Amazon.com Inc. seeks alternative ways to improve manual transactions system of granting employees resources access in the field of data science. The work constructs a modified Artificial Neural Network (ANN) by incorporating a Discrete Hopfield Neural Network (DHNN) and Clonal Selection Algorithm (CSA) with 3-Satisfiability (3-SAT) logic to initiate an Artificial Intelligence (AI) model that executes optimization tasks for industrial data. The selection of 3-SAT logic is vital in data mining to represent entries of Amazon Employees Resources Access (AERA) via information theory. The proposed model employs CSA to improve the learning phase of DHNN by capitalizing features of CSA such as hypermutation and cloning process. This resulting the formation of the proposed model, as an alternative machine learning model to identify factors that should be prioritized in the approval of employees resources applications. Subsequently, reverse analysis method (SATRA) is integrated into our proposed model to extract the relationship of AERA entries based on logical representation. The study will be presented by implementing simulated, benchmark and AERA data sets with multiple performance evaluation metrics. Based on the findings, the proposed model outperformed the other existing methods in AERA data extraction.

中文翻译：

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亚马逊员工资源通过克隆选择算法和逻辑挖掘方法访问数据提取

Amazon.com Inc. 寻求替代方法来改进在数据科学领域授予员工资源访问权限的手动交易系统。该工作通过将离散 Hopfield 神经网络 (DHNN) 和克隆选择算法 (CSA) 与 3-可满足性 (3-SAT) 逻辑相结合来构建改进的人工神经网络 (ANN)，以启动执行优化的人工智能 (AI) 模型工业数据任务。3-SAT 逻辑的选择在数据挖掘中至关重要，以通过信息论表示 Amazon 员工资源访问 (AERA) 的条目。所提出的模型通过利用 CSA 的特征（例如超变异和克隆过程）来使用 CSA 来改进 DHNN 的学习阶段。这导致了所提出的模型的形成，作为一种替代机器学习模型，以确定在批准员工资源申请时应优先考虑的因素。随后，将逆向分析方法（SATRA）集成到我们提出的模型中，以基于逻辑表示提取 AERA 条目的关系。该研究将通过实施具有多个绩效评估指标的模拟、基准和 AERA 数据集来呈现。基于研究结果，所提出的模型在 AERA 数据提取方面优于其他现有方法。具有多个性能评估指标的基准和 AERA 数据集。基于研究结果，所提出的模型在 AERA 数据提取方面优于其他现有方法。具有多个性能评估指标的基准和 AERA 数据集。基于研究结果，所提出的模型在 AERA 数据提取方面优于其他现有方法。