American Journal of Neural Networks and Applications

Methodology Article | | Peer-Reviewed |

Closing the Gap on Addiction Recovery Engagement with an AI-infused Convolutional Neural Network Technology Application—A Design Vision

Received: 6 February 2024    Accepted: 27 February 2024    Published: 7 March 2024
Views:       Downloads:

Share This Article

Abstract

Currently, real-time detection networks elaborate the technical details of the Faster Regional Convolution Neural Network (R-CNN) recognition pipeline. Within existing R-CNN literature, the evolution exhibited by R-CNN is most profound in terms of computational efficiency integrating each training stage to reduce test time and improvement in mean average precision (mAP), which can be infused into an artificially intelligent (AI), machine learning (ML), real-time, interactive, recovery capital application (app). This article introduces a Region Proposal Network (RPN) that shares full-image convolutional features with a real-time detection AI-ML infused network in an interactive, continuously self-learning wrist-wearable real-time recovery capital app for enabling cost-free region proposals (e.g., instantaneous body physiological responses, mapped connections to emergency services, sponsor, counselor, peer support, links to local and specific recovery capital assets, etc.). A fully merged RPN and Faster R-CNN deep convolutional unified network in the app can simultaneously train to aggregate and predict object bounds and objectness scores for implementing recovery capital real-time solutions (e.g., baseball card scoring dashboards, token-based incentive programs, etc.) A continuous training scheme alternates between fine-tuning RPN tasks (e.g., logging and updating personal client information, gamification orientation) and fine-tuning the detection (e.g., real-time biometric monitoring client’s behavior for self-awareness of when to connect with an addiction specialist or family member, quick response (QR) code registration for a 12-step program, advanced security encryption, etc.) in the interactive app. The very deep VGG-16 model detection system has a frame rate of 5fps within a graphic processing unit (GPU) while accomplishing sophisticated object detection accuracy on PASCAL Visual Object Classification Challenge (PASCAL VOC) and Microsoft Common Objects in Context (MS COCO) datasets. This is achieved with only 300 proposals per real-time retrieved data capture point, information bit or image. The app has real-time, infused cartographic and statistical tracking tools to generate Python Codes, which can enable a gamified addiction recovery-oriented digital conscience. Faster R-CNN and RPN can be the foundations of an interactive real-time recovery capital app that can be adaptable to multiple recovery pathways based on participant recovery plans and actions. This paper discusses some of the critical attributes and features to include in the design of a future app to support and close current gaps in needed recovery capital to help those who are dealing with many different forms of addiction recovery.

DOI 10.11648/j.ajnna.20241001.11
Published in American Journal of Neural Networks and Applications (Volume 10, Issue 1, June 2024)
Page(s) 1-14
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Convolutional Neural Networks, Recovery Capital, Addiction Recovery, Artificial Intelligence, Region Proposal Networks, Faster R-CNN, Python

