10.1109/SMART46866.2019.9117458
M. Jagtap, R. Tripathi
Gaining popularity in digital world imposes the challenge to preserve the semantic significances in the original image while display on any arbitrary device irrespective of its size or aspect ratio is the well-known ‘Image Retargeting’. When the image is to be tailored by focusing on its objectives tenacity, then the insignificant portions of the images are identified and wipe out. The historical method envisages the due respect to the pixels by considering its bottom to top style. On the contrary, the projected method builds by using top-down tactic. This appraisal is fused by ‘Classification guided Fusion Network (CFN). The feature is widely applied on 3D images which fuses left as well as right eye images which are having different viewpoints and differently designed. The disparity map acquisition algorithm fuses the images with semantic collage of the images.
{"title":"A Novel 3-D Image Retargeting by using Stereo Seam Carving with Disparity Map Acquisition (DMA) Algorithm","authors":"M. Jagtap, R. Tripathi","doi":"10.1109/SMART46866.2019.9117458","DOIUrl":"https://doi.org/10.1109/SMART46866.2019.9117458","url":null,"abstract":"Gaining popularity in digital world imposes the challenge to preserve the semantic significances in the original image while display on any arbitrary device irrespective of its size or aspect ratio is the well-known ‘Image Retargeting’. When the image is to be tailored by focusing on its objectives tenacity, then the insignificant portions of the images are identified and wipe out. The historical method envisages the due respect to the pixels by considering its bottom to top style. On the contrary, the projected method builds by using top-down tactic. This appraisal is fused by ‘Classification guided Fusion Network (CFN). The feature is widely applied on 3D images which fuses left as well as right eye images which are having different viewpoints and differently designed. The disparity map acquisition algorithm fuses the images with semantic collage of the images.","PeriodicalId":328124,"journal":{"name":"2019 8th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116928690","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 : 2019-11-01DOI: 10.1109/SMART46866.2019.9117280
S. Saxena, Mohammad Zubair Khan, Ravendra Singh
In the current scenario the demand for high performance computing system increases day by day to achieve maximum computation in minimum time. Rapid growth of Internet or Internet based services, increased the interest in network based computing or on-demand computing systems like cloud computing system. High computing servers are being deployed in large quantity for cloud computing in form of data Centers through which many different services on internet are provide to the cloud users in a very smooth and efficient manner. A large distributed system is described as a data center that includes a huge quantity of computing servers connected by an efficient network. So the consumption of energy in such data centers is enormously very high. Not only the maintenance of the data centers are too exorbitant, but also socially very harmful. High vitality costs and immense carbon footprints are brought in these data centers because the servers needed a substantial amount of electricity for their computation as well as for their cooling. As cost of energy increases and availability decreases, focus should be shifted towards the optimization of data centre servers for best performance alone with the policies of less energy consumption to justify the level of service performance with social impact. So in this paper we proposed energy aware consolidation technique for cloud data centers based on prediction of future client's requests to increase the utilization of computing servers as per request of users/clients which associated some demand of cloud resources for maintain the power consumption in cloud.
{"title":"Energy Saving Heuristics for Optimization of Cloud Data Center","authors":"S. Saxena, Mohammad Zubair Khan, Ravendra Singh","doi":"10.1109/SMART46866.2019.9117280","DOIUrl":"https://doi.org/10.1109/SMART46866.2019.9117280","url":null,"abstract":"In the current scenario the demand for high performance computing system increases day by day to achieve maximum computation in minimum time. Rapid growth of Internet or Internet based services, increased the interest in network based computing or on-demand computing systems like cloud computing system. High computing servers are being deployed in large quantity for cloud computing in form of data Centers through which many different services on internet are provide to the cloud users in a very smooth and efficient manner. A large distributed system is described as a data center that includes a huge quantity of computing servers connected by an efficient network. So the consumption of energy in such data centers is enormously very high. Not only the maintenance of the data centers are too exorbitant, but also socially very harmful. High vitality costs and immense carbon footprints are brought in these data centers because the servers needed a substantial amount of electricity for their computation as well as for their cooling. As cost of energy increases and availability decreases, focus should be shifted towards the optimization of data centre servers for best performance alone with the policies of less energy consumption to justify the level of service performance with social impact. So in this paper we proposed energy aware consolidation technique for cloud data centers based on prediction of future client's requests to increase the utilization of computing servers as per request of users/clients which associated some demand of cloud resources for maintain the power consumption in cloud.","PeriodicalId":328124,"journal":{"name":"2019 8th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125602723","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 : 2019-11-01DOI: 10.1109/SMART46866.2019.9117273
Md. Robel Mia, Amit Chakraborty Chhoton, Mahadi Hasan Mozumder, S. A. Hossain, Awolad Hossan
Bangladesh extensively depends on agriculture in terms of economy as well as food security for its huge population. For this reason, it is very important to efficiently grow a plant and enhance its yield. We often face some problem which need to be solved. We build a Mango Disease Recognition system which can recognize the mango disease. It's Very useful to the farmers because using this system they can easily identify their mango disease which is very important to produce more fruits. Using our system user can easily identify the problem and they can take action for better production. There also some existing project of similar topic but theses project are not available to the all users. More over some system recognize disease very poorly and there have less accuracy and it's a huge problem to use the system. Comparing other system our system can be use more efficiently. Recognition of Mango diseases poses two challenging problems, i.e. detection and classification of disease. In here we used K means clustering for feature extraction and SVM for classification. The novelty of our work is that here we recognize the mango diseases which is not existing and our project accuracy is 94.13%. So we think user will be benefited from our project to produce more product which can effect in our economy.
