Flexible inverse adaptive fuzzy inference model to identify the evolution of operational value at risk for improving operational risk management

Peña, Alejandro; Bonet, Isis; Lochmuller, Christian; Chiclana, Francisco; Góngora, Mario

doi:10.1016/j.asoc.2018.01.024

Cited by 15 publications

(9 citation statements)

References 31 publications

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“…." [8]. OR has emerged as a key concept for characterising risks arising from phytosanitary and climatic events in agricultural crops and is described by aggregate loss distribution (ALD).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning to Improve the Sustainability of Agricultural Crops Affected by Phytosanitary Events: A Financial-Risk Approach

et al. 2022

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Given the challenges in reducing greenhouse gases (GHG), one of the sectors that have attracted the most attention in the Sustainable Development Agenda 2030 (SDA-2030) is the agricultural sector. In this context, one of the crops that has had the most remarkable development worldwide has been oil-palm cultivation, thanks to its high productive potential and being one of the most efficient sources of palmitic acid production. However, despite the significant presence of oil palm in the food sector, oil-palm crops have not been exempt from criticism, as its cultivation has developed mainly in areas of ecological conservation around the world. This criticism has been extended to other crops in the context of the Sustainable Development Goals (SDG) due to insecticides and fertilisers required to treat phytosanitary events in the field. To reduce this problem, researchers have used unmanned aerial vehicles (UAVs) to capture multi-spectral aerial images (MAIs) to assess fields’ plant vigour and detect phytosanitary events early using vegetation indices (VIs). However, detecting phytosanitary events in the early stages still suggests a technological challenge. Thus, to improve the environmental and financial sustainability of oil-palm crops, this paper proposes a hybrid deep-learning model (stacked–convolutional) for risk characterisation derived from a phytosanitary event, as suggested by lethal wilt (LW). For this purpose, the proposed model integrates a Lagrangian dispersion model of the backward-Gaussian-puff-tracking type into its convolutional structure, which allows describing the evolution of LW in the field for stages before a temporal reference scenario. The results show that the proposed model allowed the characterisation of the risk derived from a phytosanitary event, (PE) such as lethal wilt (LW), in the field, promoting improvement in agricultural environmental and financial sustainability activities through the integration of financial-risk concepts. This improved risk management will lead to lower projected losses due to a natural reduction in insecticides and fertilisers, allowing a balance between development and sustainability for this type of crop from the RSPO standards.

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Section: Introductionmentioning

confidence: 99%

Deep Learning to Improve the Sustainability of Agricultural Crops Affected by Phytosanitary Events: A Financial-Risk Approach

et al. 2022

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“…The increase of SI generates a shift in the comments towards the NA zone of the radar chart, evidencing the impact of COVID-19 pandemic on this tourist destination and its associated products and services. This behaviour was also clearly evident for Italy (Figure 7), where the COVID-19 comments were located toward the NA zone of the radar chart (Right Side -Orange line), standing out in the comments sentiments like Fear, Sadness and Hostility, which clearly shows the impact of the pandemic on this tourism destination (Gonzalez-Ruiz et al, 2019;Pena et al, 2018aPena et al, , 2018b.…”

Section: Analysis and Discussion Of Resultsmentioning

confidence: 58%

“…Where: For Cumulative Distribution Function, the SI can be zero (CDF's -Centered), Negative (CDF's -Heavy Tails) and Positive (CDF's -Long tails) (Pena et al, 2018a(Pena et al, , 2018b.…”

Section: Skewness Indexmentioning

confidence: 99%

Evolution of the Perceptions about Tourist Destinations Affected by Risk Events Using a PANAS-tDL Deep Learning Model