References
[1] Anderson, M., Devlin, A. M., Pickering, L., McCann, M., and Wight, D. (2021). ‘It’s Not 9 to 5 Recovery’: The Role of a Recovery Community in Producing Social Bonds that Support Recovery. Drugs: Education, Prevention and Policy. 2021, 28(5), 475-485. https://doi.org/10.1080/09687637.2021.1933911
[2] Martinelli, T. F., van de Mheen, D., Best, D., Vanderplasschen, W., and Nagelhout, G. E. Are Members of Mutual Aid Groups Better Equipped for Addiction Recovery? European Cross-Sectional Study into Recovery Capital, Social Networks, and Commitment to Sobriety. Drugs: Education, Prevention and Policy. 2021, 28(5), 389-398. https://doi.org/10.1080/09687637.2020.1844638
[3] Weston, S., Honor, S., and Best, D. A Tale of Two Towns: a Comparative Study Exploring the Possibilities and Pitfalls of Social Capital Among People Seeking Recovery from Substance Misuse. Substance Use & Misuse. 2018, 53(3), 490-500. https://doi.org/10.1080/10826084.2017.1341925
[4] Best, D., Hennessy, E. A. The science of recovery capital: where do we go from here? Addiction. 2022, 117(4), 1139-1145. https://doi.org/10.1111/add.15732
[5] Cano, I., Best, D., Edwards, M., and Lehman, J. Recovery capital pathways: Modelling the components of recovery wellbeing. Drug and Alcohol Dependence. 2017, 181, 11-19. https://doi.org/10.1016/j.drugalcdep.2017.09.002
[6] Majer, J. M., Jason, L. A., and Bobak, T. J. Understanding Recovery Capital in Relation to Categorical 12-Step Involvement and Abstinence Social Support. Addiction Research & Theory. 2022, 30(3), 207-212. https://doi.org/10.1080/16066359.2021.1999935
[7] Vanderplasschen, W., Best, D. Mechanisms and Mediators of Addiction Recovery. Drugs: Education, Prevention and Policy. 2021, 28(5), 385-388. https://doi.org/10.1080/09687637.2021.1982521
[8] Revill, A. S., Patton, K. A., Connor, J. P., Sheffield, J., Wood, A. P., Castellanos-Ryan, N., and Gullo, M. J. From Impulse to Action? Cognitive Mechanisms of Impulsivity-Related Risk for Externalizing Behavior. Journal of Abnormal Child Psychology. 2020, 48, 1023-1034. https://doi.org/10.1007/s10802-020-00642-7
[9] Abrantes, A. M., Blevins, C. E. (2019). Exercise in the context of substance use treatment: key issues and future directions. Current Opinion in Psychology. 2019, 30, 103-108. https://doi.org/10.1016/j.copsyc.2019.04.001
[10] McKay, J. R. Making the Hard Work of Recovery More Attractive for Those with Substance Use Disorders. Addiction. 2017, 112(5), 751-757. https://doi.org/10.1111/add.13502
[11] Ren, S., Kaiming He, Girshick, R., & Jian Sun. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2017, 39(6), 1137–1149. https://doi.org/10.1109/TPAMI.2016.2577031
[12] Jacob, B. G., Loum, D., Kaddumukasa, M., Kamgno, J., Djeunga, H. N., Domche, A., Nwane, P., Mwangangi, J., Borjorge, S. H., Parikh, J., Casanova, J., Michael, M., Mason, T., and Mubangizi, A. Geospatial Artificial Intelligence Infused into a Smartphone Drone Application for Implementing 'Seek and Destroy' in Uganda. American Journal of Entomology. 2021, 5(4), 92-109. https://doi.org/10.11648/j.aje.20210504.11
[13] Jacob, B. G., Casanova, J., Asceng, J. R. Health security and malaria: A neural network iOS intelligent platform to create and implement seek and destroy integrated larval source management (ILSM) policies. In Disruption, Ideation and Innovation for Defence and Security, Adlakha-Hutcheon, G., Masys, A., Eds. New York, NY: Springer International Publishing; 2022, pp. 179-203.
[14] Ineza Havugimana, L. F., Liu, B., Liu, F., Zhang, J., Li, B., and Wan, P. Review of artificial intelligent algorithms for engine performance, control, and diagnosis. Energies. 2023, 16, 1206. https://doi.org/10.3390/en16031206
[15] Krizhevsky A., Sutskever I., Hinton G. E., (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems. 2012, 25, 1097-1105.
[16] Minakshi, M., Bhuiyan, T., Kariev, S., Kaddumukasa, M., Loum, D., Stanley, N. B., Chellappan, S., Habomugisha, P., Oguttu, D. W., and Jacob, B. G. High-accuracy detection of malaria mosquito habitats using drone-based multispectral imagery and artificial intelligence (AI) algorithms in an agro-village peri-urban pastureland intervention site (Akonyibedo) in Unyama sub-county, Gulu District, northern Uganda. Journal of Public Health and Epidemiology. 2020, 12(3), 202-217. https://doi.org/10.5897/JPHE2020.1213
[17] Groshkova, T., Best, D., and White, W. The Assessment of Recovery Capital: Properties and Psychometrics of a Measure of Addiction Recovery Strengths. Drug and Alcohol Review. 2013, 32(2), 187–194. https://doi.org/10.1111/j.1465-3362.2012.00489.x
[18] [D] White, W., Cloud, W. Recovery capital: A primer for addictions professionals. Counselor. 2008, 9, 22-27.
