Personality Prediction System via Curriculum Vitae (CV) Analysis Using Natural Language Processing (NLP) and Logistic Regression.

Jesse Ismaila, Sabo Victoria — Mon, 17 Feb 2025 00:00:00 +0000

When it comes to the study of humans, adjudicating one’s personality is important as it acts as a window to deciphering the individual’s mindset. The personality is a vital part of an individual when he or she works for a complex organization. There are several ways to determine an individual’s personality but the most sought after and direct method is through a simple quiz. The questions in the quiz are framed in a way that they take values with reference to the big five personality model and aid the developer in framing a personality report of the individual in question. When I take a look at the current process of hiring and selection that various organizations make use of, the employers often pick out CVs in a manual way which is monotonous, time-consuming, and consumes a lot of human resources. Our approach is rendering an automated model that motorizes the eligibility check and aptitude evaluation of an applicant in the selection process to target the drawbacks of the traditional recruitment system, a web application that analyzes both the personality and an individual’s CV has been curate. This model employs a machine learning algorithm namely “Logistic Regression” which helps to choose fair decisions to recruit a suitable candidate, and “Natural Language Processing (NLP)” uses techniques with the help of Natural Language Toolkit (NLTK) libraries to process and categorize the data. Also, the use of graphs to analyze a candidate's success makes it easier to assess his or her personality and aids in proper CV analysis. As a result, the framework lends a hand in the recruitment process, allowing the candidate's CV to be shortlisted and a reasonable decision to be reached.

Student Behaviors in College Admissions: A Survey of Agent-Based Models .

Suha Khalil Assayed, Sana'a Alsayed — Tue, 21 Jan 2025 00:00:00 +0000

The process of selecting colleges and securing admissions is influenced by numerous elements, particularly academic performance, behavioral tendencies, and equity considerations. Academic metrics such as high school grades and standardized exam results often form the cornerstone of admission criteria. However, behavioral factors, including decision-making styles, personal motivations, and self-image, play an equally critical role in shaping students' application choices. For instance, while some students may aspire to enroll in elite universities, others, constrained by financial limitations or self-imposed doubts, might opt for less competitive institutions. Social influences, access to advisory resources like school counselors, and awareness of the admissions process further shape students' choices and behaviors. Students from underserved or marginalized communities often face additional hurdles, leading them to prioritize institutions based on proximity, affordability, or program flexibility that aligns with their unique needs. This paper explores agent-based modeling techniques adopted by international universities to study secondary education pathways and student behaviors in the context of admissions. By examining these models, the research highlights how they simulate complex decision-making processes and systemic interactions to foster equitable practices in university admissions. Emphasizing behavioral dimensions, these models underscore the importance of creating fairer systems that address the diverse needs and aspirations of students while promoting inclusivity and justice in higher education.

International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence

Personality Prediction System via Curriculum Vitae (CV) Analysis Using Natural Language Processing (NLP) and Logistic Regression.

Student Behaviors in College Admissions: A Survey of Agent-Based Models .