Pub Date : 2019-04-01DOI: 10.1109/ICOEI.2019.8862762
Sanket Chandrakant Mungale, M. Sankar, D. Mudgal
The present control and management system analysis technique is the most critical work for public life, because traffic management services is programmed to provide smart service to people in their day to day life, .so artificial intelligence and deep learning methodology currently a days preferred technique, in many country has searches that sort resolution, and now more country developed this method victimizes next generation network technology, which enables a lot of reliable sources to manmade hand control.
{"title":"Study the Fundamental Conpect of Traffic Management System Analysis Methodology Victimization Using Image Development Processing","authors":"Sanket Chandrakant Mungale, M. Sankar, D. Mudgal","doi":"10.1109/ICOEI.2019.8862762","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862762","url":null,"abstract":"The present control and management system analysis technique is the most critical work for public life, because traffic management services is programmed to provide smart service to people in their day to day life, .so artificial intelligence and deep learning methodology currently a days preferred technique, in many country has searches that sort resolution, and now more country developed this method victimizes next generation network technology, which enables a lot of reliable sources to manmade hand control.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134416033","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862734
Aswathi Balachandran, Ramalakshmi, Venkatesan, M. Lakshmi, K. Jahnavi, V. Jothi
The goal of the proposed work is to minimize the energy consumption in a particular home or a region. The focus of energy consumption for smart home has been sensing depth on collecting as much as data as possible from each home or region. The paper presents have designed and deployed a smart system which leads each person to understand about the importance of intelligent buildings or smart home and their aim in reducing the energy consumption. The essence of this paper is all about collecting the different datasets for different home or different region as much as possible. It contains the information about different home appliances from a hair dryer to a refrigerator and their electricity usage for every second, temperature prediction, humidity and so on. The data that have collected has served as the foundation of this paper. And the datasets have described about those datasets and the tools that has used for implementing. The datasets and the tools are provided below so that it will be useful for further research in future on designing smart homes.
{"title":"Energy Consumption Analysis And Load Management For Smart Home","authors":"Aswathi Balachandran, Ramalakshmi, Venkatesan, M. Lakshmi, K. Jahnavi, V. Jothi","doi":"10.1109/ICOEI.2019.8862734","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862734","url":null,"abstract":"The goal of the proposed work is to minimize the energy consumption in a particular home or a region. The focus of energy consumption for smart home has been sensing depth on collecting as much as data as possible from each home or region. The paper presents have designed and deployed a smart system which leads each person to understand about the importance of intelligent buildings or smart home and their aim in reducing the energy consumption. The essence of this paper is all about collecting the different datasets for different home or different region as much as possible. It contains the information about different home appliances from a hair dryer to a refrigerator and their electricity usage for every second, temperature prediction, humidity and so on. The data that have collected has served as the foundation of this paper. And the datasets have described about those datasets and the tools that has used for implementing. The datasets and the tools are provided below so that it will be useful for further research in future on designing smart homes.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"170 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132060427","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862607
A. Aruna, K. J. Kumar
Land Mobile Radios (LMR) are used by various emergency organizations such as military, fire and ambulance services. The main function of LMR is to transmit voice over a selective range of radio frequency and they are mostly battery operated. Power amplifier (PA) circuit of LMR has drawn a major concern from engineers because they consume enormous power from battery. More research is conducted on PA to find solutions for improving Power Added Efficiency (PAE). PAE represents a figure of merit that economically shows how efficiently the PA converts RF power to DC power. With PAE parameter increased the device can be able to produce output the same amount of power with less DC power consumed. Class-E power amplifier desires the most attention among different classes of PA from engineers because of their ability of providing high PAE and more harmonic suppression. In this paper, Advance Design System (ADS) software is used for designing and simulation. Class-E PA is designed, harmonics are suppressed at the output. The final design operates at the frequency range of 136-174MHz with PAE of 92.39% by delivering 28.75dBm output power and the effectiveness of Class-E PA is boosted to suppress second order harmonics by 91.675dBc.
