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This text is written to clarify my mission that I submitted to the AWS Hackathon on Devpost. Me and my good friend had initial began the challenge as a studying step to build and deploy a machine learning challenge. We eventually ended up winning the runner up for Computer Vision for our efforts. With fast urbanization and industrialization Air pollution has develop into an alarming issue globally. Among completely different air pollutants particles , BloodVitals SPO2 device matter in (PM2.5) dimension range are able traverse deeply into our respiratory tract , transmitting hazardous chemicals into the human lungs and blood causing respiratory and cardiovascular health points . We intention to develop picture-based air high quality analysis utility , in particular a one which estimates the concentration of particulate matter with diameters in range of 2.5 micrometers. It may well inform you what your breathing from photos generated using consumer grade hardware. Convolutional Neural Network (CNN) with the excessive computational energy of Amazon EC2 DL1 instance to resolve a computer vision downside of classifying natural photographs into 3 totally different categories ;wholesome , average and danger based mostly on their PM2.5 concentrations.

We created our own dataset by combining data from 2 sources . We learned that, BloodVitals SPO2 device some reasons for overfitting may very well be high imbalance in information , we had 2232 healthy pictures where because the average and hazard categories had solely 1480 and 591 pictures respectively . 2232. Then we educated our model following the identical technique as before but this time we used ImageNet weights un-freezed the final one hundred layers for training , then superb-tuned the mannequin with a lower learning charge ,we additionally used early stopping callback with a patience of three . Now, there are some more things we have done to improve our mannequin. We migrated our mannequin onto the dl1 instance and initially trained utilizing 1 gaudi processor. This considerably improved our efficiency and effectivity .We were capable of rapidly test , experiment and BloodVitals SPO2 device tweak adjustments . 76 %. We then transformed our model into a tensorflow lite mannequin and Blood Vitals used a huggingface demo instrument known as gradio to shortly reveal our utility.

Initially as we tried training our mannequin we noticed a few of the image information within the dataset had been corrupted .We automated the process of scanning through and BloodVitals SPO2 deleting these recordsdata after which proceeded to coaching mannequin . We used EfficientNet architecture since its renown for BloodVitals experience its efficiency in low spec machines, this known as switch learning . 30 layers and set the weights parameter to false. 60 % but our training accuracy was high, the mannequin might be overfitting. As now we have seen we had been ready an accuracy of about 76 % which is fairly good contemplating the info we had. Now , why do we need this application and BloodVitals SPO2 the way is it completely different from the prevailing air monitoring system systems, we already know Exposure to effective particles could cause long run well being results resembling lung and coronary heart downside however they also cause short time period well being effects such as eye, nostril, throat irritation.

The prevailing air high quality monitoring strategies are extremely dependent on monitoring stations, BloodVitals SPO2 device which are once more situated far away due to the high setup cost and costly gear. For a particular location, the air quality depends on how air moves through the realm and the way people are influencing the air , so we cannot depend on one thing that was predicted a while again or BloodVitals SPO2 device in some place that isn't our site of interest .This utility helps you analyse the air quality around you with a single picture and BloodVitals SPO2 device extra importantly is portable. We can carry it wherever we want to and know what your being exposed to . Honestly I don’t know, I think there is potential for this app to integrate into our routine. People would possibly need options to help plan their day out, integrate with their phone’s calender and recommend one of the best time to plan an outdoor activity. Right now the Artificial intelligence that runs the machine runs on the azure cloud. Sooner or BloodVitals insights later we would like to have the ability to convey it into the app and run it natively.

Certain constituents in the blood have an effect on the absorption of gentle at various wavelengths by the blood. Oxyhemoglobin absorbs mild extra strongly in the infrared area than within the pink region, whereas hemoglobin exhibits the reverse behavior. Therefore, extremely oxygenated blood with a high concentration of oxyhemoglobin and a low concentration of hemoglobin will tend to have a excessive ratio of optical transmissivity in the red area to optical transmissivity in the infrared region. These alternating parts are amplified and then segregated by sampling devices working in synchronism with the red/infrared switching, so as to offer separate indicators on separate channels representing the red and infrared gentle transmission of the physique structure. After low-go filtering to remove sign parts at or above the switching frequency, each of the separate alerts represents a plot of optical transmissivity of the body structure at a selected wavelength versus time. AC component prompted solely by optical absorption by the blood and varying at the pulse frequency or heart rate of the organism.