{"title":"An Approach for Mango Disease Recognition using K-Means Clustering and SVM Classifier","authors":"Md. Robel Mia, Amit Chakraborty Chhoton, Mahadi Hasan Mozumder, S. A. Hossain, Awolad Hossan","doi":"10.1109/SMART46866.2019.9117273","DOIUrl":"https://doi.org/10.1109/SMART46866.2019.9117273","url":null,"abstract":"Bangladesh extensively depends on agriculture in terms of economy as well as food security for its huge population. For this reason, it is very important to efficiently grow a plant and enhance its yield. We often face some problem which need to be solved. We build a Mango Disease Recognition system which can recognize the mango disease. It's Very useful to the farmers because using this system they can easily identify their mango disease which is very important to produce more fruits. Using our system user can easily identify the problem and they can take action for better production. There also some existing project of similar topic but theses project are not available to the all users. More over some system recognize disease very poorly and there have less accuracy and it's a huge problem to use the system. Comparing other system our system can be use more efficiently. Recognition of Mango diseases poses two challenging problems, i.e. detection and classification of disease. In here we used K means clustering for feature extraction and SVM for classification. The novelty of our work is that here we recognize the mango diseases which is not existing and our project accuracy is 94.13%. So we think user will be benefited from our project to produce more product which can effect in our economy.","PeriodicalId":328124,"journal":{"name":"2019 8th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127050766","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 : 2019-11-01DOI: 10.1109/SMART46866.2019.9117513
S. Chowdhury, Zerin Nasrin Tumpa, F. Khatun, S. F. Rabby
Crime is one of the major challenges of the world which is affecting the normal life and socio-economic development. Therefore, many governments are trying to use advanced technology to address or tackle such issues to maintain the peace of the country. So the analysis on Crime data has a great impact and value for the current scenario of the world. Nowadays, online newspaper is very popular among the people and contents varieties of crime news which can be a great source to understand the types and occurrence of crime. The aim of this paper is to monitor the crime, based on the headlines of the online newspaper provided in Twitter. Our approach is based on sentiment analysis by applying lexicon based methods and understand the crime categorized in a day, month, location and week. This piece of research work will help to deep understanding the pattern of the crime as well as the possibilities of occurrence of the crime in the specific time or day which will bear a great value to ensure the security purpose.
