2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)最新文献_第4页

Water Irrigation and Flood Prevention using IOT 利用物联网进行灌溉和防洪

2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2020-01-01 DOI: 10.1109/Confluence47617.2020.9057842

Sarthak Gupta, Virain Malhotra, Vasudha Vashisht

{"title":"Water Irrigation and Flood Prevention using IOT","authors":"Sarthak Gupta, Virain Malhotra, Vasudha Vashisht","doi":"10.1109/Confluence47617.2020.9057842","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057842","url":null,"abstract":"India is one of the largest producers of agricultural products. Main source of India’s GDP is its vast agricultural produce that accounts to 16% of the total. About 58 percent of the India’s workforce is involved in agriculture. But due to variable climatic condition of the country farmers are unprepared for these harsh and inevitable conditions. The farmers don’t have any effective way to deal with natural disasters such as drought and flooding which results in damaging of the crop and steep loss to the farmers. This research paper proposes a system through which we can reduce the problems of the farmers by automated smart irrigation system in drought conditions and smart suction pump which will suck out the excess water during flooding conditions. A database will be maintained for thorough analysis of amount of water irrigated in the fields, measurement of amount of rainfall, amount of water sucked during flooding and humidity level of soil in timeline manner. This database will be used for prediction of such climatic conditions and informing the farmers to take appropriate measures so that they can reduce or nullify the losses under such conditions.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115799856","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: 8

Classification Of Plant Leaf Diseases Using Machine Learning And Image Preprocessing Techniques 基于机器学习和图像预处理技术的植物叶片病害分类

2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2020-01-01 DOI: 10.1109/Confluence47617.2020.9057889

Pushkar Sharma, P. Hans, Subhash Chand Gupta

{"title":"Classification Of Plant Leaf Diseases Using Machine Learning And Image Preprocessing Techniques","authors":"Pushkar Sharma, P. Hans, Subhash Chand Gupta","doi":"10.1109/Confluence47617.2020.9057889","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057889","url":null,"abstract":"Agriculture is one of the main factor that decides the growth of any country. In India itself around 65% of the population is based on agriculture. Due to various seasonal conditions the crops get infected by various kind of diseases. These diseases firstly affect the leaves of the plant and later infected the whole plant which in turn affect the quality and quantity of crop cultivated. As there are large number of plants in the farm, it becomes very difficult for the human eye to detect and classify the disease of each plant in the field. And it is very important to diagnose each plant because these diseases may spread. Hence in this paper we are introducing the artificial intelligence based automatic plant leaf disease detection and classification for quick and easy detection of disease and then classifying it and performing required remedies to cure that disease. This approach of ours goals towards increasing the productivity of crops in agriculture. In this approach we have follow several steps i.e. image collection, image preprocessing, segmentation and classification.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123833385","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: 49

An Approach To Extract Optimal Test Cases Using AI 一种利用人工智能提取最优测试用例的方法

2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2020-01-01 DOI: 10.1109/Confluence47617.2020.9058244

Amandeep Kaur

引用次数: 3

Comparative Study of K-Means Clustering Using Iris Data Set for Various Distances 不同距离下Iris数据集K-Means聚类的比较研究

2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2020-01-01 DOI: 10.1109/Confluence47617.2020.9058328

Adrija Chakraborty, Neetu Faujdar, Akash Punhani, Shipra Saraswat

引用次数: 4

Implementation of PingER on Android Mobile Devices Using Firebase

2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2020-01-01 DOI: 10.1109/Confluence47617.2020.9058306

Ananthnarayan Rajappa, A. Upadhyay, A. Sabitha, Abhay Bansal, B. White, L. Cottrell

引用次数: 5

A Literature Review and Taxonomy on Workload Prediction in Cloud Data Center 云数据中心工作负荷预测的文献综述与分类

2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2020-01-01 DOI: 10.1109/Confluence47617.2020.9057938

