Filters








16,490 Hits in 6.3 sec

The Last State of Artificial Intelligence in Project Management [article]

Mohammad Reza Davahli
2020 arXiv   pre-print
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]

Barakat. J. Akinsanya, Luiz J.P. Araújo, Mariia Charikova, Susanna Gimaeva, Alexandr Grichshenko, Adil Khan, Manuel Mazzara, Ozioma Okonicha N, Daniil Shilintsev
2020 arXiv   pre-print
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

Tahira Mahboob, Sabheen Gull, Sidrish Ehsan, Bushra Sikandar
2017 International Journal of Advanced Computer Science and Applications  
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

Tam Nguyen Van, Toan Nguyen Quoc
2021 Journal of Applied Science and Technology Trends  
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

M. N. A. Khan, A. M. Mirza, I. Saleem
2020 Zenodo  
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  
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

B.M.G. PRASAD, P.V.S. SREENIVAS
2019 INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY  
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

Desheng Wu, David L. Olson, Alexandre Dolgui
2017 Engineering applications of artificial intelligence  
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

Jesús Gil, Javier Martínez Torres, Rubén González-Crespo
2020 International Journal of Interactive Multimedia and Artificial Intelligence  
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

Rifat SONMEZ, Burak SÖZGEN
2017 Journal of Civil Engineering and Management  
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

P.C. Pendharkar, G.H. Subramanian, J.A. Rodger
2005 IEEE Transactions on Software Engineering  
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]

Parag C. Pendharkar, Girish H. Subramanian, James A. Rodger
2003 Lecture Notes in Computer Science  
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

Daniel Magaña Martínez, Juan Carlos Fernandez-Rodriguez
2015 International Journal of Interactive Multimedia and Artificial Intelligence  
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  
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

Jiahao Shan, Hongling Wang, Mu En Wu
2022 Wireless Communications and Mobile Computing  
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
« Previous Showing results 1 — 15 out of 16,490 results