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

Machine Learning Techniques to Understand Partial and Implied Data Values for Conversion of Natural Language to SQL Queries on HPCC Systems 理解HPCC系统上自然语言到SQL查询转换的部分和隐含数据值的机器学习技术

2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) Pub Date : 2019-12-01 DOI: 10.1109/CSITSS47250.2019.9031035

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

引用次数: 3

Design and Implementation of Octave Plugin for HPCC Systems HPCC系统中八度音阶插件的设计与实现

2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) Pub Date : 2019-12-01 DOI: 10.1109/CSITSS47250.2019.9031031

K. Sathvik, G. Shobha, Jyoti Shetty, Dan Camper

引用次数: 0

Decision Support System to Agronomically Optimize Crop Yield based on Nitrogen and Phosphorus 基于氮磷的作物产量优化决策支持系统

2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) Pub Date : 2019-12-01 DOI: 10.1109/CSITSS47250.2019.9031054

Meeradevi, V. Sanjana, Monica R. Mundada

{"title":"Decision Support System to Agronomically Optimize Crop Yield based on Nitrogen and Phosphorus","authors":"Meeradevi, V. Sanjana, Monica R. Mundada","doi":"10.1109/CSITSS47250.2019.9031054","DOIUrl":"https://doi.org/10.1109/CSITSS47250.2019.9031054","url":null,"abstract":"India has always been a high agricultural value country. Agriculture and its industries are one of the country's leading sources of life and economy. The prediction of crops can lead to a positive response that can help farmers improve yields and reduce losses. Natural chemicals like Nitrogen(N), Phosphorus(P), Potassium(K), pH along with soil type (clay soil, black soil, red soil, silt soil, etc.,) and average rainfall dataset are used to predict yield. This project concentrates on balancing macro nutrients (NPK) which is very important requirement for the crop growth. Different doses of these nutrients will be considered foryield prediction of various regions of Karnataka. Since, macro nutrients play major role, by varying the amount of nutrients supplied for crops and sharing this knowledge to farmers will help them to increase crop yield. This proposed work will help farmers to identify the right amount of nutrients supply to particular crops like maize, rice and wheat. Farmers are losing their yield by having lack of knowledge about nutrients requirement for particular crops, this paper concentrates on rice, maize and wheat crop for various regions of Karnataka for prediction. Right quantity of nutrients supply will help farmers to increase their yield.","PeriodicalId":236457,"journal":{"name":"2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS)","volume":"102 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":"122485914","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

AI-NLP Analytics: A thorough Comparative Investigation on India-USA Universities Branding on the Trending Social Media Platform “Instagram” AI-NLP分析:印度-美国大学在热门社交媒体平台“Instagram”上的品牌推广对比研究

2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) Pub Date : 2019-12-01 DOI: 10.1109/CSITSS47250.2019.9031050

G. S. Thejas, Kundan Kumar, S. S. Iyengar, Prajwal Badrinath, N. Sunitha

{"title":"AI-NLP Analytics: A thorough Comparative Investigation on India-USA Universities Branding on the Trending Social Media Platform “Instagram”","authors":"G. S. Thejas, Kundan Kumar, S. S. Iyengar, Prajwal Badrinath, N. Sunitha","doi":"10.1109/CSITSS47250.2019.9031050","DOIUrl":"https://doi.org/10.1109/CSITSS47250.2019.9031050","url":null,"abstract":"The branding of the universities through social media has enormous influence among individuals. Social media has prospered through different networking sites such as Twitter, Facebook, Instagram, etc. However, in recent times, Instagram has become popular due to its unique features, which includes posting images, writing description alongside these images, and many more. People can even comment on these posts. The emotions expressed by people through their posts represent their real feelings and can be used for the analysis of public opinion regarding any topic. In our paper, we are applying the concepts of NLP on these textual descriptions and comments to perform a comparative investigation on the branding of the colleges/universities of India and USA. Our method provides a completely innovative way to view social media data. Our analysis can help the colleges/universities of both the country in providing an insight into their recognition among students through Instagram considering that the younger generation is largely influenced by Instagram. It can also help them in their rapid development and future goals.","PeriodicalId":236457,"journal":{"name":"2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS)","volume":"255 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":"116731886","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: 1

A Multi-time-scale Time Series Analysis for Click Fraud Forecasting using Binary Labeled Imbalanced Dataset 基于二值标记不平衡数据集的点击欺诈多时间尺度时间序列分析

2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) Pub Date : 2019-12-01 DOI: 10.1109/CSITSS47250.2019.9031036

G. S. Thejas, Jayesh Soni, Kianoosh G. Boroojeni, S. S. Iyengar, Kanishk Srivastava, Prajwal Badrinath, N. Sunitha, N. Prabakar, Himanshu Upadhyay

