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2024 | OriginalPaper | Buchkapitel

Application of Gradient Boosting Classifier-Based Computational Intelligence to Detect Drug Addiction Threat in Society

verfasst von : Ashutosh Kumar, Abhigyan Sinha, Tamoghno Bakshi, Sibashish Choudhury, Sushruta Mishra, Laith Abualigah

Erschienen in: Proceedings of Third International Conference on Computing and Communication Networks

Verlag: Springer Nature Singapore

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Abstract

Currently, drug and alcohol addiction has become a major menace to society's youth. As responsible citizens of this country, we must act now to keep these young brains from succumbing to this lethal addiction. In this research, a method based on intelligent analytics is designed for forecasting the threat of drug addiction using machine learning algorithms. Initially, we identify several reasons for drug addiction by speaking with health professionals, individuals addicted to drugs, and reading related studies and articles. Then we gather information from both addicts and abstainers. After preprocessing the data, we use Random Forest, Naive Bayes, Adaptive Boosting, and Gradient Boosting machine and assess the interpretation of each of these classifiers using certain well-known performance criteria. Outcome with Gradient Boosting showed promising with the highest accuracy rate of 95.4% and minimum error rate of 0.0854.

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Acion, L., Kelmansky, D., van der Laan, M., Sahker, E., Jones, D., Arndt, S.: Use of a machine learning framework to predict substance use disorder treatment success. PLoS One 12(4), e0175383 (2017). https://doi.org/10.1371/journal.pone.0175383.PMID:28394905;PMCID:PMC5386258CrossRef

Jing, Y., Hu, Z., Fan, P., Xue, Y., Wang, L., Tarter, R.E., Kirisci, L., Wang, J., Vanyukov, M., Xie, X.Q.: Analysis of substance use and its outcomes by machine learning I. Childhood evaluation of liability to substance use disorder. Drug Alcohol Depend. 2020 January 1;206, 107605. https://doi.org/10.1016/j.drugalcdep.2019.107605. Epub 2019 Oct 22. PMID: 31839402; PMCID: PMC6980708

Arif, M., Sany, S.I., Sharmin, F., Rahman, S., Habib, T.: Prediction of addiction to drugs and alcohol using machine learning: a case study on Bangladeshi population. Int. J. Electr. Comput. Eng. (IJECE). 11, 4471 (2021). https://doi.org/10.11591/ijece.v11i5.pp4471-4480

Nath, P., Kilam, S., Swetapadma, A.: A machine learning approach to predict volatile substance abuse for drug risk analysis. In: 2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), Kolkata, India, pp. 255–258 (2017).https://doi.org/10.1109/ICRCICN.2017.8234516

Hassanpour, S., Tomita, N., DeLise, T., Crosier, B., Marsch, L.A.: Identifying substance use risk based on deep neural networks and Instagram social media data. Neuropsychopharmacology 44(3), 487–494 (2019). https://doi.org/10.1038/s41386-018-0247-x. Epub 2018 Oct 24. PMID: 30356094; PMCID: PMC6333814

Rekabdar, B., Albright, D.L., McDaniel, J.T., Talafha, S., Jeong, H.: From machine learning to deep learning: a comprehensive study of alcohol and drug use disorder. Healthc. Anal. 2, 100104 (2022). ISSN 2772-4425, https://doi.org/10.1016/j.health.2022.100104

Han, D.-H., Lee, S., Seo, D.-C.: Using machine learning to predict opioid misuse among U.S. adolescents. Prevent. Med. 130, 105886 (2020). ISSN 0091-7435, https://doi.org/10.1016/j.ypmed.2019.105886

Shahriar, A., Faisal, F., Uddin Mahmud, S., Chakrabarty, A., Alam, Md. G.R.: A machine learning approach to predict vulnerability to drug addiction, pp. 1–7 (2019). https://doi.org/10.1109/ICCIT48885.2019.9038605

Mohan, S., Shailaja, L.: Prediction of drug addiction using supervised learning. Int. Res. J. Innov. Eng. Technol. (IRJIET) 6(5), 213–216 (2022). ISSN (online): 2581-3048 https://doi.org/10.47001/IRJIET/2022.605030

10.

Mishra, N., Mishra, S., Tripathy, H.K.: Rice yield estimation using deep learning. In: Innovations in Intelligent Computing and Communication: First International Conference, ICIICC 2022, Bhubaneswar, Odisha, India, December 16–17, 2022, Proceedings, pp. 379–388. Springer International Publishing, Cham (2023)

11.

Chakraborty, S., Mishra, S., Tripathy, H.K.: COVID-19 outbreak estimation approach using hybrid time series modelling. In: Innovations in Intelligent Computing and Communication: First International Conference, ICIICC 2022, Bhubaneswar, Odisha, India, December 16–17, 2022, Proceedings, pp. 249–260. Springer International Publishing, Cham (2023)

12.

Verma, S., Mishra, S.: An exploration analysis of social media security. In: Predictive Data Security using AI: Insights and Issues of Blockchain, IoT, and DevOps, pp. 25–44. Springer Nature Singapore, Singapore (2022)

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Singh, P., Mishra, S.: A comprehensive study of security aspects in blockchain. In: Predictive Data Security using AI: Insights and Issues of Blockchain, IoT, and DevOps, pp. 1–24. Springer Nature Singapore, Singapore (2022)

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Swain, T., Mishra, S.: Evolution of machine learning algorithms for enhancement of self-driving vehicles security. In: 2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC), pp. 1–5. IEEE (2022)

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Sahoo, S., Mishra, S.: A comparative analysis of PGGAN with other data augmentation technique for brain tumor classification. In: 2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC), pp. 1–7. IEEE (2022)

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Mohapatra, S.K., Mishra, S., Tripathy, H.K.: Energy consumption prediction in electrical appliances of commercial buildings using LSTM-GRU model. In: 2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC), pp. 1–5. IEEE (2022)

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Adrija, M., Yash, A., Sushruta, M.: 8 pragmatic study of IoT in healthcare security with an explainable AI perspective. In: Explainable Artificial Intelligence for Biomedical Applications, pp. 145–166. River Publishers (2023)

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Bhavya, M., Pranshu, S., Sushruta, M., Sibanjan, D.: 17 comparative analysis of breast cancer diagnosis driven by the smart IoT-based approach. In: Explainable Artificial Intelligence for Biomedical Applications, pp. 353–374. River Publishers (2023)

Titel: Application of Gradient Boosting Classifier-Based Computational Intelligence to Detect Drug Addiction Threat in Society
verfasst von: Ashutosh Kumar
Abhigyan Sinha
Tamoghno Bakshi
Sibashish Choudhury
Sushruta Mishra
Laith Abualigah
Verlag: Springer Nature Singapore
Buch: Proceedings of Third International Conference on Computing and Communication Networks
Print ISBN: 978-981-9708-91-8

Electronic ISBN: 978-981-9708-92-5

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-97-0892-5_14

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