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<p>Challenges become more intricate in uncertain environments, mainly when human subjective evaluations play a crucial role. Cloud models offer a powerful framework for managing complex situations by describing the transformation between qualitative and quantitative knowledge, addressing uncertainty through randomness and fuzziness. This research proposes a novel Reciprocal Linguistic Cloud Model based on a reciprocal linguistic term set to facilitate the transition between reciprocal linguistic variables and clouds, enabling the construction of cloud descriptors. Further, a generalized reciprocal linguistic scale function is introduced. A Cloud Model scale is constructed, integrating a relative importance scale with the proposed reciprocal linguistic scale function. A real-time application is presented to explore uncertainty in human decision-making in criminal analysis. We integrate machine learning algorithms to analyse crimes in India&#xa0;and to predict future crime rates under uncertainty. Further, a comparative analysis demonstrates the superiority of our proposed research model.</p>

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A novel reciprocal linguistic cloud model and its integration with machine learning

  • 期刊论文

  • 发表日期：

    2025年6月6日

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  • Shweta Kalson,
  • Anjana Gupta

摘要

Challenges become more intricate in uncertain environments, mainly when human subjective evaluations play a crucial role. Cloud models offer a powerful framework for managing complex situations by describing the transformation between qualitative and quantitative knowledge, addressing uncertainty through randomness and fuzziness. This research proposes a novel Reciprocal Linguistic Cloud Model based on a reciprocal linguistic term set to facilitate the transition between reciprocal linguistic variables and clouds, enabling the construction of cloud descriptors. Further, a generalized reciprocal linguistic scale function is introduced. A Cloud Model scale is constructed, integrating a relative importance scale with the proposed reciprocal linguistic scale function. A real-time application is presented to explore uncertainty in human decision-making in criminal analysis. We integrate machine learning algorithms to analyse crimes in India and to predict future crime rates under uncertainty. Further, a comparative analysis demonstrates the superiority of our proposed research model.