International Journal of Software Engineering & Applications最新文献_第2页

Impact of Artificial Intelligence on the Automation of Digital Health System 人工智能对数字医疗系统自动化的影响

International Journal of Software Engineering & Applications Pub Date : 2022-11-30 DOI: 10.5121/ijsea.2022.13602

Mehmood Ali Mohammed, Murtuza Ali Mohammed, Vazeer Ali Mohammed

{"title":"Impact of Artificial Intelligence on the Automation of Digital Health System","authors":"Mehmood Ali Mohammed, Murtuza Ali Mohammed, Vazeer Ali Mohammed","doi":"10.5121/ijsea.2022.13602","DOIUrl":"https://doi.org/10.5121/ijsea.2022.13602","url":null,"abstract":"Automating digital systems in healthcare plays a significant role in transforming the quality-of-care services delivered to patients across the board. This role is anticipated to be accomplished by the development and implementation of artificial intelligence in healthcare which has the potential to impact the provision of healthcare services. This paper sought to investigate the impact of adopting and implementing artificial intelligence on the automation of digital health systems within the different levels of healthcare. The general objective of the research study was to investigate the impact of artificial intelligence in the automation of digital health systems. The specific goals were to understand the concept of artificial intelligence and how it automates digital strategies, to determine the AI systems that have been developed and implemented in the healthcare systems, to establish the factors that influence the adoption of AI in healthcare, and to find out the outcomes of implementing AI in digital health systems. The research employed the descriptive research design. The study population included healthcare workers, policymakers, IT specialists, and management teams in the healthcare sector in the State of Kentucky. The sampling technique for the study was the purposive sampling technique. The study collected data using semi-structured interviews administered through Google Teams and Zoom. Data analysis was analyzed using the computer-assisted software for analyzing qualitative data, NVivo. The findings were that AI as a technological concept has the potential to impact the automation of digital health systems and is key to automating health services such as the diagnosis and treatment of illnesses and management of claims and payments. The study recommended that policy supports the application of artificial intelligence in healthcare, thus enabling the automation of several healthcare services and thus improving the delivery of care.","PeriodicalId":434551,"journal":{"name":"International Journal of Software Engineering & Applications","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121237643","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

From the Art of Software Testing to Test-as-a-Service in Cloud Computing 从软件测试的艺术到云计算中的测试即服务

International Journal of Software Engineering & Applications Pub Date : 2022-11-30 DOI: 10.5121/ijsea.2022.13601

Janete Amaral, Alberto S. Lima, José Deivison de Souza, Lincoln S. Rocha

引用次数: 0

An Improved Repository Structure to Identify, Select and Integrate Components in Component-based Development 在基于组件的开发中识别、选择和集成组件的改进存储库结构

International Journal of Software Engineering & Applications Pub Date : 2022-11-30 DOI: 10.5121/ijsea.2022.13603

MuhammadMaigatari Dauda, Reda M Salama, Rizwan Qureshi

{"title":"An Improved Repository Structure to Identify, Select and Integrate Components in Component-based Development","authors":"MuhammadMaigatari Dauda, Reda M Salama, Rizwan Qureshi","doi":"10.5121/ijsea.2022.13603","DOIUrl":"https://doi.org/10.5121/ijsea.2022.13603","url":null,"abstract":"An ultimate goal of software development is to build high quality products. The customers of software industry always demand for high-quality products quickly and cost effectively. The component-based development (CBD) is the most suitable methodology for the software companies to meet the demands of target market. To opt CBD, the software development teams have to customize generic components that are available in the market and it is very difficult for the development teams to choose the suitable components from the millions of third party and commercial off the shelf (COTS) components. On the other hand, the development of in-house repository is tedious and time consuming. In this paper, we propose an easy and understandable repository structure to provide helpful information about stored components like how to identify, select, retrieve and integrate components. The proposed repository will also provide previous assessments of developers and end-users about the selected component. The proposed repository will help the software companies by reducing the customization effort, improving the quality of developed software and preventing integrating unfamiliar components.","PeriodicalId":434551,"journal":{"name":"International Journal of Software Engineering & Applications","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127515984","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

Threats and Opportunities with AI-based Cyber Security Intrusion Detection: A Review 基于人工智能的网络安全入侵检测的威胁与机遇

International Journal of Software Engineering & Applications Pub Date : 2022-09-30 DOI: 10.5121/ijsea.2022.13502

