2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC)最新文献_第5页

Cost Effective Algorithms for Participant Selection Problem in Mobile Crowd Sensing Environment 移动人群感知环境下参与者选择问题的成本有效算法

2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2018-12-01 DOI: 10.1109/PDGC.2018.8745988

Sanjoy Mondal, Saurav Ghosh, Sunirmal Khatua, R. Das, U. Biswas

{"title":"Cost Effective Algorithms for Participant Selection Problem in Mobile Crowd Sensing Environment","authors":"Sanjoy Mondal, Saurav Ghosh, Sunirmal Khatua, R. Das, U. Biswas","doi":"10.1109/PDGC.2018.8745988","DOIUrl":"https://doi.org/10.1109/PDGC.2018.8745988","url":null,"abstract":"Recent developments in the areas of smart phones and wearable devices have paved the way to many emerging applications in Mobile Crowd Sensing (MCS) domain. There has been a phenomenal increase in the number of people using these devices. In this work we consider an MCS application to cover a set of points of interest (targets) with the objective of selecting a minimum set of participants among the mobile devices. This problem, coined as Participant Selection Problem (PSP) to provide desired coverage can be solved optimally using an Integer Linear Programming (ILP). But for large problem size, solving the ILP becomes impractical from the point of execution time as well as the requirement of having the necessary information about all the participants in one place. In this paper, we have proposed a distributed participants selection algorithm (DPSA) to be run at each participants where, the participants after exchanging messages with its neighbors, decides to remain active or idle. We have shown that, those participants which selects themselves as active suffice to cover all the desired targets. We have run a series of experiments to measure the performance of the proposed algorithm where we measure along with number of participants selected also the total energy consumption. Simulation results reveals the close proximity of DPSA compared to the optimal one providing the same coverage.","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":"125425035","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

Framework for proficient proof of identity of duplicate and near-duplicate images and image distances using high-disguisable image fragment 使用高伪装图像片段熟练证明重复和接近重复图像和图像距离的框架

2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2018-12-01 DOI: 10.1109/PDGC.2018.8745792

V. Narayana, Sreevani Gaddameedhi, V. K. Koppula, K. S. Raju

引用次数: 2

Stage-wise Development of a Remote Controlled Robotic Arm 遥控机械臂的阶段性发展

2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2018-12-01 DOI: 10.1109/PDGC.2018.8745871

Shivam Sharma, Shashwat Sahai, Jaisal Joshi, N. Hema

引用次数: 2

An Aggregated Similarity Based Hierarchical Clustering Technique for Relational DDBS Design 关系型DDBS设计中基于聚合相似度的分层聚类技术

2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2018-12-01 DOI: 10.1109/PDGC.2018.8745981

A. Amer, M. Mohamed, A. Sewisy, Khaled Al Asri

引用次数: 4

Performance Evaluation of Classification Algorithm in Weka using Parallel Performance Profiling and Computing Technique 基于并行性能分析和计算技术的Weka分类算法性能评价

2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2018-12-01 DOI: 10.1109/PDGC.2018.8745940

N. Upadhyay, Ravi Shankar Singh

{"title":"Performance Evaluation of Classification Algorithm in Weka using Parallel Performance Profiling and Computing Technique","authors":"N. Upadhyay, Ravi Shankar Singh","doi":"10.1109/PDGC.2018.8745940","DOIUrl":"https://doi.org/10.1109/PDGC.2018.8745940","url":null,"abstract":"At present, the size of the data is growing rapidly. In such a situation, it is necessary to keep an eye on the speed of the data without undermining it. It is also important to note that while processing the data, its quality remains intact. This is the reason that data mining technology has become very important in the field of predictions in the scientific mining area, commercial and environment sectors. In this case, the need for parallel processing becomes important. Therefore, the aim of this paper is to analyze and perform computation times of different classification algorithms on many datasets using parallel profiling and computing techniques. Performance analysis is based on many factors, such as the unique nature of the dataset, the size, and type of the class, the diversity of the data in the data set, and so on. Many researchers are working on the optimization of classification algorithms which are not showing accurate results according to the processor (core) capacity. So, in this paper, we have displayed some simulation results which discuss the processor's size, efficiency, and workload as well as the complexity of input instructions in the group; Which will help researchers to optimize the code for maximum use of the core. At the end of the paper, we have given a comparative study of the optimized algorithm based on a parallel approach and tuned algorithm based on parallel performance.","PeriodicalId":303401,"journal":{"name":"2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"7 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":"126358303","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

