We are specialists in the practical application of Artificial Intelligence including Diagnostics AI, Machine Learning, Deep Learning and Data Science Software used within Climate Change processes and operations

Using past diverse data to learn from, AI can rapidly use current data and scenarios to accurately diagnose highly variable issues.

The power of diagnostics

Being able to automatically diagnose an issue based on past diagnoses, events and knowledge is an extremely powerful tool in all aspects of business and industry operations.

By learning from past operations and processes, including positive events, negative events, and all the in-between variable and unpredictable events that happened before; diagnosis is more insightful, more meaningful, more accurate, and more likely to result in a beneficial output: e.g. financial gain, increased efficiency, lower risk, improved processes, better customer journey mapping and experience, or greater consumer satisfaction.

Importantly, a faster, accurate diagnosis potentially saves time, cost and other resources in the future.

The more past data, diagnoses, experience, knowledge, events and processes to learn from the more accurate and informed we can make our current diagnosis.

AI makes automatic diagnosis possible

Learning from past diagnoses, gained-knowledge and experiences in order to make a new diagnosis might sound straightforward - it is of course exactly what a human brain does - but making this process automatic and asking a machine to do this is extremely complex and demanding, mainly due to the large amount of variability involved.

If a traditional software programming approach was used, the computer would continually be asking "if this, then do that; if x and y, do z". This stepwise methodology is limited, tedious and resource-heavy as the program will only do what it has explicitly been told to look for by the programmer; making it next to impossible to account for all possibilities and variations, even in the simplest of diagnoses. You certainly couldn't use this approach for complex and diverse diagnosis scenarios.

With AI and Machine Learning technology it is now possible to learn from near infinite amounts of variable, constantly changing data in order to make much more accurate diagnoses, automatically.

ELDR Diagnoser is a powerful Deep Learning package that can help diagnose issues, scenarios and events based on variable past and complex data it has learnt from

ELDR Diagnoser is a variant of Fennaio's core AI predictive analytics software - ELDR Predictor. Using our powerful ELDR AI Engine, which is a Deep Learning Artificial Neural Network, ELDR Diagnoser uses both Supervised and Unsupervised learning to make and link complex relationships from data (of any size or complexity) passed to it.

When ELDR has learnt (trained) from the data, it is then primed to receive current-status data from which to make a diagnosis from.

A simple example using ELDR Diagnoser

Consider you are a Health Care Provider such as a GP Practice in the NHS and over the last 5 years you have used an online system to help triage about 100,000 patients and manage the patient journey from the point of entry to your surgery and beyond. However you have noticed a variable degree of success of this system, and would now like to make much better informed triage decisions to make sure the patient receives the most appropriate treatment but also save your practice time and money. You ask about 50 symptom and other related questions on the system and over time you have recorded whether the system triaged correctly or where the patient eventually ended up.

With ELDR Diagnoser, you simply pass the entire known data set (online symptom questionnaire answers and eventual outcome) in, it will self-sort and label the data, self-optimise, self-learn and using a combination of Supervised and Unsupervised Learning will make all the links between all data sets, accounting for wide variation; in this example of 50 data points with 100,000 rows of data there are ~ (50 x 50 x 100,000) 250 million possible combinations. You can then probe ELDR for a diagnosis by sending in a single current data set (or this case a single questionnaire) which will tell you, from past experience, where the patient would likely eventually end up.

ELDR Diagnoser is highly dynamic, autonomous, configurable, graphical and easily integrated

Dynamic

ELDR Diagnoser can handle and learn from multiple sources, sizes and complexities of data for numerous prediction requirements simultaneously. Data can be changed at any time and it can continually learn. The software can carry out supervised and unsupervised learning.

Autonomous

By default ELDR is plug and play - you can simply give it appropriately formatted data and it will automatically learn from it, including self optimisation and self scaling.

Configurable

In many cases you may be happy with plug and play, however almost everything in ELDR is configurable; from which data fields to use as inputs and outputs, to colours, displays, output format, learning modes, learning accuracy, all the way through to Artificial Neural Network dynamics and dimensions.

Graphical

ELDR Predictor uses a rich intuitive GUI Dashboard from which to manage the whole AI process (data preparation, learning, outputting and testing), including a comprehensive suite of gamified charts and other visual displays to monitor everything.

Easily Integrated

AI Integration is our speciality. We understand that AI can be used in a variety of ways and in numerous system-types and processes. We build our software to be entirely modular and there are multiple integration methods and points ranging from network-based RESTFulAPI integration to direct coupling at the code level, depending on the response time required, amongst other considerations.