Pub Date : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862044
Suja Cherukullapurath Mana, T. Sasiprabha
An efficient meta data systems will helps to improve the usability of data. Metadata will describe the data being stored and it will helps in adding more value and usability to the stored data. Data retrieval also will be made easy by using an efficient metadata system. This survey paper will study some of the metadata standards and perform a comparison based on the usability of each metadata standards.
{"title":"A Study on Various Semantic Metadata Standards to Improve Data Usability","authors":"Suja Cherukullapurath Mana, T. Sasiprabha","doi":"10.1109/ICCIDS.2019.8862044","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862044","url":null,"abstract":"An efficient meta data systems will helps to improve the usability of data. Metadata will describe the data being stored and it will helps in adding more value and usability to the stored data. Data retrieval also will be made easy by using an efficient metadata system. This survey paper will study some of the metadata standards and perform a comparison based on the usability of each metadata standards.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128753615","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 : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862048
R. Gomathi, P. Ajitha, G. H. S. Krishna, I. Harsha Pranay
Recommendation systems are being enforced to offer personalized set of services to the users. They are basically build to produce recommendations or suggestions (like restaurants, places…) that comply with user’s concern and that can be applied to multiple fields. To enhance the quality and service of Recommendation systems and to resolve any issues related to it, various effective techniques linked to data management can be made use of. The current paper proposes a machine learning algorithms to resolve the issue of personalized Restaurant selection relying upon tripadvisor.com search data. The facilities provided by the hotel along with user’s comments are being utilized. The NLP - Natural Language Processing is imbibed for examining and tagging all the previous user’s comments (whether positive or negative) for every hotel, thereafter computing the overall % of the comments and storing the output. In the process of Restaurant recommendation, first the user chooses the hotel’s features according to his interest and centered on this, the corresponding hotels are fetched and the user comments are examined to identify the hotel with the highest ranking. Eventually, the highest rated hotel is being recommended to the user by the restaurant recommended system. The proposed sentimental score measure NLP algorithm is used for finding the aspect and sentiments of the user comments. Natural language processing (NLP) is one of the machines learning technique to analyze, understand, and derive meaning from human language in a smart and useful way. The evaluation results reveal that the proposed NLP algorithm improves the performance when compared to existing algorithms. The focus of the research work is to offer list of recommended restaurants that is more precise and accessible. The conclusion and results reveal that the suggested approach yields high accuracy.
{"title":"Restaurant Recommendation System for User Preference and Services Based on Rating and Amenities","authors":"R. Gomathi, P. Ajitha, G. H. S. Krishna, I. Harsha Pranay","doi":"10.1109/ICCIDS.2019.8862048","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862048","url":null,"abstract":"Recommendation systems are being enforced to offer personalized set of services to the users. They are basically build to produce recommendations or suggestions (like restaurants, places…) that comply with user’s concern and that can be applied to multiple fields. To enhance the quality and service of Recommendation systems and to resolve any issues related to it, various effective techniques linked to data management can be made use of. The current paper proposes a machine learning algorithms to resolve the issue of personalized Restaurant selection relying upon tripadvisor.com search data. The facilities provided by the hotel along with user’s comments are being utilized. The NLP - Natural Language Processing is imbibed for examining and tagging all the previous user’s comments (whether positive or negative) for every hotel, thereafter computing the overall % of the comments and storing the output. In the process of Restaurant recommendation, first the user chooses the hotel’s features according to his interest and centered on this, the corresponding hotels are fetched and the user comments are examined to identify the hotel with the highest ranking. Eventually, the highest rated hotel is being recommended to the user by the restaurant recommended system. The proposed sentimental score measure NLP algorithm is used for finding the aspect and sentiments of the user comments. Natural language processing (NLP) is one of the machines learning technique to analyze, understand, and derive meaning from human language in a smart and useful way. The evaluation results reveal that the proposed NLP algorithm improves the performance when compared to existing algorithms. The focus of the research work is to offer list of recommended restaurants that is more precise and accessible. The conclusion and results reveal that the suggested approach yields high accuracy.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126802057","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 : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862006
K. Madheswaran, K. Veerappan, V. Sathiesh Kumar
Human elephant conflict occurs due to migration of elephants from their habitat to human living areas in search of food and water. In order to reduce the Human-Elephant Conflict, a real time prototype is built to migrate the elephant to human living areas is minimized by generating honey bee sound and tiger growl sound to which the elephant’s dislikes. Four object detection algorithms such as SSD mobilenet v2 model, SSDlite mobilenet v2 model, SSD inception v2 model, and Fast R-CNN inception v2 are considered. SSDlite mobilenet v2 model produced the best results with precision = 0.854 AP, recall = 0.718 AR, f1-score = 0.780, prediction time = 34.49ms for a frame rate = 31.15fps. Real time implementation is carried out using Raspberry Pi 3 with SSDlite mobilenet v2 architecture.