{"title":"Crime Monitoring from Newspaper Data based on Sentiment Analysis","authors":"S. Chowdhury, Zerin Nasrin Tumpa, F. Khatun, S. F. Rabby","doi":"10.1109/SMART46866.2019.9117513","DOIUrl":"https://doi.org/10.1109/SMART46866.2019.9117513","url":null,"abstract":"Crime is one of the major challenges of the world which is affecting the normal life and socio-economic development. Therefore, many governments are trying to use advanced technology to address or tackle such issues to maintain the peace of the country. So the analysis on Crime data has a great impact and value for the current scenario of the world. Nowadays, online newspaper is very popular among the people and contents varieties of crime news which can be a great source to understand the types and occurrence of crime. The aim of this paper is to monitor the crime, based on the headlines of the online newspaper provided in Twitter. Our approach is based on sentiment analysis by applying lexicon based methods and understand the crime categorized in a day, month, location and week. This piece of research work will help to deep understanding the pattern of the crime as well as the possibilities of occurrence of the crime in the specific time or day which will bear a great value to ensure the security purpose.","PeriodicalId":328124,"journal":{"name":"2019 8th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129162851","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}
In today's world business are becoming online based. Companies sell their products and seek for consumer's feedback. When all the consumer writes their review about that's a product, It's becomes difficult to say that product is good or not based on their review. That's where Deep learning come. By using this, we can extract opinion or sentiment from the text which is written by the consumer. This is sentiment analysis. It can classify the emotional status of that review. Our project detects opinion from consumer's review whether it is good or bad. We use SVM, Naive Bayes algorithm and some methods. We use the Naive Bayes algorithm because we want to know how often words occur in the document. And then we use SVM for classifying whether words are positive or negative. For our researching purpose, we use the Amazon consumer review data set, which was available online. Some methods that we are using for preprocessing and cleaned the document where just words are left. We trained our model so well with twenty-four thousand data. So, it will give us the best accuracy and we make this model with the best algorithm and after that, it gives the accuracy of 98.39%. This project will help us in real life when we are having trouble with product reviews. Our machine will help us to determine which review is good and which review is bad and make a category of a positive and negative review and saves our time.
{"title":"Comparative Sentiment Analysis using Difference Types of Machine Learning Algorithm","authors":"Rakib Hossain, Fowjael Ahamed, Raihana Zannat, Md. Golam Rabbani","doi":"10.1109/SMART46866.2019.9117274","DOIUrl":"https://doi.org/10.1109/SMART46866.2019.9117274","url":null,"abstract":"In today's world business are becoming online based. Companies sell their products and seek for consumer's feedback. When all the consumer writes their review about that's a product, It's becomes difficult to say that product is good or not based on their review. That's where Deep learning come. By using this, we can extract opinion or sentiment from the text which is written by the consumer. This is sentiment analysis. It can classify the emotional status of that review. Our project detects opinion from consumer's review whether it is good or bad. We use SVM, Naive Bayes algorithm and some methods. We use the Naive Bayes algorithm because we want to know how often words occur in the document. And then we use SVM for classifying whether words are positive or negative. For our researching purpose, we use the Amazon consumer review data set, which was available online. Some methods that we are using for preprocessing and cleaned the document where just words are left. We trained our model so well with twenty-four thousand data. So, it will give us the best accuracy and we make this model with the best algorithm and after that, it gives the accuracy of 98.39%. This project will help us in real life when we are having trouble with product reviews. Our machine will help us to determine which review is good and which review is bad and make a category of a positive and negative review and saves our time.","PeriodicalId":328124,"journal":{"name":"2019 8th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129092060","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 : 2019-11-01DOI: 10.1109/SMART46866.2019.9117414
C. Verma, Veronika Stoffová, Z. Illés, Mandeep Singh
Feature extraction has a vibrant part in Machine learning (ML) to identify the data patterns with optimum accuracy. We proposed some significant features to predict the student's institution or university based on their answers in the technological survey. Four experiments were performed in IBM SPSS Modeler version 18.2 using 4 ML to resolve the binary classification problem. In the university prediction problem., the uppermost accuracy of 94.26% is provided by eXtreme Gradient Boosting Tree (XGBT) and suggested 18 significant features out of a total of 37. Further., the Artificial Neural Network (ANN) with boosting scored second maximum accuracy of 93.96% and recommended 10 significant features; Support Vector Machine (SVM) provided third-highest accuracy of 92.45% with the recommendation of 12 features; and Random Tree (RT) attained the least accuracy 92.15% with recommendation of 10 important features. The findings of the paper conclude that the XGBT classifier outperformed others in prediction. Also., a noteworthy dissimilarity was found between XGBT's accuracy and SVM's accuracy., RT's accuracy.
