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Int. J. Cloud Appl. Comput.最新文献

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A Cloud-Based Predictive Model for the Detection of Breast Cancer 基于云的乳腺癌检测预测模型
Int. J. Cloud Appl. Comput. Pub Date : 2022-01-01 DOI: 10.4018/ijcac.310041
Kuldeep Pathoee, Deepesh Rawat, Anupama Mishra, Varsha Arya, M. Rafsanjani, A. Gupta
{"title":"A Cloud-Based Predictive Model for the Detection of Breast Cancer","authors":"Kuldeep Pathoee, Deepesh Rawat, Anupama Mishra, Varsha Arya, M. Rafsanjani, A. Gupta","doi":"10.4018/ijcac.310041","DOIUrl":"https://doi.org/10.4018/ijcac.310041","url":null,"abstract":"Invasive cancer is the biggest cause of death worldwide, especially among women. Early cancer detection is vital to health. Early identification of breast cancer improves prognosis and survival odds by allowing for timely clinical therapy. For accurate cancer prediction, machine learning requires quick analytics and feature extraction. Cloud-based machine learning is vital for illness diagnosis in rural areas with few medical facilities. In this research, random forests, logistic regression, decision trees, and SVM are employed, and the authors assess the performance of various algorithms using confusion measures and AUROC to choose the best machine learning model for breast cancer prediction. Precision, recall, accuracy, and specificity are used to calculate results. Confusion matrix is based on predicted cases. The ML model's performance is evaluated. For simulation, the authors used the Wisconsin Dataset of Breast Cancer (WDBC). Through experiments, it can be seen that the SVM model reached 98.24% accuracy with an AUC of 0.993, while the logistic regression achieved 94.54% accuracy with an AUC of 0.998.","PeriodicalId":442336,"journal":{"name":"Int. J. Cloud Appl. Comput.","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125179668","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 Integration of Vehicular Ad-Hoc Networks and Cloud Computing 车载Ad-Hoc网络与云计算融合研究进展
Int. J. Cloud Appl. Comput. Pub Date : 2022-01-01 DOI: 10.4018/ijcac.300771
Limali Sahoo, S. K. Panda, K. Das
{"title":"A Review on Integration of Vehicular Ad-Hoc Networks and Cloud Computing","authors":"Limali Sahoo, S. K. Panda, K. Das","doi":"10.4018/ijcac.300771","DOIUrl":"https://doi.org/10.4018/ijcac.300771","url":null,"abstract":"The vehicular network is becoming a prominent area of research in the field of smart transportation systems. In such systems, smart vehicles are embedded with the onboard unit, which includes computing systems, communication devices, and storage equipment, and these vehicles are used to provide efficient traffic management and road safety. For many years, several solutions are addressed by many practitioners regarding different issues and challenges that arise in a vehicular network. One of the solutions is vehicular cloud computing (VCC). VCC is a promising technology that has created a great impact on the services and applications of vehicular ad-hoc networks (VANETs) by exploiting the vehicular resources effectively. In this paper, we present a survey on VCC, which integrates VANETs and cloud computing (CC). The basic overview and characteristics of VANETs are discussed. The architecture and several features of VCC are explored. We also discuss various privacy and security-related issues in VCC. Lastly, we focus on open issues, existing work, and future research directions.","PeriodicalId":442336,"journal":{"name":"Int. J. Cloud Appl. Comput.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128422238","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
Business Process Discovery Using Process Mining Techniques and Distributed Framework 使用流程挖掘技术和分布式框架的业务流程发现
Int. J. Cloud Appl. Comput. Pub Date : 2022-01-01 DOI: 10.4018/ijcac.300772
Ishak H. A. Meddah, Fatiha Guerroudji
{"title":"Business Process Discovery Using Process Mining Techniques and Distributed Framework","authors":"Ishak H. A. Meddah, Fatiha Guerroudji","doi":"10.4018/ijcac.300772","DOIUrl":"https://doi.org/10.4018/ijcac.300772","url":null,"abstract":"The processing of big data across different axes is becoming more and more difficult and the introduction of the Hadoop MapReduce framework seems to be a solution to this problem. With this framework, large amounts of data can be analyzed and processed. It does this by distributing computing tasks between a group of virtual servers operating in the cloud or a large group of devices. The mining process forms an important bridge between data mining and business process analysis. Its techniques make it possible to extract information from event reports. The extraction process generally consists of two phases: identification or discovery and innovation or education. Our first task is to extract small patterns from the log effects. These templates represent the implementation of the tracking from a business process report file. In this step we use the available technologies. Patterns are represented by finite state automation or regular expressions. And the final model is a combination of just two different styles.","PeriodicalId":442336,"journal":{"name":"Int. J. Cloud Appl. Comput.","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129213906","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
Density-Based Machine Learning Scheme for Outlier Detection in Smart Forest Fire Monitoring Sensor Cloud 智能森林火灾监测传感器云中基于密度的离群点检测机器学习方案
Int. J. Cloud Appl. Comput. Pub Date : 2022-01-01 DOI: 10.4018/ijcac.305218
Rajendra Kumar Dwivedi
