2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)最新文献

Threats Paradigmin IoT Ecosystem 威胁范式物联网生态系统

2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2018-08-01 DOI: 10.1109/ICRITO.2018.8748558

Anshul Jain, Tanya Singh, Satyendra K. Sharma

引用次数: 4

Design Pattern Detection using Machine Learning Techniques 使用机器学习技术进行设计模式检测

2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2018-08-01 DOI: 10.1109/ICRITO.2018.8748282

Shivam Chaturvedi, Amrita Chaturvedi, Anurag Tiwari, Shalini Agarwal

引用次数: 7

Comparative Study of Task Scheduling Algorithms through Cloudsim 基于Cloudsim的任务调度算法比较研究

2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2018-08-01 DOI: 10.1109/ICRITO.2018.8748514

R. Pratap, T. Zaidi

{"title":"Comparative Study of Task Scheduling Algorithms through Cloudsim","authors":"R. Pratap, T. Zaidi","doi":"10.1109/ICRITO.2018.8748514","DOIUrl":"https://doi.org/10.1109/ICRITO.2018.8748514","url":null,"abstract":"Delivery of reliable services in cloud environment is a major issue. Reliability may be achieved by implementing the fault tolerance. Due to the abundant growth of traffic and service request on cloud datacenters, balancing the load in cloud environment is one of the serious challenges as failure may occur due to increase in power consumption, node failure, machine failure etc. Therefore there is a needof a policy for balancing the load among the datacenters and various solutions to balance the load have been proposed by researchers. Load distributionis the mechanism of dispersal the load between different nodes based on certain parameters such as underloaded(node) and overloaded (node). In this research articlewe have discussed the concept of dispersal of load and then perform a comparative analysis of various task-scheduling policies such as First Come First Serve, Shortest Job First and Round Robin onCloudsim.The simulation results on Cloudsim depicted that RR task-scheduling is much better than the FCFS and SJF whether we are using the Time shared policy or Space shared policy for execution of cloudlet.","PeriodicalId":439047,"journal":{"name":"2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116957350","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}

引用次数: 9

On the Application of Cross-Project Validation for Predicting Maintainability of Open Source Software using Machine Learning Techniques 基于机器学习技术的跨项目验证在开源软件可维护性预测中的应用

2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2018-08-01 DOI: 10.1109/ICRITO.2018.8748749

R. Malhotra, K. Lata

{"title":"On the Application of Cross-Project Validation for Predicting Maintainability of Open Source Software using Machine Learning Techniques","authors":"R. Malhotra, K. Lata","doi":"10.1109/ICRITO.2018.8748749","DOIUrl":"https://doi.org/10.1109/ICRITO.2018.8748749","url":null,"abstract":"Design and development of models to predict software maintenance effort is an impending research area as these models help to predict maintenance effort of software system at earlier stages of its development. The predictions of these models help in allocation of limited resources in an optimal way in the test and maintenance phases of software development. Although numeral software maintainability prediction models have been successfully developed in the past using machine learning (ML) and statistical techniques but there is always threat to generalizability of result have prevailed, as these models are validated on the same data set on which they are trained. This study endeavors to improve generalizability of the software maintainability prediction by cross-project validation where prediction model developed on one software project is validated against the other project. To meet our objective we have taken three open source projects written in java language.The performance of the models is evaluated using prevalent the performance measures. Based on the statistical tests; it is quite conclusive that cross project validation can be successfully applied to predict software maintenance effort of open source software.","PeriodicalId":439047,"journal":{"name":"2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"445 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127445675","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}

引用次数: 6

Predicting Fluctuations in Cryptocurrencies' Price using users' Comments and Real-time Prices 使用用户评论和实时价格预测加密货币价格波动

2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2018-08-01 DOI: 10.1109/ICRITO.2018.8748792

Pavitra Mohanty, Darshan Patel, Parth Patel, Sudipta Roy

{"title":"Predicting Fluctuations in Cryptocurrencies' Price using users' Comments and Real-time Prices","authors":"Pavitra Mohanty, Darshan Patel, Parth Patel, Sudipta Roy","doi":"10.1109/ICRITO.2018.8748792","DOIUrl":"https://doi.org/10.1109/ICRITO.2018.8748792","url":null,"abstract":"This paper shows the prediction of fluctuation in the future price of cryptocurrencies. Users’ comments and tweets from twitter using Apache Flume and Price data was fetched from exchanges. Bitcoin first documented by allies Satoshi Nakamoto, the first decentralized currency payment system has gained a considerable attention in the financial system, economics, social media and computer science due to its combination of peer-to-peer nature, encryption technology, and monetary unit. Predicting the price of Bitcoin and other cryptocurrencies is a great challenge because it is immensely complex and dynamic in nature. In this paper, we have tried to predict the future price of cryptocurrencies like Bitcoin using LSTM (Long Short-Term Memory) and used Twitter data to predict public mood. By combining both market sentiment and social sentiment because bitcoin price shows mixed properties. We also have selected some other important features from the blockchain information which has a major impact on Bitcoin’s supply and demand and using them to train model that improves the predictive power of the future Bitcoin price. We have performed a deep study of how data from social media affect the price of Bitcoin and so we have included the twitter data in model training. Our model shows that how well LSTM predict the price of Bitcoin considering the high volatility. The precision given by our model is 60% and accuracy is 50%. More focus is not given to accuracy, in this case, considering the highly volatile market.","PeriodicalId":439047,"journal":{"name":"2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124773288","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}

