Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.Artificial neural network (ANN)

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    Classifying behavioural traits of small-scale farmers: Use of a novel artificial neural network (ANN) classifier
    (Institute of Electrical and Electronics Engineers Inc., 2016) Jena, P.R.; Majhi, R.
    This paper develops and employs a novel artificial neural network (ANN) model to study farmers' behaviour towards decision making on maize production in Kenya. The paper has compared the accuracy level of ANN based model and the statistical model and found out that the ANN model has achieved higher accuracy and efficiency. The findings from the study reveal that the farmers are mostly influenced by their demographic and food security for decision making. Further to examine the relative importance of different demographic and food security characteristics, an ANOVA test is undertaken. The results found that education and food security indices are instrumental in influencing farmers' decision making. © 2016 IEEE.
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    Numerical Modeling on Buckling Behavior of Structural Stiffened Panel
    (Springer Science and Business Media Deutschland GmbH, 2023) Alagundi, S.; Palanisamy, T.
    Stiffened panels are essential building elements in weight-sensitive structures. They have various applications in marine, aircraft, and other structures. Plate structures can undergo buckling when subjected to axial compression loads and then exhibit out of plane displacements. The present work aims to study the buckling behavior of the stiffened panel. The finite element model of the stiffened panel is developed, and buckling analysis is performed using ANSYS software. This model is validated with the published experimental work. Once the model is validated, total of 320 numbers of models of stiffened panels with varying plate thickness, stiffener height, stiffener thickness, and distance between stiffeners are modeled in ANSYS-2020, and buckling analysis is performed. An artificial neural network model is proposed to predict the buckling load of the stiffened panel. Neural network model is created in MATLAB software, and it is trained, tested, and validated, and its performance is checked by statistical relations like coefficient of correlation and mean square error. Proposed ANN model shows high accuracy in the prediction of buckling load. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.