Pub Date : 2017-01-01DOI: 10.1109/CONFLUENCE.2017.7943218
Luxit Kapoor, Sanjeev Thakur
Biomedical Image Processing is a growing and demanding field. It comprises of many different types of imaging methods likes CT scans, X-Ray and MRI. These techniques allow us to identify even the smallest abnormalities in the human body. The primary goal of medical imaging is to extract meaningful and accurate information from these images with the least error possible. Out of the various types of medical imaging processes available to us, MRI is the most reliable and safe. It does not involve exposing the body to any sorts of harmful radiation. This MRI can then be processed, and the tumor can be segmented. Tumor Segmentation includes the use of several different techniques. The whole process of detecting brain tumor from an MRI can be classified into four different categories: Pre-Processing, Segmentation, Optimization and Feature Extraction. This survey involves reviewing the research by other professionals and compiling it into one paper.
{"title":"A survey on brain tumor detection using image processing techniques","authors":"Luxit Kapoor, Sanjeev Thakur","doi":"10.1109/CONFLUENCE.2017.7943218","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943218","url":null,"abstract":"Biomedical Image Processing is a growing and demanding field. It comprises of many different types of imaging methods likes CT scans, X-Ray and MRI. These techniques allow us to identify even the smallest abnormalities in the human body. The primary goal of medical imaging is to extract meaningful and accurate information from these images with the least error possible. Out of the various types of medical imaging processes available to us, MRI is the most reliable and safe. It does not involve exposing the body to any sorts of harmful radiation. This MRI can then be processed, and the tumor can be segmented. Tumor Segmentation includes the use of several different techniques. The whole process of detecting brain tumor from an MRI can be classified into four different categories: Pre-Processing, Segmentation, Optimization and Feature Extraction. This survey involves reviewing the research by other professionals and compiling it into one paper.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"24 1","pages":"582-585"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89457796","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 : 2017-01-01DOI: 10.1109/CONFLUENCE.2017.7943158
Meetu Kandpal, Monica Gahlawat, Kalyani Patel
In the era of Big Data analytics predictive modeling plays an important role to predict the future demand and behavior by using historical data. As majority of the IT companies running behind cloud services, the cloud service providers like Amazon, Google cloud, Microsoft Azure etc may be interested to know the future demand of the computing resources so that they can derive new pricing schemes to gain more profit. The providers have different pricing schemes to charge for computing resourcese. g., Amazon provides three pricing schemes, namely, on-demand pricing, reserved pricing and auction pricing in the same way Microsoft has different schemes like Pay-As-You-Go Subscriptions, Prepaid Subscriptions. The paper presents survey of role of predictive modeling in cloud service pricing. The survey result clearly shows that predictions made by various author are closer to actual outcomes, which highlights the importance of predictive modeling to forecast future demand of cloud computing resources and deciding the price of resources.
{"title":"Role of predictive modeling in cloud services pricing: A survey","authors":"Meetu Kandpal, Monica Gahlawat, Kalyani Patel","doi":"10.1109/CONFLUENCE.2017.7943158","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943158","url":null,"abstract":"In the era of Big Data analytics predictive modeling plays an important role to predict the future demand and behavior by using historical data. As majority of the IT companies running behind cloud services, the cloud service providers like Amazon, Google cloud, Microsoft Azure etc may be interested to know the future demand of the computing resources so that they can derive new pricing schemes to gain more profit. The providers have different pricing schemes to charge for computing resourcese. g., Amazon provides three pricing schemes, namely, on-demand pricing, reserved pricing and auction pricing in the same way Microsoft has different schemes like Pay-As-You-Go Subscriptions, Prepaid Subscriptions. The paper presents survey of role of predictive modeling in cloud service pricing. The survey result clearly shows that predictions made by various author are closer to actual outcomes, which highlights the importance of predictive modeling to forecast future demand of cloud computing resources and deciding the price of resources.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"267 1","pages":"249-254"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79810869","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 : 2017-01-01DOI: 10.1109/CONFLUENCE.2017.7943144
Tamanna, O. Sangwan
Accurate software reliability prediction with a single universal software reliability growth model is very difficult. In this ρ aper we reviewed different models which uses computational intelligence for the prediction purpose and describe how these techniques outperform conventional statistical models. Parameters, efficacy measures with methodologies are concluded in tabular form.