[19] Granfield, R., Cloud, W. The Elephant That No One Sees: Natural Recovery among Middle-Class Addicts. Journal of Drug Issues. 1996, 26(1), 45-61. https://doi.org/10.1177/002204269602600104
[20] Granfield, R., Cloud, W. Coming Clean: Overcoming Addiction Without Treatment. 1st ed. New York, NY: NYU Press; 1999.
[21] Moos, R. H. Theory-Based Active Ingredients of Effective Treatments for Substance Use Disorders. Drug and Alcohol Dependence. 2007, 88(2-3), 109–121. https://doi.org/10.1016/j.drugalcdep.2006.10.010
[22] Kaskutas, L. A., Bond, J., & Humphreys, K. Social Networks as Mediators of the Effect of Alcoholics Anonymous. Addiction. 2002, 97(7), 891–900. https://doi.org/10.1046/j.1360-0443.2002.00118.x
[23] Chang, T. Y., Chen, G. Y., Chen, J. J., Young, L. H., and Chang, L. T. Application of Artificial Intelligence Algorithms and Low-Cost Sensors to Estimate Respirable Dust in the Workplace. Environment International. 2023, 182, 108317. https://doi.org/10.1016/j.envint.2023.108317
[24] Bermejo-Peláez, D., Marcos-Mencía, D., Álamo, E., Pérez-Panizo, N., Mousa, A., Dacal, E., Lin, L., Vladimirov, A., Cuadrado, D., Mateos-Nozal, J., Galán, J. C., Romero-Hernandez, B., Cantòn, R., Luengo-Oroz, M., and Rodriguez-Dominguez, M. A Smartphone-Based Platform Assisted by Artificial Intelligence for Reading and Reporting Rapid Diagnostic Tests: Evaluation Study in SARS-CoV-2 Lateral Flow Immunoassays. JMIR Public Health and Surveillance. 2022, 8(12), e38533. https://doi.org/10.2196/38533
[25] Lin, T. Y., Goyal, P., Girshick, R., He, K., and Dollár, P. Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, 2017; pp. 2980-2988.
[26] Jacob, B. G., Habomushiga, P. Location intelligence powered by maching learning automation for mapping malaria mosquito habitats employing an unmanned aerial vehicle (UAV) for implementing “seek and destroy” for commercial roadside ditch foci and real time larviciding rock pit quarry habitats in peri-domestic agro-pastureland ecosystems in northern Uganda. In Sensemaking for Security, Masys, A. J., Ed. New York, NY: Springer International Publishing; 2021, pp. 133-148.
[27] Saabith, A. S., Fareez, M. M. M., and Vinothraj, T. Python Current Trend Applications- An Overview. International Journal of Advance Engineering and Research Development. 2019, 6(10), 6-12.
[28] Patkar, U., Singh, P., Panse, H., Bhavsar, S., and Pandey, C. Python for Web Development. International Journal of Computer Science and Mobile Computing. 2022, 11(4), 36-48. https://doi.org/10.47760/ijcsmc.2022.v11i04.006
[29] Nair, V., Hinton, G. E. (2010). Rectified Linear Units Improve Restricted Boltzmann Machines. In Proceedings of the 27th International Conference on Machine Learning, Haifa, Israel, 2010; pp. 807-814.
[30] He K., Gkioxari, G., Dollár, P., and Girshick, R. Mask R-CNN. In 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017; pp. 2980-2988. https://doi.org/10.1109/ICCV.2017.322
[31] Braun, V., Clarke, V. Using thematic analysis in psychology. Qualitative Research in Psychology. 2006, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa
[32] R. Girshick. Fast R-CNN, In Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile, 2015; pp. 1440-1448.
[33] Liu, S., Jia, J., Fidler, S., & Urtasun, R. SGN: Sequential Grouping Networks for Instance Segmentation. In Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, 2017; pp. 3496-3504.
[34] He, K., Zhang, X., Ren, S., and Sun, J. Deep Residual Learning for Image Recognition. In the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016; pp. 770–778.
[35] Bi, X, Hu, J., Xiao, B., Li, W., and Gao, X. IEMask R-CNN: Information-enhanced mask R-CNN. IIEE Transactions on Big Data. 2022, 9(2), 688-700. https://doi.org/10.1109/TBDATA.2022.3187413
[36] Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollar, P., and Zitnick, C. L. Microsoft COCO: Common objects in context. In European Conference of Computer Vision. New York, NY: Springer International Publishing; 2014, 740-755.
[37] ] Hosang, J., Benenson, R., Dollar, P., and Schiele, B. What makes for effective detection proposals? IIEE Transactions on Pattern Analysis and Machine Intelligence. 2016, 38(4), 814-830. https://doi.org/10.1109/TPAMI.2015.2465908
[38] Hennessy, E. A. Recovery Capital: A Systematic Review of the Literature. Addiction Research Theory. 2017, 25(5), 349–60. https://doi.org/10.1080/16066359.2017.1297990
[39] Wang, X., Girshick, R., Gupta, A., & He, K. Non-local neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018; pp. 7794-7803.
[40] Dalphonse, L., Campbell, D. A., Kerr, B. J., Kerr, J. L., and Gadbois, C. More Than an Opioid Crisis: Population Health and Economic Indicators Influencing Deaths of Despair. Sociological Inquiry. 2024, 94(1), 45–65. https://doi.org/10.1111/soin.12546
Cite This Article
  • APA Style

