Towards an uncertainty reduction framework for land-cover change prediction using possibility theory

Ferchichi, Ahlem; Boulila, ‪Wadii; Farah, Imed Riadh

doi:10.1007/s40595-016-0088-7

Cited by 14 publications

(6 citation statements)

References 33 publications

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“…This uncertainty can be propagated from one-step to another, which can influence the final decision on genes related to non-small cell lung cancer. Integrating uncertainty propagation in our proposed approach will constitute a further challenging perspective for other related works [27]- [29].…”

Section: Discussionmentioning

confidence: 99%

A Novel Multi-Stage Fusion based Approach for Gene Expression Profiling in Non-Small Cell Lung Cancer

et al. 2019

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Background: Non-small cell lung cancer is defined at the molecular level by mutations and alterations to oncogenes, including AKT1, ALK, BRAF, EGFR, HER2, KRAS, MEK1, MET, NRAS, PIK3CA, RET, and ROS1. A better understanding of non-small cell lung cancer requires a thorough consideration of these oncogenes. However, the complexity of the problem arises from high-dimensional gene vector space, which complicates the identification of cluster boundaries, and hence gene expression cluster membership. This paper aims to analyze potential biological biomarkers for tumorigenesis in lung cancer based on different treatment solutions. Results: Genes BRAF, RET, and ROS1 show an overexpression transition by one cluster from non-treatment to treatment states, followed by a stabilization in the 3 treatment states at the same cluster. Genes MET, ALK, and PIK3CA show an overexpression transition by two clusters from non-treatment to treatment states, followed by a stabilization in the 3 treatment states at the same cluster. SME1 shows an under-expression transition by two clusters from non-treatment to the treatment states, a stabilization in the 3 treatment states at the same cluster. Conclusions: We present a novel fusion-based approach for gene expression profiling of non-small cell lung cancer under non-thermal plasma treatment. The main contribution of the proposed approach is to exploit Dempster-Shafer evidence theory-based data fusion to combine information from different samples in the considered dataset. This minimizes uncertainty and enhances the reliability and validity of decisions, leading to a better description of genes related to non-small cell lung cancer. We also propose use of fuzzy c-means-with-range clustering to track changes of genes' states under different non-thermal plasma treatments. INDEX TERMS Gene expression, Dempster Shafer, evidence theory, data fusion, clustering, non-small cell lung cancer.

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Section: Discussionmentioning

confidence: 99%

A Novel Multi-Stage Fusion based Approach for Gene Expression Profiling in Non-Small Cell Lung Cancer

et al. 2019

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“…This imperfection is accentuated with the huge amount of big data. Therefore, building reliable decision support systems in RS fields requires modeling imperfection in different stages of the decision support process [43] [48] [49].…”

Section: Discussionmentioning

confidence: 99%

A novel decision support system for the interpretation of remote sensing big data

2017

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Applications of remote sensing (RS) data cover several fields such as: cartography, surveillance, land-use planning, archaeology, environmental studies, resources management, etc. However, the amount of RS data has grown considerably due to the increase of aerial and satellite sensors. With this continuous increase, the necessity of having automated tools for the interpretation and analysis of RS big data is clearly obvious. The manual interpretation becomes a time consuming and expensive task. In this paper, a novel tool for interpreting and analyzing RS big data is described. The proposed system allows knowledge gathering for decision support in RS fields. It helps users easily make decisions in many fields related to RS by providing descriptive, predictive and prescriptive analytics. The paper outlines the design and development of a framework based on three steps: RS data acquisition, modeling, and analysis & interpretation. The performance of the proposed system has been demonstrated through three models: clustering, decision tree and association rules. Results show that the proposed tool can provide efficient decision support (descriptive and predictive) which can be adapted to several RS users' requests. Additionally, assessing these results show good performances of the developed tool.

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“…The digital twin is able to understand, learn, and reason what-if questions in intuitive approaches. Remote sensing (RS) data are processed comprehensively and generate valuable intelligence for various research areas, such as the prediction of land cover change Boulila et al ( 2017 ); Ferchichi et al ( 2017 ), disaster damage detection Vetrivel et al ( 2017 ). With the increasingly adopted technique of digital twins, RS provides the opportunity to implement the specific situation of forests and plants at present, and to summarize the characteristics of historical changes.…”

Section: Introductionmentioning

confidence: 99%

Applying Digital Twins to Research the Relationship Between Urban Expansion and Vegetation Coverage: A Case Study of Natural Preserve

Zhao

Wang

et al. 2022

Front. Plant Sci.

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With the growth of the world population, cities expand and encroach on forests and plants, causing many environmental problems. Digital Twin, as the rapidly developing technique in recent years, provides the opportunity to implement the specific situation of forests and plants at present or in the future, which has great performance on predictive analysis and optimization. From the consideration of plants and forests, this study provides a comprehensive case study to research the relationship between urban development boundary and natural environment in a natural preserve in a coastal city. Multispectral data of the study area is collected by Unmanned Aerial Vehicle (UAV), combining satellite remote sensing (RS) historical data and geographic data to establish the digital twin model for plant identification. In conjunction with local Master planning of land use, the results of modeling are used to analyze the influences of urban construction on the natural environment, and the inappropriate aspects of the planning are discovered and summarized. In addition, 6 suggestions for effective management and planning strategies are presented. As plants and forests are effective factors of natural conditions, this study offered an objective assessment for the sustainability and rationality of urban planning with some guidance and bases.

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Towards an uncertainty reduction framework for land-cover change prediction using possibility theory

Cited by 14 publications

References 33 publications

A Novel Multi-Stage Fusion based Approach for Gene Expression Profiling in Non-Small Cell Lung Cancer

A Novel Multi-Stage Fusion based Approach for Gene Expression Profiling in Non-Small Cell Lung Cancer

A novel decision support system for the interpretation of remote sensing big data

Applying Digital Twins to Research the Relationship Between Urban Expansion and Vegetation Coverage: A Case Study of Natural Preserve

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