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

Operating of a Drone Using Human Intent Recognition and Characteristics of an EEG Signal 利用人类意图识别和脑电图信号特征的无人机操作

2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2020-11-06 DOI: 10.1109/PDGC50313.2020.9315321

Ashutosh Shankhdhar, Arushi Mangla, Akhilesh Kumar Singh, Ayushi Srivastava

{"title":"Operating of a Drone Using Human Intent Recognition and Characteristics of an EEG Signal","authors":"Ashutosh Shankhdhar, Arushi Mangla, Akhilesh Kumar Singh, Ayushi Srivastava","doi":"10.1109/PDGC50313.2020.9315321","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315321","url":null,"abstract":"Drones are applied for normal subjects likewise as forces exercise. Consultations with drones are liable to deal with and compromise since they're broadly used for self-directed conduct. Still, it is of great consequence to take on an automatic pilot framework which is vigorous to potential digital assault. Right now, we tend to propose an individual's implicit intent recognition model dependent on a multi-modular data that is a blend of the eye movement data and the EEG signal acquired from some eye-locators and EEG scanners separately. The eye movement will be used to extricate some highlights like fixation length and fixation count relating to the visual stimuli, and similarly, we will examine the graph signals observed on part synchronicity technique and consolidating this, we will also train a few classifiers such as the SVM classifier, Naïve Bayesian and Gaussian Mixture Model that might effectively recognize an individual's implicit intention into 2 characterized classes - navigational and informational intention, which will ultimately be used for training a drone. Also, we will be displaying a biometric framework to scramble letters between a drone and an electronic ground station which can be achieved by creating a key from the EEG signal of a user. Then, at the endpoint, once the correspondence with a drone is assaulted a security system facilitates it to a sheltered ‘home’ area.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"32 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":"127109136","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

Optimizing Trace Tool-overhead for Lock-Intensive Multi-threaded Parallel Applications 优化锁定密集型多线程并行应用程序的跟踪工具开销

2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2020-11-06 DOI: 10.1109/PDGC50313.2020.9315323

Ajit Singh, P. Chakraborty

引用次数: 0

Plant Disease Detection Using Clustering Based Segmentation and Neural Networks 基于聚类分割和神经网络的植物病害检测

2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2020-11-06 DOI: 10.1109/PDGC50313.2020.9315856

Aditya Mohan, Kushagra Srivastava, Garima Malhotra, N. U. Khan

{"title":"Plant Disease Detection Using Clustering Based Segmentation and Neural Networks","authors":"Aditya Mohan, Kushagra Srivastava, Garima Malhotra, N. U. Khan","doi":"10.1109/PDGC50313.2020.9315856","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315856","url":null,"abstract":"Farmer suicides in India had ranged between 1.4 and 1.8 per hundred thousand people, accounting to 11.2 % of all suicides in India due to reasons like debt, low produce prices, crops failure and alcohol addiction. Among these, crop failure is attributed to various factors including unpredictable weather conditions, poor farming practices, pests and diseases along withill use of fertilizers and late disease diagnosis. Various systems have been proposed and implemented for immediate identification of the disease, using mobile devices for disease identification and consequent action, but the majority of proposed approaches involve segmentation techniques coupled with classical machine learning algorithms, which focused on the entire plant or fruit image, not primarily on the diseased part, thus embedding pixels which introduce possible bias in each data point leading to an imprecise training dataset and consequently faulty training. In this paper we propose a method of leveraging a combination of clustering based segmentation for identification of the diseased part exclusively and consequent feature extraction over it along with using neural networks over classical algorithms, thereby increasing feature complexity and thus better training, increasing training accuracy and leaving scope for further integration of huge amount of data which can added later on.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"91 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":"116089236","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 Switch Based Power Aware VM Consolidation Method for Cloud Datacenter 一种基于交换机的云数据中心电源感知虚拟机整合方法

2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2020-11-06 DOI: 10.1109/PDGC50313.2020.9315776

Shally, S. Sharma, Sunil Kumar

引用次数: 0

Heterogeneous Stacked Ensemble Classifier for Software Defect Prediction 软件缺陷预测的异构堆叠集成分类器

2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2020-11-06 DOI: 10.1007/s11042-021-11488-6