    Jacob, B., McDonald, H., Bohn, J. (2024). Closing the Gap on Addiction Recovery Engagement with an AI-infused Convolutional Neural Network Technology Application—A Design Vision. American Journal of Neural Networks and Applications, 10(1), 1-14. https://doi.org/10.11648/j.ajnna.20241001.11

    Copy | Download

    ACS Style

    Jacob, B.; McDonald, H.; Bohn, J. Closing the Gap on Addiction Recovery Engagement with an AI-infused Convolutional Neural Network Technology Application—A Design Vision. Am. J. Neural Netw. Appl. 2024, 10(1), 1-14. doi: 10.11648/j.ajnna.20241001.11

    Copy | Download

    AMA Style

    Jacob B, McDonald H, Bohn J. Closing the Gap on Addiction Recovery Engagement with an AI-infused Convolutional Neural Network Technology Application—A Design Vision. Am J Neural Netw Appl. 2024;10(1):1-14. doi: 10.11648/j.ajnna.20241001.11

    Copy | Download

  • @article{10.11648/j.ajnna.20241001.11,
      author = {Benjamin Jacob and Heather McDonald and Joe Bohn},
      title = {Closing the Gap on Addiction Recovery Engagement with an AI-infused Convolutional Neural Network Technology Application—A Design Vision},
      journal = {American Journal of Neural Networks and Applications},
      volume = {10},
      number = {1},
      pages = {1-14},
      doi = {10.11648/j.ajnna.20241001.11},
      url = {https://doi.org/10.11648/j.ajnna.20241001.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20241001.11},
      abstract = {Currently, real-time detection networks elaborate the technical details of the Faster Regional Convolution Neural Network (R-CNN) recognition pipeline. Within existing R-CNN literature, the evolution exhibited by R-CNN is most profound in terms of computational efficiency integrating each training stage to reduce test time and improvement in mean average precision (mAP), which can be infused into an artificially intelligent (AI), machine learning (ML), real-time, interactive, recovery capital application (app). This article introduces a Region Proposal Network (RPN) that shares full-image convolutional features with a real-time detection AI-ML infused network in an interactive, continuously self-learning wrist-wearable real-time recovery capital app for enabling cost-free region proposals (e.g., instantaneous body physiological responses, mapped connections to emergency services, sponsor, counselor, peer support, links to local and specific recovery capital assets, etc.). A fully merged RPN and Faster R-CNN deep convolutional unified network in the app can simultaneously train to aggregate and predict object bounds and objectness scores for implementing recovery capital real-time solutions (e.g., baseball card scoring dashboards, token-based incentive programs, etc.) A continuous training scheme alternates between fine-tuning RPN tasks (e.g., logging and updating personal client information, gamification orientation) and fine-tuning the detection (e.g., real-time biometric monitoring client’s behavior for self-awareness of when to connect with an addiction specialist or family member, quick response (QR) code registration for a 12-step program, advanced security encryption, etc.) in the interactive app. The very deep VGG-16 model detection system has a frame rate of 5fps within a graphic processing unit (GPU) while accomplishing sophisticated object detection accuracy on PASCAL Visual Object Classification Challenge (PASCAL VOC) and Microsoft Common Objects in Context (MS COCO) datasets. This is achieved with only 300 proposals per real-time retrieved data capture point, information bit or image. The app has real-time, infused cartographic and statistical tracking tools to generate Python Codes, which can enable a gamified addiction recovery-oriented digital conscience. Faster R-CNN and RPN can be the foundations of an interactive real-time recovery capital app that can be adaptable to multiple recovery pathways based on participant recovery plans and actions. This paper discusses some of the critical attributes and features to include in the design of a future app to support and close current gaps in needed recovery capital to help those who are dealing with many different forms of addiction recovery.
