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

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 596 ◽  
Author(s):  
Nur Ezlin Zamri ◽  
Mohd. Asyraf Mansor ◽  
Mohd Shareduwan Mohd Kasihmuddin ◽  
Alyaa Alway ◽  
Siti Zulaikha Mohd Jamaludin ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Science ◽  
Clonal Selection ◽  
Data Extraction ◽  
Hopfield Neural Network ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Proposed Model ◽  
Artificial Neural Network Ann ◽  
Logical Representation

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.

scholarly journals Hybrid clonal selection algorithm with Hopfield neural network for 3-satisfiability data mining on Amazon’s Employees Resources Access

2020 ◽  
Author(s):  
Nur Ezlin Zamri ◽  
Mohd. Asyraf Mansor ◽  
Mohd Shareduwan Mohd Kasihmuddin ◽  
Saratha Sathasivam ◽  
Samaila Abdullahi
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Clonal Selection ◽  
Hopfield Neural Network ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm

Neural Network Ensemble Based on Clonal Selection Algorithm for Sneak Circuit Analysis

2014 ◽  
Vol 511-512 ◽  
pp. 913-917 ◽  
Author(s):  
Bing Jie Liu ◽  
Wen Zhong Lu ◽  
Hai Yan Ji
Keyword(s):  
Neural Network ◽  
Clonal Selection ◽  
Circuit Analysis ◽  
Clonal Selection Algorithm ◽  
Neural Network Ensemble ◽  
Selection Algorithm ◽  
Functional Components

The paper proposed a novel neural network ensemble algrithm (NNNEA) whose individual was generated by clonal selection algorithm. NNNEA can produced individuals of ensemble with better difference than other algrithm. NNNEA was used for predicting ciruit functions and finding sneak circuit. The inputs of NNNEA are states of switches, and the outputs are states of functional components. NNNEA predicted all possible functions of circuit. The sneak circuits can be discovered by comparing the predicted with designed functions. Although there are several limitations of NNNNEA, the results revealed that NNNNEA can exactly discover sneak circuits.

Improved clonal selection algorithm optimizing Neural Network for solving terminal anti-missile collaborative intercepting assistant decision-making model

2017 ◽  
Vol 08 (03) ◽  
pp. 1750033
Author(s):  
Jinke Xiao ◽  
Weimin Li ◽  
Xinrong Xiao
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Clonal Selection ◽  
Population Diversity ◽  
Self Determination ◽  
Experimental Simulation ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Decision Making Model ◽  
Crossover And Mutation

Programming terminal high-low collaborative intercepting strategy scientifically and constructing assistant decision-making model with self-determination and intellectualization is onekey problem to enhance operational efficiency. Assistant decision-making model has been constructed after analysis on collaborative intercepting principle; then Improved Clonal Selection Algorithm Optimizing Neural Network (ICLONALG-NN) is designed to solve the terminal anti-missile collaborative intercepting assistant decision-making model through introducing crossover operator to increase population diversity, introducing modified combination operator to make use of the information before crossover and mutation, introducing population update operator into traditional CLONALG to optimize Neural Network parameters. Experimental simulation confirms the superiority and practicability of the assistant decision-making model solved by ICLONALG-NN.

scholarly journals Energy Based Logic Mining Analysis with Hopfield Neural Network for Recruitment Evaluation

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 40
Author(s):  
Siti Zulaikha Mohd Jamaludin ◽  
Mohd Shareduwan Mohd Kasihmuddin ◽  
Ahmad Izani Md Ismail ◽  
Mohd. Asyraf Mansor ◽  
Md Faisal Md Basir
Keyword(s):  
Neural Network ◽  
Data Extraction ◽  
Hopfield Neural Network ◽  
Data Set ◽  
Reverse Analysis ◽  
Insurance Agency ◽  
Logical Representation ◽  
Relationship Of ◽  
Extraction Model ◽  
The Relationship

An effective recruitment evaluation plays an important role in the success of companies, industries and institutions. In order to obtain insight on the relationship between factors contributing to systematic recruitment, the artificial neural network and logic mining approach can be adopted as a data extraction model. In this work, an energy based k satisfiability reverse analysis incorporating a Hopfield neural network is proposed to extract the relationship between the factors in an electronic (E) recruitment data set. The attributes of E recruitment data set are represented in the form of k satisfiability logical representation. We proposed the logical representation to 2-satisfiability and 3-satisfiability representation, which are regarded as a systematic logical representation. The E recruitment data set is obtained from an insurance agency in Malaysia, with the aim of extracting the relationship of dominant attributes that contribute to positive recruitment among the potential candidates. Thus, our approach is evaluated according to correctness, robustness and accuracy of the induced logic obtained, corresponding to the E recruitment data. According to the experimental simulations with different number of neurons, the findings indicated the effectiveness and robustness of energy based k satisfiability reverse analysis with Hopfield neural network in extracting the dominant attributes toward positive recruitment in the insurance agency in Malaysia.

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