Performance assessment of neuro fuzzy based image fusion of satellite images

Babu, Ch. Ramesh; Rao, D. Srinivasa; Ravi, T.; Gopi, G.

doi:10.19101/ijatee.2018.539005

Cited by 2 publications

(1 citation statement)

References 12 publications

(16 reference statements)

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“…In the proposed ANFIS-OPR, from one part, the FFA technique gives generalizability and copes with model overfitting, and from the second part, the Sugeno ANFIS model provides adaptive learning and interpretability. Babu et al (2018) combined PCA and ANFIS for satellite image fusion and improved the RMSE to 10.38, but it is known that the PCA technique provides less generalizability than FFA. Abdul Mokhtar et al (2016) modelled the reservoir water release decision through two ANFIS models using the BP and hybrid optimisation methods and obtained RMSEs respectively equal to 0.656 and 0.712, without addressing the ANFIS model generalizability.…”

Section: Discussion and Contributionsmentioning

confidence: 99%

A Sugeno ANFIS Model Based on Fuzzy Factor Analysis for IS/IT Project Portfolio Risk Prediction

Zaidouni,

Janati Idrissi,

Bellabdaoui

2024

JICT

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Risk inherence jeopardises Information System (IS) and Information Technology (IT) Project Portfolio Management (PPM) to realise thestrategic objectives. Previous studies have mainly provided Artificial Intelligence (AI) and statistical models to predict the overall riskof IS/IT project portfolio, whereas neuro-fuzzy models were rarely used. This paper proposes a Sugeno Adaptive Neuro-Fuzzy InferenceSystem (ANFIS) model based on Fuzzy Factor Analysis (FFA) named ANFIS-OPR to predict the overall risk of IS/IT project portfolio fromhistorical IS/IT project risk data. The ANFIS-OPR inputs are the relevant factor loadings resulting from the FFA application on the IS/IT projects risks set to cope with the curse of dimensionality. Then, the Sugeno ANFIS model is adopted to give strategic interpretability to the predicted IS/IT project portfolio overall risk by implementing the IS/IT Project Management Office (PMO) expert knowledge, represented by fuzzy rules, on the relationship between IS/IT project portfolio strategic alignment and the IS/IT projects risks. The ANFISOPR outputs are the predicted Overall Portfolio Risk (OPR) and Root Mean Square Error (RMSE). The paper also presents an IS/IT PMO case study that shows the proposed ANFIS-OPR efficacy, which predicted the OPR values closely to the OPR estimates with an accepted RMSE of 0.108. The proposed ANFIS-OPR is a novel intelligent decision-making tool that enables the IS/IT PMO to monitor the OPR, considering its linkage with strategic alignment; thus, contingency plans can be carried out appropriately while ensuring that the IS/IT project portfolio is strategically aligned.

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Section: Discussion and Contributionsmentioning

confidence: 99%