Amazon Employees Resources Access Data Extraction via Clonal Selection Algorithm and Logic Mining Approach

Abstract

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.

Artificial Immune System in Doing 2-Satisfiability Based Reverse Analysis Method via a Radial Basis Function Neural Network

“…Step 1: convert all raw dataset to binary, split into a training dataset, and test dataset with the outcome learn P (60%) and test P (40%) [16,18]. Step 2: initialize the input data, width, and center of the neurons, and designation of all the neurons with binary data from Step 1.…”

“…Step 1: convert all raw dataset to binary, split into a training dataset, and test dataset with the outcome P learn (60%) and P test (40%) [16,18].…”

“…In every dataset, 60% of the data points in the datasets will be utilized for training, and 40% will be used for testing. The k Mean clustering [18] will be used to convert the dataset into binary representation. As for missing data in the dataset, the neuron state will be defined randomly.…”

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