This paper proposes IPAMS (Interview Performance Assessment using Gen AI), which is an AI-driven platform that automates interview evaluations using advanced technologies like Convolutional Neural Networks (CNN) to gain insights on facial emotions and expressions, Large Language Models (LLM) to generate and process interview questions, YOLO (You Only Look Once) for real-time object detection, and APIs for speech-to-text transcription and behavioral analysis. The system captures video responses and analyzes key elements such as sentiment, speech patterns, body posture, and facial expressions, generating a detailed report. This report highlights a candidate’s strengths and areas of improvement and is sent directly to their email with actionable insights. IPAMS modernizes recruitment by providing unbiased assessments, saving time and resources for recruiters. For candidates, it offers a valuable mock interview tool, delivering feedback on technical skills, confidence, stress levels, and nonverbal communication. By combining cutting-edge AI and analytics, IPAMS delivers an efficient, objective, and insightful solution for recruitment and self-assessment, benefiting all stakeholders in the interview process.

In this research, a Temporal Convolutional Network (TCN) is combined with a Transformer model with multi-head attention to present a novel approach to stock price forecasting. The primary objective is to address the challenges of recognizing complex patterns and long-term interdependence inherent in the volatility of financial time series data. By fusing the powerful attention mechanisms of Transformers with the sequential processing capabilities of TCNs, the hybrid model provides a powerful solution. This method performs better than conventional deep learning models, including Long Short-Term Memories and standalone TCNs, according to extensive testing on historical stock market data. The outcomes highlight the efficacy of this approach for trustworthy stock market forecasting by demonstrating notable gains in prediction accuracy and model stability.