{"title":"Efficiency enchancement of Class-E power amplifier in VHF radio frequency spectrum for land mobile radio system","authors":"A. Aruna, K. J. Kumar","doi":"10.1109/ICOEI.2019.8862607","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862607","url":null,"abstract":"Land Mobile Radios (LMR) are used by various emergency organizations such as military, fire and ambulance services. The main function of LMR is to transmit voice over a selective range of radio frequency and they are mostly battery operated. Power amplifier (PA) circuit of LMR has drawn a major concern from engineers because they consume enormous power from battery. More research is conducted on PA to find solutions for improving Power Added Efficiency (PAE). PAE represents a figure of merit that economically shows how efficiently the PA converts RF power to DC power. With PAE parameter increased the device can be able to produce output the same amount of power with less DC power consumed. Class-E power amplifier desires the most attention among different classes of PA from engineers because of their ability of providing high PAE and more harmonic suppression. In this paper, Advance Design System (ADS) software is used for designing and simulation. Class-E PA is designed, harmonics are suppressed at the output. The final design operates at the frequency range of 136-174MHz with PAE of 92.39% by delivering 28.75dBm output power and the effectiveness of Class-E PA is boosted to suppress second order harmonics by 91.675dBc.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124181076","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862597
Anu V Kottath, Prince V Jose
Big Data is a term used to represent huge size of data and still growing exponentially with time. In short, all data sets are large and complex. The existing traditional data management tools are not able to store and process the large data sets effectively. In Data sets which contains incomplete data and they having random-distributed missing nodes in its dimensions. It is very hard to get back datas from this type of data set when it is large. Dominance value is the most influential value in the data set. A deep analysis is need to identify top-k dominance value in the data set. Some of the existing methods to find the top-k dominant values are Pair wise comparison, Skyline based algorithm, Upper bound based algorithm, Bitmap index guided algorithm. But the major problems of these methods are mainly applicable only to small data sets, complexity increases with increasing data, require numerous comparisons between values, slower data processing respectively. In this review discuss in detail the existing methods to find the dominance values on incomplete data set.
{"title":"Review on Finding Dominance on Incomplete Big Data","authors":"Anu V Kottath, Prince V Jose","doi":"10.1109/ICOEI.2019.8862597","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862597","url":null,"abstract":"Big Data is a term used to represent huge size of data and still growing exponentially with time. In short, all data sets are large and complex. The existing traditional data management tools are not able to store and process the large data sets effectively. In Data sets which contains incomplete data and they having random-distributed missing nodes in its dimensions. It is very hard to get back datas from this type of data set when it is large. Dominance value is the most influential value in the data set. A deep analysis is need to identify top-k dominance value in the data set. Some of the existing methods to find the top-k dominant values are Pair wise comparison, Skyline based algorithm, Upper bound based algorithm, Bitmap index guided algorithm. But the major problems of these methods are mainly applicable only to small data sets, complexity increases with increasing data, require numerous comparisons between values, slower data processing respectively. In this review discuss in detail the existing methods to find the dominance values on incomplete data set.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121052153","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862570
Pavan Sai Beri, Arun Mishra
Autonomous systems like self-driving cars, unmanned aerial and marine vehicles, smart robots etc., are rapidly emerging in scientific and industrial sectors for mission-critical applications, in recent times. Critical systems are developed using component-based software engineering paradigm by most of the software developers. Each activity in a component-based system is performed by different components of the system and each dynamic component integration with the system gives an opportunity for adversaries to insert malicious code into the system for execution through the components. In present work, a security model is proposed using concept of slack space of software components, for authentication of components to safely integrate with an autonomous system. By using this methodology, a mission-critical autonomous system can detect tampered components and prevent integrating them.
{"title":"Dynamic Software Component Authentication for Autonomous Systems using Slack space","authors":"Pavan Sai Beri, Arun Mishra","doi":"10.1109/ICOEI.2019.8862570","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862570","url":null,"abstract":"Autonomous systems like self-driving cars, unmanned aerial and marine vehicles, smart robots etc., are rapidly emerging in scientific and industrial sectors for mission-critical applications, in recent times. Critical systems are developed using component-based software engineering paradigm by most of the software developers. Each activity in a component-based system is performed by different components of the system and each dynamic component integration with the system gives an opportunity for adversaries to insert malicious code into the system for execution through the components. In present work, a security model is proposed using concept of slack space of software components, for authentication of components to safely integrate with an autonomous system. By using this methodology, a mission-critical autonomous system can detect tampered components and prevent integrating them.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121237307","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862534
C. James, David Nettikadan
Recent reports confirm the fact that school students are the most vulnerable to social crimes happening across the globe and our country too. Many of these cases happen during their ply from their residence to school and vice versa. In multiple cases these social crimes including sexual harassment happened in their school bus itself. Considering this serious situation, we are proposing a real time monitoring system using image processing techniques. — Identifying a student with an image has been popularized through the mass media like camera. This system monitors the images inside the vehicle and identifies the students and their movements inside the bus. The system recognizes the student faces and their count are also monitored. The system will also raise an alarm to get the attention of the public if it is so essential. Technologies are available in the Open-Computer-Vision (OpenCV) library and implement those using Python. For face detection, Haar-Cascades classifier was used and for face recognition Eigenfaces, and Local binary pattern histograms were used. each stage of the system described by some flowcharts. And also face recognition used in automation attendance system which eliminates most of the drawbacks that the manual attendance systems pose, easy manipulation of attendance records, proxy-attendances, and insecure system.
