Recently I built a Facial Recognition Mobile App using Oracle Visual Builder having set up the Facial recognition APIs using Tensorflow taking some inspiration from FaceNet. As highlighted above the app does the following: record a video of your face and send it to the API that generates various images and classifies them based on the label we provide at runtime. And in turn, invoke another API that is going to train the machine learning model to update the dataset with the new images and label provided. These two APIs will build a facial recognition Database. Once I have this, I can capture the face and compare that with the dataset I have captured earlier in my Facial recognition Database to output if the face exists in our system.
In the last post, we talked briefly about the Oracle Digital Assistant. In this post, I would like to walk you through the provisioning process of the Autonomous Oracle Digital Assistant.
If you are following the Oracle Autonomous Mobile and Chatbot Platform announcements you would have now realised that we have announced the availability of the Oracle Digital Assistant platform as a new SKU under the PaaS offerings.
In this post, I will delve deeper into the Oracle Digital Assistant offering and answer what I anticipate will be common questions about the changes.
Reading Alessia’s recent post about her experiments with beacons reminded me of a post that I have been meaning to write for a while, regarding my previous dabbling with building location-aware applications. Beacons are a powerful tool by which to provide fine-grained location services to applications, but need to be used carefully, and really need to be part of a larger mix of technologies in order to provide the richest experiences. In this post, I will look at the weaknesses I have previously encountered using beacons, and outline some of the strategies I have used to mitigate those weaknesses.
“Just like lighthouses have helped sailors navigate the world for thousands of years, electronic beacons can be used to provide precise location and contextual cues within apps to help you navigate the world.” (The Google Beacons development team)
What I find thrilling about beacons is that in their simplest sense they are unaware of themselves or any devices around them and we – humans – are not aware of them. So how is possible that these tiny transmitters are being used to help people in their daily lives? According to reports, 5 million beacons attached to the walls around the world are used to offer great help to people with regard to an array of things like travel, shopping, parking, entertainment, transportation, inventory management, assets tracking, indoor navigation and at last but not least in the healthcare space through more efficient processes and improved patient-care.
Wellness First is a fictitious gym that utilizes beacons to improve the customer experience. In this post we’ll take a close look at the Ionic Framework Mobile App I’ve built that uses Estimote beacons to target a customer located near the room where a Yoga class is just about to start and offers an unbeatable discount.
In this post we will explore utilising Mobile Cloud Service (MCS), which provides RAML based tooling, in order to define an API, and create mock services for that API which we can run anywhere. While Mobile Cloud Service provides a wide array of incredibly useful tools for rolling out APIs, and simplifying a number of common mobile development tasks; in this post I am going to focus exclusively on the API design tooling which far outstrips pretty much every other API design tool I have played with.