Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing最新文献_第2页

Personalized Knee Angle Prediction Models Using Machine Learning 使用机器学习的个性化膝盖角度预测模型

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549233

Antarleen Pal, C. Prakash

引用次数: 0

Intuitionistic Fuzzy Representation of Plant Images captured using Unmanned Aerial Vehicle for Measuring Mango Crop Health 用无人机测量芒果作物健康的植物图像的直观模糊表示

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549324

Vinita Vinita, Suma Dawn

{"title":"Intuitionistic Fuzzy Representation of Plant Images captured using Unmanned Aerial Vehicle for Measuring Mango Crop Health","authors":"Vinita Vinita, Suma Dawn","doi":"10.1145/3549206.3549324","DOIUrl":"https://doi.org/10.1145/3549206.3549324","url":null,"abstract":"The bacterial, viral, or fungal plant diseases are still taking a heavier toll on food production in developing countries like India. India loses, almost, 35% of the annual crop yield due to plant diseases. Early detection of plant diseases remains difficult due to the lack of lab infrastructure and expertise. Though in the past three decades, there have been marked improvements in agricultural production with the aid of disease-resistant methods to pause the significant reduction in both the quantity and quality of needed items.The major focus of this work is to identify the mango crop health issues at an earlier stage so that the diseases can be recognized using the Intuitionistic fuzzy set (IFS) approach over traditional segmentation techniques.The IFS method is used to compare with conventional segmentation techniques such as K-means clustering, Otsu's thresholding, region growing, and Felzenswalb segmentation to achieve the best results in the segmentation of the diseased area on the crop.As the Intuitionistic fuzzy logic allows a certain amount of incomplete information, the imprecision in the grey level definitions of UAV crop images can be accounted for in defining the delicacy of boundaries of disease-affected areas. Initially, all the experimental UAV images are pre-processed and segmented by using four types of segmentation techniques.The experimental results were obtained by the conventional segmentation techniques and intuitionistic fuzzy set approach as well. The experimental result shows the best visible strained region (around 95-98% affected area) from UAV captured field-area images along with statistical comparing parameters.This also infers the strength of IFS to handle disease complications in the crop which can further help farmers of India to increase their yield production.","PeriodicalId":199675,"journal":{"name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127058402","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 New High Performance Active Building Block Suitable for Low Voltage Low Power Signal Processing 一种适用于低电压低功率信号处理的新型高性能有源模块

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549320

Atul Kumar, B. Chaturvedi, Shafali Jagga

引用次数: 0

Detection of COVID-19 Protocols Violation in Real Time using Deep Learning 基于深度学习的COVID-19协议违规实时检测

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549282

N. Singh, Anurag Goel

引用次数: 0

Virtual Machine Placement Techniques Based on Biological Models: Comprehensive Research and Study 基于生物模型的虚拟机放置技术:综合研究

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549232

Madala Guru Brahmam, Vijay Anand Rajasekaran

{"title":"Virtual Machine Placement Techniques Based on Biological Models: Comprehensive Research and Study","authors":"Madala Guru Brahmam, Vijay Anand Rajasekaran","doi":"10.1145/3549206.3549232","DOIUrl":"https://doi.org/10.1145/3549206.3549232","url":null,"abstract":"Cloud computing is a recent trend of managing virtual spaces for holding information, accessing them through different devices. With green computing as a predominant design approach, managing energy efficiently is the proven solution to reduce emission of greenhouse gases. Count of physical machines can be optimized into a considerable number of data centers through which dynamic migration of information can be regulated. Consolidation process, followed by effective placement of VMs, can further improve the quality of services offered by cloud service providers. In the same context, placing the virtual machines within a specific region in considerable proximity of physical machines, is a renowned strategy for achieving energy efficiency in virtual environments. Optimization algorithms are, at times, inspired from biological models to deliver quality of service parameters and refining cost of communications, energy utilizations, managing resources and hence meeting the user expectations in terms of deadlines. A detailed review of biological models for constructing the taxonomy of virtual machine placement techniques is presented in this literature survey. The fundamental ideologies of placing virtual machines, their pros and cons, achievement of tangible and intangible factors, meeting the requirements and expectations of end users, issues and challenges in the design and implementation are discussed in detail. Different strategies, their approaches and optimization algorithms, comparisons of performance in real time and simulated platforms are presented for better understanding of the models. The common list of parameters which have to be satisfied for efficient functioning and energy management are listed. Finally, the article concludes with future prospects of biological models.","PeriodicalId":199675,"journal":{"name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125456036","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

The State of Test Automation in DevOps: A Systematic Literature Review DevOps中测试自动化的状态:系统的文献综述

