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Enhanced Deep Type-2 Fuzzy Logic System For Global Interpretability
2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Year: 2021 | Conference Paper |
Cited by: Papers (2)
The recent advances in the field of Artificial Intelligence (AI) have led to the rapid deployment of AI systems in a variety of fields such as healthcare, financial, education etc. However, many of the AI systems are black boxes which restricts the use of these AI in applications that are highly regulated (such as financial, justice, medical, autonomous vehicles etc.) where it is necessary to prov...Show More
Hybrid Deep Learning Type-2 Fuzzy Logic Systems For Explainable AI
2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Year: 2020 | Conference Paper |
Cited by: Papers (15)
The recent years have witnessed a rapid rise in the use of Artificial Intelligence (AI) systems, in particular Machine Learning (ML) models. The vast majority of AI systems employ black box models that lack transparency in operation and decision making. This lack of transparency curtails the use of these AI systems in regulated applications (such as medical, financial applications, etc.) where it ...Show More
Comparing the Performance Potentials of Singleton and Non-singleton Type-1 and Interval Type-2 Fuzzy Systems in Terms of Sculpting the State Space
This paper provides a novel and better understanding of the performance potential of a nonsingleton (NS) fuzzy system over a singleton (S) fuzzy system. It is done by extending sculpting the state space works from S to NS fuzzification and demonstrating uncertainties about measurements, modeled by NS fuzzification: first, fire more rules more often, manifested by a reduction (increase) in the size...Show More
Stacked Auto Encoder Based Hybrid Genetic Algorithm for Workforce Optimization
Ravikiran Chimatapu;Hani Hagras;Andrew Starkey;Gilbert Owusu
2018 10th Computer Science and Electronic Engineering (CEEC)
Year: 2018 | Conference Paper |
Cited by: Papers (2)
Work area optimization is a sub domain of Workforce optimization where the goal is the efficient utilization of resources (engineers) which leads to significant savings in operational costs and a corresponding increase in revenue. In this paper we present a Hybrid Genetic Algorithm where we will generate prior knowledge about the work area optimization problem using Deep Neural Network to provide ...Show More
Interval Type-2 Fuzzy Logic Based Stacked Autoencoder Deep Neural Network For Generating Explainable AI Models in Workforce Optimization
Ravikiran Chimatapu;Hani Hagras;Andrew Starkey;Gilbert Owusu
2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Year: 2018 | Conference Paper |
In Utility based industries that employ a large mobile workforce, efficient utilization of field engineers is key to optimal service delivery. The utilization of the engineers can be improved by predicting the future performance of work areas by using machine learning tools such as Deep Neural Networks (DNNs).The dramatic success of DNNs has led to an explosion of its applications. However, the ef...Show More
A Big-Bang Big-Crunch Type-2 Fuzzy Logic System for Generating Interpretable Models in Workforce Optimization
Ravikiran Chimatapu;Hani Hagras;Andrew Starkey;Gilbert Owusu
2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Year: 2018 | Conference Paper |
Cited by: Papers (8)
Efficient utilization of resources (engineers) is critical to optimal service delivery in service-based industries, such as water, electricity or telecom companies. One of the ways in which efficiency can be improved is by optimizing the geographic area in which the engineers operate. This process is known as Work Area (WA) optimization and it is a sub-domain of workforce optimization.In previous ...Show More
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