2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)最新文献_第2页

A Compressed domain Based Robust and Imperceptible Digital Video Watermarking Scheme 一种基于压缩域的鲁棒不可感知数字视频水印方案

2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2020-11-06 DOI: 10.1109/PDGC50313.2020.9315778

Rakesh Ahuja, Manish Sharma, Mohd. Junedul Haque

引用次数: 1

Welcome Message 欢迎信息

2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2020-11-06 DOI: 10.1109/pdgc50313.2020.9315790

H. Saini, Chief Guest

引用次数: 0

Stock Price prediction using LSTM and SVR 基于LSTM和SVR的股票价格预测

2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2020-11-06 DOI: 10.1109/PDGC50313.2020.9315800

G. Bathla

引用次数: 26

A Review on Advanced Techniques of Requirement Elicitation and Specification in Software Development Stages 软件开发阶段需求引出和需求说明的先进技术综述

2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2020-11-06 DOI: 10.1109/PDGC50313.2020.9315741

G. C. Sampada, T. I. Sake, Megha Chhabra

引用次数: 3

Swarm Intelligence based Hierarchical Routing Protocols Study in WSNs 基于群智能的wsn分层路由协议研究

2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2020-11-06 DOI: 10.1109/PDGC50313.2020.9315750

Deepak Mehta, S. Saxena

{"title":"Swarm Intelligence based Hierarchical Routing Protocols Study in WSNs","authors":"Deepak Mehta, S. Saxena","doi":"10.1109/PDGC50313.2020.9315750","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315750","url":null,"abstract":"The applications of Wireless Sensor Networks have been envisioned in numerous spheres of life in modern time. Wireless sensors enabled IoT based applications are changing the way modern life is being lived with applications into battle field surveillance, habitat monitoring, structural health, monitoring of vital parameters of human body and many more. Major constraint with these resource constrained networks is energy depletion during transmission of information with energy depletion increasing as the distance to which data is communicated increases. Energy efficient routing protocols have proved to be a formidable mechanism to save energy in WSNs. Moreover, hierarchical routing protocols are considered to be providing highest energy efficiency among all types of routing protocols. In recent times extensive research on nature inspired, swarm intelligence-based routing protocols has been observed. These optimization-based protocols not only have been established to be more energy efficient but have also performed better on several performance parameters including throughput, packet delivery ratio, delay and other QoS parameters, thus optimizing energy and other QoS based factors in a Wireless Sensor Network. This paper presents a taxonomy of approaches based on swarm intelligence and a analyzes the state-of-the-art swarm intelligence-based hierarchical routing protocols.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115627557","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

IoT and Cloud Based Healthcare Solution for Diabetic Foot Ulcer 糖尿病足溃疡的物联网和云医疗解决方案

2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2020-11-06 DOI: 10.1109/PDGC50313.2020.9315824

Punit Gupta, Navaditya Gaur, R. Tripathi, M. Goyal, Ankit Mundra

引用次数: 0

RPSO Optimization with machine learning in WSN 基于机器学习的无线传感器网络RPSO优化

2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2020-11-06 DOI: 10.1109/PDGC50313.2020.9315774

Y. Pant, Ravindra Sharma

{"title":"RPSO Optimization with machine learning in WSN","authors":"Y. Pant, Ravindra Sharma","doi":"10.1109/PDGC50313.2020.9315774","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315774","url":null,"abstract":"This work emphasizes to increase the network lifetime by using an appropriate data collection scheme and machine learning technique. The routing mechanism is one of the best approaches to decrease energy consumption and increase the lifetime of the network as well. We have used PSO with an updated scheme where we are selecting the random values to find best fitness value then the final route will be calculated. Genetic methods like mutation and crossover are implemented over the final routes to get alternate routes and then performance will be calculated. We have compared the lifetime and stability of network with existing protocols like Low Energy Adaptive Clustering Hierarchy (LEACH), Power-Efficient Gathering in Sensor Information Systems (PEGASIS), and Ant Colony Routing (ACR). In this work, we have added active-sleep feature with our network to enhance the network lifetime and the machine learning technique is used to predict the data of the network in sleep state. MATLAB is used to validate our mathematical framework; we have performed analytical simulations by choosing the network area, the number of nodes in each cluster. The lifetime and stability period is analyzed and compared with other optimization methods.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125758252","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

