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Modeling cognitive functionalities of prosthetic arm using conceptual spaces

Ishwarya M.S., Aswani Kumar Ch.

Journal of Automation Mobile Robotics and Intelligent Systems

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2019

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Vol. 13, No. 4

11--21

EN

Conceptual space framework is used for representing knowledge in cognitive systems. In this paper, we have adapted conceptual space framework for prosthetic arm considering its cognitive abilities such as receiving signals, recognizing and decoding the signal and responding with the corresponding action in order to develop a conceptual space of the prosthetic arm. Cognitive functionalities such as learning, memorizing and distinguishing configurations of prosthetic arm are achieved via its conceptual space. To our knowledge, this work is the first attempt to adapt the conceptual spaces to model cognitive functionalities of prosthetic arm. Adding to this, we have made use of different notion of concept that reflects the topological structure in concepts. To model the actions of the prosthetic arm functionalities, we have made use of force patterns to represent action. Similarly, to model the distinguishing ability, we make use of the relationship between the attributes conveyed by adapted different notion of concept.

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Knowledge discovery in data using formal concept analysis and random projections

Aswani Kumar Ch.

International Journal of Applied Mathematics and Computer Science

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2011

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Vol. 21, no 4

745-756

EN

In this paper our objective is to propose a random projections based formal concept analysis for knowledge discovery in data. We demonstrate the implementation of the proposed method on two real world healthcare datasets. Formal Concept Analysis (FCA) is a mathematical framework that offers a conceptual knowledge representation through hierarchical conceptual structures called concept lattices. However, during the design of a concept lattice, complexity plays a major role.

3

Latent semantic indexing using eigenvalue analysis for efficient information retrieval

Aswani Kumar Ch., Srinivas S.

International Journal of Applied Mathematics and Computer Science

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2006

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Vol. 16, no 4

551-558

EN

Text retrieval using Latent Semantic Indexing (LSI) with truncated Singular Value Decomposition (SVD) has been intensively studied in recent years. However, the expensive complexity involved in computing truncated SVD constitutes a major drawback of the LSI method. In this paper, we demonstrate how matrix rank approximation can influence the effectiveness of information retrieval systems. Besides, we present an implementation of the LSI method based on an eigenvalue analysis for rank approximation without computing truncated SVD, along with its computational details. Significant improvements in computational time while maintaining retrieval accuracy are observed over the tested document collections.