Human Behavior modeling and Language Cognition is a significant component in the application domain, like social, behavioral, and healthcare research. However, accurate prediction to understand the human behavior determinant roles and cognitive states helps analyze humans' behaviors as a critical challenge by various conventional methods. Hence in this research, the Probability learned Neural Model (PLNM) had been proposed to address the critical issues related to testing, training, and classify the cognitive states for accurate prediction of roles and rules of the human mind. This conceptual model describes the user's psychological behavior by utilizing activities, action, inter and intra activity behavior, and language modeling. Furthermore, the architecture shows how the probability model enables users to predict the next steps and effectively detect user psychological anomalies. The lab-scale numerical results show that accurate prediction in human psychological behavior and its quality shows the proposed framework's stability.

人类行为建模和语言认知是应用程序领域中的重要组成部分，例如社会，行为和医疗保健研究。然而，了解人类行为决定因素的作用和认知状态的准确预测有助于通过各种常规方法来分析人类行为，并将其作为一项关键挑战。因此，在这项研究中，已经提出了概率学习神经模型（PLNM），以解决与测试，训练和分类认知状态有关的关键问题，以准确预测人脑的作用和规则。此概念模型通过利用活动，动作，活动间和活动内行为以及语言建模来描述用户的心理行为。此外，该架构显示了概率模型如何使用户能够预测下一步并有效地检测用户的心理异常。实验室规模的数值结果表明，对人类心理行为及其质量的准确预测表明了所提出框架的稳定性。