    },
     year = {2024}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Closing the Gap on Addiction Recovery Engagement with an AI-infused Convolutional Neural Network Technology Application—A Design Vision
    AU  - Benjamin Jacob
    AU  - Heather McDonald
    AU  - Joe Bohn
    Y1  - 2024/03/07
    PY  - 2024
    N1  - https://doi.org/10.11648/j.ajnna.20241001.11
    DO  - 10.11648/j.ajnna.20241001.11
    T2  - American Journal of Neural Networks and Applications
    JF  - American Journal of Neural Networks and Applications
    JO  - American Journal of Neural Networks and Applications
    SP  - 1
    EP  - 14
    PB  - Science Publishing Group
    SN  - 2469-7419
    UR  - https://doi.org/10.11648/j.ajnna.20241001.11
    AB  - Currently, real-time detection networks elaborate the technical details of the Faster Regional Convolution Neural Network (R-CNN) recognition pipeline. Within existing R-CNN literature, the evolution exhibited by R-CNN is most profound in terms of computational efficiency integrating each training stage to reduce test time and improvement in mean average precision (mAP), which can be infused into an artificially intelligent (AI), machine learning (ML), real-time, interactive, recovery capital application (app). This article introduces a Region Proposal Network (RPN) that shares full-image convolutional features with a real-time detection AI-ML infused network in an interactive, continuously self-learning wrist-wearable real-time recovery capital app for enabling cost-free region proposals (e.g., instantaneous body physiological responses, mapped connections to emergency services, sponsor, counselor, peer support, links to local and specific recovery capital assets, etc.). A fully merged RPN and Faster R-CNN deep convolutional unified network in the app can simultaneously train to aggregate and predict object bounds and objectness scores for implementing recovery capital real-time solutions (e.g., baseball card scoring dashboards, token-based incentive programs, etc.) A continuous training scheme alternates between fine-tuning RPN tasks (e.g., logging and updating personal client information, gamification orientation) and fine-tuning the detection (e.g., real-time biometric monitoring client’s behavior for self-awareness of when to connect with an addiction specialist or family member, quick response (QR) code registration for a 12-step program, advanced security encryption, etc.) in the interactive app. The very deep VGG-16 model detection system has a frame rate of 5fps within a graphic processing unit (GPU) while accomplishing sophisticated object detection accuracy on PASCAL Visual Object Classification Challenge (PASCAL VOC) and Microsoft Common Objects in Context (MS COCO) datasets. This is achieved with only 300 proposals per real-time retrieved data capture point, information bit or image. The app has real-time, infused cartographic and statistical tracking tools to generate Python Codes, which can enable a gamified addiction recovery-oriented digital conscience. Faster R-CNN and RPN can be the foundations of an interactive real-time recovery capital app that can be adaptable to multiple recovery pathways based on participant recovery plans and actions. This paper discusses some of the critical attributes and features to include in the design of a future app to support and close current gaps in needed recovery capital to help those who are dealing with many different forms of addiction recovery.
    
    VL  - 10
    IS  - 1
    ER  - 

    Copy | Download

Author Information
  • Sections