Incremental regularized Data Density-Based Clustering neural networks to aid in the construction of effort forecasting systems in software development

Souza, Paulo Vitor de Campos; Guimarães, Augusto Júnio; Araújo, Vanessa Souza; Rezende, Thiago Silva; Araújo, Vinícius Jonathan Silva

doi:10.1007/s10489-019-01449-w

Cited by 17 publications

(5 citation statements)

References 58 publications

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“…Authors in [ 53 ] applied four backpropagation neural networks to explore correlation that how plug load data, occupancy rates, and local weather factors affects to predict electricity usage. Authors in [ 54 ] studied the challenges to reduce complexity, failures, and time in the field of software development and used fuzzy-neural network composed by fuzzy rules to seek the solutions to overcome these challenges.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Risk Prediction by Using Artificial Neural Network in Global Software Development

Iftikhar

Alam

Ahmed

et al. 2021

Computational Intelligence and Neuroscience

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The demand for global software development is growing. The nonavailability of software experts at one place or a country is the reason for the increase in the scope of global software development. Software developers who are located in different parts of the world with diversified skills necessary for a successful completion of a project play a critical role in the field of software development. Using the skills and expertise of software developers around the world, one could get any component developed or any IT-related issue resolved. The best software skills and tools are dispersed across the globe, but to integrate these skills and tools together and make them work for solving real world problems is a challenging task. The discipline of risk management gives the alternative strategies to manage risks that the software experts are facing in today’s world of competitiveness. This research is an effort to predict risks related to time, cost, and resources those are faced by distributed teams in global software development environment. To examine the relative effect of these factors, in this research, neural network approaches like Levenberg–Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient have been implemented to predict the responses of risks related to project time, cost, and resources involved in global software development. Comparative analysis of these three algorithms is also performed to determine the highest accuracy algorithms. The findings of this study proved that Bayesian Regularization performed very well in terms of the MSE (validation) criterion as compared with the Levenberg–Marquardt and Scaled Conjugate Gradient approaches.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Risk Prediction by Using Artificial Neural Network in Global Software Development

Iftikhar

Alam

Ahmed

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…In addition to tests with traditional approaches, the results will also be compared with other hybrid models of neural networks and fuzzy systems, where we can highlight an evolving fuzzy neural network model (EFNN) [39], one that works with incremental fuzzification (IFNN) [134] and one that works with self-organizing fuzzification (SFNN) [135]. All models use the extreme learning machine to define the weights of the output layer, have three layers, and are composed of unineurons in the second layer and Gaussian neurons in the first layer.…”

Section: Definitions and Models Used In The Testsmentioning

confidence: 99%

Detection of Anomalies in Large-Scale Cyberattacks Using Fuzzy Neural Networks

Souza

Guimarães

Rezende

et al. 2020

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The fuzzy neural networks are hybrid structures that can act in several contexts of the pattern classification, including the detection of failures and anomalous behaviors. This paper discusses the use of an artificial intelligence model based on the association between fuzzy logic and training of artificial neural networks to recognize anomalies in transactions involved in the context of computer networks and cyberattacks. In addition to verifying the accuracy of the model, fuzzy rules were obtained through knowledge from the massive datasets to form expert systems. The acquired rules allow the creation of intelligent systems in high-level languages with a robust level of identification of anomalies in Internet transactions, and the accuracy of the results of the test confirms that the fuzzy neural networks can act in anomaly detection in high-security attacks in computer networks.

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“…A model of fuzzy neural networks normalized based on incremental clustering of data density was used to assist in predicting efforts for the construction of software [18] it was proposed for De Campos Souza et al…”

Section: Fuzzification Techniques In Fuzzy Neural Networkmentioning

confidence: 99%

Autonomous Data Density pruning fuzzy neural network for Optical Interconnection Network

et al. 2020

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Traditionally, fuzzy neural networks have parametric clustering methods based on equally spaced membership functions to fuzzify inputs of the model. In this sense, it produces an excessive number calculations for the parameters' definition of the network architecture, which may be a problem especially for realtime large-scale tasks. Therefore, this paper proposes a new model that uses a nonparametric technique for the fuzzification process. The proposed model uses an autonomous data density approach in a pruned fuzzy neural network, wich favours the compactness of the model. The performance of the proposed approach is evaluated through the usage of databases related to the Optical Interconnection Network. Finally, binary patterns classification tests for the identification of Temporal Distribution (Asynchronous or Client-Server) were performed and compared with stateof-the-art fuzzy neural-based and traditional machine learning approaches. Results demonstrated that the proposed model is an efficient tool for these challenging classification tasks.

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Incremental regularized Data Density-Based Clustering neural networks to aid in the construction of effort forecasting systems in software development

Cited by 17 publications

References 58 publications

Risk Prediction by Using Artificial Neural Network in Global Software Development

Risk Prediction by Using Artificial Neural Network in Global Software Development

Detection of Anomalies in Large-Scale Cyberattacks Using Fuzzy Neural Networks

Autonomous Data Density pruning fuzzy neural network for Optical Interconnection Network

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