{"title":"Computational intelligence based approaches to software reliability","authors":"Tamanna, O. Sangwan","doi":"10.1109/CONFLUENCE.2017.7943144","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943144","url":null,"abstract":"Accurate software reliability prediction with a single universal software reliability growth model is very difficult. In this ρ aper we reviewed different models which uses computational intelligence for the prediction purpose and describe how these techniques outperform conventional statistical models. Parameters, efficacy measures with methodologies are concluded in tabular form.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"108 1","pages":"171-176"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79580209","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 : 2017-01-01DOI: 10.1109/CONFLUENCE.2017.7943145
T. Pattanshetti, V. Attar
Enormous amount of data is being generated at a tremendous rate by multiple sources, often this data exists in different formats thus making it quite difficult to process the data using traditional methods. The platforms used for processing this type of data rely on distributed architecture like Cloud computing, Hadoop etc. The processing of big data can be efficiently carried out by exploring the characteristics of underlying platforms. With the advent of efficient algorithms, software metrics and by identifying the relationship amongst these measures, system characteristics can be evaluated in order to improve the overall performance of the computing system. By focusing on these measures which play important role in determining the overall performance, service level agreements can also be revised. This paper presents a survey of different performance modeling techniques of big data applications. One of the key concepts in performance modeling is finding relevant parameters which accurately represent performance of big data platforms. These extracted relevant performances measures are mapped onto software qualify concepts which are then used for defining service level agreements.
{"title":"Survey of performance modeling of big data applications","authors":"T. Pattanshetti, V. Attar","doi":"10.1109/CONFLUENCE.2017.7943145","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943145","url":null,"abstract":"Enormous amount of data is being generated at a tremendous rate by multiple sources, often this data exists in different formats thus making it quite difficult to process the data using traditional methods. The platforms used for processing this type of data rely on distributed architecture like Cloud computing, Hadoop etc. The processing of big data can be efficiently carried out by exploring the characteristics of underlying platforms. With the advent of efficient algorithms, software metrics and by identifying the relationship amongst these measures, system characteristics can be evaluated in order to improve the overall performance of the computing system. By focusing on these measures which play important role in determining the overall performance, service level agreements can also be revised. This paper presents a survey of different performance modeling techniques of big data applications. One of the key concepts in performance modeling is finding relevant parameters which accurately represent performance of big data platforms. These extracted relevant performances measures are mapped onto software qualify concepts which are then used for defining service level agreements.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"7 1","pages":"177-181"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78906843","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 : 2017-01-01DOI: 10.1109/CONFLUENCE.2017.7943228
K. Dang, Shanu Sharma
With the tremendous increase in video and image database there is a great need of automatic understanding and examination of data by the intelligent systems as manually it is becoming out of reach. Narrowing it down to one specific domain, one of the most specific objects that can be traced in the images are people i.e. faces. Face detection is becoming a challenge by its increasing use in number of applications. It is the first step for face recognition, face analysis and detection of other features of face. In this paper, various face detection algorithms are discussed and analyzed like Viola-Jones, SMQT features & SNOW Classifier, Neural Network-Based Face Detection and Support Vector Machine-Based face detection. All these face detection methods are compared based on the precision and recall value calculated using a DetEval Software which deals with precised values of the bounding boxes around the faces to give accurate results.
{"title":"Review and comparison of face detection algorithms","authors":"K. Dang, Shanu Sharma","doi":"10.1109/CONFLUENCE.2017.7943228","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943228","url":null,"abstract":"With the tremendous increase in video and image database there is a great need of automatic understanding and examination of data by the intelligent systems as manually it is becoming out of reach. Narrowing it down to one specific domain, one of the most specific objects that can be traced in the images are people i.e. faces. Face detection is becoming a challenge by its increasing use in number of applications. It is the first step for face recognition, face analysis and detection of other features of face. In this paper, various face detection algorithms are discussed and analyzed like Viola-Jones, SMQT features & SNOW Classifier, Neural Network-Based Face Detection and Support Vector Machine-Based face detection. All these face detection methods are compared based on the precision and recall value calculated using a DetEval Software which deals with precised values of the bounding boxes around the faces to give accurate results.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"10 1","pages":"629-633"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78949971","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 : 2017-01-01DOI: 10.1109/CONFLUENCE.2017.7943186
Daniel Fraunholz, Marc Zimmermann, S. D. Antón, Jörg Schneider, H. Dieter Schotten
Recently, the increase of interconnectivity has led to a rising amount of IoT enabled devices in botnets. Such botnets are currently used for large scale DDoS attacks. To keep track with these malicious activities, Honeypots have proven to be a vital tool. We developed and set up a distributed and highly-scalable WAN Honeypot with an attached backend infrastructure for sophisticated processing of the gathered data. For the processed data to be understandable we designed a graphical frontend that displays all relevant information that has been obtained from the data. We group attacks originating in a short period of time in one source as sessions. This enriches the data and enables a more in-depth analysis. We produced common statistics like usernames, passwords, username/password combinations, password lengths, originating country and more. From the information gathered, we were able to identify common dictionaries used for brute-force login attacks and other more sophisticated statistics like login attempts per session and attack efficiency.
