Evaluation of Ethnicity and Issues of Political Development in Punjab, Pakistan

Abstract

The purpose of this research is to evaluate ethnicity and its impact on the political structure of Punjab, Pakistan. This topic was required by the subverting tendencies of the circumstances that, in reality, endanger the survival of the minority groups in Punjab. In order to upgrade provincial political development, the facets that proliferate its existence in policies and hold it can be abolished. They have not yielded any efficacious outcomes in spite of elucidation that has been consistently provided. Subsequently, they need to search for a more practical alternative by focusing on the divisions that are present in ethnic societies. Ethnic democracy should be present that is a governmental system that links the ethnic groups to their democratic and political rights. Quantitative research was used in this research article. The researcher collected the data by distributing questionnaire among people. For future research, this research concludes with a debate of recommendations.

Classification of Ethnicity Using Efficient CNN Models on MORPH and FERET Datasets Based on Face Biometrics

“…Soft biometrics, such as gender, ethnicity, age, and expression, have recently gained attention from the pattern recognition community because of their wide range of retail and video surveillance applications and the difficulty in designing effective and reliable algorithms in challenging real-world scenarios [1]. The face is the part of the human body that contains the most semantic information about an individual.…”

“…More than 3,000,000 face images have been annotated within four ethnicity groups, namely African American, East Asian, Caucasian Latin, and Asian Indian, to cover this gap in the facial soft biometrics analysis of the VGGFace2 Mivia Ethnicity Recognition (VMER) dataset [6]. With the help of three people from diverse ethnic backgrounds, the final annotations can be derived free from the well-known other-race effect [1,2]. Due to the inherent challenges associated with ethnicity classification, two efficient CNN models were developed for predicting the ethnicity of a face using the MORPH and FERET datasets.…”