Work place: School of Computer Science and Engineering, Lovely Professional University Phagwara, Punjab, India
E-mail: er.karanbajaj@gmail.com
Website:
Research Interests:
Biography
Dr. Karan Bajaj, affiliated with Lovely Professional University, focuses on areas like Edge/Fog Computing, IoT, and Machine Learning. His research includes topics such as IoT-based offloading frameworks, WSN integration with IoT, and intrusion detection systems. His most cited work is the "Implementation analysis of IoT-based offloading frameworks on cloud/edge computing," with 112 citations as of 2022. Bajaj has contributed significantly to the field, particularly in integrating wireless sensor networks with IoT and analyzing machine learning models' performance in big data contexts.
By Karan Bajaj Mukesh Kumar Shaily Jain Vivek Bhardwaj Sahil Walia
DOI: https://doi.org/10.5815/ijisa.2025.02.06, Pub. Date: 8 Apr. 2025
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.
[...] Read more.Subscribe to receive issue release notifications and newsletters from MECS Press journals