{"title":"Density-Based Machine Learning Scheme for Outlier Detection in Smart Forest Fire Monitoring Sensor Cloud","authors":"Rajendra Kumar Dwivedi","doi":"10.4018/ijcac.305218","DOIUrl":"https://doi.org/10.4018/ijcac.305218","url":null,"abstract":"Sensor Cloud is an integration of sensor networks with cloud where sensed data is stored and processed in the cloud. The applications of sensor cloud can be seen in forest fire monitoring, healthcare system, and other Internet-of-Things systems. Outliers may present within this data due to malicious activities, low-quality sensors, or node deployment in harsh environments. Such outliers must be detected timely for effective decision making. Many clustering-based machine learning schemes for outlier detection have been devised. However, accuracy of these techniques can be further improved. This paper proposes a density-based machine learning scheme (DBS) for outlier detection which is implemented in Python and executed on the two datasets of different forest fire monitoring networks. DBS makes density-based clusters of all data points where outliers lie in low-density region. The use of a density-based model in the proposed approach improves precision, throughput, and accuracy. DBS outperforms the existing Mean Shift and K Means based clustering schemes with maximum accuracy 98.40%.","PeriodicalId":442336,"journal":{"name":"Int. J. Cloud Appl. Comput.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131806457","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 Multi-Objective Adaptive Upper Threshold Approach for Overloaded Host Detection in Cloud Computing 云计算中多目标自适应上阈值检测方法
Int. J. Cloud Appl. Comput. Pub Date : 2022-01-01 DOI: 10.4018/ijcac.311038
Rajeshwari Sissodia, M. Rauthan, V. Barthwal
{"title":"A Multi-Objective Adaptive Upper Threshold Approach for Overloaded Host Detection in Cloud Computing","authors":"Rajeshwari Sissodia, M. Rauthan, V. Barthwal","doi":"10.4018/ijcac.311038","DOIUrl":"https://doi.org/10.4018/ijcac.311038","url":null,"abstract":"Cloud data centers (CDC) have become an increasingly critical issue because of their large-scale deployment, which has resulted in increased energy consumption (EC) and SLA. The SLA and EC can be greatly reduced by using an efficient virtual machine consolidation (VMC) approach. This study presents a multi-objective adaptive upper threshold (UTh) technique for identifying overloaded hosts. The dynamic virtual machine consolidation (DVMC) is then obtained by combining a modified overloaded host detection technique with a different VM selection method (i.e., minimum migration time (Mmt) and minimum utilization (Mu)). The simulation results indicate that the modified Interquartile range (Iqr) overloaded host detection algorithm outperforms the existing overloaded host detection algorithms (i.e., InterQuartile range (Iqr), local regression (Lr), and dynamic voltage frequency scale (DVFS) algorithms) in terms of EC, SLA, and the number of virtual machine (VM) migrations.","PeriodicalId":442336,"journal":{"name":"Int. J. Cloud Appl. Comput.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131000311","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
Catholic Impact Evolution Through Public Twitter Data During COVID-19 在2019冠状病毒病期间，天主教通过公共推特数据影响演变
Int. J. Cloud Appl. Comput. Pub Date : 2022-01-01 DOI: 10.4018/ijcac.305211
E. Marín, Cristina Blanco González-Tejero, María Guijarro García, F. J. S. García
{"title":"Catholic Impact Evolution Through Public Twitter Data During COVID-19","authors":"E. Marín, Cristina Blanco González-Tejero, María Guijarro García, F. J. S. García","doi":"10.4018/ijcac.305211","DOIUrl":"https://doi.org/10.4018/ijcac.305211","url":null,"abstract":"During the Covid-19 crisis, many networks have sprung up disseminating information. This study examines the influence of religion during the Covid-19 pandemic. It understands religion as a factor capable of mitigating frustrations and critical situations in society. To this end, a data mining analysis was developed for a set of 107,786 tweets collected from the social platform Twitter in the framework of user-generated content (UGC), linked to the Covid-19 related tweets published by @Pontifex and @Pontifex_es. To achieve this goal, hidden insight data extraction and sentiment analysis are carried out, along with the application of Social Network Analysis (SNA) techniques. The main outcome of the study is the positive correlation between the repercussion of the Pope’s tweets and the evolution of the Covid-19 incidence in Europe. Finally, the Latent Dirichlet Allocation (LDA) algorithm identifies the relevant topics in the analysis.","PeriodicalId":442336,"journal":{"name":"Int. J. Cloud Appl. Comput.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129558257","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
Cost-Effective Spot Instances Provisioning Using Features of Cloud Markets 使用云市场的特性提供具有成本效益的现货实例
Int. J. Cloud Appl. Comput. Pub Date : 2022-01-01 DOI: 10.4018/ijcac.308276
Abdullah Alourani, A. Kshemkalyani
{"title":"Cost-Effective Spot Instances Provisioning Using Features of Cloud Markets","authors":"Abdullah Alourani, A. Kshemkalyani","doi":"10.4018/ijcac.308276","DOIUrl":"https://doi.org/10.4018/ijcac.308276","url":null,"abstract":"Cloud computing offers a variable-cost payment scheme that allows cloud customers to specify the price they are willing to pay for renting spot instances at much lower costs than fixed payment schemes, and depending on the varying demand from cloud customers, cloud platforms could revoke spot instances at any time. To alleviate the effect of spot instance revocations, applications often employ different fault-tolerance mechanisms to minimize or even eliminate the lost work for each spot instance revocation. However, these fault-tolerance mechanisms incur additional overhead related to application completion time and deployment cost. This article proposes a novel cloud market-based approach for provisioning spot instances using features of cloud markets to reduce the deployment cost and completion time of applications. The simulation results show that the approach reduces the deployment cost and completion time compared to approaches based on fault-tolerance mechanisms.","PeriodicalId":442336,"journal":{"name":"Int. J. Cloud Appl. Comput.","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125758015","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
