A Deep Learning Modified Neural Network Used for Efficient Effort Estimation

Kodmelwar, Manohar K.; Joshi, Sarang; Khanna, Vivek

doi:10.1166/jctn.2018.7651

Cited by 8 publications

(14 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4 (b). While Deep-MNN [49] achieved accuracy up to 85% when Dataset2 is used as shown in Fig. 4 (b).…”

Section: Methodsmentioning

confidence: 99%

“…In this experiment, the proposed model is compared with Deep-MNN [49], in terms of execution time required for training the model. This experiment was repeated to verify the performance on different datasets.…”

Section: Methodsmentioning

confidence: 99%

“…The Deep-MNN [49] and our proposed GWDNNSB performances are analyzed using Dataset1 and Dataset2. Table 8 depicts the comparison results of nine benchmark functions using Dataset1.…”

Section: Methodsmentioning

confidence: 99%

“…ANN is associated with several challenges including falling in local minima, and overfitting. Therefore, many researchers recommend using nature-inspired meta-heuristic algorithms for the following: (i) ANN training [46,53], (ii) feature selection in classification [52], and (iii) weights selection [47,49]. Wani et al, [46] used functional link ANN model for software effort estimation.…”

Section: State Of the Artmentioning

confidence: 99%

“…Emary et al [47] use a modified GWO algorithm that utilizes the reinforcement learning principles, to optimize the selection of weights for neural networks and selecting relevant features for model building. Kodmelwar et al [49] proposed a DL modified NN technique (Deep MNN) using CS Algorithm for initializing the network weights and HPSO for better classification of dataset parameters. CS is used to initialize the weights of the network.…”

Section: State Of the Artmentioning

confidence: 99%

See 4 more Smart Citations

Metaheuristic Algorithms in Optimizing Deep Neural Network Model for Software Effort Estimation

et al. 2021

View full text Add to dashboard Cite

Effort estimation is the most critical activity for the success of overall solution delivery in software engineering projects. In this context, the paper's main contributions to the literature on software effort estimation are twofold. First, this paper examines the application of meta-heuristic algorithms to have a logical and acceptable parametric model for software effort estimation. Secondly, to unravel the benefits of nature-inspired meta-heuristic algorithms usage in optimizing Deep Learning (DL) architectures for software effort estimation, this paper presents a Deep Neural Network (DNN) model for software effort estimation based on meta-heuristic algorithms. In this paper, Grey Wolf Optimizer (GWO) and StrawBerry (SB) meta-heuristic algorithms are applied for having a logical and acceptable parametric model for software effort estimation. To validate the performances of these two algorithms, a set of nine benchmark functions having wide dimensions is applied. Results from GWO and SB algorithms are compared with five other meta-heuristic algorithms used in literature for software effort estimation. Experimental results showed that the GWO has comprehensive superiority in terms of accuracy in estimation. The proposed DNN model (GWDNNSB) using meta-heuristic algorithms for initial weights and learning rate selection, produced better results compared to existing work on using DNN for software effort estimation.

show abstract

“…4 (b). While Deep-MNN [49] achieved accuracy up to 85% when Dataset2 is used as shown in Fig. 4 (b).…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…The Deep-MNN [49] and our proposed GWDNNSB performances are analyzed using Dataset1 and Dataset2. Table 8 depicts the comparison results of nine benchmark functions using Dataset1.…”

Section: Methodsmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

See 3 more Smart Citations

Metaheuristic Algorithms in Optimizing Deep Neural Network Model for Software Effort Estimation

et al. 2021

View full text Add to dashboard Cite

show abstract

MFLion‐DMN: Mayfly Lion‐optimized deep maxout network for prediction of software development effort

R.,

Polala

2024

J Software Evolu Process

View full text Add to dashboard Cite

The precise evaluation of predicting software development effort is a serious process in software engineering. The underestimates require more time, which seems to be the negotiation of complete efficient design and complete software testing. Thus, this paper devises a novel model for inspecting the effort taken for the design of software. The Mayfly Lion Optimization (MFLion) algorithm is devised, which ensembles the Mayfly Algorithm (MA) with the Lion Optimization Algorithm (LOA) for precise estimation. The deep maxout network (DMN) is adapted wherein the different weights are produced out of which the most suitable is infused considering the MFLion during model training. The optimal features are selected using recursive feature elimination (RFE) wherein the best features are selected and the remaining irrelevant features are eliminated from the list. The proposed MFLion obtained better performance with the smallest mean magnitude of relative error (MMRE) of 5.909 and the smallest root mean square error (RMSE) of 75.505, respectively. Each technique is produced using a separate database generated using the Promise software engineering repository. The outcomes produced from the assessment of the accuracies of models suggested that the MFLion‐DMN is a substitute to forecast software effort, which is extensively devised in engineering platforms.

show abstract

Evaluation of estimation in software development using deep learning-modified neural network

Sreekanth¹,

Devi

Shukla

et al. 2022

Appl Nanosci

View full text Add to dashboard Cite

A Deep Learning Modified Neural Network Used for Efficient Effort Estimation

Cited by 8 publications

References 0 publications

Metaheuristic Algorithms in Optimizing Deep Neural Network Model for Software Effort Estimation

Metaheuristic Algorithms in Optimizing Deep Neural Network Model for Software Effort Estimation

MFLion‐DMN: Mayfly Lion‐optimized deep maxout network for prediction of software development effort

Evaluation of estimation in software development using deep learning-modified neural network

Contact Info

Product

Resources

About