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.

This research paper explores Blockchain (BC) technology-based identity verification's role in streamlining and securing the employee onboarding process within Human Resource (HR) management. It addresses this technology's potential benefits, challenges, and limitations in enhancing HR practices. This study is grounded in the theoretical foundation of BC technology and its applications. It examines existing identity verification systems in HR management and delves into the potential implications of adopting BC-based solutions. This research employs a comprehensive design encompassing a discussion of the background, research problem, objectives, and significance. A detailed overview of BC technology and its applications and an analysis of existing identity verification systems are presented. The study employs a well-defined research design, including a sampling strategy, sample size determination, data collection methods, and data analysis techniques. The study's findings reveal that BC-based identity verification has the potential to streamline and secure the employee onboarding process in HR management. However, the investigation also identified scalability, interoperability, and data security challenges. These findings contribute to understanding the feasibility of adopting BC technology in HR practices. The study informs HR managers and BC developers on the potential benefits and hurdles of implementing BC-based identity verification, enabling them to make informed decisions.