It is evident how crucial education is to a person's overall development. The knowledge of the economy and society is still in its infancy. In terms of social and economic elements, education has emerged as the most significant factor for individual and national growth. Given this context, it would be worthwhile to examine the New Education Policy 2020 for the benefits and impacts it has on the various stakeholders. Such analysis is important to fulfill the needs and objectives of NEP-2020. Despite having many universities and schools, Indian education still needs some improvements. Many Indian children still do not have access to education, and more importantly, the education system in India has not undergone significant reform in the last few decades, so changes must be made to keep up with the changing needs of society. The purpose of this study is to use the neutrosophic PESTEL analysis technique to mathematically identify and rank the major factors required to be identified for the successful implementation of NEP. Numerous factors that are grouped into six primary categories—political, economic, social, technological, legal, and environmental. These are presented by a thorough literature review of the subject. The present work employs neutrosophic PESTEL analysis, to identify the main obstacles to the implementation and execution of NEP-2020 in India. The study shows that social and economic factors, with 84% and 60% respectively play a significant role while political and technical factors are also important and come in second place since they each represent 25% and 34% of the barriers to the implementation of the NEP-2020. The last two factors are legal and environmental, contributing only 13% and 3%, respectively. The primary goal of the study is to identify and statistically rank the biggest obstacles to NEP-2020 implementation in India. In many aspects, this research will help government organizations and policymakers prioritize the main obstacles early in the implementation process as well as during execution, ensuring that the results are as anticipated and that the project is finished within the allotted time limit.

This paper presents a comprehensive analysis of the scholarly footprint of ChatGPT, an AI language model, using bibliometric and scientometric methods. The study aims to understand the evolution of research output, citation patterns, collaborative networks, application domains, and future research directions related to ChatGPT. By analyzing data from the Scopus database, 533 relevant articles were identified for analysis. The findings reveal the prominent publication venues, influential authors, and countries contributing to ChatGPT research. Collaborative networks among researchers and institutions are visualized, highlighting patterns of co-authorship. The application domains of ChatGPT, such as customer support and content generation, are examined. Moreover, the study identifies emerging keywords and potential research areas for future exploration. The methodology employed includes data extraction, bibliometric analysis using various indicators, and visualization techniques such as Sankey diagrams. The analysis provides valuable insights into ChatGPT's influence in academia and offers researchers guidance for further advancements. This study stimulates discussions, collaborations and innovations to enhance ChatGPT's capabilities and impact across domains.

Research work in the field of offline Arabic handwriting recognition has seen an exponential growth during past decades. Despite much work dedicated to the recognition of handwritten Arabic text, there remains a lot to achieve in this regard. Though there exists a lot of work that is confined to the Arabic text, this paper presents an approach towards classifying and recognizing text written in one of the famous scripts of Arabic language i.e. Kufic script. The approach based on character segmentation does not perform well in recognizing Kufic text due to various complexities. The proposed system is based on word segmentation and employs a Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) feature extraction techniques. Later in the paper a comparison is given between the numerical results of proposed technique with previous Arabic text recognition techniques to show the effectiveness of present work. This approach yields effective results with 97.05% accuracy in recognizing the Arabic text written in Kufic script using Polynomial kernel of SVM classifier. Experimental results show that the proposed system for recognition of Kufic script performs better than the previous recognition systems for Arabic text.