Organizations often receive hundreds of resumes for a single job opening, making manual candidate screening slow and difficult to manage. Conventional recruitment methods generally depend on keyword matching or manual evaluation, both of which may overlook qualified applicants because of differences in resume structure and terminology. This research presents a Resume Ranking Engine that automates candidate screening using Machine Learning (ML) and Natural Language Processing (NLP). The proposed application extracts candidate information from PDF and DOCX resumes, preprocesses the extracted text, and compares it with a job description using TF-IDF vectorization and cosine similarity. In addition to textual relevance, the system evaluates technical skills, educational qualifications, and professional experience through a weighted scoring strategy. The application is implemented using React, Flask, Python, and Supabase, providing recruiters with a simple interface for uploading resumes and obtaining ranked candidate lists. The proposed approach reduces manual effort, improves consistency during recruitment, and provides an effective decision-support tool for candidate shortlisting.
Overview AutoML-Lite is a powerful, user-friendly desktop application designed to democratize machine learning by automating the entire modeling pipeline. Built with Python and PyQt6, it provides a comprehensive GUI-based environment for data preprocessing, feature engineering, m…
Preparing examination question papers manually is a time-consuming and challenging task for educators. Faculty members must ensure balanced syllabus coverage, appropriate marks distribution, and proper assessment of students across different cognitive levels. Manual preparation o…
This record provides the supplementary materials and machine-readable results accompanying the revised manuscript: "Explainable ensemble machine learning for pregnancy screening in dairy cows using routine Dairy Herd Improvement records." The deposit includes Supplementary Tables…
This paper develops a machine learning framework for detecting and predicting liquidity sweep events in XAUUSD using event-based market microstructure analysis. Using 15-minute data from 2014–2024, the study formalizes liquidity sweeps as a binary classification problem evaluated…
Large language models (LLMs) are increasingly deployed in user-facing applications, which exposes them to prompt injection and jailbreak attacks that override system instructions, exfiltrate data, or elicit disallowed behaviour. Existing defences are largely single-mechanism: a r…
The importance of power transformers in electrical power systems cannot be overstated, as their failures can lead to considerable economic losses and disruptions. The typical malfunctions encountered by a power transformer comprise dielectric issues, thermal losses due to copper…