2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS)最新文献_第2页

Massively Scalable Parallel KMeans on the HPCC Systems Platform HPCC系统平台上的大规模可扩展并行KMeans

2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) Pub Date : 2019-12-01 DOI: 10.1109/CSITSS47250.2019.9031047

Lili Xu, A. Apon, Flavio Villanustre, R. Dev, Arjuna Chala

引用次数: 4

Intelligent Clinical Diagnosis System 智能临床诊断系统

2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) Pub Date : 2019-12-01 DOI: 10.1109/CSITSS47250.2019.9031022

Prakarsh Anand, Sirsha Chatterjee, I. Agarwal, Shantanu, Aman Anupam, Krupa A. Jain

引用次数: 1

CSITSS Proceedings 2020

2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) Pub Date : 2019-12-01 DOI: 10.1109/CSITSS47250.2019.9031039

引用次数: 0

A Comparative Study of Spark Schedulers' Performance 星火调度器性能比较研究

2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) Pub Date : 2019-12-01 DOI: 10.1109/CSITSS47250.2019.9031028

Ashmita Raju, Ramya Ramanathan, R. Hemavathy

引用次数: 2

An approach to enable secure and reliable communication on IoT Devices 一种在物联网设备上实现安全可靠通信的方法

2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) Pub Date : 2019-12-01 DOI: 10.1109/CSITSS47250.2019.9031034

B. R. Harsha, A. Damodaran, S. Ranganath, Vaishnav Raut, Shashanka Holla

引用次数: 2

Indian Food Image Classification with Transfer Learning 基于迁移学习的印度食物图像分类

2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) Pub Date : 2019-12-01 DOI: 10.1109/CSITSS47250.2019.9031051

J. Rajayogi, G. Manjunath, G. Shobha

{"title":"Indian Food Image Classification with Transfer Learning","authors":"J. Rajayogi, G. Manjunath, G. Shobha","doi":"10.1109/CSITSS47250.2019.9031051","DOIUrl":"https://doi.org/10.1109/CSITSS47250.2019.9031051","url":null,"abstract":"Image classification has become easier with deep learning and availability of larger datasets and computational resources. The Convolutional neural network is the most popular and widely used image classification technique in the recent days. In this paper image classification is performed on Indian food dataset using different transfer learning techniques. The food plays important role in human's life as it provides us different nutrients and hence it is necessary for every individual to keep a watch on their eating habits. Therefore, food classification is a quintessential thing for a healthier life style. Unlike the traditional methods of building a model from the scratch, pre trained models are used in this project which saves the computation time and cost and also has given better results. The Indian food dataset of 20 classes with 500 images in each class is used for training and validating. The models used are IncceptionV3, VGG16, VGG19 and ResNet. After experimentation it was found that Google InceptionV3 outperformed other models with an accuracy of 87.9% and loss rate of 0.5893.","PeriodicalId":236457,"journal":{"name":"2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132906751","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: 20

Natural Language to Structured Query Language using Elasticsearch for descriptive columns 使用Elasticsearch对描述性列进行自然语言到结构化查询语言的转换

2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) Pub Date : 2019-12-01 DOI: 10.1109/CSITSS47250.2019.9031030

Sourabh S Badhya, A. Prasad, Shetty Rohan, Y. Yashwanth, N. Deepamala, G. Shobha

{"title":"Natural Language to Structured Query Language using Elasticsearch for descriptive columns","authors":"Sourabh S Badhya, A. Prasad, Shetty Rohan, Y. Yashwanth, N. Deepamala, G. Shobha","doi":"10.1109/CSITSS47250.2019.9031030","DOIUrl":"https://doi.org/10.1109/CSITSS47250.2019.9031030","url":null,"abstract":"In the present day, data is captured in large amounts in the form of numbers, text, images etc. The data captured is either very simple to extract any useful information or it is very complex such that it takes a lot of time to generate information. In addition to this, data is being stored either in relational databases like MySQL, PostgreSQL or in document-oriented databases like MongoDB, Cassandra. The extraction of data from these databases requires some special knowledge of writing queries that are designed for that particular database. Hence there is a need for a system which extracts information from an input natural language query and convert it into a query which can be understood by the database in a fast and an efficient way. This paper proposes a way to create such a system by making use of Elasticsearch which is used for extracting data from descriptive columns. This paper is mainly focused on creating optimized SQL queries from natural language inputs using Elasticsearch where the extraction of data happens by means of searching through keywords present in input query throughout all the descriptive columns.","PeriodicalId":236457,"journal":{"name":"2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133597480","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

Reinforcement Learning Based Channel Selection for Design of Routing Protocol in Cognitive Radio Network 基于强化学习的认知无线网络路由协议信道选择设计

2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) Pub Date : 2019-12-01 DOI: 10.1109/CSITSS47250.2019.9031024

S. Talekar, Sujatha P. Terdal

引用次数: 3

Development of GNSS Test Tool and Automatic Suit GNSS测试工具及自动套件的研制

2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) Pub Date : 2019-12-01 DOI: 10.1109/CSITSS47250.2019.9031011

Neetin Kumar, Deepak Gupta, Sujata D. Badiger

引用次数: 0

A Survey of Popular Image and Text analysis Techniques 流行的图像和文本分析技术综述

2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) Pub Date : 2019-12-01 DOI: 10.1109/CSITSS47250.2019.9031023

R. Suresh, N. Keshava

{"title":"A Survey of Popular Image and Text analysis Techniques","authors":"R. Suresh, N. Keshava","doi":"10.1109/CSITSS47250.2019.9031023","DOIUrl":"https://doi.org/10.1109/CSITSS47250.2019.9031023","url":null,"abstract":"Image processing has made huge progress in recent times and has captured the attention of the international research community in recent times. In this survey paper the winning Convolutional Neural Network (CNN) architectures of the popular ImageNet Large Scale Visual Recognition Competition (ILSVRC) competition along with the innovations they introduced are discussed in detail. CNNs to the likes of the ZFNet, Inception V1, V2, V3 and V4 versions, ResNet versions, Inception ResNet versions, VGG versions are all covered. Also object detection is looked at as a preeminent task and popular models like RCNN, Fast RCNN, Faster RCNN and the YOLO versions along with their award winning architectures are discussed. Text classification is another major practice that is followed before making business decisions. In this survey paper section III explains the text classification techniques. preprocessing techniques such as removing stop words, stemming, lemmatization. Two variants of vectorization method such as vectorization-delta, vectorization-center. paper also explains the hyperparameter tuning for optimization of support vector machine(SVM), Logistic regression(LR), random forest(RF) and Boosted Regression Tree (BRT). Explored fine tuning parameter which enhance the performance of machine learning models.","PeriodicalId":236457,"journal":{"name":"2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS)","volume":"10 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133227406","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