引用次数: 7

Power-aware Aggregated SEARCH: Enhancing Spectrum and Energy Efficiency of Sensor Networks 功率感知聚合搜索:增强传感器网络频谱和能量效率

2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2018-08-01 DOI: 10.1109/ICRITO.2018.8748746

Neeti Gupta, Vidushi Sharma

引用次数: 0

Truncated Wallace Based Single Precision Floating Point Multiplier 截断华莱士基于单精度浮点乘法器

2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2018-08-01 DOI: 10.1109/ICRITO.2018.8748843

Abhay Sharma, T. Rawat

引用次数: 2

Comparison of a Magnetically Geared PM Wind Generator with Radial Flux Generator 磁齿轮永磁风力发电机与径向磁通发电机的比较

2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2018-08-01 DOI: 10.1109/ICRITO.2018.8748428

R. Zeinali, H. Ertan

引用次数: 2

Dynamic Clustering of n-Dimensional Data on Tangential Space 切向空间上n维数据的动态聚类

2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2018-08-01 DOI: 10.1109/ICRITO.2018.8748444

Mayank Sharma, Amit Srivastava, S. Shankar, S. Khatri

{"title":"Dynamic Clustering of n-Dimensional Data on Tangential Space","authors":"Mayank Sharma, Amit Srivastava, S. Shankar, S. Khatri","doi":"10.1109/ICRITO.2018.8748444","DOIUrl":"https://doi.org/10.1109/ICRITO.2018.8748444","url":null,"abstract":"Clustering of n-dimensional data into classes is consistent problem of research, Large number of efficient clustering techniques are in literature and still more are in development. K-means and Spherical K-means are standard clustering methods which are frequently used. Euclidean distance and cosine distance are mainly used by clustering methods. Data distribution is always non-linear and distributed in n-dimensional hyper sphere. Euclidean distance did not take care of topology of the hyper space. Clustering of data using spherical K-means clustering is done through mapping all data points in hyper sphere to the nearest cosine angular distance, but both do not take care of geodesic distance between the points on the surface of the hyper sphere. In this paper new mathematical dynamic clustering approach has been proposed which take care of topology of the data distribution between various clusters and geodesic distance between the points with in the cluster. Theoretical and mathematical results are discussed and empirically verified on the iris data set.","PeriodicalId":439047,"journal":{"name":"2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125502473","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}

引用次数: 0

Understanding Relation between Public Sentiments and Government Policy Reforms 论民情与政府政策改革的关系

2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2018-08-01 DOI: 10.1109/ICRITO.2018.8748655

Mansi Srivastava, S. Khatri, Sapna Sinha, Armaan Singh Ahluwalia, P. Johri

{"title":"Understanding Relation between Public Sentiments and Government Policy Reforms","authors":"Mansi Srivastava, S. Khatri, Sapna Sinha, Armaan Singh Ahluwalia, P. Johri","doi":"10.1109/ICRITO.2018.8748655","DOIUrl":"https://doi.org/10.1109/ICRITO.2018.8748655","url":null,"abstract":"Government of any democratic country usually needs to take decisions for the welfare of its citizens and sometimes for political gain. So, whenever government implements any major policy reforms, government collects feedback from citizens of country through surveys. For these surveys data is gathered through different agencies and from the data available on social network sites. Now a days Twitter has become the major source of open data, as it is the platform where people share their views. In this paper tweets posted by people on recently introduced policy by government of India on ‘Demonetization’ are extracted at regular intervals. Especially, whenever any policy change is implemented by government, tweets from the date of reform till the date of next reform is considered for the study. The extracted tweets are analyzed and change in sentiments of citizens related to reform is measured. Data is collected using Twitter API (Application Program Interface) in Python and sentiment analysis is performed using R Programming. Correlation between policy reform related to demonetization and public sentiments are found and the type of correlation is determined.","PeriodicalId":439047,"journal":{"name":"2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114269305","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}

引用次数: 3