vivek Pradhan

The modern job search process is complex and time-consuming, requiring candidates to manually search, evaluate, and apply for multiple opportunities. This research proposes an AI-driven automated job search system that uses natural language processing and semantic matching to identify relevant job openings based on a candidate’s profile. The system automatically submits applications and sends personalized referral requests to recruiters.

?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom. ABSTRACT The Binary Emax model is widely employed in dose-response analysis during Phase II clinical studies to identify the optimal dose for subsequence confirmatory trials.

Abstract The Binary Emax model is widely employed in dose–response analysis during drug development, where missing data often pose significant challenges. Addressing nonignorable missing binary responses—where the likelihood of missing data is related to unobserved outcomes—is particularly important, yet existing methods often lead to biased estimates. This issue is compounded when using the regulatory-recommended ‘‘imputing as treatment failure’’ approach, known as non-responder imputation (NRI).