{"title":"Region Based Convolutional Neural Network for Human-Elephant Conflict Management System","authors":"K. Madheswaran, K. Veerappan, V. Sathiesh Kumar","doi":"10.1109/ICCIDS.2019.8862006","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862006","url":null,"abstract":"Human elephant conflict occurs due to migration of elephants from their habitat to human living areas in search of food and water. In order to reduce the Human-Elephant Conflict, a real time prototype is built to migrate the elephant to human living areas is minimized by generating honey bee sound and tiger growl sound to which the elephant’s dislikes. Four object detection algorithms such as SSD mobilenet v2 model, SSDlite mobilenet v2 model, SSD inception v2 model, and Fast R-CNN inception v2 are considered. SSDlite mobilenet v2 model produced the best results with precision = 0.854 AP, recall = 0.718 AR, f1-score = 0.780, prediction time = 34.49ms for a frame rate = 31.15fps. Real time implementation is carried out using Raspberry Pi 3 with SSDlite mobilenet v2 architecture.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114398291","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 : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862042
K. Indira, P. Ajitha, V. Reshma, A. Tamizhselvi
Vehicular ad hoc network is one of most recent research areas to deploy intelligent Transport System. Due to their highly dynamic topology, energy constrained and no central point coordination, routing with minimal delay, minimal energy and maximize throughput is a big challenge. Software Defined Networking (SDN) is new paradigm to improve overall network lifetime. It incorporates dynamic changes with minimal end-end delay, and enhances network intelligence. Along with this, intelligence secure routing is also a major constraint. This paper proposes a novel approach to Energy efficient secured routing protocol for Software Defined Internet of vehicles using Restricted Boltzmann Algorithm. This algorithm is to detect hostile routes with minimum delay, minimum energy and maximum throughput compared with traditional routing protocols.
{"title":"An Efficient Secured Routing Protocol for Software Defined Internet of Vehicles","authors":"K. Indira, P. Ajitha, V. Reshma, A. Tamizhselvi","doi":"10.1109/ICCIDS.2019.8862042","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862042","url":null,"abstract":"Vehicular ad hoc network is one of most recent research areas to deploy intelligent Transport System. Due to their highly dynamic topology, energy constrained and no central point coordination, routing with minimal delay, minimal energy and maximize throughput is a big challenge. Software Defined Networking (SDN) is new paradigm to improve overall network lifetime. It incorporates dynamic changes with minimal end-end delay, and enhances network intelligence. Along with this, intelligence secure routing is also a major constraint. This paper proposes a novel approach to Energy efficient secured routing protocol for Software Defined Internet of vehicles using Restricted Boltzmann Algorithm. This algorithm is to detect hostile routes with minimum delay, minimum energy and maximum throughput compared with traditional routing protocols.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128958161","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 : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862038
J. Kirupakar, S. Shalinie
In today’s IIoT world, most of the IoT platform providers like Microsoft, Amazon and Google are focused towards connecting devices and extract data from the devices and send the data to the Cloud for analytics. Only there are few companies concentrating on Security measures implemented on Edge Node. Gartner estimates that by 2020, more than 25 percent of all enterprise attackers will make use of the Industrial IoT. As Cyber Security Threat is getting more important, it is essential to ensure protection of data both at rest and at motion. The reflex of Cyber Security in the Industrial IoT Domain is much more severe when compared to the Consumer IoT Segment. The new bottleneck in this are security services which employ computationally intensive software operations and system services [1]. Resilient services consume considerable resources in a design. When such measures are added to thwart security attacks, the resource requirements grow even more demanding. Since the standard IIoT Gateways and other sub devices are resource constrained in nature the conventional design for security services will not be applicable in this case. This paper proposes an intelligent architectural paradigm for the Constrained IIoT Gateways that can efficiently identify the Cyber-Attacks in the Industrial IoT domain.