Bibhu Dash, Md Meraj Ansari, P. Sharma, Azad Ali

引用次数: 46

Federated Learning for Privacy-Preserving: A Review of PII Data Analysis in Fintech 面向隐私保护的联邦学习:金融科技领域PII数据分析综述

International Journal of Software Engineering & Applications Pub Date : 2022-07-31 DOI: 10.5121/ijsea.2022.13401

Bibhu Dash, P. Sharma, Azad Ali

{"title":"Federated Learning for Privacy-Preserving: A Review of PII Data Analysis in Fintech","authors":"Bibhu Dash, P. Sharma, Azad Ali","doi":"10.5121/ijsea.2022.13401","DOIUrl":"https://doi.org/10.5121/ijsea.2022.13401","url":null,"abstract":"There has been tremendous growth in the field of AI and machine learning. The developments across these fields have resulted in a considerable increase in other FinTech fields. Cyber security has been described as an essential part of the developments associated with technology. Increased cyber security ensures that people remain protected, and that data remains safe. New methods have been integrated into developing AI that achieves cyber security. The data analysis capabilities of AI and its cyber security functions have ensured that privacy has increased significantly. The ethical concept associated with data privacy has also been advocated across most FinTech regulations. These concepts and considerations have all been engaged with the need to achieve the required ethical requirements. The concept of federated learning is a recently developed measure that achieves the abovementioned concept. It ensured the development of AI and machine learning while keeping privacy in data analysis. The research paper effectively describes the issue of federated learning for confidentiality. It describes the overall process associated with its development and some of the contributions it has achieved. The widespread application of federated learning in FinTech is showcased, and why federated learning is essential for overall growth in FinTech.","PeriodicalId":434551,"journal":{"name":"International Journal of Software Engineering & Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128568742","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: 54

Smart ULT Management for Ultra-Large-Scale Software 超大规模软件智能ULT管理

International Journal of Software Engineering & Applications Pub Date : 2022-07-31 DOI: 10.5121/ijsea.2022.13402

N. Luo, Linlin Zhang

引用次数: 0

Enhanced Software Design for Boosted Continuous Software Delivery 增强软件设计，促进持续软件交付

International Journal of Software Engineering & Applications Pub Date : 2022-03-31 DOI: 10.5121/ijsea.2022.13202

N. Luo, Yue Xiong

引用次数: 1

Determining the Risky Software Projects using Artificial Neural Networks 利用人工神经网络确定风险软件项目

International Journal of Software Engineering & Applications Pub Date : 2022-03-31 DOI: 10.5121/ijsea.2022.13201

Etkin Sakucoglu, Laman Valizada, Ayse Buharali Olcaysoy, O. Kalipsiz

{"title":"Determining the Risky Software Projects using Artificial Neural Networks","authors":"Etkin Sakucoglu, Laman Valizada, Ayse Buharali Olcaysoy, O. Kalipsiz","doi":"10.5121/ijsea.2022.13201","DOIUrl":"https://doi.org/10.5121/ijsea.2022.13201","url":null,"abstract":"Determining risky software projects early is a very important factor for project success. In this study it is aimed to choose the most correctly resulting modelling method that will be useful for early prediction of risky software projects to help companies to avoid losing time and money on unsuccessful projects and also facing legal requirements because of not being able to fullfill their responsibilites to their customers While making the research for this subject, it is seen that in previous researches, usually traditional modelling techniques were preferred. But it is observed that these methods were mostly resulted with high misclassification ratio. To overcome this problem, this study proposes a three-layered neural network (NN) architecture with a backpropagation algorithm. NN architecture was trained by using two different data sets which were OMRON data set (collected by OMRON) and 2016-2020 ES.LV data set (collected by the authors) separately. For the made of this study firstly the most relevant classification method (Gaussian Naive Bayes Algorithm) and the most relevant neural network method (Scaled Conjugate Gradient Backpropagation Algorithm) was chosen and both data sets were trained by using each method seperately for the purpose of observing which type of modelling architecture would give better results. Experimental results of this study showed that the neural network approach is useful for predicting whether a project is risky or not risky.","PeriodicalId":434551,"journal":{"name":"International Journal of Software Engineering & Applications","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123066708","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

Vinayaka : A Semi-Supervised Projected Clustering Method Using Differential Evolution 一种基于差分进化的半监督投影聚类方法

International Journal of Software Engineering & Applications Pub Date : 2012-07-31 DOI: 10.5121/IJSEA.2012.3406

Satish Gajawada, Durga Toshniwal

引用次数: 12

International Journal of Software Engineering &amp; Applications最新文献

International Journal of Software Engineering & Applications最新文献