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Presentation 10 15 v1
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The Density functional theory section of JARVIS (JARVIS-DFT) consists of thousands of VASP based calculations for 3D-bulk, single layer (2D), nanowire (1D) and molecular (0D) systems. Most of the calculations are carried out with optB88vDW functional. JARVIS-DFT includes materials data such as: energetics, diffraction pattern, radial distribution function, band-structure, density of states, carrier effective mass, temperature and carrier concentration dependent thermoelectric properties, elastic constants and gamma-point phonons.
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Presentation 10 15 v1
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Geet Lahoti
The Density functional theory section of JARVIS (JARVIS-DFT) consists of thousands of VASP based calculations for 3D-bulk, single layer (2D), nanowire (1D) and molecular (0D) systems. Most of the calculations are carried out with optB88vDW functional. JARVIS-DFT includes materials data such as: energetics, diffraction pattern, radial distribution function, band-structure, density of states, carrier effective mass, temperature and carrier concentration dependent thermoelectric properties, elastic constants and gamma-point phonons.
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Data Mining is the Process to extract knowledge from the data or data stream, Mining high utility itemsets focus on the itemset with high profit only, as data or itemset may be static or in a stream mine this type if data has become a significant research topic. In this research paper we have presented different methods available for mining high utility dataset , to develop algorithm for this requires logic to mine the high utility itemsets from data streams, We have compared following algorithms, One pass Algorithm, Two Phase Algorithm, Mining top k-Utility Frequent itemset, and Sliding window based algorithms like MHUI-BIT(Mining High Utility Itemset Based on BITVector), MHUI-TID(Mining HighUtility Itemset based on TID-List) for lexicographical tree based summary data structure. And the FHM algorithm which we will used for our proposed work “Fast High Utility Miner) Using estimated utility Co-occurrence Pruning.
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Presentation 10 15 v1
1.
Developing Structure-Property Linkage
for Glass Fibre Reinforced Composites ME 8883/CSE 8803: Materials Informatics Alicia White and Geet Lahoti 20th October 2014
2.
Glass Fibre Reinforced
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Analytics on Samples
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4.
Analytics on Samples
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5.
Analytics on Samples
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6.
Analytics on Samples
• Peak Fitting (Sample 1)
7.
Analytics on Samples
• Segmentation based on Gaussian Likelihood Maximization • Gaussian Function 1 푎 f(x) = 푒− (푥−푏)2 2푐2 where, a=height, b=center, c=width 2-Phase Segmented Image (Sample 1)
8.
Analytics on Samples
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9.
Analytics on Samples
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Analytics on Samples
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