This paper presents the implementation and evaluation of a Multi-key Multi-modalities Biometric Encryption System designed for business enterprises, leveraging cloud storage for secure and scalable data management. The system integrates multiple biometric modalities fingerprint, iris scan, and face recognition to enhance data security through advanced multi-key encryption techniques, utilizing algorithms such as Advanced Encryption Standard (AES) and Rivest-Shamir-Adleman (RSA). The encrypted biometric data is securely stored in the cloud, providing enterprises with efficient storage solutions. The system's performance was evaluated across several parameters including encryption/decryption time, biometric match accuracy, data transfer speeds, energy consumption, cost, and user satisfaction. The results demonstrate that multi-modal systems offer superior accuracy and security compared to single-modality systems, reducing error rates and enhancing reliability. However, multi-modal authentication incurs higher costs, energy consumption, and slightly longer processing times. Despite these trade-offs, the system achieved high user satisfaction, particularly in high-security environments where data protection is a priority. The findings indicate that the proposed system is a viable solution for businesses seeking a secure, scalable, and efficient method of protecting sensitive data.

Suicide remains a critical global public health issue, claiming vast number of lives each year. Traditional assessment methods, often reliant on subjective evaluations, have limited effectiveness. This study examines the potential of Bidirectional Encoder Representations from Transformers (BERT) in revolutionizing suicide risk prediction by extracting textual biomarkers from relevant data. The research focuses on the efficacy of BERT in classifying suicide-related text data and introduces a novel BERT-based approach that achieves state-of-the-art accuracy, surpassing 97%. These findings highlight BERT's exceptional capability in handling complex text classification tasks, suggesting broad applicability in mental healthcare. The application of Artificial Intelligence (AI) in mental health poses unique challenges, including the absence of established biological markers for suicide risk and the dependence on subjective data, which necessitates careful consideration of potential biases in training datasets. Additionally, ethical considerations surrounding data privacy and responsible AI development are paramount. This study emphasizes the substantial potential of BERT and similar Natural Language Processing (NLP) techniques to significantly improve the accuracy and effectiveness of suicide risk prediction, paving the way for enhanced early detection and intervention strategies. The research acknowledges the inherent limitations of AI-based approaches and stresses the importance of ongoing efforts to address these issues, ensuring ethical and responsible AI application in mental health.