2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS)最新文献_第5页

Prediction of breast cancer using Find-S and Candidate elimination algorithm 使用Find-S和候选消除算法预测乳腺癌

2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) Pub Date : 2019-12-01 DOI: 10.1109/CSITSS47250.2019.9031046

C. L. Nithya, Sunanda Dixit, B.I. Khodhanpur

引用次数: 0

Information Retrieval in Kannada using Ontology 基于本体的卡纳达语信息检索

2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) Pub Date : 2019-12-01 DOI: 10.1109/CSITSS47250.2019.9031044

H. C. Vijayalakshmi, Bhavana S Dixit

引用次数: 0

ElGamal-based Privacy-Preseving Scheme (EPPS) for Edge-Cloud-of-Things (ECoT) 基于elgamal的边缘物联网(ECoT)隐私保护方案(EPPS)

2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) Pub Date : 2019-12-01 DOI: 10.1109/CSITSS47250.2019.9031020

N. Jayashree, B. Babu

引用次数: 0

Post Disaster Management Using Satellite Imagery and Social Media Data 利用卫星图像和社交媒体数据进行灾后管理

2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) Pub Date : 2019-12-01 DOI: 10.1109/CSITSS47250.2019.9031042

Malika Makker, Ramya Ramanathan, Sindhu Dinesh

{"title":"Post Disaster Management Using Satellite Imagery and Social Media Data","authors":"Malika Makker, Ramya Ramanathan, Sindhu Dinesh","doi":"10.1109/CSITSS47250.2019.9031042","DOIUrl":"https://doi.org/10.1109/CSITSS47250.2019.9031042","url":null,"abstract":"Efficient post disaster management is imperative to deal with the havoc a disaster creates. Providing the disaster response agencies with the necessary tools and advanced technologies could accelerate the process of rescuing people in affected areas and providing them with the necessary assistance. Social media provides NGOs, individuals, etc., a platform to reach out to people in other parts, expressing the needs of the affected people. Using social media data, the disaster response agencies could gather all the immediate needs of the people. The solution proposed makes use of satellite imagery and social media data to assist the disaster response agencies. The system is capable of tracing usable roads in flooded areas and distinguishes areas based on the extent of damage using satellite imagery, and analyzing areas where people could be stranded and identifying items of need using social media data. Tracing of usable roads has been carried out using maximum number of connected pixels concept. Distinguishing areas based on extent of damage is carried by comparing pre and post disaster satellite images. For analyzing items of need and identifying locations of stranded people, Twitter data was filtered based on certain keywords. It's an all-in-one solution, which could be used by response agencies to gather real time information about the on-ground situation.","PeriodicalId":236457,"journal":{"name":"2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS)","volume":"20 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":"115383218","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 Machine Learning Approach to Water Leak Localization 漏水定位的机器学习方法

2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) Pub Date : 2019-12-01 DOI: 10.1109/CSITSS47250.2019.9031010

D. Shravani, Y. Prajwal, S. Prapulla, N S Girish Rao Salanke, G. Shobha, Syed Farhan Ahmad

引用次数: 7

A novel utility metric to measure information loss for generalization and suppression techniques in Privacy Preserving Data publishing 为隐私保护数据发布的泛化和抑制技术提供了一种新的度量信息损失的实用度量

2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) Pub Date : 2019-12-01 DOI: 10.1109/CSITSS47250.2019.9031014

Veena Gadad, C. Sowmyarani

{"title":"A novel utility metric to measure information loss for generalization and suppression techniques in Privacy Preserving Data publishing","authors":"Veena Gadad, C. Sowmyarani","doi":"10.1109/CSITSS47250.2019.9031014","DOIUrl":"https://doi.org/10.1109/CSITSS47250.2019.9031014","url":null,"abstract":"Privacy has become a prime importance in this digital era. Personally Identifiable Information (PII) gets collected in various firms such as educational institutions, government organizations, hospitals etc‥ The collected data is often published and utilized for the purpose of analysis, research, decision making, advertisement or for the purpose of business. It is the duty of the data curator to store and to publish the data safely. A person who is having accessibility to the data that is published must not be able to identify or learn any new personal or sensitive information of an individual. Statistical Disclosure Control(SDC) is a suite of anonymization techniques. The aim of SDC is post processing the data containing sensitive or personal information and effectively protect the privacy of the participating data subject. However these techniques leads to “information loss”, i.e., any analysis or research carried on such a data might not give a appropriate result. This paper aims to discuss various metrics available to assess the information loss in the processed data and an attempt has been made to propose our technique for measuring the loss.","PeriodicalId":236457,"journal":{"name":"2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS)","volume":"55 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":"129302566","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

