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Article History

Received: 2 June 2025

Accepted: 5 January 2026

First Online: 14 January 2026

Declarations

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: This is to certify that the submitted research paper titled “Hybrid Ensemble Model for Automated Assessment of Delinquency Levels in Adolescents via Random Subspace Learning” is an outcome of the government-funded project titled “Development of machine learning algorithms for the early diagnosis of delinquent behavior in juveniles.” This project has been approved for financial support under the CSRI scheme of the Department of Science and Technology (DST), New Delhi, vide file NO-CSRI/2017/400. Since it is a previously DST-approved, non-clinical, and non-invasive study, hence ethics committee approval was not recommended by the Departmental Academic Committee (DAC). However, it was recommended to acquire behavioral data from the adolescents under the supervision of a clinical psychologist. The study and its methods were approved by the PhD doctoral committee of the relevant institution, Birla Institute of Technology, Mesra, Ranchi, India, and Dr. Madhu Kumari Gupta, Senior Clinical Psychologist, Ranchi, India. The study followed all necessary guidelines and regulations. Informed consent was taken from each individual participant after explaining to them about the research itself as well as their rights to participate or not. Informed consent was also obtained from the parents of all participants under 18 years of age. Additionally, informed consent was also obtained from the heads of the participating schools. Participants and their guardians were informed about the study purpose, voluntary participation, confidentiality, and the right to withdraw. The study was conducted following the Indian Council of Medical Research (ICMR), National Ethical Guidelines for Biomedical and Health Research Involving Human Participants (2017), and applicable national regulations. The study was performed in accordance with relevant guidelines and regulations.

: It was informed to all the participants and their parents that no individual identifiable data would be published, and the data would be used solely for developing predictive models. We clarified that no individual identifiable data will be published, and all data collected were anonymized to preserve their dignity, integrity, and right to self-determination. Therefore, consent to publish individual data was not required.

: The authors declare no competing interests.