2018 IEEE Punecon最新文献_第7页

Remote Diagnostics and Monitoring of Sucker Rod Pump System (RDMSRP System) 有杆泵系统(RDMSRP系统)远程诊断与监测

2018 IEEE Punecon Pub Date : 2018-11-01 DOI: 10.1109/PUNECON.2018.8745377

Santosh Babulal Gupta, M. Mhetre

引用次数: 1

Secure and Transparent Election System for India using Block chain Technology 使用区块链技术的印度安全透明的选举系统

2018 IEEE Punecon Pub Date : 2018-11-01 DOI: 10.1109/PUNECON.2018.8745404

Ishaan Srivastava, Bhumika Saini, Shraddha P. Phansalkar, Sonali Patwe

引用次数: 1

Standards and Techniques to remove Data Remanence in Cloud Storage 消除云存储中数据残留的标准和技术

2018 IEEE Punecon Pub Date : 2018-11-01 DOI: 10.1109/PUNECON.2018.8745370

Seema B. Joshi

{"title":"Standards and Techniques to remove Data Remanence in Cloud Storage","authors":"Seema B. Joshi","doi":"10.1109/PUNECON.2018.8745370","DOIUrl":"https://doi.org/10.1109/PUNECON.2018.8745370","url":null,"abstract":"Data security is one of the main concerns for organizations when organizations deploy cloud to store data. Particularly, data leakage is a critical issue in public clouds due to data remanence. The retentive property of storage media is observed as a major threat because it may include sensitive information such as user id, user passwords, secret keys, web URLs, etc. Healthcare records, financial records, defense records are some of the examples where data remanence has serious repercussions. Though consumers vacate cloud volumes, vendors do not securely recycle storage devices. Hence, cold boot attack, harvesting attacks, side-channel attacks are likely to occur in the cloud. It is required to ensure about data integrity and confidentiality of sensitive data within a cloud by organizations or users. It is observed that encryption and data persistence avoidance technology for different memory devices like Hard Disk Drive (HDD), Static and/or Dynamic Random-Access Memory (SDRAM) are widely used. In this paper, we discuss and provide various standards and techniques proposed in the literature to remove the data remanence in the cloud storage. Moreover, we present a comparison of parametric evaluation of overwriting standards to remove data remanence.","PeriodicalId":166677,"journal":{"name":"2018 IEEE Punecon","volume":" 17","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113950495","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

Comprehensive and Comparative Study of Cloud Simulators 云模拟器的综合比较研究

2018 IEEE Punecon Pub Date : 2018-11-01 DOI: 10.1109/PUNECON.2018.8745422

Niti Naik, M. Mehta

引用次数: 0

Real-Time Disease Forecasting using Climatic Factors: Supervised Analytical Methodology 使用气候因素的实时疾病预测:监督分析方法

2018 IEEE Punecon Pub Date : 2018-11-01 DOI: 10.1109/PUNECON.2018.8745369

Garima Makkar

{"title":"Real-Time Disease Forecasting using Climatic Factors: Supervised Analytical Methodology","authors":"Garima Makkar","doi":"10.1109/PUNECON.2018.8745369","DOIUrl":"https://doi.org/10.1109/PUNECON.2018.8745369","url":null,"abstract":"Weather being an uncontrollable factor, often has direct effects on human mortality rates, physical health, mental injury and other health outcomes. Extreme climate incidences and gradual changes in weather are making us more vulnerable to disease outbreaks. In general, there are three ways by which variation in climate affects such diseases: by affecting the virus, the vector or host and spread of a disease. According to 1996 World health organization (WHO) report, 30 new diseases have come into existence in the past 20 years. Additionally, there has been a re-emergence and redistribution of various arthropod-borne diseases such as dengue, malaria etc. on a global scale. Events like rainfall, humidity, temperature etc. have well-defined role in the transference cycle. Any changes in these events can lead to increase in incidence of these diseases.The worldwide pandemic about abroviral diseases demands the need for developing early warning system (EWS) for infectious diseases by considering climate change. Past studies incorporates only the historical weather statistics into account. However because of increasing uncertainty and climate variability, the traditional systems in this context are getting outstripped. In this paper, we’ll propose our methodology for predicting number of dengue cases that are likely to occur in real time on the basis of five-day weather forecast. Our analysis is applicable globally and enables comprehensive scenarios of daily disease outbreaks to be explored using real-time weather API, preparing society against any health related risks arising due to variability in climate.","PeriodicalId":166677,"journal":{"name":"2018 IEEE Punecon","volume":"470 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133637274","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

