3 research outputs found
Knowledge Graph semantic enhancement of input data for improving AI
Intelligent systems designed using machine learning algorithms require a
large number of labeled data. Background knowledge provides complementary, real
world factual information that can augment the limited labeled data to train a
machine learning algorithm. The term Knowledge Graph (KG) is in vogue as for
many practical applications, it is convenient and useful to organize this
background knowledge in the form of a graph. Recent academic research and
implemented industrial intelligent systems have shown promising performance for
machine learning algorithms that combine training data with a knowledge graph.
In this article, we discuss the use of relevant KGs to enhance input data for
two applications that use machine learning -- recommendation and community
detection. The KG improves both accuracy and explainability
Smart element aware gate controller for intelligent wheeled robot navigation
The directing of a wheeled robot in an unknown moving environment with physical barriers is a difficult proposition. In particular, having an optimal or near-optimal path that avoids obstacles is a major challenge. In this paper, a modified neuro-controller mechanism is proposed for controlling the movement of an indoor mobile robot. The proposed mechanism is based on the design of a modified Elman neural network (MENN) with an effective element aware gate (MEEG) as the neuro-controller. This controller is updated to overcome the rigid and dynamic barriers in the indoor area. The proposed controller is implemented with a mobile robot known as Khepera IV in a practical manner. The practical results demonstrate that the proposed mechanism is very efficient in terms of providing shortest distance to reach the goal with maximum velocity as compared with the MENN. Specifically, the MEEG is better than MENN in minimizing the error rate by 58.33%
Knowledge Graph Semantic Enhancement of Input Data for Improving AI
Intelligent systems designed using machine learning algorithms require a large number of labeled data. Background knowledge provides complementary, real-world factual information that can augment the limited labeled data to train a machine learning algorithm. The term Knowledge Graph (KG) is in vogue as for many practical applications, it is convenient and useful to organize this background knowledge in the form of a graph. Recent academic research and implemented industrial intelligent systems have shown promising performance for machine learning algorithms that combine training data with a knowledge graph. In this article, we discuss the use of relevant KGs to enhance the input data for two applications that use machine learning-recommendation and community detection. The KG improves both accuracy and explainability