Somya Goyal, P. Bhatia

引用次数: 12

Epilepsy Seizure Detection by using Bayesian Optimize Bi-LSTM Model 基于贝叶斯优化Bi-LSTM模型的癫痫发作检测

2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2020-11-06 DOI: 10.1109/PDGC50313.2020.9315779

Vidhi Sood, D. Kumar, V. Athavale, S. Gupta

{"title":"Epilepsy Seizure Detection by using Bayesian Optimize Bi-LSTM Model","authors":"Vidhi Sood, D. Kumar, V. Athavale, S. Gupta","doi":"10.1109/PDGC50313.2020.9315779","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315779","url":null,"abstract":"In medical Science field, the EEG signal classification is present with a plethora of applications. The health monitoring is depending on modern technology like EEG and ECG signal recording. The EEG signals are analyzed to identify the abnormal condition of the human brains. The unusual activity of the brain is known as the seizure The electrical signal generated in the braincauses epilepsy. In this proposed work, a deep learning model Bi-LSTM is projected for the epilepsy signal classification. The Bonn university EEG dataset is used for the testing purpose. The EEG signal classification has three significant steps data pre-processing, features extraction, and classification. Data pre-processing is done by DCT and filter conversion. The Hurst exponent and ARMA feature sets are extracted from the pre-process EEG signal. A Bayesian optimization tuned Bi-LSTM model is suggested for the EEG signal classification task. The epileptic EEG signals are recognized by the proposed method. The hyperparameters of the Bi- LSTM model is tuned by the Bayesian optimization rule. Three different class ictal, pre-ictal, and inter-ictal are classified from the EEG signal data. A comparative study is also provided for the epilepsy signal classification task. The classification accuracy of for ictal is 94%, pre-ictal is 92%, and inter-ictal is 91%, which more significant than the LSTM and SVM based classifier model.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"65 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":"130675095","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

Synchronization in Parallel Programming Models for Heterogeneous Many-Cores 异构多核并行编程模型中的同步

2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2020-11-06 DOI: 10.1109/PDGC50313.2020.9315797

Mohamed Naseer, Zayed Khaled, Islam Tharwat Abdel Halim, Ayman M. Bahaa-Eldin

引用次数: 1

[Copyright notice] (版权)

2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2020-11-06 DOI: 10.1109/pdgc50313.2020.9315830

引用次数: 0

Exploring the Role of Vegetation Indices in Plant Diseases Identification 探讨植被指数在植物病害鉴定中的作用

2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2020-11-06 DOI: 10.1109/PDGC50313.2020.9315814

Sangeeta Vaibhav Meena, V. Dhaka, Deepak Sinwar

{"title":"Exploring the Role of Vegetation Indices in Plant Diseases Identification","authors":"Sangeeta Vaibhav Meena, V. Dhaka, Deepak Sinwar","doi":"10.1109/PDGC50313.2020.9315814","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315814","url":null,"abstract":"The economy of the agriculture industry is badly affected by plant diseases. Effective management practices involve regular monitoring of the plant's health with early detection of pathogens for reducing the spread of diseases. Traditionally, several invasive plant disease diagnostic techniques are used that involve the devastation of leaf samples with chemical treatment. Apart from that, non-invasive disease detection techniques are more feasible and practical ways of monitoring plant diseases in real time applications without affecting the growth of plants. Imaging and spectroscopic are non-invasive disease identification methods used for discovering harmful organisms that affect the health of plants. For identifying diseases, biophysical parameters of plants are extracted through vegetation indices. A vegetation index is a spectral computation that can be done using two or more spectral bands that are sensitive to plant vigor and biomass. Vegetation indices are used to estimate water contents of soils, monitor drought, classify vegetation, examine climate trends, crop management, identify changes in biodiversity, etc. The paper aims to discuss various methods used for detecting plant diseases. Some commonly used vegetation indices are also discussed along with the role of vegetation indices in identifying plant diseases.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"176 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":"134172852","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

Hybrid Genetic and Simulated Annealing Algorithm for Capacitated Vehicle Routing Problem 有能力车辆路径问题的混合遗传和模拟退火算法

2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC) Pub Date : 2020-11-06 DOI: 10.1109/PDGC50313.2020.9315798

Mohammad Sajid, A. Jafar, Surbhi Sharma

引用次数: 2