Health Security and Malaria: A Neural Network iOS Intelligent Platform to Create and Implement Seek and Destroy Integrated Larval Source Management (ILSM) Policies

Jacob, Benjamin; Casanova, Jesse; Asceng, Jane Ruth

doi:10.1007/978-3-031-06636-8_9

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…Advances in the field, such as Faster R-CNN (Regional Convolution Neural Network), can upgrade standard R-CNN in a real-time recovery capital app [13]. Faster R-CNNs take advantage of a graphic processing unit (GPU), while the region proposal methods are usable in standard AI and ML-infused apps [12].…”

Section: Algorithmsmentioning

confidence: 99%

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

Jacob,

McDonald,

Bohn

2024

AJNNA

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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.

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Section: Algorithmsmentioning

confidence: 99%

“…For the very deep VGG-16 model, the proposed app information directory and object detection real-time retrieval system will have a frame rate of 5fps while attaining state-of-the-art object detection accuracy on PASCAL VOC and MS COCO datasets with approximately 300 proposals [13].…”

Section: Unified Networkmentioning

confidence: 99%

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

Jacob,

McDonald,

Bohn

2024

AJNNA

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“…The disease has significant economic impacts, including reduced productivity and increased healthcare costs, with an estimated total economic burden of US$ 12 billion annually in sub-Saharan Africa [6]. In Uganda, malaria remains one of the leading causes of morbidity and mortality, with an estimated 8 million cases and over 15,000 deaths reported annually [7]. A national survey estimated that malaria accounts for over 40% of outpatient visits and approximately 15-20% of hospital admissions in the country [8].…”

Section: Introductionmentioning

confidence: 99%

Facilitators and barriers to integrated malaria prevention in Wakiso district, Uganda: A photovoice study

Musoke,

Lubega,

Niyongabo

et al. 2024

PLOS Glob Public Health

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Malaria continues to cause significant morbidity and mortality globally, particularly in sub-Saharan Africa. Appropriate combinations of non-chemical and chemical methods of malaria vector control in the context of integrated vector management have been recommended by the World Health Organization. The aim of the study was to explore facilitators and barriers to using integrated malaria prevention in Wakiso district, Uganda. This qualitative study employed photovoice among 20 community members in Kasanje Town Council, Wakiso District. The photos taken by participants for 5 months using smartphones were discussed during monthly meetings with the researchers. The discussions were audio-recorded, and resulting data analysed using thematic analysis with the support of NVivo (2020) QSR International. Findings indicated that various conventional and non-conventional measures were being used for preventing malaria such as: insecticide treated nets; clearing overgrown vegetation; draining stagnant water; mosquito coils; smouldering of cow dung; spraying insecticides; plant repellents near houses; eating of prophylactic herbs; as well as closing doors and windows on houses early in the evening. Facilitators supporting the use of several malaria prevention methods holistically included: low cost and accessibility of some methods such as slashing overgrown vegetation; and support provided for certain methods such as receiving free mosquito nets from the government. Barriers to using several malaria prevention methods holistically included: inadequate knowledge of some methods such as housing improvement; allergic reactions to chemical-based methods such as insecticide treated nets; unaffordability of some methods such as insecticide sprays; and inaccessibility of certain methods such as body repellents. These barriers to integrated malaria prevention need to be addressed to achieve greater impact from the combination of methods in endemic communities.

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Health Security and Malaria: A Neural Network iOS Intelligent Platform to Create and Implement Seek and Destroy Integrated Larval Source Management (ILSM) Policies

Cited by 2 publications

References 13 publications

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

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

Facilitators and barriers to integrated malaria prevention in Wakiso district, Uganda: A photovoice study

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