{"title":"Student Monitoring System for School Bus Using Facial Recognition","authors":"C. James, David Nettikadan","doi":"10.1109/ICOEI.2019.8862534","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862534","url":null,"abstract":"Recent reports confirm the fact that school students are the most vulnerable to social crimes happening across the globe and our country too. Many of these cases happen during their ply from their residence to school and vice versa. In multiple cases these social crimes including sexual harassment happened in their school bus itself. Considering this serious situation, we are proposing a real time monitoring system using image processing techniques. — Identifying a student with an image has been popularized through the mass media like camera. This system monitors the images inside the vehicle and identifies the students and their movements inside the bus. The system recognizes the student faces and their count are also monitored. The system will also raise an alarm to get the attention of the public if it is so essential. Technologies are available in the Open-Computer-Vision (OpenCV) library and implement those using Python. For face detection, Haar-Cascades classifier was used and for face recognition Eigenfaces, and Local binary pattern histograms were used. each stage of the system described by some flowcharts. And also face recognition used in automation attendance system which eliminates most of the drawbacks that the manual attendance systems pose, easy manipulation of attendance records, proxy-attendances, and insecure system.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128978565","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862533
Mamatha Sai Yarabarla, L. Ravi, A. Sivasangari
Breast cancer is one of the most common and leading causes of cancer among women. Currently, it has become the common health issue, and its incidence has increased recently. Prior identification is the best way to manage breast cancer results. Computer-aided detection or diagnosis (CAD) systems plays a major role in prior identification of breast cancer and can be used for reduction of death rate among women. The main intention of this paper is to make use of the recent advances in the development of CAD systems and related techniques. The mainstay of the project is to predict whether the person is having breast cancer or not. Machine learning is nothing but training the machines to learn and perform by itself without any explicit program or instruction. So here, predicting whether a person is suffering with breast cancer or not is done with the help of the trained data.
{"title":"Breast Cancer Prediction via Machine Learning","authors":"Mamatha Sai Yarabarla, L. Ravi, A. Sivasangari","doi":"10.1109/ICOEI.2019.8862533","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862533","url":null,"abstract":"Breast cancer is one of the most common and leading causes of cancer among women. Currently, it has become the common health issue, and its incidence has increased recently. Prior identification is the best way to manage breast cancer results. Computer-aided detection or diagnosis (CAD) systems plays a major role in prior identification of breast cancer and can be used for reduction of death rate among women. The main intention of this paper is to make use of the recent advances in the development of CAD systems and related techniques. The mainstay of the project is to predict whether the person is having breast cancer or not. Machine learning is nothing but training the machines to learn and perform by itself without any explicit program or instruction. So here, predicting whether a person is suffering with breast cancer or not is done with the help of the trained data.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129397703","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862720
Ruchi Vishwakarma, A. Jain
With the tremendous growth of IoT botnet DDoS attacks in recent years, IoT security has now become one of the most concerned topics in the field of network security. A lot of security approaches have been proposed in the area, but they still lack in terms of dealing with newer emerging variants of IoT malware, known as Zero-Day Attacks. In this paper, we present a honeypot-based approach which uses machine learning techniques for malware detection. The IoT honeypot generated data is used as a dataset for the effective and dynamic training of a machine learning model. The approach can be taken as a productive outset towards combatting Zero-Day DDoS Attacks which now has emerged as an open challenge in defending IoT against DDoS Attacks.