Peña

Mesias

Patino

et al. 2021

J INFORM SYSTEMS ENG

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The tourism industry has dynamized the economy of the countries by offering places, as well as related tourism experiences, products, and services. In the context of the COVID-19 pandemic, some of these tourist destinations were affected by subjective perceptions of users on social networks, within stands out Twitter. To achieve an objective perception from user comments posted on Twitter in front of a tourist destination, we propose a PANAS-tDL (Positive and Negative Affect Schedule -Deep Learning) model which integrates into a single structure a neural model inspired by a Stacked neural deep learning model (SDL), as well as the PANAS-t methodology. For this process, a database of comments was available for four destinations (Colombia, Italy, Spain, USA), and its tourist's products and services, before and in the context of COVID-19 pandemic throughout the year 2020. The proposed model made it possible to generate objective perceptions of the tourist destinations and their products and services using an automatic classification of comments in each category defined by the PANAS-t methodology (11-sentiments). The results show how users' perceptions were towards the negative sentiment zone defined by this methodology, according to the evolution of the COVID-19 pandemic worldwide throughout the year 2020. The proposed model also integrated an automatic process of normalisation, lemmatisation and tokenisation (Natural language process -NLP) for the objective characterization of perceptions, and due to its capacity for adaption and learning, it can be extended for the evaluation of new tourist destinations, products or services using comments from different social networks.

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“…Balibek and K€ oksalan 2010Multi-objective stochastic programming Consiglio and Staino (2012) Stochastic programming Valladão, Veiga, and Veiga (2014) Stochastic programming Consiglio, Lotfi, and Zenios (2018) Linear programming Venture capital and initial public offerings Ko, Lin, and Yang (2011) Game theory Aouni, Colapinto, and Torre (2014) Fuzzy goal programming Bast i, Kuzey, and Delen (2015) Support vector machines Afful-Dadzie and Afful-Dadzie 2016Multicriteria analysis Quintana, Ch avez, Luque Baena, and Luna (2018) ANFIS, genetic optimization Tian, Xu, and Fujita (2018) Fuzzy systems Zhong, Liu, Zhong, and Xiong (2018) Bayesian inference, Markov Chain Monte Carlo Operational and liquidity risk modeling Chavez-Demoulin, Embrechts, and Ne slehov a 2006Extreme value theory Shevchenko (2009) Bayesian inference Aquaro et al 2010Bayesian networks Shevchenko 2011Bayesian inference Sanford and Moosa (2012) Bayesian networks Janabi, Hernandez, Berger, and Nguyen (2017) Copula modeling Eling and Jung (2018) Copula modeling Peña, Bonet, Lochmuller, Chiclana, and G ongora (2018) Adaptive fuzzy inference model Azar and Dolatabad 2019Fuzzy cognitive maps Derivatives and volatility modeling Bandi and Bertsimas (2014) Linear programming Quek, Pasquier, and Kumar (2007) Neural networks Liu, Cao, Ma, and Shen (2019) Wavelets Neural networks Kim and Won (2018) Deep learning Bezerra and Albuquerque (2017) Support vector machines Zeng and Klabjan (2019) Support vector machines Financial fraud detection Gaganis (2009) Multicriteria analysis, machine learning Dikmen and K€ uc¸€ ukkocao glu (2010) Integer programming Glancy and Yadav (2011) Text mining Abbasi, Albrecht, Vance, and Hansen 2012Stacked generalization Sahin, Bulkan, and Duman (2013) Decision trees Balla, Gaganis, Pasiouras, and Zopounidis (2014) Multicriteria analys...…”

Section: Study Methodologymentioning

confidence: 99%

Computational approaches and data analytics in financial services: A literature review

Andriosopoulos

Doumpos

Pardalos

et al. 2019

Journal of the Operational Research Society

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The level of modeling sophistication in financial services has increased considerably over the years. Nowadays, the complexity of financial problems and the vast amount of data require an engineering approach based on analytical modeling tools for planning, decision making, reporting, and supervisory control. This article provides an overview of the main financial applications of computational and data analytics approaches, focusing on the coverage of the recent developments and trends. The overview covers different methodological tools and their uses in areas, such as portfolio management, credit analysis, banking, and insurance.

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Flexible inverse adaptive fuzzy inference model to identify the evolution of operational value at risk for improving operational risk management

Cited by 15 publications

References 31 publications

Deep Learning to Improve the Sustainability of Agricultural Crops Affected by Phytosanitary Events: A Financial-Risk Approach

Deep Learning to Improve the Sustainability of Agricultural Crops Affected by Phytosanitary Events: A Financial-Risk Approach

Evolution of the Perceptions about Tourist Destinations Affected by Risk Events Using a PANAS-tDL Deep Learning Model

Computational approaches and data analytics in financial services: A literature review

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