{"title":"Distributed and highly-scalable WAN network attack sensing and sophisticated analysing framework based on Honeypot technology","authors":"Daniel Fraunholz, Marc Zimmermann, S. D. Antón, Jörg Schneider, H. Dieter Schotten","doi":"10.1109/CONFLUENCE.2017.7943186","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943186","url":null,"abstract":"Recently, the increase of interconnectivity has led to a rising amount of IoT enabled devices in botnets. Such botnets are currently used for large scale DDoS attacks. To keep track with these malicious activities, Honeypots have proven to be a vital tool. We developed and set up a distributed and highly-scalable WAN Honeypot with an attached backend infrastructure for sophisticated processing of the gathered data. For the processed data to be understandable we designed a graphical frontend that displays all relevant information that has been obtained from the data. We group attacks originating in a short period of time in one source as sessions. This enriches the data and enables a more in-depth analysis. We produced common statistics like usernames, passwords, username/password combinations, password lengths, originating country and more. From the information gathered, we were able to identify common dictionaries used for brute-force login attacks and other more sophisticated statistics like login attempts per session and attack efficiency.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"27 1","pages":"416-421"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83303294","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 : 2017-01-01DOI: 10.1109/CONFLUENCE.2017.7943185
Rajesh Mishra, Bhupendra Singh, Shobhna Tiwari
Safe operation of rail vehicles is a matter of concern. Communication based train control (CBTC) network is a data communication based automated control network that ensures safety of rail vehicles. In this network status and control command are transmitted using WLAN technology. It has been observed that WLAN is less successful for high speed as because of its design constrains packet drops cannot be avoided. In present work, analysis of random packet drops in CBTC systems during handover process is evaluated. Unlike the existing work that only consider packet drop formulation under specific condition, we analyze system behavior under worst case scenario by varying one parameter over the range and analyze its impact on the packet drop and other related parameters of CBTC system. Simulation results are presented and compared against existing results under specific conditions.
{"title":"Performance improvement of communication based high speed train control system with packet drops during handover under worst case scenario","authors":"Rajesh Mishra, Bhupendra Singh, Shobhna Tiwari","doi":"10.1109/CONFLUENCE.2017.7943185","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943185","url":null,"abstract":"Safe operation of rail vehicles is a matter of concern. Communication based train control (CBTC) network is a data communication based automated control network that ensures safety of rail vehicles. In this network status and control command are transmitted using WLAN technology. It has been observed that WLAN is less successful for high speed as because of its design constrains packet drops cannot be avoided. In present work, analysis of random packet drops in CBTC systems during handover process is evaluated. Unlike the existing work that only consider packet drop formulation under specific condition, we analyze system behavior under worst case scenario by varying one parameter over the range and analyze its impact on the packet drop and other related parameters of CBTC system. Simulation results are presented and compared against existing results under specific conditions.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"47 1","pages":"412-415"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73496775","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 : 2017-01-01DOI: 10.1109/CONFLUENCE.2017.7943224
Siddhant Bansal, Garima Mehta
The security of multimedia content such as images while transmission is a cause of concern in current times. Traditional watermarking techniques help in identification of source as well as maintaining patient metadata for biomedical images. Similarly, traditional image encryption techniques allow privacy of patients. There is a need for a two-layer security approach with joint watermarking and encryption, to improve over contemporary methods. This paper presents comparative analysis of various joint encryption and watermarking algorithms of biomedical images to fin d the best pair of algorithms based on previous research. Comparative results also indicate that joint encryption and watermarking algorithms are suitable for security of biomedical images.
