Artificial Intelligence, Machine Learning, Neural Networks & Deep Learning

Artificial Intelligence technologies are rapidly evolving at present thanks to various use cases of this technology and the evolution of the computing capability in recent years.  We often hear terms such as ML, deep learning, and AI used interchangeably when, in fact, they are still-developing techniques under the larger umbrella of AI.

The technology behemoths like Google, IBM and Microsoft have invested considerable amount of time and money into developing platforms for the Artificial Intelligence technology.  Noted achievements were from Google through their AI algorithms that beat a human in the Japanese game “Go” by using Deep Learning techniques.  IBM Watson is a notable AI engine from IBM.

We at have invested our effort into integrating the latest AI techniques with the Enterprise software products we have.  The Artificial Intelligence techniques are used for enhancing various Planning, Scheduling and Optimization algorithms.  Various Machine Learning algorithms that are used for predictive analysis are summarized in below Table.

Classification Regression Clustering Preprocessing
Description Identify to which category a new observation belongs, on the basis of a training set of data Modeling the relationship between a scalar dependent variable y and one or more explanatory variables x Grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups Feature extraction and normalization
Applicability spam detection, image recognition, credit scoring, disease identification drug response, stock prices, supermarket revenue customer segmentation, grouping experiment outcomes, grouping shopping items transform input data, such as text, for use with machine learning algorithms
Algorithms SVM, nearest neighbor, decision tree classification, neural network linear regression, decision tree regression, nearest neighbor, neural network k-means Normalization preprocessor

Table 1: Machine Learning Algorithms.

In addition to these above mentioned algorithms, the Genetic algorithm is used widely to optimise various scheduling problems.

Reliability analysis and prediction of failures is a critical function for Continuing Airworthiness of an air asset. Traditionally the reliability analysis is carried out manually by reliability engineers and results are published to the maintenance staff periodically. Though this method has helped in reducing premature failures and unscheduled maintenance, it is laborious and prone to human errors.

Artificial Intelligence powered Reliability Analysis is the future. Reliability analysis systems can use Machine Learning techniques to improve the accuracy of predictions.


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