{"title":"Situation Aware Intrusion Detection System Design for Industrial IoT Gateways","authors":"J. Kirupakar, S. Shalinie","doi":"10.1109/ICCIDS.2019.8862038","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862038","url":null,"abstract":"In today’s IIoT world, most of the IoT platform providers like Microsoft, Amazon and Google are focused towards connecting devices and extract data from the devices and send the data to the Cloud for analytics. Only there are few companies concentrating on Security measures implemented on Edge Node. Gartner estimates that by 2020, more than 25 percent of all enterprise attackers will make use of the Industrial IoT. As Cyber Security Threat is getting more important, it is essential to ensure protection of data both at rest and at motion. The reflex of Cyber Security in the Industrial IoT Domain is much more severe when compared to the Consumer IoT Segment. The new bottleneck in this are security services which employ computationally intensive software operations and system services [1]. Resilient services consume considerable resources in a design. When such measures are added to thwart security attacks, the resource requirements grow even more demanding. Since the standard IIoT Gateways and other sub devices are resource constrained in nature the conventional design for security services will not be applicable in this case. This paper proposes an intelligent architectural paradigm for the Constrained IIoT Gateways that can efficiently identify the Cyber-Attacks in the Industrial IoT domain.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131352789","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 : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862143
G. Jayalakshmi, V. Sathiesh Kumar
This paper focuses on the classification of dermoscopic images to identify the type of Skin lesion whether it is benign or malignant. Dermoscopic images provide deep insight for the analysis of any type of skin lesion. Initially, a custom Convolutional Neural Network (CNN) model is developed to classify the images for lesion identification. This model is trained across different train-test split and 30% split of train data is found to produce better accuracy. To further improve the classification accuracy a Batch Normalized Convolutional Neural Network (BN-CNN) is proposed. The proposed solution consists of 6 layers of convolutional blocks with batch normalization followed by a fully connected layer that performs binary classification. The custom CNN model is similar to the proposed model with the absence of Batch normalization and presence of Dropout at Fully connected layer. Experimental results for the proposed model provided better accuracy of 89.30%. Final work includes analysis of the proposed model to identify the best tuning parameters.