{"title":"Comparative analysis of joint encryption and watermarking algorithms for security of biomedical images","authors":"Siddhant Bansal, Garima Mehta","doi":"10.1109/CONFLUENCE.2017.7943224","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943224","url":null,"abstract":"The security of multimedia content such as images while transmission is a cause of concern in current times. Traditional watermarking techniques help in identification of source as well as maintaining patient metadata for biomedical images. Similarly, traditional image encryption techniques allow privacy of patients. There is a need for a two-layer security approach with joint watermarking and encryption, to improve over contemporary methods. This paper presents comparative analysis of various joint encryption and watermarking algorithms of biomedical images to fin d the best pair of algorithms based on previous research. Comparative results also indicate that joint encryption and watermarking algorithms are suitable for security of biomedical images.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"615 1","pages":"609-612"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77362456","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 : 2017-01-01DOI: 10.1109/CONFLUENCE.2017.7943207
Uma Ojha, Savita Goel
Breast cancer is the most common cancer in women and thus the early stage detection in breast cancer can provide potential advantage in the treatment of this disease. Early treatment not only helps to cure cancer but also helps in its prevention of its recurrence. Data mining algorithms can provide great assistance in prediction of earl y stage breast cancer that always has been a challenging research problem. The main objective of this research is to find how precisely can these data mining algorithms predict the probability of recurrence of the disease among the patients on the basis of important stated parameters. The research highlights the performance of different clustering and classification algorithms on the dataset. Experiments show that classification algorithms are better predictors than clustering algorithms. The result indicates that the decision tree (C5.0) and SVM is the best predictor with 81% accuracy on the holdout sample and fuzzy c-means came with the lowest accuracy of37% among the algorithms used in this paper.
{"title":"A study on prediction of breast cancer recurrence using data mining techniques","authors":"Uma Ojha, Savita Goel","doi":"10.1109/CONFLUENCE.2017.7943207","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943207","url":null,"abstract":"Breast cancer is the most common cancer in women and thus the early stage detection in breast cancer can provide potential advantage in the treatment of this disease. Early treatment not only helps to cure cancer but also helps in its prevention of its recurrence. Data mining algorithms can provide great assistance in prediction of earl y stage breast cancer that always has been a challenging research problem. The main objective of this research is to find how precisely can these data mining algorithms predict the probability of recurrence of the disease among the patients on the basis of important stated parameters. The research highlights the performance of different clustering and classification algorithms on the dataset. Experiments show that classification algorithms are better predictors than clustering algorithms. The result indicates that the decision tree (C5.0) and SVM is the best predictor with 81% accuracy on the holdout sample and fuzzy c-means came with the lowest accuracy of37% among the algorithms used in this paper.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"61 1","pages":"527-530"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88489020","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 : 2017-01-01DOI: 10.1109/CONFLUENCE.2017.7943119
Rivindu Perera, P. Nand, Wen-Hsin Yang, Kohichi Toshioka
The consumption of Linked Data has dramatically increased with the increasing momentum towards semantic web. Linked data is essentially a very simplistic format for representation of knowledge in that all the knowledge is represented as triples which can be linked using one or more components from the triple. To date, most of the efforts has been towards either creating linked data by mining the web or making it available for users as a source of knowledgebase for knowledge engineering applications. In recent times there has been a growing need for these applications to interact with users in a natural language which required the transformation of the linked data knowledge into a natural language. The aim of the RealText project described in this paper, is to build a scalable framework to transform Linked Data into natural language by generating lexicalization patterns for triples. A lexicalization pattern is a syntactical pattern that will transform a given triple into a syntactically correct natural language sentence. Using DBpedia as the Linked Data resource, we have generated 283 accurate lexicalization patterns for a sample set of 25 ontology classes. We performed human evaluation on a test sub-sample with an inter-rater agreement of 0.86 and 0.80 for readability and accuracy respectively. This results showed that the lexicalization patterns generated language that are accurate, readable and emanates qualities of a human produced language.
{"title":"Lexicalizing linked data for a human friendly web","authors":"Rivindu Perera, P. Nand, Wen-Hsin Yang, Kohichi Toshioka","doi":"10.1109/CONFLUENCE.2017.7943119","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943119","url":null,"abstract":"The consumption of Linked Data has dramatically increased with the increasing momentum towards semantic web. Linked data is essentially a very simplistic format for representation of knowledge in that all the knowledge is represented as triples which can be linked using one or more components from the triple. To date, most of the efforts has been towards either creating linked data by mining the web or making it available for users as a source of knowledgebase for knowledge engineering applications. In recent times there has been a growing need for these applications to interact with users in a natural language which required the transformation of the linked data knowledge into a natural language. The aim of the RealText project described in this paper, is to build a scalable framework to transform Linked Data into natural language by generating lexicalization patterns for triples. A lexicalization pattern is a syntactical pattern that will transform a given triple into a syntactically correct natural language sentence. Using DBpedia as the Linked Data resource, we have generated 283 accurate lexicalization patterns for a sample set of 25 ontology classes. We performed human evaluation on a test sub-sample with an inter-rater agreement of 0.86 and 0.80 for readability and accuracy respectively. This results showed that the lexicalization patterns generated language that are accurate, readable and emanates qualities of a human produced language.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"29 1","pages":"30-35"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91540813","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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