{"title":"ICT and Mobile Technology Features Predicting the University of Indian and Hungarian Student for the Real-Time","authors":"C. Verma, Veronika Stoffová, Z. Illés, Mandeep Singh","doi":"10.1109/SMART46866.2019.9117414","DOIUrl":"https://doi.org/10.1109/SMART46866.2019.9117414","url":null,"abstract":"Feature extraction has a vibrant part in Machine learning (ML) to identify the data patterns with optimum accuracy. We proposed some significant features to predict the student's institution or university based on their answers in the technological survey. Four experiments were performed in IBM SPSS Modeler version 18.2 using 4 ML to resolve the binary classification problem. In the university prediction problem., the uppermost accuracy of 94.26% is provided by eXtreme Gradient Boosting Tree (XGBT) and suggested 18 significant features out of a total of 37. Further., the Artificial Neural Network (ANN) with boosting scored second maximum accuracy of 93.96% and recommended 10 significant features; Support Vector Machine (SVM) provided third-highest accuracy of 92.45% with the recommendation of 12 features; and Random Tree (RT) attained the least accuracy 92.15% with recommendation of 10 important features. The findings of the paper conclude that the XGBT classifier outperformed others in prediction. Also., a noteworthy dissimilarity was found between XGBT's accuracy and SVM's accuracy., RT's accuracy.","PeriodicalId":328124,"journal":{"name":"2019 8th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129490168","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 : 2019-11-01DOI: 10.1109/SMART46866.2019.9117326
G. Goswami, P. Goswami
In distribution system network the necessity and adoption of non-linear loads is becoming very common in present scenario. Since these loads accounts for the insertion of harmonics in line current, which results in unbalanced voltage and reactive power but their use can not be ignored as we are entering in the era of modernization. Thus it becomes very essential to have a solution to get a healthy system with less disturbance in line current and compensated reactive power. This paper presents design of a transformer less shunt active power filter to improve transient specifications and to get the system more stable with improved power factor. The action of ShAPF is controlled using PID/Fuzzy/ANN controller and the comparison has been made among the results obtained from the simulation model of all three methods.
{"title":"Transient Specifications and Reactive Power Compensation Using ShAPF for Non-Linear Load Applications","authors":"G. Goswami, P. Goswami","doi":"10.1109/SMART46866.2019.9117326","DOIUrl":"https://doi.org/10.1109/SMART46866.2019.9117326","url":null,"abstract":"In distribution system network the necessity and adoption of non-linear loads is becoming very common in present scenario. Since these loads accounts for the insertion of harmonics in line current, which results in unbalanced voltage and reactive power but their use can not be ignored as we are entering in the era of modernization. Thus it becomes very essential to have a solution to get a healthy system with less disturbance in line current and compensated reactive power. This paper presents design of a transformer less shunt active power filter to improve transient specifications and to get the system more stable with improved power factor. The action of ShAPF is controlled using PID/Fuzzy/ANN controller and the comparison has been made among the results obtained from the simulation model of all three methods.","PeriodicalId":328124,"journal":{"name":"2019 8th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"306 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123082168","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 : 2019-11-01DOI: 10.1109/SMART46866.2019.9117308
Harsh Karna, Vidhu Baggan, A. Sahoo, P. Sarangi
Some of the most effective routing protocols used in data transmission are (RIPv2), OSPF and EIGRP which stand for Routing Information Protocol, Open Shortest Path First (OSPF) and Enhanced Interior Gateway Routing Protocol (EIGRP) respectively. The main objective of this study is to execute a performance analysis of these protocols using parameters like Throughput, Jitter, Convergence Time, End-to-End delay and Packet Depletion through the simulated network models. Ten Generic routers are used in our simulated network topology using GNS-3(Graphical Network Stimulator). Based on the results, it can be observed that EIGRP routing protocol delivers a more superior performance as compared to OSPF routing protocol for real world applications. However, based on network variations we observe that EIGRP requires more computation than OSPF and hence consumes immense system power.
{"title":"Performance Analysis of Interior Gateway Protocols (IGPs) using GNS-3","authors":"Harsh Karna, Vidhu Baggan, A. Sahoo, P. Sarangi","doi":"10.1109/SMART46866.2019.9117308","DOIUrl":"https://doi.org/10.1109/SMART46866.2019.9117308","url":null,"abstract":"Some of the most effective routing protocols used in data transmission are (RIPv2), OSPF and EIGRP which stand for Routing Information Protocol, Open Shortest Path First (OSPF) and Enhanced Interior Gateway Routing Protocol (EIGRP) respectively. The main objective of this study is to execute a performance analysis of these protocols using parameters like Throughput, Jitter, Convergence Time, End-to-End delay and Packet Depletion through the simulated network models. Ten Generic routers are used in our simulated network topology using GNS-3(Graphical Network Stimulator). Based on the results, it can be observed that EIGRP routing protocol delivers a more superior performance as compared to OSPF routing protocol for real world applications. However, based on network variations we observe that EIGRP requires more computation than OSPF and hence consumes immense system power.","PeriodicalId":328124,"journal":{"name":"2019 8th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"47 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116400950","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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