{"title":"Performance analysis of Convolutional Neural Network (CNN) based Cancerous Skin Lesion Detection System","authors":"G. Jayalakshmi, V. Sathiesh Kumar","doi":"10.1109/ICCIDS.2019.8862143","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862143","url":null,"abstract":"This paper focuses on the classification of dermoscopic images to identify the type of Skin lesion whether it is benign or malignant. Dermoscopic images provide deep insight for the analysis of any type of skin lesion. Initially, a custom Convolutional Neural Network (CNN) model is developed to classify the images for lesion identification. This model is trained across different train-test split and 30% split of train data is found to produce better accuracy. To further improve the classification accuracy a Batch Normalized Convolutional Neural Network (BN-CNN) is proposed. The proposed solution consists of 6 layers of convolutional blocks with batch normalization followed by a fully connected layer that performs binary classification. The custom CNN model is similar to the proposed model with the absence of Batch normalization and presence of Dropout at Fully connected layer. Experimental results for the proposed model provided better accuracy of 89.30%. Final work includes analysis of the proposed model to identify the best tuning parameters.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134615315","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 : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862140
Mohan S Acharya, Asfia Armaan, Aneeta S Antony
Prospective graduate students always face a dilemma deciding universities of their choice while applying to master’s programs. While there are a good number of predictors and consultancies that guide a student, they aren’t always reliable since decision is made on the basis of select past admissions. In this paper, we present a Machine Learning based method where we compare different regression algorithms, such as Linear Regression, Support Vector Regression, Decision Trees and Random Forest, given the profile of the student. We then compute error functions for the different models and compare their performance to select the best performing model. Results then indicate if the university of choice is an ambitious or a safe one.
{"title":"A Comparison of Regression Models for Prediction of Graduate Admissions","authors":"Mohan S Acharya, Asfia Armaan, Aneeta S Antony","doi":"10.1109/ICCIDS.2019.8862140","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862140","url":null,"abstract":"Prospective graduate students always face a dilemma deciding universities of their choice while applying to master’s programs. While there are a good number of predictors and consultancies that guide a student, they aren’t always reliable since decision is made on the basis of select past admissions. In this paper, we present a Machine Learning based method where we compare different regression algorithms, such as Linear Regression, Support Vector Regression, Decision Trees and Random Forest, given the profile of the student. We then compute error functions for the different models and compare their performance to select the best performing model. Results then indicate if the university of choice is an ambitious or a safe one.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114709441","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 : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862087
Ashwin Ashok, M. Guruprasad, C. Prakash, S. Shylaja
Insights from real-time disease surveillance systems are very useful for the public to take preventive measures against the diseases and it also benefits the pharmaceutical manufacturers in improving the sales of medicines for the particular disease and ensuring adequate availability of medicines when they are needed.A disease outbreak is an event wherein there is a rise in the number of positive cases for a disease in a short span of time. An outbreak can be limited to a particular region or time of the year. Diseases can be detected by several approaches, social media being preferred method due to availability of real-time data. Hence, data from social media, especially Twitter can be used to detect live events and monitor them efficiently. In order to detect diseases precisely, this paper proposes an approach wherein tweets, which are collected and pre-processed, can be effectively vectorized and clustered into the appropriate diseases with the use Agglomerative Clustering technique. The tweets can also be visualized using their geo information in order to generate zones which have high density of diseases. Such a surveillance system can be of use for early prediction of disease outbreaks, in turn facilitating faster and better handling of the situation.
{"title":"A Machine Learning Approach for Disease Surveillance and Visualization using Twitter Data","authors":"Ashwin Ashok, M. Guruprasad, C. Prakash, S. Shylaja","doi":"10.1109/ICCIDS.2019.8862087","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862087","url":null,"abstract":"Insights from real-time disease surveillance systems are very useful for the public to take preventive measures against the diseases and it also benefits the pharmaceutical manufacturers in improving the sales of medicines for the particular disease and ensuring adequate availability of medicines when they are needed.A disease outbreak is an event wherein there is a rise in the number of positive cases for a disease in a short span of time. An outbreak can be limited to a particular region or time of the year. Diseases can be detected by several approaches, social media being preferred method due to availability of real-time data. Hence, data from social media, especially Twitter can be used to detect live events and monitor them efficiently. In order to detect diseases precisely, this paper proposes an approach wherein tweets, which are collected and pre-processed, can be effectively vectorized and clustered into the appropriate diseases with the use Agglomerative Clustering technique. The tweets can also be visualized using their geo information in order to generate zones which have high density of diseases. Such a surveillance system can be of use for early prediction of disease outbreaks, in turn facilitating faster and better handling of the situation.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132245772","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 : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862109
N. Mangayarkarasi, G. Raghuraman, S. Kavitha
In recent years transmission is becoming one of the demanding ways of mobility all over the world. There are various pipeline systems built to carry water, gas and sewage water to reach out every nook and corner of the state. Unfortunately most of these resources are lost during the transmission only, due to the damages found in the pipelines. The advent of Computer Vision and Internet of Things (IoT) over the years has increased the scope of automation in every field. Being influenced by that, the existing inspection systems are getting smarter day by day. This paper gives an overall view about the existing techniques used in identification of the defects occurring in the pipelines. It discusses about the existing image processing techniques used to detect the defects present in the pipelines as quoted from various papers. It also briefs about the various sensors that are being used in the current scenarios for the continuous monitoring of the pipelines thus describing its pros and cons. Finally, the limitations of the existing methods and the scope of research in this domain have been outlined.