{"title":"A Multi-time-scale Time Series Analysis for Click Fraud Forecasting using Binary Labeled Imbalanced Dataset","authors":"G. S. Thejas, Jayesh Soni, Kianoosh G. Boroojeni, S. S. Iyengar, Kanishk Srivastava, Prajwal Badrinath, N. Sunitha, N. Prabakar, Himanshu Upadhyay","doi":"10.1109/CSITSS47250.2019.9031036","DOIUrl":"https://doi.org/10.1109/CSITSS47250.2019.9031036","url":null,"abstract":"Click fraud refers to the practice of generating random clicks on a link in order to extract illegitimate revenue from the advertisers. We present a generalized model for modeling temporal click fraud data in the form of probability or learning based anomaly detection and time series modeling with time scales like minutes and hours. The proposed approach consists of seven stages: Pre-processing, data smoothing, fraudulent pattern identification, homogenizing variance, normalizing auto-correlation, developing the AR and MA models and fine tuning along with evaluation of the models. The objective of the proposed work is to first, model multi-time-scale time series data on AR/MA by relying only on time and the label without the need of too many attributes and secondly, to model different time scales separately on Auto-regression (AR) and Moving Average (MA) models. Then, we evaluate the models by tuning forecasting errors and also by minimizing Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) to obtain a best fit model for all time scale data. Through our experiments we also demonstrated that the Probability based model approach is better as compared to the Learning based probabilistic estimator model.","PeriodicalId":236457,"journal":{"name":"2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS)","volume":"18 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":"124009582","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: 12

Evaluation of Internal Factors in Driver Distraction 驾驶员分心的内部因素评价

2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) Pub Date : 2019-12-01 DOI: 10.1109/CSITSS47250.2019.9031049

H. S. Srihari, Aishwarya Seth, C. Sowmyarani

引用次数: 0

Hand Recognition and Motion Analysis using Faster RCNN 使用更快RCNN的手部识别和运动分析

2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) Pub Date : 2019-12-01 DOI: 10.1109/CSITSS47250.2019.9031033

M. Srividya, M. Anala, N. Dushyanth, Datla V. Satya K. Raju

引用次数: 2

Hybrid Classifier for Identification of Heart Disease 心脏病识别的混合分类器

2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) Pub Date : 2019-12-01 DOI: 10.1109/CSITSS47250.2019.9031037

Y. Sharma, Rikku Veliyambara, R. Shettar

{"title":"Hybrid Classifier for Identification of Heart Disease","authors":"Y. Sharma, Rikku Veliyambara, R. Shettar","doi":"10.1109/CSITSS47250.2019.9031037","DOIUrl":"https://doi.org/10.1109/CSITSS47250.2019.9031037","url":null,"abstract":"Cardiovascular disease has increased rapidly in the past few decades. It has become a leading cause of death globally. Heart disease has affected the global heterogeneous population irrespective of age and gender. According to World Health Organization, an estimated 17.3 million people died from cardiovascular diseases in 2008, representing 30% of all global deaths. The accurate and timely prediction of these diseases has become a challenge for medical organizations. A mere assumption of absence or presence of disease is an approach used by many hospitals to give prediction results. The predictions of the heart disease are dependent mainly on the prominent factors involved and their effect weightage. Finding out the patterns and extracting knowledge from those patterns is the major task at hand. Data mining techniques have proven to be a good means for this knowledge discovery. This study makes use of the prominent features of two data mining techniques, namely, K-Means Clustering and Decision Tree. These methods, one being unsupervised learning and the other supervised learning, use very different approaches to predict the results. The positive factors of both the techniques have been used to build a Hybrid Classifier. The aim is to provide an algorithm which gives the best accuracy and performance for the Heart disease identification system.","PeriodicalId":236457,"journal":{"name":"2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS)","volume":"7 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":"132688552","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

Time Series Forecasting methods suitable for prediction of CPU usage 适合预测CPU使用情况的时间序列预测方法

2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) Pub Date : 2019-12-01 DOI: 10.1109/CSITSS47250.2019.9031015

Sriram N Rao, G. Shobha, Srinivas Prabhu, N. Deepamala

引用次数: 5

Using virtual reality to boost the effectiveness of brain-computer interface applications 利用虚拟现实技术提高脑机接口应用的有效性

2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) Pub Date : 2019-12-01 DOI: 10.1109/CSITSS47250.2019.9031021

Sriram N Rao, S. Prapulla, G. Shobha, Shivani Hariprasad, Maghup Gupta, Sai Aneesh Reddy

{"title":"Using virtual reality to boost the effectiveness of brain-computer interface applications","authors":"Sriram N Rao, S. Prapulla, G. Shobha, Shivani Hariprasad, Maghup Gupta, Sai Aneesh Reddy","doi":"10.1109/CSITSS47250.2019.9031021","DOIUrl":"https://doi.org/10.1109/CSITSS47250.2019.9031021","url":null,"abstract":"Noninvasive brain-computer interface (BCI) uses data from electroencephalographic (EEG) sensors to train a model using machine learning and pattern detection algorithms to recognize certain patterns. These patterns correspond to a command to an actuator like wheels of a wheelchair, keyboard or mouse. Virtual reality is used to make a virtual environment (VE) where the users can control virtual objects. BCI generated commands can be used to control the virtual objects with a suitable interface. Unity 3D software provides the interface and the VE. The noninvasive nature of input collection makes the input prone to noise and error. The user needs considerable amount of training to learn to control the wheelchair efficiently. This training is done in a VE where the user controls a virtual wheelchair. The accuracy of wheelchair control before and after training in the VE is compared. It is observed that the user can control the physical wheelchair with better accuracy after training in the VE.","PeriodicalId":236457,"journal":{"name":"2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS)","volume":"40 7 Pt 1 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":"125747752","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: 1