FRAMEWORK FOR TALENT PREDICTION AND TOP PERFORMER SEGMENTATION USING HYBRID CLASSIFIER: with Multilingual Voice based Chatbot - Couverture souple

Dr. Leena Rahul Deshmukh

Couverture souple

ISBN 13 : 9798905842320

Editeur : Notion Press, 2026

Synopsis

Here is the description shortened by about 50 characters while preserving the meaning: Framework for Talent Prediction and Top Performer Segmentation Using Hybrid Classifier with Multilingual Voice-Based Chatbot for Employee Performance Appraisal presents an innovative approach to modernizing human resource management through Artificial Intelligence (AI), Machine Learning (ML), and conversational technologies. The book introduces a framework that combines hybrid classification algorithms with a multilingual voice-based chatbot to automate employee self-appraisal, predict talent potential, and identify top-performing employees with greater accuracy and transparency.The framework addresses the limitations of traditional appraisal systems, including subjective evaluations, language barriers, and manual processing. By integrating speech recognition, natural language processing, HR analytics, and predictive modeling, the system enables employees to interact in their preferred language while providing actionable insights for HR professionals.The book covers AI-driven performance evaluation, talent prediction, workforce segmentation, decision support systems, ethical considerations, and practical implementation strategies. It is intended for researchers, academicians, HR professionals, data scientists, postgraduate students, and industry practitioners seeking intelligent, inclusive, data-driven solutions for employee performance management in the era of digital transformation.

Détails de l'édition

• Éditeur: Notion Press
• Date d'édition: 2026
• Langue: anglais
• ISBN 13: 9798905842320
• Reliure: Broché
• Nombre de pages: 168
• Coordonnées du fabricant: non disponible
• Personne responsable: GPSR Kontakt; gpsr@libri.de; ; Europaallee 1; Bad Hersfeld; 36244; Allemagne