{"title":"Influence of Computer Vision and IoT for Pipeline Inspection-A Review","authors":"N. Mangayarkarasi, G. Raghuraman, S. Kavitha","doi":"10.1109/ICCIDS.2019.8862109","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862109","url":null,"abstract":"In recent years transmission is becoming one of the demanding ways of mobility all over the world. There are various pipeline systems built to carry water, gas and sewage water to reach out every nook and corner of the state. Unfortunately most of these resources are lost during the transmission only, due to the damages found in the pipelines. The advent of Computer Vision and Internet of Things (IoT) over the years has increased the scope of automation in every field. Being influenced by that, the existing inspection systems are getting smarter day by day. This paper gives an overall view about the existing techniques used in identification of the defects occurring in the pipelines. It discusses about the existing image processing techniques used to detect the defects present in the pipelines as quoted from various papers. It also briefs about the various sensors that are being used in the current scenarios for the continuous monitoring of the pipelines thus describing its pros and cons. Finally, the limitations of the existing methods and the scope of research in this domain have been outlined.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131325150","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 : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862126
C. Sivaranjani, Lekshmi Kalinathan, R. Amutha, Ruba Soundar Kathavarayan, K. J. Jegadish Kumar
The lighting condition of the environment are uncontrolled, so the segmentation of a leaf from the background is considered as a complex task. Here we propose a system which can identify the plant species based on the input leaf sample. An improved vegetation index, ExG-ExR is used to obtain more vegetative information from the images. The reason here is, it fixes a built-in zero threshold and hence there is no need to use otsu or any threshold value selected by the user. Inspite of the existence of more vegetative information in ExG with otsu method, our ExG-ExR index works well irrespective of the lighting background. Therefore, the ExG-ExR index identifies a binary plant region of interest. The original color pixel of the binary image serves as the mask which isolates leaves as sub-images. The plant species are classified by the color and texture features on each extracted leaf using Logistic Regression classifier with the accuracy of 93.3%.
{"title":"Real-Time Identification of Medicinal Plants using Machine Learning Techniques","authors":"C. Sivaranjani, Lekshmi Kalinathan, R. Amutha, Ruba Soundar Kathavarayan, K. J. Jegadish Kumar","doi":"10.1109/ICCIDS.2019.8862126","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862126","url":null,"abstract":"The lighting condition of the environment are uncontrolled, so the segmentation of a leaf from the background is considered as a complex task. Here we propose a system which can identify the plant species based on the input leaf sample. An improved vegetation index, ExG-ExR is used to obtain more vegetative information from the images. The reason here is, it fixes a built-in zero threshold and hence there is no need to use otsu or any threshold value selected by the user. Inspite of the existence of more vegetative information in ExG with otsu method, our ExG-ExR index works well irrespective of the lighting background. Therefore, the ExG-ExR index identifies a binary plant region of interest. The original color pixel of the binary image serves as the mask which isolates leaves as sub-images. The plant species are classified by the color and texture features on each extracted leaf using Logistic Regression classifier with the accuracy of 93.3%.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130442710","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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