Multi Feature Immigration Handling and Recommendation Model for Improved Migration Management Using Remittance and Psychology

Abstract

Immigrant management and recommendation has been approached with several approaches. However, the existing models does not handle the problem with most efficient way as they does not consider variety of features related to the problem. To support the problem, an efficient multi feature immigration handling and recommendation model (MFIHRM) is presented in this article. The MFIHRM model focused on considering remittance provided to the immigrant and psychology of the migrant in handling them with more efficient way. To perform this, the immigrant data set has been utilized to obtain features like purpose of migration, educational standard, economic support, lifestyle, criminal records, remittance of country, and behavior of migrant. The data set has been preprocessed to eliminate the noisy records by applying Deep Averaging Technique. From the normalized data set, the method extracts the above mentioned features. With the features extracted, the method performs Immigrant Management by computing various support measures like Migration Target Support (MTS), Educational Support (ES), Economic Support (EcS), Social Support (SS), Remittance Support (RS). Using these support values, the method computes Migration Handling Measure (MHM). Similarly, the method performs Migration Recommendation by computing Migration Score (MS) for various countries to produce recommendations. The proposed method improves the performance of migration management and recommendation handling.