{"title":"A Honeypot with Machine Learning based Detection Framework for defending IoT based Botnet DDoS Attacks","authors":"Ruchi Vishwakarma, A. Jain","doi":"10.1109/ICOEI.2019.8862720","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862720","url":null,"abstract":"With the tremendous growth of IoT botnet DDoS attacks in recent years, IoT security has now become one of the most concerned topics in the field of network security. A lot of security approaches have been proposed in the area, but they still lack in terms of dealing with newer emerging variants of IoT malware, known as Zero-Day Attacks. In this paper, we present a honeypot-based approach which uses machine learning techniques for malware detection. The IoT honeypot generated data is used as a dataset for the effective and dynamic training of a machine learning model. The approach can be taken as a productive outset towards combatting Zero-Day DDoS Attacks which now has emerged as an open challenge in defending IoT against DDoS Attacks.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116320905","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862612
Devi Archana Kar, R. Patro, Subhashree Subudhi, P. Biswal
For accurate classification of remote sensing data, Hyperspectral Images (HSI) have become very popular. It can capture the reflected electromagnetic spectrum from the object in several contiguous spectral bands. But processing of hundreds of bands is computationally expensive and also it contains several noisy and redundant bands. Often the water absorption bands are manually removed by the researchers in advance. In this work, a histogram based automatic noisy band removal algorithm is developed for the HSI. This algorithm can be used as a preprocessing step prior to hyperspectral image classification. At first, by using the histogram information, noisy bands are removed. Next, after obtaining the desired number of non-noisy bands, a Gaussian Filter is applied on obtained bands to extract spatial-spectral features. Finally, to evaluate the algorithm, classification is performed using a SVM classifier. For experimental validation of results, Indian Pines and Salinas datasets are used. The obtained result clearly reveals the effectiveness of the proposed automatic noisy band removal algorithm.
{"title":"Histogram based automatic noisy band removal for remotely sensed hyperspectral images","authors":"Devi Archana Kar, R. Patro, Subhashree Subudhi, P. Biswal","doi":"10.1109/ICOEI.2019.8862612","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862612","url":null,"abstract":"For accurate classification of remote sensing data, Hyperspectral Images (HSI) have become very popular. It can capture the reflected electromagnetic spectrum from the object in several contiguous spectral bands. But processing of hundreds of bands is computationally expensive and also it contains several noisy and redundant bands. Often the water absorption bands are manually removed by the researchers in advance. In this work, a histogram based automatic noisy band removal algorithm is developed for the HSI. This algorithm can be used as a preprocessing step prior to hyperspectral image classification. At first, by using the histogram information, noisy bands are removed. Next, after obtaining the desired number of non-noisy bands, a Gaussian Filter is applied on obtained bands to extract spatial-spectral features. Finally, to evaluate the algorithm, classification is performed using a SVM classifier. For experimental validation of results, Indian Pines and Salinas datasets are used. The obtained result clearly reveals the effectiveness of the proposed automatic noisy band removal algorithm.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116676155","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862580
S. Patil, U. Kulkarni
Machine Learning is one of the finest fields of Computer Science world which has given the innumerable and invaluable solutions to the mankind to solve its complex problems. Decision Tree is one such modern solution to the decision making problems by learning the data from the problem domain and building a model which can be used for prediction supported by the systematic analytics. In order to build a model on a huge dataset Decision Tree algorithm needs to be transformed to manifest itself into distributed environment so that higher performance of training the model is achieved in terms of time, without compromising the accuracy of the Decision Tree built. In this paper, we have proposed an enhanced version of distributed decision tree algorithm to perform better in terms of model building time without compromising the accuracy.
{"title":"Accuracy Prediction for Distributed Decision Tree using Machine Learning approach","authors":"S. Patil, U. Kulkarni","doi":"10.1109/ICOEI.2019.8862580","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862580","url":null,"abstract":"Machine Learning is one of the finest fields of Computer Science world which has given the innumerable and invaluable solutions to the mankind to solve its complex problems. Decision Tree is one such modern solution to the decision making problems by learning the data from the problem domain and building a model which can be used for prediction supported by the systematic analytics. In order to build a model on a huge dataset Decision Tree algorithm needs to be transformed to manifest itself into distributed environment so that higher performance of training the model is achieved in terms of time, without compromising the accuracy of the Decision Tree built. In this paper, we have proposed an enhanced version of distributed decision tree algorithm to perform better in terms of model building time without compromising the accuracy.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116982937","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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