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The Last State of Artificial Intelligence in Project Management
[article]
2020
arXiv
pre-print
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However, the most popular PM processes among included papers were project effort prediction and cost estimation, and the most popular AI techniques were support vector machines, neural networks, and genetic ...
project procurements management, and project communication management. ...
), and a
three-layer backpropagation neural
network (BPNN) or another regression
model, and support vector machine
(SVM)
[28]
Monitoring Cost
Earned value and
earned schedule
Support vector machines ...
arXiv:2012.12262v1
fatcat:q3qxxsb6rbailg7leuoputgphy
Machine Learning and value generation in Software Development: a survey
[article]
2020
arXiv
pre-print
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This paper reviews the literature between 2000 and 2019 on the use the learning models that have been employed for programming effort estimation, predicting risks and identifying and detecting defects. ...
Machine Learning (ML) has become a ubiquitous tool for predicting and classifying data and has found application in several problem domains, including Software Development (SD). ...
Specifically, the performance of Support Vector Machine is frequently noted in regards to predicting schedule and budget risks. ...
arXiv:2001.08980v1
fatcat:mriy3kfcwzbytcp2gleqblqn3e
Predictive Approach towards Software Effort Estimation using Evolutionary Support Vector Machine
2017
International Journal of Advanced Computer Science and Applications
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Other than that a model is proposed to predict the effort using minimum number of parameters in software project development. ...
The project effort measurement is one of the most important estimates done in project management domain. ...
A structural model was proposed that correlated the soft and hard skills of risk management with project success. ...
doi:10.14569/ijacsa.2017.080554
fatcat:23vcsgh42nhw5dxvpfokdlsyn4
Research Trends on Machine Learning in Construction Management: A Scientometric Analysis
2021
Journal of Applied Science and Technology Trends
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In addition, five main aspects in construction management have been applied machine learning techniques, namely, assess and reduce risk, safety management for construction sites, cost estimation and prediction ...
approach. ...
neural network (BPNN) or another regression model, support vector machine (SVM). ...
doi:10.38094/jastt203105
fatcat:igk6feeydnegzffdmc5kkxdc5a
Software Risk Analysis with the use of Classification Techniques: A Review
2020
Zenodo
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Risk analysis and management is a critical aspect of the software development process. Various risks are associated with every phase of the software development lifecycle. ...
The early identification of risks in each phase of software development coupled with mitigating plans can help to reduce the cost of the product and increase software quality. ...
A logistic regression method and six machine learning methods (Decision Tree, Support Vector Machine, Group Method of Data Handling Method, Artificial Neural Network, Cascade Correlation Network, and Gene ...
doi:10.5281/zenodo.3934583
fatcat:4mz565mkzvbvvngszld5vedmn4
An Effective Software Effort Estimation based on Functional Points using Soft Computing Techniques
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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In this paper, Functional Points have been selected for effort estimation and implemented using soft computing techniques like Neural Networks and Neuro Fuzzy techniques. ...
Still in this 21st century, it is a great challenge for the Project Managers to make the software projects successful. ...
Sultan et al [12] , used three machine learning models like Linear Regression Model, Support Vector Machine (SVM) and Artificial Neural Network (ANN) to estimate the development effort. ...
doi:10.35940/ijitee.j9674.0881019
fatcat:fen4e4tqlzge7ockdhjkincouu
AN IMPLEMENTATION OF SOFTWARE EFFORT DURATION AND COST ESTIMATION WITH STATISTICAL AND MACHINE LEARNING APPROACHES
2019
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY
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Presently there are two types of software EDC models to estimate the software those are statistical approaches and machine learning approaches. ...
Machine learning techniques are suitable in software engineering because it produces accurate results, which estimates by training rules and iterations. ...
Support Vector Machine (SVM) Support vector machines are used to perform classification and regression analysis by constructing hyper plane or various hyper planes in a high dimensional data space. ...
doi:10.34218/ijcet.10.1.2019.010
fatcat:uahondbojran7nb5h3yfkenurq
Artificial intelligence in engineering risk analytics
2017
Engineering applications of artificial intelligence
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The authors would like to thank all the referees and the Editor-in-Chief A. Abraham, for their energy and efforts in bringing out this special issue. This work is supported by the Ministry of ...
For example, artificial intelligence models such as neural networks and support vector machines have been widely used for establishing the early warning system for monitoring a company's financial status ...