Machine Learning in Wireless Sensor Networks: A Retrospective 无线传感器网络中的机器学习:回顾

2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2020-11-06 DOI: 10.1109/PDGC50313.2020.9315767

Aina Mehta, Jasminder Kaur Sandhu, Luxmi Sapra

引用次数: 4

Comparative Study of Genetic Algorithm and Artificial Neural Network for Multi-class Classification based on Type-2 Diabetes Treatment Recommendation model 基于2型糖尿病治疗推荐模型的遗传算法与人工神经网络多类分类比较研究

2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2020-11-06 DOI: 10.1109/PDGC50313.2020.9315837

Siddhi Khanse, Payal Bhandari, Rumjhum Singru, Neha Runwal, Atharva Dharane

{"title":"Comparative Study of Genetic Algorithm and Artificial Neural Network for Multi-class Classification based on Type-2 Diabetes Treatment Recommendation model","authors":"Siddhi Khanse, Payal Bhandari, Rumjhum Singru, Neha Runwal, Atharva Dharane","doi":"10.1109/PDGC50313.2020.9315837","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315837","url":null,"abstract":"Multi-class Classification is often used for classification and categorization purposes under Machine Learning wherein vast datasets can be classified into multiple labels/classes. It is often perceived as more complex than binary classification and is still being explored and studied. The main objective of this paper is to perform a comparative study of Genetic Algorithm and Artificial Neural Network to identify the algorithm that enhances the accuracy of multi-class classification. The experimental results obtained in the comparative study are evaluated using our model developed for Type-2 Diabetes Individualistic Treatment Recommendation, which successfully implements multiclass classification of patients into 7 classes(Treatment Line). Presently, doctors prescribe drugs by using their knowledge and experience, but they require a faster and more efficient system to assist them in taking the final decision by providing a suitable suggestion about the treatment line. The dataset used by our model consists of 24 input attributes and 7 output class of 2430 individuals having different characteristics like hypertension etc to make it as diverse as possible. While comparing the benefits and drawbacks of these two algorithms on our model, we have considered factors such as accuracy, training, testing and complexity. Among the two types of classifier the ANN classifier leverages the performance of the system by giving the most accurate result and generating the prediction accuracy of 92%. Thus, based on the comparative study ANN classifier demonstrates better prediction results than evolutionary Genetic Algorithm.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"19 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132496990","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: 0

A Review on Machine Learning Techniques for Prediction of Cardiovascular Diseases 心血管疾病预测的机器学习技术综述

2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2020-11-06 DOI: 10.1109/PDGC50313.2020.9315747

Savita, Ganga Sharma, Geeta Rani, Vijaypal Singh Dhaka

{"title":"A Review on Machine Learning Techniques for Prediction of Cardiovascular Diseases","authors":"Savita, Ganga Sharma, Geeta Rani, Vijaypal Singh Dhaka","doi":"10.1109/PDGC50313.2020.9315747","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315747","url":null,"abstract":"Cardiovascular disease is a major cause of death worldwide. The detection of these diseases at a premature phase is imperative to rescue the lives of people. Implying machine learning classification techniques into health care organization gives extraordinary results which assist health care professionals for immediate and accurate diagnosis of these diseases. Healthcare organizations generate a huge amount of data which is still not perfectly utilized by researchers. Machine learning techniques and tools help in extracting effective knowledge from datasets for more precise results. Exploring numerous combinations of algorithms and finding out efficient techniques from the recent research papers is the objective of this research. The novelty of our work is associated with uses of optimization algorithms over classification algorithms such as Genetic algorithm (GA), Naïve Bayes (NB), Random Forest (RF), Artificial Neural Networks (ANN), Support Vector Machine SVM), etc. used so far. Feature optimization techniques (Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO)) with machine learning techniques (K-Nearest Neighbor (KNN) and Random Forest (RF)) give maximum accuracy of 99.65% which is examined from the survey work. The future works can emphasize on developing an advanced model by integrating different optimization techniques using machine learning which could help the health care professionals in making felicitous decisions.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130230409","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