2019 International Conference on Computational Intelligence in Data Science (ICCIDS)最新文献

Candidate Generation for Instance Matching on Semantic Web 语义Web实例匹配的候选生成

2019 International Conference on Computational Intelligence in Data Science (ICCIDS) Pub Date : 2019-02-01 DOI: 10.1109/ICCIDS.2019.8862131

B. Vijaya, P. Gharpure

引用次数: 0

Reinforcement Learning Approach to Improve Transmission Control Protocol 改进传输控制协议的强化学习方法

2019 International Conference on Computational Intelligence in Data Science (ICCIDS) Pub Date : 2019-02-01 DOI: 10.1109/ICCIDS.2019.8862007

S. V. Jansi Rani, R. S. Milton, L. Yamini, K. Shivaani

{"title":"Reinforcement Learning Approach to Improve Transmission Control Protocol","authors":"S. V. Jansi Rani, R. S. Milton, L. Yamini, K. Shivaani","doi":"10.1109/ICCIDS.2019.8862007","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862007","url":null,"abstract":"Transmission Control Protocol(TCP) plays an important role in everyday life, right from accessing ones mails to browsing the internet. With revolutionary mechanisms to ensure safe and consistent delivery of data and reducing the loss in the data transferred, TCP has indeed paved way for a paradigm shift in the way data is delivered over a network. TCP is proven to work in traditional environments involving conventional wired transmission, with well formulated packet loss restricting mechanisms implemented in the form of congestion control techniques. It is, however, found wanting in environments which involve a degree of heterogeneity (composed of wired and wireless nodes) or in purely wireless networks, involving multimedia data transmission. The performance improvement is achieved by developing a system that can classify losses as occurring due to congestion or due to the wireless nature and consequently controlling the congestion window size. This work seeks to create such a system based on reinforcement learning, where it first learns to differentiate and then predict wireless and congestion loss and consequently, predict the ideal size of congestion window thereby increasing the throughput of the system.","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":"125574758","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: 2

A Hybrid Machine Learning Approach for Classifying Aerial Images of Flood-Hit Areas 洪涝地区航拍图像分类的混合机器学习方法

2019 International Conference on Computational Intelligence in Data Science (ICCIDS) Pub Date : 2019-02-01 DOI: 10.1109/ICCIDS.2019.8862138

J. Akshya, P. Priyadarsini

引用次数: 32

Handwritten Mathematical Recognition Tool 手写数学识别工具

2019 International Conference on Computational Intelligence in Data Science (ICCIDS) Pub Date : 2019-02-01 DOI: 10.1109/ICCIDS.2019.8862155

M. Abirami, S. Jaganathan

引用次数: 2

MoneyBall - Data Mining on Cricket Dataset MoneyBall -对板球数据集的数据挖掘

2019 International Conference on Computational Intelligence in Data Science (ICCIDS) Pub Date : 2019-02-01 DOI: 10.1109/ICCIDS.2019.8862065

D. Thenmozhi, P. Mirunalini, S. M. Jaisakthi, Srivatsan Vasudevan, V. Veeramani Kannan, S. Sagubar Sadiq

引用次数: 10

TaskDo: A Daily Task Recommender System TaskDo:每日任务推荐系统

2019 International Conference on Computational Intelligence in Data Science (ICCIDS) Pub Date : 2019-02-01 DOI: 10.1109/ICCIDS.2019.8862073

M. Kuhail, Nikhil Sai Santosh Gurram

引用次数: 3

Med-Recommender System for Predictive Analysis of Hospitals and Doctors 用于医院和医生预测分析的药物推荐系统

2019 International Conference on Computational Intelligence in Data Science (ICCIDS) Pub Date : 2019-02-01 DOI: 10.1109/ICCIDS.2019.8862121

S. Swarnalatha, I. Kesavarthini, S. Poornima, N. Sripriya

{"title":"Med-Recommender System for Predictive Analysis of Hospitals and Doctors","authors":"S. Swarnalatha, I. Kesavarthini, S. Poornima, N. Sripriya","doi":"10.1109/ICCIDS.2019.8862121","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862121","url":null,"abstract":"A recommender system is proposed and developed to help users to find the best hospital for a particular treatment. Finding a best hospital that can cure one’s ailment is of paramount importance. A good hospital is one in which there are always enough staff on duty with the right skills, knowledge and experience. Customer experience is how customers perceive their interactions with a company or an organization. A customer’s experience is reflected in the comments that he makes about the organization through online public forums. Med–recommender system aims to provide accurate analysis of hospitals by taking into account the reviews by thousands of patients, which were written by the patients themselves in various online forums. Our recommendation system performs sentiment analysis on the reviews of various patients using Natural Language Processing techniques to classify them as positive and negative reviews. It weighs the ranking of hospitals on three different parameters namely polarity, subjectivity and intensity. The hospital with the best ranking for curing a particular disease is then given as result to the user asking for a recommendation. The system is evaluated using 300 online reviews about hospitals and specialties and found to yield 90% of accuracy. The proposed system also helps the users to understand the quality of a certain hospital by providing star ratings for the hospital when the user needs.","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":"115032049","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

Multiple Real-time object identification using Single shot Multi-Box detection 基于单次多盒检测的多实时目标识别

2019 International Conference on Computational Intelligence in Data Science (ICCIDS) Pub Date : 2019-02-01 DOI: 10.1109/ICCIDS.2019.8862041

S. Kanimozhi, G. Gayathri, T. Mala

引用次数: 27

INSIGHTS! - a modern deep learning approach to data analysis using Feature Name Substitution Network 见解!-使用特征名称替代网络进行数据分析的现代深度学习方法

2019 International Conference on Computational Intelligence in Data Science (ICCIDS) Pub Date : 2019-02-01 DOI: 10.1109/ICCIDS.2019.8862071

K. M. Yatheendra Pravan, Udhayakumar Shanmugam, P. Rajaraman

{"title":"INSIGHTS! - a modern deep learning approach to data analysis using Feature Name Substitution Network","authors":"K. M. Yatheendra Pravan, Udhayakumar Shanmugam, P. Rajaraman","doi":"10.1109/ICCIDS.2019.8862071","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862071","url":null,"abstract":"The core of technological advancements in the current trend is based on the manipulation of the inestimable amount of data that is generated every second around us. Gaining interesting insights from the data is of utmost importance and the need of the hour. The proposed system makes use of advancements in the domain of deep learning by implementing various algorithms and methodologies to automate the process of data analytics. The intended insights platform is developed using various deep learning frameworks such as Tensorflow, Keras and delivered to the end user as a web platform using Django Framework. The underlying algorithm of insights which makes the automation of analytics possible relies on the efficacy of feature name substitution network implemented using LSTM and the enhanced correlation analysis. These are then used to determine a measure called Insight Relevance Index (IRI) which then updates the global rule set records in the centralized data store accordingly. Employing the proposed system will definitely aid the profit and future growth of an institution or an organization.","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":"116851797","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

A review of recent trends in EEG based Brain-Computer Interface 基于脑电图的脑机接口研究进展综述

2019 International Conference on Computational Intelligence in Data Science (ICCIDS) Pub Date : 2019-02-01 DOI: 10.1109/ICCIDS.2019.8862054

P. Lahane, Jay Jagtap, Aditya Inamdar, Nihal Karne, Ritwik Dev

引用次数: 19