Department of Civil Engineering

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    Low-cost Artificial Intelligence Enhanced Hardware Design for Data Augmentation
    (IEEE, 2023) Gupta, Rajiv; Gupta, Anu
    This paper presents a novel low-cost hardware implementation of data augmentation using artificial neural networks for a low-power, low-cost Water Quality Indexing application. Multilayer Perceptron (MLP) feedforward network with backpropagation learning has been designed to predict the data of DO and EC using pH and ORP as the input vector. This reduces the requirement for costly sensor electrodes, decreasing the design's cost. The design has been implemented on both ASIC and Embedded platforms. The Augmentation ANN predicts DO and EC with a 98% accuracy rate and achieves a 92% reduction in cost. The results have been presented and compared with standard WQI device.
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    Tender Evaluation by Visual Decision Support System
    (IJCCIE, 2016) Gupta, Rajiv; Gupta, Anu
    The process of evaluating tenders is a multi-criteria decision making process in which project performance is influenced by cost, time and quality. It involves a wide range of criteria, which are not only quantitative but can also be defined qualitatively. The present paper deals with visual decision support system, which can consider such criteria to evaluate the tenders. With the help of the proposed decision support method, the uncertainty of criteria can be reduced. By using two case studies the tender evaluation is then fully investigated using Visual decision support system approach.