Geospatial Artificial Intelligence Infused into a Smartphone Drone Application for Implementing 'Seek and Destroy' in Uganda

Abstract

This study provided important insights into new, real time, control measures at reducing larval, vector density [Macro Seek and Destroy (S&D) and blood parasite level [Micro S&D] in a malaria treated and suspected intervened population. Initially, this study employed a low-cost (< $1000) drone (DJI Phantom) for eco-geographically locating, water bodies including natural water bodies, irrigated rice paddies, cultivated swamps, ditches, ponds, and other geolocations, which are among the common breeding sites for Anopheles mosquitoes in Gulu district of Northern Uganda. Our hypothesis was that by integrating real time, scaled up, sentinel site, spectral signature, unmanned aerial vehicle (UAV) or drone imagery with satellite data using geospatial artificial intelligence [geo-AI] infused into an iOS application (app), a local, vector control officer could retrieve a ranked list of visually similar, breeding site, aquatic foci of An.gambiae s.l. arabiensis s.s. fuentsus s.s. mosquitoes, and their respective district-level, capture point, GPS indexed, centroid coordinates. We real time retrieved (hence, no lag time between seasonal, aquatic, Anopheles, larval habitat, mapping and treatment of foci) each georeferenced sentinel site signature which was subsequently archived in the drone dashboard spectral library using the smartphone app. Each georeferenced, UAV sensed, capture point was inspected using a mobile field team (i.e., trained local village residents led by a vector control officer) on the same day the habitats were geo-AI signature mapped, spatially forecasted and treated. A second hypothesis was that a real time, environmentally friendly, habitat alteration [i.e., Macro S&D] could reduce vector larval habitat density and blood parasite levels in treated and not suspected malaria patients at an entomological intervention site. A

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

“…The IoU threshold would be set as at least 0.7. In Jacob et al, when the IoU was 1, then a perfect classification and emplacement of the bounding box occurred [12]. Lower IoU values in a web-configurable, interactive dashboard app can indicate poorer performance [11].…”

“…The proposed implementation within an interactive recovery capital app is novel would be nature. Based on Jacob et al, the convolutional feature information maps used by region-based detectors can be employed for generating robust region proposals in an interactive, web-configurable, real-time, artificial intelligence (AI)-machine learning (ML) dashboard app [12]. On top of these convolutional features, the construction of the recovery capital app using an RPN by adding additional convolutional layers that simultaneously regress region bounds and objectness scores at each location on a county-level, 1 kilometer (km) x 1km stratified grid is proposed [11].…”

“…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]. Non-graphical uses of GPUs in a Faster R-CNN-enabled, web-based mobile app would include the training of neural networks and real-time data mining for multiple recovery capital-related incentives.…”

“…In total, using 300 anchors for training. In so doing, anchors will properly be directed to the RPN is proposed [12].…”

“…The goal of training the input recovery capital data is to minimize the loss of training data [16]. In previous real-time app development, Jacob et al noticed that the default binary cross entropy loss function, which is standard, had increased after 15,000 iterations for training real-time, drone-retrieved signatures of the malaria mosquito oviposition habitats [12]. To minimize the loss, the authors applied a novel focal loss function that penalized instances of false negatives, which in this case was an information data capture point (scaled-up capture point sentinel site breeding site aquatic foci) classified as background.…”