2020
DOI: 10.35940/ijeat.c5974.029320
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Short term Predication of Risk Management Integrating Artificial Neural Network ANN

Malaya Nayak,
Tariq Abdullah

Abstract: The IT industry has boomed in the past few years with an ever increasing number of risk management applications being developed. There are inherent risks in software development projects and failure to deliver software projects within deadline or failure to develop software according to specifications can be costly. The software risks may occur during the project process. The management process of software risks consists the risk refinement, risk identification, risk monitoring, risk maintenance, risk estimati… Show more

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Cited by 2 publications
(2 citation statements)
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“…The procedure of assessing the risk is divided into five major phases as explained under: [14]. Among these, the most common risk factors which have been used for the forecasting classified are financial and economic risks, schedule risks, user risks, performance risks, and complexity risks [15][16][17][18][19].…”
Section: Analysis Procedures For Prediction Of Total Project Riskmentioning
confidence: 99%
“…The procedure of assessing the risk is divided into five major phases as explained under: [14]. Among these, the most common risk factors which have been used for the forecasting classified are financial and economic risks, schedule risks, user risks, performance risks, and complexity risks [15][16][17][18][19].…”
Section: Analysis Procedures For Prediction Of Total Project Riskmentioning
confidence: 99%
“…Xuesong et al calculated the index risk value of each partner in the cooperative alliance from the perspective of the third party, constructed the early warning system of information sharing risk, and gave different early warning information according to different degrees of risk, so as to curb the risk caused by information sharing [10]. Nayak et al constructed a technological innovation risk early warning model integrating functions, early warning index system, and risk response strategies, which pointed out the direction for the response and strategy research of enterprise technological innovation risks [11]. Shen et al established a risk early warning system for enterprise technological innovation projects using a rough neural network, which is both effective and feasible, pointing out the direction for the risk early warning management of enterprise technological innovation projects [12].…”
Section: Literature Reviewmentioning
confidence: 99%