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549321

A. Patel, Sulabh Tyagi

{"title":"The State of Test Automation in DevOps: A Systematic Literature Review","authors":"A. Patel, Sulabh Tyagi","doi":"10.1145/3549206.3549321","DOIUrl":"https://doi.org/10.1145/3549206.3549321","url":null,"abstract":"DevOps is becoming increasingly popular in the software industry to deliver quality software in less time. However, building a DevOps pipeline is not easy. Many factors need to be considered in the process, and many decisions need to be made. Making the DevOps lifecycle successful, \"Test Automation\" played a pivot role in different stages of the DevOps pipeline. With automated testing, continuous testing can be achieved - a crucial driver for delivering high-quality software rapidly and a vital factor in reducing risks, increasing productivity, and lowering costs. Through automated testing tools, the entire process of testing can be automated. This paper presents a review of the use of Test Automation in a DevOps environment, it also provides glimpses of automated testing tools. The results are limited to articles published in peer-reviewed conferences, scientific journals, and books published between 2013 and 2021. The articles have been synthesized by categorizing the articles based on the research method, benefits of automated testing in DevOps, and the tools used for performing testing in an automated form. Our results shed light on different studies related to testing automation as one of the DevOps practices and its impact on software quality.","PeriodicalId":199675,"journal":{"name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123277987","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

Minimizing Cold Start Times in Serverless Deployments 最小化无服务器部署中的冷启动时间

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549234

Daniyaal Khan, Basant Subba, Sangeeta Sharma

引用次数: 0

Diagnosis of Covid-19 using Deep Learning 利用深度学习诊断Covid-19

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549275

Divya Sharma, Kritika Shelly, Ekta Gandotra, Deepak Gupta

{"title":"Diagnosis of Covid-19 using Deep Learning","authors":"Divya Sharma, Kritika Shelly, Ekta Gandotra, Deepak Gupta","doi":"10.1145/3549206.3549275","DOIUrl":"https://doi.org/10.1145/3549206.3549275","url":null,"abstract":"In the global health disaster of the Coronavirus infection-2019 (Covid-19) pandemic, the health sector is avidly seeking new technologies and strategies to detect and manage the spread of the Coronavirus outbreak. Artificial Intelligence (AI) is currently one of the most essential aspects of global technology since it can track and monitor the rate at which the Coronavirus develops as well as determines the danger and severity of Coronavirus patients. In this paper, we have proposed a two-stage end-to-end Deep Learning (DL) model which can be used to predict the presence and severity of Covid-19 infection in a patient as early and accurately as possible so that the spread of this viral infection can be slowed down. Hence, based on the Computed Tomography (CT) scans or chest X-rays provided by the user as an input, the DL models are built that can forecast the presence of Covid-19 in that respective patient accurately and efficiently. In this paper, 5 DL models i.e., VGG16, InceptionV3, Xception, ResNet50, and Convolution Neural Networks (CNN) are built and their comparative analysis is carried out for the diagnosis of Covid-19. On the Google Colab GPU, the models are trained for 100 epochs on a total of 1686 images of chest X-rays and CT scans. The experimental results show that out of all these models, the model based on the Xception algorithm is the most accurate one in determining the presence of the disease and provides an accuracy of 81% and 89% on CT scans and Chest x-rays respectively.","PeriodicalId":199675,"journal":{"name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121051942","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 GWO based efficient approach to identify terrorist incident hotspots in India 基于GWO的印度恐怖事件热点识别方法

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549264

Ankita Wadhwa, M. Thakur

{"title":"A GWO based efficient approach to identify terrorist incident hotspots in India","authors":"Ankita Wadhwa, M. Thakur","doi":"10.1145/3549206.3549264","DOIUrl":"https://doi.org/10.1145/3549206.3549264","url":null,"abstract":"For a given set of geospatial locations (like, crime activities, terrorist activities, bomb blast locations, etc.), identification of such circular zones where accumulation of points inside the circle is very much greater than outside is important. Such zones are known as hotspots and their detection is known as circular hotspot detection (CHD). Timely detection of circular hotspots is crucial in many societal applications like epidemiology, terrorism, criminology etc. The state-of-the-art method for circular hotspot detection viz. SaTScan is computationally expensive due to enumeration of all possible circles called candidate circular hotspots. Due to its high cost SaTScan is not suitable for applications like terrorist activity hotspot identification, where well-timed identification of hotspots is crucial to prioritize the security efforts put by government and security agencies. Therefore, in this paper, we present an efficient and effective Grey Wolf Optimizer based approach called GWO-CHD for terrorism hotspot detection. The results of GWO-CHD are compared with SaTScan in terms of time required to detect the hotspot and its quality (measured using relative error). All the experiments are performed using terrorist activity data of Indian subcontinent from 2016-2021. Results indicate that hotspots identified by GWO-CHD and SaTScan are almost at par in terms of quality; however, GWO-CHD proved to be much more efficient than SaTScan in terms of computational time.","PeriodicalId":199675,"journal":{"name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","volume":"146 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115583091","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

Detecting Android Malicious Applications using Dynamic Malware Analysis and Machine Learning 使用动态恶意软件分析和机器学习检测Android恶意应用程序

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549271

Meghna Dhalaria, Ekta Gandotra

引用次数: 0