Efficient Resource Management Using Improved Bio-Inspired Algorithms for the Fog Computing Environment 在雾计算环境中使用改进的生物启发算法的有效资源管理
Int. J. Cloud Appl. Comput. Pub Date : 2022-01-01 DOI: 10.4018/ijcac.297104
Chetan M. Bulla, M. N. Birje
{"title":"Efficient Resource Management Using Improved Bio-Inspired Algorithms for the Fog Computing Environment","authors":"Chetan M. Bulla, M. N. Birje","doi":"10.4018/ijcac.297104","DOIUrl":"https://doi.org/10.4018/ijcac.297104","url":null,"abstract":"The resource monitoring and management services together play a vital role in improving the overall performance of fog computing services. The monitoring system continuously keeps track of all resources by collecting and analyzing the status information and alert the user when the performance decreases. Resource management involves load balancing, resource scheduling and allocation and it requires accurate resource status which is provided by resource monitoring system to take scheduling and allocation decisions. The resource management activities are NP-hard problems and require optimal techniques to improve resource utilization and reduce energy consumption and latency. This paper proposes resource management model using improved bio-inspired algorithms and fog monitoring model to improve resource utilization and reduce energy consumption. The simulation results show that the proposed model is effective in terms of execution time, response time and energy consumption compared to the state of art techniques.","PeriodicalId":442336,"journal":{"name":"Int. J. Cloud Appl. Comput.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129622577","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
An Optimization Model for Task Scheduling in Mobile Cloud Computing 移动云计算中的任务调度优化模型
Int. J. Cloud Appl. Comput. Pub Date : 2022-01-01 DOI: 10.4018/ijcac.297102
R. Alakbarov
{"title":"An Optimization Model for Task Scheduling in Mobile Cloud Computing","authors":"R. Alakbarov","doi":"10.4018/ijcac.297102","DOIUrl":"https://doi.org/10.4018/ijcac.297102","url":null,"abstract":"The rapid increase in the number of mobile users in mobile cloud computing (MCC), the cloud servers' remoteness, and the Internet loading have caused significant delays in the delivery of processed data to the user. The selection of the most suitable cloudlet that allows running users' applications rapidly in the cloudlet is still an urgent problem. In the paper we propose a strategy for selecting a cloudlet with high computing productivity, which provides a fast solution, considering the complexity degree of the application (file type). Here also noted that the balanced distribution of users' application software in the cloudlet network ensures the reduction of delays. In the paper, for the proposed strategy, a mathematical model of the optimal distribution of applications in the cloudlet has been proposed considering the loading degree of cloudlets which provides energy consumption on mobile devices, uploading the issue to the cloud, obtaining results, reducing network delays.","PeriodicalId":442336,"journal":{"name":"Int. J. Cloud Appl. Comput.","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124047242","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: 6
λHive: Formal Semantics of an Edge Computing Model Based on JavaScript λHive:基于JavaScript的边缘计算模型的形式语义
Int. J. Cloud Appl. Comput. Pub Date : 2022-01-01 DOI: 10.4018/ijcac.312564
Matías Teragni, Claudia Pons
{"title":"λHive: Formal Semantics of an Edge Computing Model Based on JavaScript","authors":"Matías Teragni, Claudia Pons","doi":"10.4018/ijcac.312564","DOIUrl":"https://doi.org/10.4018/ijcac.312564","url":null,"abstract":"Edge computing is a paradigm that brings computation and data storage closer to the location where it is needed to improve response times and save bandwidth. It applies virtualization technology that makes it easier to deploy and run a wider range of applications on the edge servers and take advantage of largely unused computational resources. This article describes the design and formalization of Hive, a distributed shared memory model that can be transparently integrated with JavaScript using a standard out of the box runtime. To define such a model, a formal definition of the JavaScript language was used and extended to include modern capabilities and custom semantics. This extended model is used to prove that the distributed shared memory can operate on top of existing and unmodified web browsers, allowing the use of any computer and smartphone as a part of the distributed system. The proposed model guarantees the eventual synchronization of data across all the system and provides the possibility to have a stricter consistency using standard http operations.","PeriodicalId":442336,"journal":{"name":"Int. J. Cloud Appl. Comput.","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124224454","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
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