Multiphase and Multiple Encryption 多相和多重加密

2018 IEEE Punecon Pub Date : 2018-11-01 DOI: 10.1109/PUNECON.2018.8745400

Harbir Kaur, Hirday Pal Singh Gill, D. Sarmah

引用次数: 4

A Deep Learning Approach for Face Detection using YOLO 一种基于YOLO的人脸检测深度学习方法

2018 IEEE Punecon Pub Date : 2018-11-01 DOI: 10.1109/PUNECON.2018.8745376

Dweepna Garg, Parth Goel, Sharnil Pandya, Amit Ganatra, K. Kotecha

{"title":"A Deep Learning Approach for Face Detection using YOLO","authors":"Dweepna Garg, Parth Goel, Sharnil Pandya, Amit Ganatra, K. Kotecha","doi":"10.1109/PUNECON.2018.8745376","DOIUrl":"https://doi.org/10.1109/PUNECON.2018.8745376","url":null,"abstract":"Deep learning is nowadays a buzzword and is considered a new era of machine learning which trains the computers in finding the pattern from a massive amount of data. It mainly describes the learning at multiple levels of representation which helps to make sense on the data consisting of text, sound and images. Many organizations are using a type of deep learning known as a convolutional neural network to deal with the objects in a video sequence. Deep Convolution Neural Networks (CNNs) have proved to be impressive in terms of performance for detecting the objects, classification of images and semantic segmentation. Object detection is defined as a combination of classification and localization. Face detection is one of the most challenging problems of pattern recognition. Various face related applications like face verification, facial recognition, clustering of face etc. are a part of face detection. Effective training needs to be carried out for detection and recognition. The accuracy in face detection using the traditional approach did not yield a good result. This paper focuses on improving the accuracy of detecting the face using the model of deep learning. YOLO (You only look once), a popular deep learning library is used to implement the proposed work. The paper compares the accuracy of detecting the face in an efficient manner with respect to the traditional approach. The proposed model uses the convolutional neural network as an approach of deep learning for detecting faces from videos. The FDDB dataset is used for training and testing of our model. A model is fine-tuned on various performance parameters and the best suitable values are taken into consideration. It is also compared the execution of training time and the performance of the model on two different GPUs.","PeriodicalId":166677,"journal":{"name":"2018 IEEE Punecon","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132677245","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: 57

CDACI: Concept Drift Detection and Adaptation to Classify Imbalanced Data streams CDACI:概念漂移检测与自适应对不平衡数据流进行分类

2018 IEEE Punecon Pub Date : 2018-11-01 DOI: 10.1109/PUNECON.2018.8745380

Kiran Bhowmick, M. Narvekar, Aqsa Bhimdiwala, Chandrasekhar Raman

引用次数: 0

The Effect of ICT (Information and Communications Technology)in Evaluation of learners in Engineering Education 信息通信技术在工程教育学习者评价中的作用

2018 IEEE Punecon Pub Date : 2018-11-01 DOI: 10.1109/PUNECON.2018.8745378

Mahendra Patil, M. Meena, Nikita Patil

引用次数: 0

A Comprehensive Analysis Between Popular Symmetric Encryption Algorithms 流行对称加密算法的综合分析

2018 IEEE Punecon Pub Date : 2018-11-01 DOI: 10.1109/PUNECON.2018.8745324

Shiraz Saptarshi Ghosh, Harshwardhan Parmar, Prasham Shah, Krishna Samdani

引用次数: 5