81 research outputs found
Determinig the efficacy of mathematical programming approaches for multi-group classification:
No full textManagers have been grappling with the problem of extracting patterns out of the vast database generated by their systems. The advent of powerful information systems in organizations and the consequent agglomeration of vast pool of data since the mid-1980s have created renewed interest in the usefulness of discriminant analysis (DA). Expert systems have come to the aid of managers in their day-to-day decision making with many successful applications in financial planning, sales management, and other areas of business operations (Erenguc and Koehler 1990).
Currently, no comprehensive research study exists that tests the robustness of multi-group classification analysis. Our research aims to bridge the gaps in the existing works and take a step further by extending our study to four-group classification problems. The main purpose of this research is to determine the efficacy of mathematical programming classification models, more specifically, LP methods vis-à-vis statistical approaches such as discriminant analysis (Mahalanobis) and logistic regression, an artificial intelligence (AI) technique such as a neural network, and a non-parametric technique such as k-nearest neighborhood (k-NN) for four-group classification problems. This research also proposes an integrated (hybrid) model that combines a non-parametric classification technique and a LP approach to enhance the overall classification performance. Furthermore, the study extends an existing two-group LP model (Bal et al. 2006) based on the work of (Lam and Moy 1996b) and apply it to four-group classification problems. These models are tested through robust computational experiments under varying data conditions using a financial product example. The characteristics of a real dataset are used to simulate (Monte Carlo method) multiple sample runs for four group classification problems with three continuous independent variables.
The experimental results show that LP approaches in general and the proposed integrated method in particular consistently have lower misclassification rates for most data characteristics. Furthermore, the integrated method utilizes the strengths of both the methods: k-NN and linear programming, thereby considerably improving the classification accuracy.Ph.D.Includes bibliographical references (p. 83-97)by Dinesh R. Pa
Fabrication of PbS quantum dots and their applications in solar cells based on ZnO nanorod arrays
No full textSeaweed minerals: unlocking functional food potential from an Indian perspective
No full textAbstract Minerals and trace elements are thought to be necessary for human nutrition, and seaweeds are well known for their accumulation capacity and the rate may vary based on the locations. Green seaweeds are recognized for their iron and magnesium content, whilst brown and red seaweeds prefer to accumulate manganese, iodine, sodium, potassium, and zinc. These properties provide significant opportunities for the functional food development industry to create new ingredients and generate employment. Additionally, certain seaweeds are considered as potential candidates for addressing the iodine deficiency through regular consumption, thus seaweeds hold great potential as functional foods. This review examines the role of minerals in seaweed farming followed by their impact on seaweed growth and nutritional value, the health benefits of mineral-enriched seaweed, and its market potential as a functional food. It also discusses about the limitations, challenges, pathways for popularization, and future opportunities for seaweed as a functional food. Author name: Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author 1 Given name: [S. Shek Mohamed] Last name [Ibrahim]. Author 2 Given name: [R. Suhail] Last name [Haq]. Author 3 Given name: [S. Dinesh] Last name [Kumar]. Also, kindly confirm the details in the metadata are correct. The author's given name modified accordingly. Graphical Abstrac
Modelling delay and noise in arbitrarily coupled RC trees.
Get PDFClosed-form equations for second-order transfer functions of general arbitrarily coupled resistance-capacitance (RC) trees with multiple drivers are reported. The models allow precise delay and noise calculations for systems of coupled interconnects with guaranteed stability and represent the minimum complexity associated with this class of circuits. Their accuracy is extensively compared against other relevant models and is found to be better or comparable to more expensive models. All results are derived from a theoretical approach, and their physical basis is examined. The simplicity, accuracy, and generality of the models make them suitable for use in early signal integrity analyses of complex systems and incremental physical optimization
Zincblende to Wurtzite phase shift of CdSe thin films prepared by electrochemical deposition
No full textBulk to nanostructured vanadium pentaoxide-nanowires (V2O5-NWs) for high energy density supercapacitors
No full textStructural and optical properties of nanostructured CdSe thin films prepared by electrochemical deposition
No full textThickness dependence of magnetic anisotropy and damping in sputter deposited CoFeB thin films
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