Avneesh Vashistha, Pushpneel Verma

引用次数: 6

Exploratory Data Analysis and Machine Learning on Titanic Disaster Dataset 泰坦尼克号灾难数据集的探索性数据分析和机器学习

2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2020-01-01 DOI: 10.1109/Confluence47617.2020.9057955

Karman Singh, Renuka Nagpal, Rajni Sehgal

引用次数: 8

Comparative Study of Data Mining Techniques for Predicting Explosions in Coal Mines 煤矿爆炸预测数据挖掘技术的比较研究

2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2020-01-01 DOI: 10.1109/Confluence47617.2020.9057921

S. Namazi, L. Brankovic, B. Moghtaderi, J. Zanganeh

{"title":"Comparative Study of Data Mining Techniques for Predicting Explosions in Coal Mines","authors":"S. Namazi, L. Brankovic, B. Moghtaderi, J. Zanganeh","doi":"10.1109/Confluence47617.2020.9057921","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057921","url":null,"abstract":"Global warming is a long-term environmental hazard demonstrated by a gradual increase in the temperature of the Earth. It is caused by the accumulation of greenhouse gases in the atmosphere, including carbon dioxide and methane. Although, in terms of the volume, methane is considered secondary to carbon dioxide, it is about 21 times more damaging when compared over a 100-year period. Fugitive methane emissions from underground coal mines significantly contribute to global warming. Amongst all the known methods to reduce the fugitive methane, application of thermal oxidation (or, simply, burning) is deemed the most effective and practical. This process produces water vapour and carbon dioxide, which has significantly lower adverse impact on the atmosphere than methane. The thermal oxidisers operate at high temperatures, which may introduce a risk of fire and explosion to the mine. In order to mitigate such risk, a thorough understanding of the methane explosion characteristics is essential. Methane fire and explosion experiments under conditions pertinent to underground coal mines are expensive, risky and necessitate significant effort, and thus require enormous preparation and safety procedures. It is cheaper and safer to analyse existing data to discover patterns and predict explosions than to conduct new extensive experiments. In this paper, we present a comparative study of data mining and machine learning techniques used for these purposes.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116616476","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

Comparative Analysis for KeyTerms Extraction Methods for Personalized Search Engines 个性化搜索引擎关键字提取方法的比较分析

2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2020-01-01 DOI: 10.1109/Confluence47617.2020.9057810

Shaurya Uppal, Arti Jain, Anuja Arora

引用次数: 3

CSA based PID Controller Design Technique for optimizing Various Integral Errors 基于CSA的各种积分误差优化PID控制器设计技术

2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2020-01-01 DOI: 10.1109/Confluence47617.2020.9057816

A. Kaur, R. Kaur, Swati Sondhi

{"title":"CSA based PID Controller Design Technique for optimizing Various Integral Errors","authors":"A. Kaur, R. Kaur, Swati Sondhi","doi":"10.1109/Confluence47617.2020.9057816","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057816","url":null,"abstract":"Control design plays a significant role in almost all types of industries. Proportional-integral-derivative (PID) controllers are an integral part of process control loops. PID controllers are popular for their simplicity of implementation and broad applicability. In recent years, various metaheuristic algorithms and modified hybrid algorithms have been applied to design the controllers. The aim of this paper is to design a controller with high versatility, accuracy and good control quality. In this research paper, first, a novel tuning method based on Crow Search Algorithm (CSA) is proposed to optimize parameters of PID controller: $K_{p}, K_{i}$ and Kd. Each crow represents a feasible solution for the PID parameters. Second, four objective functions have been explored and the effectiveness and convergence rates of CSA-PID controller is evaluated therein for two different control problems. Last, comparison has been carried out between CSA optimized PID The main advantage of CSA is its simplicity, faster convergence rate, ease of implementation and easy understanding. As per findings based on statistical analysis, Crow search Algorithm (CSA) has been found to be more reliable. Simulation results based on two control problems and four evaluation functions have been tested for set point tracking, load rejection capability, noise suppression and modelling errors.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131624891","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