2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC)最新文献_第7页

Comparison and Evaluation of Real Time Reservation Technologies in the Intelligent Public Transport System 智能公交系统中实时预约技术的比较与评价

2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2018-12-01 DOI: 10.1109/PDGC.2018.8745888

Akshay Mewada, S. Tanwar, Z. Narmawala

{"title":"Comparison and Evaluation of Real Time Reservation Technologies in the Intelligent Public Transport System","authors":"Akshay Mewada, S. Tanwar, Z. Narmawala","doi":"10.1109/PDGC.2018.8745888","DOIUrl":"https://doi.org/10.1109/PDGC.2018.8745888","url":null,"abstract":"The demand of Public Transport System (PTS) is increasing day by day because it is quite easy, frequently available and cheap in cost as compared to the private transport system. It attracts attentions both from academia and Industry. But at the same time have many issues such as-travel time estimation, route selection, real-time tracking of vehicles, and efficient way of reservation. The solution of these problems helps the drivers as well as to users to use PTS in timely and efficient manner. Although many state-of-the-art proposals exist in the literature to address these issues there is scope available to identify the parameters responsible for an efficient way of real-time PTS reservation. Keeping the above-mentioned points into consideration, this paper provides the detailed taxonomy of PTS and also give proper categorization of the existing approaches. This gives the much-needed clarity with regards to the issues related to Intelligent PTS and their solutions available in the literature. The parameters used for framing the main taxonomy are such as-quality & fare, route selection, seat selection, and seat availability. We also compared all standard algorithm and approaches available for PTS. We also highlight the open issues and challenges encountered by the researchers. These help the beginners who need to begin the work in this.","PeriodicalId":303401,"journal":{"name":"2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125944603","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: 5

Investigations on designs of Dielectric Resonator Antennas for WiMax & WLAN applications 用于WiMax和WLAN应用的介质谐振器天线设计研究

2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2018-12-01 DOI: 10.1109/PDGC.2018.8745754

Dishant Khosla, Kulwinder Singh Malhi

引用次数: 1

Classification of EEG for Upper Limb Motor Imagery: An Approach for Rehabilitation 上肢运动意象脑电分类:一种康复方法

2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2018-12-01 DOI: 10.1109/PDGC.2018.8745936

Yogesh Paul, R. Jaswal

引用次数: 1

Comparison for Confidential Cryptography in Multimedia Cloud Environment 多媒体云环境下保密密码的比较

2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2018-12-01 DOI: 10.1109/PDGC.2018.8745909

Shilpi Harnal, R. Chauhan

引用次数: 0

The Review of Various Methods and Color Models in Face Recogntion 人脸识别中各种方法和颜色模型的综述

2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2018-12-01 DOI: 10.1109/PDGC.2018.8745847

Prabhjot Singh, M. Kaur, Jaspreet Singh

{"title":"The Review of Various Methods and Color Models in Face Recogntion","authors":"Prabhjot Singh, M. Kaur, Jaspreet Singh","doi":"10.1109/PDGC.2018.8745847","DOIUrl":"https://doi.org/10.1109/PDGC.2018.8745847","url":null,"abstract":"The human being face is passing on extreme information related to the person expressing state. The sub category of biometrics as face recognition is considered as a major challenge in the area of several applications significantly for the identification and verification purposes such as for law enforcement, high security in banking systems, authentication for security system and particularly for the identification and verification of the person faces with other faces. In this review paper, mainly consists of three phases: (i) Face representation (ii) Extract the features and (iii) detection and recognition. The facial images, extract the unique features. Classification process compared with the images face images from the large facial datasets. Facial recognition is a real-time application such as a reliable and most important case for security. In this review paper, first, define an overview of facial recognition and describe the functionality. Consequently, it defines face recognition methods which are currently used to add their merits and demerits. The huge number of facial recognition methods, adding LBP, LDA, PCA and EIGEN faces for recognition. The various facial expression situation and illumination of images some methods specified here also enhances the effectiveness of facial recognition.","PeriodicalId":303401,"journal":{"name":"2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121216342","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