AI techniques that are useful include, but are not limited to expert systems, artificial neural networks, support vector machines, evolutionary computations, fuzzy systems, knowledge based systems, case-based ...
doi:10.1016/j.engappai.2017.09.001
fatcat:s35mrwdhprdcjbwaw47gfsq7tq
The Application of Artificial Intelligence in Project Management Research: A Review
2020
International Journal of Interactive Multimedia and Artificial Intelligence
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techniques used today and the areas of project management in which agents are being applied. ...
The field of artificial intelligence is currently experiencing relentless growth, with innumerable models emerging in the research and development phases across various fields, including science, finance ...
Evolutionary Fuzzy Support Vector Machines Inference Model (EFSIM). ...
doi:10.9781/ijimai.2020.12.003
fatcat:zb2jicffbffohbngkvovdmdqmq
A support vector machine method for bid/no bid decision making
2017
Journal of Civil Engineering and Management
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The performance of the support vector machine classifier is compared with the performances of the worth evaluation model, linear regression, and neural network classifiers. ...
The method is implemented for bid/no bid decision making of offshore oil and gas platform fabrication projects to achieve a parsimonious support vector machine classifier. ...
Along the with support vector machine classifier, linear regression, and feed-forward neural network classifiers are developed to evaluate prediction performance of the support vector machine classifier ...
doi:10.3846/13923730.2017.1281836
fatcat:3wiknfsjovfgxpbmuk4wos4y6y
A probabilistic model for predicting software development effort
2005
IEEE Transactions on Software Engineering
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We develop a causal model from the literature and, using a data set of 33 real-world software projects, we illustrate how decision-making risks can be incorporated in the Bayesian networks. ...
We compare the predictive performance of the Bayesian model with popular nonparametric neural-network and regression tree forecasting models and show that the Bayesian model is a competitive model for ...
First, using a set of real-world software projects, we benchmark the performance of Bayesian point software development forecasts with popular nonparametric neural network and the CART approaches. ...
doi:10.1109/tse.2005.75
fatcat:qs3dy7w2i5hexle6p5zy7fkdiu
A Probabilistic Model for Predicting Software Development Effort
[chapter]
2003
Lecture Notes in Computer Science
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We develop a causal model from the literature and, using a data set of 33 real-world software projects, we illustrate how decision-making risks can be incorporated in the Bayesian networks. ...
We compare the predictive performance of the Bayesian model with popular nonparametric neural-network and regression tree forecasting models and show that the Bayesian model is a competitive model for ...
First, using a set of real-world software projects, we benchmark the performance of Bayesian point software development forecasts with popular nonparametric neural network and the CART approaches. ...
doi:10.1007/3-540-44843-8_63
fatcat:2hpoioqxxbdcbebeb5et5py2sa
Artificial Intelligence Applied to Project Success: A Literature Review
2015
International Journal of Interactive Multimedia and Artificial Intelligence
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Project control and monitoring tools are based on expert judgement and parametric tools. Projects are the means by which companies implement their strategies. ...
However project success rates are still very low. ...
It relies on PDRI for determining a rate of project definition before the project starts. To make this prediction, it uses two models based on neural networks and Support Vector Machine. ...
doi:10.9781/ijimai.2015.3510
fatcat:oxqqtdizbra2zbyz2cyleup7a4
Scrum based scaling using agile method to test software projects and its future solutions using in artificial neural networks
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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They construe 70% of software based on agile methods. Its estimation results are justified in the machine learning processes such as Bayesian regression using back propagation neural network. ...
Every day software demands have has been growing in this field, provides new innovative ideas and support to incorporate the customer needs in software development. ...
The neural network along with to verify the results the performance review model and multiple regression model to verify the results. ...
doi:10.35940/ijitee.h6853.078919
fatcat:653dcomqxrfmxn7mfkpsay6lri
Software Enterprise Risk Detection Model Based on BP Neural Network
2022
Wireless Communications and Mobile Computing
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With the rapid development of software industry, software enterprises have many problems in risk management, and enterprises are facing a huge crisis. ...
In order to detect the risk of software enterprise, a risk detection model based on BP neural network is proposed in this paper. ...
Acknowledgments This work is supported by the Soochow University. ...
doi:10.1155/2022/9147090
fatcat:zchj6lh7f5bn3mhgd5vsdh7mn4
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