An improved functional link artificial neural networks with intuitionistic fuzzy clustering for software cost estimation

Kaushik, Anupama; Soni, Ajay; Soni, Rachna

doi:10.1007/s13198-014-0298-2

Cited by 12 publications

(7 citation statements)

References 18 publications

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“…In the recommended method, the entire dataset has been splited into two parts, where 70% of data have been utilized for training stage and 30% of data have been utilized for testing stage. The proposed method was compared with several optimization 40 algorithms such as, “PSO‐HI‐HL, 41 GWO‐HI‐HL, 42 FOA‐HI‐HL, 35 and MFO‐HI‐HL 36 ” as well as machine learning algorithms like, “fuzzy, 43,44 DNN, 45,46 LSTM, 47 and ELM 48,49 ” in terms of various error measures like, “MEP, SMAPE, MASE, MAE, RMSE, L1 Norm, L2 Norm, L‐Infinity Norm, MRE, and MMRE” to determine the betterment of the proposed method. “Learning rate is a hyper‐parameter that controls how much we are adjusting the weights of our network concerning the loss gradient.…”

Section: Resultsmentioning

confidence: 99%

Automated software effort estimation for agile development system by heuristically improved hybrid learning

Gupta¹,

Mahapatra²

2022

Concurrency and Computation

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Summary In the process of modern software development, an important role is played by software effort estimation. The failure or success of the projects is mostly based on the schedule outcomes and effort estimation accuracy. The effort estimation problem still remains as a challenging issue because of the limitations of various standard measures for forecasting the efforts in the plan‐driven software development. This article emphasizes on novel software effort estimation framework using heuristically improved hybrid learning model. This article proposes heuristically improved hybrid learning (HI‐HL) with deep belief network and artificial neural network for software effort estimation. The weight optimization strategy of DBN and ANN to attain highly accurate estimation. Here, the weight optimization is performed by integrating two standard optimization algorithms such as forest optimization algorithm and moth‐flame optimization, so called as solution index‐based forest moth flame optimization with the purpose of solving the fitness function concerning the “Magnitude of Relative Error and Mean Absolute Error (MAE).” From the table results, for dataset 1, the SMAPE of SI‐FMFO‐HI‐HL is correspondingly secured 60.75%, 34.26%, 56.77%, and 61.44% improved than ELM, LSTM, DNN, and fuzzy. The simulation findings indicated that the recommended estimation model outperformed the baseline approaches on the three publically available datasets.

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Section: Resultsmentioning

confidence: 99%

Automated software effort estimation for agile development system by heuristically improved hybrid learning

Gupta¹,

Mahapatra²

2022

Concurrency and Computation

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“…Sadiq and Shahid [14] analysed the software cost and risk using esrcTool that paved the way for the estimation of the cost of the software and paved the way for the design of the software, but the esrcTool was not applicable to real-world problems. An SCE using the Functional Link Artificial Neural Network (FLANN) and the Intuitionistic Fuzzy C-Means Clustering (IFCM) was introduced in [1]. The proposed FLANN-IFCM was found to be more effective in estimating the software, but the membership functions were not defined.…”

Section: Related Workmentioning

confidence: 99%

“…The cost estimation is the main aspect in the software management projects, which contributes a lot to the project managers in analysing the feasibility of the project and thereby improves the effectiveness of the software development process [1]. Software Cost Estimation (SCE) creates and applies a required model for estimating the resources needed for a fully functional software system [2].…”

Section: Introductionmentioning

confidence: 99%

“…The cost models and the estimation techniques of software engineering are employed for a number of applications, such as project planning and authority, investment analysis for software improvement and so on [3]. Thus, the target over time is the need for effective cost estimation methods [1,4]. The SCE techniques are sub-divided into algorithmic and non-algorithmic.…”

Section: Introductionmentioning

confidence: 99%

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Whale–crow optimization (WCO)-based Optimal Regression model for Software Cost Estimation

Ahmad¹,

Bamnote²

2019

Sādhanā

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Software Cost Estimation (SCE) is the emerging concern of the software companies during the development phase of the software, as it requires effort and cost factors for modelling the software. These factors are modelled using the Artificial Intelligence models, which seem to be less accurate and non-reliable by increasing the risk factor of the software projects. Thus, for estimating the software cost, meta-heuristics are employed. This paper proposes an algorithm, termed as whale-crow optimization (WCO) algorithm, which is the integration of the whale optimization algorithm (WOA) and the crow search algorithm (CSA). The main function of the WCO algorithm is to determine the Optimal Regression coefficients for the regression models, such as the Linear Regression model and the Kernel Logistic Regression model, to develop an Optimal Regression model to estimate the software cost. The experimentation is carried out using four datasets taken from the Promise software engineering repository to perform effective performance analysis. Analysis is carried out regarding the mean magnitude of relative error (MMRE) that proves that the proposed method of SCE is effective, attaining the average MMRE at a rate of 0.2442 for the proposed Linear Regression model and 0.2692 for the proposed Kernel Regression model. adhana(0123456789().,-volV)FT3 ](0123456789().,-volV)

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“…[101] Chaira [47] Kacprzyk et al [137] Wang et al [304] Kaushik et al [143] Liu et al [178] Prabu et al [244] Rangasamy et al [250] Shang et al [270] Son et al [280] Tripathy et al…”

Section: Intuitionistic Fuzzy Clusteringmentioning

confidence: 99%

Informational Paradigm, management of uncertainty and theoretical formalisms in the clustering framework: A review

D’Urso

2017

Information Sciences

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Evidential clustering or credal clustering or Belief clustering Credal Sets (CSs) theory, Dempster-2004 Denoeux, Masson [ 98 ] 1967 Shafer (DS) theory 2008 Denoeux, Masson [190] 1976 Dempster, [90] , Shafer [268] 1997 Cozman [68] Credibilistic clustering Credibilistic (Cred) theory 2007 Zhou et al. [ 350 ] 2004 Liu [174] 2014 Niakan et al. [216] 2015 Zhou et al. [348] Type-2 fuzzy clustering Type-2 Fuzzy Sets (T2FSs) theory 2007 Hwang and Rhee [359] (interval Type-2 fuzzy clustering) 1975 Zadeh [353] 2009 Min et al. [198] (interval Type-2 possibilistic clustering) 2012 Linda and Manic [166] (Type-2 fuzzy clustering or general Type-2 fuzzy clustering) 2014 Ji et al. [134] (interval Type-2 fuzzy possibilistic clustering) Neutrosophic clustering Neutrosophic Sets (NSs) theory 2012 Shan et al. [269] 1998 Smarandache [274] 2015 Guo and Ş engür [119] Hesitant fuzzy clustering Hesitant Fuzzy Sets (HFSs) theory 2013 Chen et al. [55] 2009 Torra and Narukawa [290] Interval-based fuzzy clustering Interval Sets (ISs) theory 2015 Silva et al. [273] 1966 Moore [211] 2015 Picture fuzzy clustering 2013 Picture Fuzzy Sets (PFSs) theory Son [277] Cuong and Kreinovich [73]

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An improved functional link artificial neural networks with intuitionistic fuzzy clustering for software cost estimation

Cited by 12 publications

References 18 publications

Automated software effort estimation for agile development system by heuristically improved hybrid learning

Automated software effort estimation for agile development system by heuristically improved hybrid learning

Whale–crow optimization (WCO)-based Optimal Regression model for Software Cost Estimation

Informational Paradigm, management of uncertainty and theoretical formalisms in the clustering framework: A review

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