Predictive Data Modeling: Educational Data Classification and Comparative Analysis of Classifiers Using Python 预测数据建模:使用Python的教育数据分类和分类器的比较分析

2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2018-12-01 DOI: 10.1109/PDGC.2018.8745727

P. Guleria, M. Sood

{"title":"Predictive Data Modeling: Educational Data Classification and Comparative Analysis of Classifiers Using Python","authors":"P. Guleria, M. Sood","doi":"10.1109/PDGC.2018.8745727","DOIUrl":"https://doi.org/10.1109/PDGC.2018.8745727","url":null,"abstract":"Due to an increase in the number of data sources and digital community, there is a huge amount of unstructured data at almost every synergy and in such outline, data mining becomes an important field of Machine Learning. Machine learning can be used for data mining following its two approaches i.e. Supervised learning and Unsupervised learning to find out meaningful information from huge accumulated unstructured data. To increase the quality of education and to find a solution to problems soaring from the complicated educational dataset and contentious environment among the academic institutions, educational data mining is receiving great attention. Educational data mining helps in facilitation and utilization of resources related to student performance, predicting placement results and finding new educational trends. In this paper, classification of student's data in terms of internal assessment given by faculty members and visualization of an educational dataset using Python following multiple Data based classification prediction models and comparative results of classifier models are performed. The classifier models using python which can transform learning are compared and the model having best accuracy measure is considered for predictive analytics and classification of the overall performance of class.","PeriodicalId":303401,"journal":{"name":"2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124489035","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: 4

Sentiment Analysis Using Lexicon Based Approach 基于词典的情感分析方法

2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2018-12-01 DOI: 10.1109/PDGC.2018.8745971

Vijendra Singh, Gurdeep Singh, Priyanka Rastogi, Devanshi Deswal

引用次数: 11

Modelling of Optical Fiber Characteristics Using QR Optimized Memory Polynomial 基于QR优化记忆多项式的光纤特性建模

2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2018-12-01 DOI: 10.1109/PDGC.2018.8745755

Amandeep Singh Sappal, Manjit Singh

引用次数: 0

An Efficient Virtual Machine Management Algorithm for Vehicular Clouds 一种高效的车载云虚拟机管理算法

2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2018-12-01 DOI: 10.1109/PDGC.2018.8745987

S. K. Panda, Sanju Parida, Sourav Kumar Bhoi, S. K. Nayak, Satyabrata Das

{"title":"An Efficient Virtual Machine Management Algorithm for Vehicular Clouds","authors":"S. K. Panda, Sanju Parida, Sourav Kumar Bhoi, S. K. Nayak, Satyabrata Das","doi":"10.1109/PDGC.2018.8745987","DOIUrl":"https://doi.org/10.1109/PDGC.2018.8745987","url":null,"abstract":"Vehicular cloud computing (VCC) is one of the prominent fields of research, which combines both vehicular ad hoc network and cloud computing. In VCC, the vehicles are acting like virtual machine hosts to process the tasks that are given by the roadside units (RSUs). The vehicles and RSUs coordinate each other to share several information. Further, the RSUs are connected to the cloud through the Internet. As the vehicles under consideration are not residing in the same area or street or zone or grid, the problem of managing virtual machine hosts is very much challenging to process the tasks. In this paper, we introduce virtual machine management algorithm for vehicular clouds. Moreover, we present three variants of the proposed algorithm, namely first fit strategy, best fit strategy and worst fit strategy, respectively. The performance of these variants is evaluated by means of three performance metrics through simulation runs with a wide variety of vehicles, vehicle speeds, length of the grids and data sizes. The result of these variants is extensively compared. The comparison results show the superiority of the best fit strategy among all the variants of the proposed algorithm.","PeriodicalId":303401,"journal":{"name":"2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115696260","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: 4

Human Detection and Motion Tracking Using Machine Learning Techniques: A Review 使用机器学习技术的人体检测和运动跟踪:综述

2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2018-12-01 DOI: 10.1109/PDGC.2018.8745852

R. Mahajan, Devanand Padha

引用次数: 4