Analysis and Prediction of Earthquake Impact-a Machine Learning approach 地震影响的分析与预测——一种机器学习方法

2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) Pub Date : 2019-12-01 DOI: 10.1109/CSITSS47250.2019.9031026

Anmol Gaba, Arnab Jana, Rahul Subramaniam, Yash Agrawal, Merin Meleet

{"title":"Analysis and Prediction of Earthquake Impact-a Machine Learning approach","authors":"Anmol Gaba, Arnab Jana, Rahul Subramaniam, Yash Agrawal, Merin Meleet","doi":"10.1109/CSITSS47250.2019.9031026","DOIUrl":"https://doi.org/10.1109/CSITSS47250.2019.9031026","url":null,"abstract":"An earthquake is a natural disaster known on account of the devastating effect it has on naturally occurring structures and manmade structures such as buildings, bungalows and residential locations to name a few. Earthquakes are measured using seismometers, that detect the vibrations due to seismic waves travelling through the earth's crust. In this work, the damage that is caused by an earthquake was classified into damage grades, ranging in values from one to five. A previously acquired data set was used, wherein a series of parameters were taken into consideration to predict the damage grade of a given building, which is associated with a Unique Identification String. The prediction was done using a survey of existing machine learning classifier algorithms. The machine learning algorithms used in this work were Logistic Regression, Naive Bayes Classifier, Random Forest Classifier and K-Nearest Neighbors. Based on an evaluation of a set of attributes, the most appropriate algorithm was considered. A detailed analysis was done on the predicted attribute by the given algorithm, followed by data analysis that provided details that could help mitigate the impact of an earthquake in future.","PeriodicalId":236457,"journal":{"name":"2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS)","volume":"3 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":"131225276","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

Identification of Criminal Activity Hotspots using Machine Learning to Aid in Effective Utilization of Police Patrolling in Cities with High Crime Rates 利用机器学习识别犯罪活动热点，帮助在高犯罪率城市有效利用警察巡逻

2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) Pub Date : 2019-12-01 DOI: 10.1109/CSITSS47250.2019.9031057

Ramshankar Yadhunath, S. Srikanth, A. Sudheer, Suja Palaniswamy

{"title":"Identification of Criminal Activity Hotspots using Machine Learning to Aid in Effective Utilization of Police Patrolling in Cities with High Crime Rates","authors":"Ramshankar Yadhunath, S. Srikanth, A. Sudheer, Suja Palaniswamy","doi":"10.1109/CSITSS47250.2019.9031057","DOIUrl":"https://doi.org/10.1109/CSITSS47250.2019.9031057","url":null,"abstract":"Criminal activity has always been a major deterrent in human progress and the constant presence of criminal activities stemming out of multifarious causes has been a hindrance for human sustainable living. The problem further aggravates when there is a dearth of police force to prevent crime. In countries like India where the police to population ratio is much less than the United Nations' Standard, the need of the hour is to efficiently utilize the existing force to prevent crimes. In this paper, we propose a solution that would facilitate effective distribution of police forces in a city among multiple districts based on the extent to which each district is prone to crime at a given hour, in a given day, for a given month. We have used the Chicago Crime Dataset in this work. The problem has been modelled as an imbalanced classification problem and supervised machine learning algorithms such as Logistic Regression, Naive Bayes, K Nearest Neighbours, Support Vector Machines, Decision Trees, Random Forests, Gradient Boosting Trees have been employed and their performances have been evaluated. In particular, the Gradient Boosting Tree has achieved the best performance in our case.","PeriodicalId":236457,"journal":{"name":"2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS)","volume":"75 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":"121497802","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