Probabilistic Neural Network Approach for Classifying Ventricular Tachyarrhythmias 分类室性心动过速的概率神经网络方法

2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2018-12-01 DOI: 10.1109/PDGC.2018.8745882

Shipra Saraswat, Prasiddhi Shahi

引用次数: 0

Analysis of cardiovascular diseases using artificial neural network 应用人工神经网络分析心血管疾病

2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2018-12-01 DOI: 10.1109/PDGC.2018.8745900

Jyotismita Talukdar, B. Dewangan

{"title":"Analysis of cardiovascular diseases using artificial neural network","authors":"Jyotismita Talukdar, B. Dewangan","doi":"10.1109/PDGC.2018.8745900","DOIUrl":"https://doi.org/10.1109/PDGC.2018.8745900","url":null,"abstract":"In this paper, a study has been made on the possibility and accuracy of early prediction of several Heart Disease using Artificial Neural Network. (ANN). The study has been made in both noise free and noisy environment. The data collected for this analysis are from five Hospitals. Around 1500 heart patient's data has been collected and studied. The data is analysed and the results have been compared with the Doctor's diagnosis. It is found that, in noise free environment, the accuracy varies from 74% to 92%.and in noisy environment (2dB), the results of accuracy varies from 62% to 82%. In the present study, four basic attributes considered are Blood Pressure (BP), Fasting Blood Sugar (FBS), Thalach (THAL) and Cholesterol (CHOL.). sIt has been found that highest accuracy(93%), has been achieved in case of PPI(Post-Permanent-Pacemaker Implementation), around 79% in case of CAD(Coronary Artery disease), 87% in DCM(Dilated Cardiomyopathy), 89% in case of RHD&MS(Rheumatic heart disease with Mitral Stenosis), 75% in case of RBBB +LAFB (Right Bundle Branch Block + Left Anterior Fascicular Block), 72% for CHB(Complete Heart Block) etc. The lowest accuracy has been obtained in case of ICMP(Ischemic Cardiomyopathy), about 38% and AF(Atrial Fibrillation), about 60 to 62%.","PeriodicalId":303401,"journal":{"name":"2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"188 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":"133566303","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

Diagnosis of Breast Cancer and Diabetes using Hybrid Feature Selection Method 应用混合特征选择方法诊断乳腺癌和糖尿病

2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2018-12-01 DOI: 10.1109/PDGC.2018.8745830

Divya Jain, V. Singh

{"title":"Diagnosis of Breast Cancer and Diabetes using Hybrid Feature Selection Method","authors":"Divya Jain, V. Singh","doi":"10.1109/PDGC.2018.8745830","DOIUrl":"https://doi.org/10.1109/PDGC.2018.8745830","url":null,"abstract":"Diagnosis of diseases at an early stage is a crucial task in the medical field. A hybrid machine learning framework is presented for the diagnosis of breast cancer and diabetes using efficient feature selection and classification technique. This research identifies significant risk factors related to both chronic disease datasets by applying different feature selection techniques and hybridization of ReliefF Feature Ranking with Principal Component Analysis (PCA) method. To evaluate the effectiveness of the presented feature selection method, k-nearest neighbor method for classification is used. The hybridization enhances the accuracy of the classifier with the proposed feature selection technique for both chronic disease datasets. The performance of the presented hybrid framework is found to be best in comparison to five other techniques - Correlation Based feature Selection (CBS), Fast Correlation Based Feature Selection (FCBF), Mutual Information Based Feature Selection (MIFS), MODTree Filtering Approach and ReliefF Feature Selection. Moreover, the proposed ReliefF-PCA method eliminates 25% and 33.3% of irrelevant features for diabetes and breast cancer dataset respectively.","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":"128404002","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: 15

Classification of Indian Herbal Plants based on powder microscopic images using Transfer Learning 基于粉末显微图像的印度草本植物的迁移学习分类

2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2018-12-01 DOI: 10.1109/PDGC.2018.8745922

Rohan Marwaha, B. Fataniya

引用次数: 4

Emotion Model for Artificial Intelligence and their Applications 面向人工智能的情感模型及其应用

2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2018-12-01 DOI: 10.1109/PDGC.2018.8745840

Advait Gopal Ranade, Maitri Patel, Archana Magare

引用次数: 3