VIBGYOR indexing technique for image mining

Author(s):  
Balvant Tarulatha ◽  
Namrata Shroff ◽  
M B Chaudhary
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.


2021 ◽  
Vol 121 ◽  
pp. 54-58
Author(s):  
Kun Zhang ◽  
Kai Chen ◽  
Binghui Fan

1997 ◽  
Author(s):  
Srinivasan Raghavan ◽  
Robert F. Cromp ◽  
Sridhar Srinivasan ◽  
Raadhakrishnan Poovendran ◽  
William J. Campbell ◽  
...  

2021 ◽  
Vol 15 (1) ◽  
pp. 98-111
Author(s):  
Dong He ◽  
Maureen Daum ◽  
Walter Cai ◽  
Magdalena Balazinska

We design, implement, and evaluate DeepEverest, a system for the efficient execution of interpretation by example queries over the activation values of a deep neural network. DeepEverest consists of an efficient indexing technique and a query execution algorithm with various optimizations. We prove that the proposed query execution algorithm is instance optimal. Experiments with our prototype show that DeepEverest, using less than 20% of the storage of full materialization, significantly accelerates individual queries by up to 63X and consistently outperforms other methods on multi-query workloads that simulate DNN interpretation processes.


2020 ◽  
Vol 13 (10) ◽  
pp. 1669-1681
Author(s):  
Zijing Tan ◽  
Ai Ran ◽  
Shuai Ma ◽  
Sheng Qin

Pointwise order dependencies (PODs) are dependencies that specify ordering semantics on attributes of tuples. POD discovery refers to the process of identifying the set Σ of valid and minimal PODs on a given data set D. In practice D is typically large and keeps changing, and it is prohibitively expensive to compute Σ from scratch every time. In this paper, we make a first effort to study the incremental POD discovery problem, aiming at computing changes ΔΣ to Σ such that Σ ⊕ ΔΣ is the set of valid and minimal PODs on D with a set Δ D of tuple insertion updates. (1) We first propose a novel indexing technique for inputs Σ and D. We give algorithms to build and choose indexes for Σ and D , and to update indexes in response to Δ D. We show that POD violations w.r.t. Σ incurred by Δ D can be efficiently identified by leveraging the proposed indexes, with a cost dependent on log (| D |). (2) We then present an effective algorithm for computing ΔΣ, based on Σ and identified violations caused by Δ D. The PODs in Σ that become invalid on D + Δ D are efficiently detected with the proposed indexes, and further new valid PODs on D + Δ D are identified by refining those invalid PODs in Σ on D + Δ D. (3) Finally, using both real-life and synthetic datasets, we experimentally show that our approach outperforms the batch approach that computes from scratch, up to orders of magnitude.


Author(s):  
Jonathan Reyes ◽  
Kareem Ahmed

This paper presents the correlation of the intensity ratio of the C2* and CH* radicals to fuel-air measurements over a range of pressures using 93% octane gasoline as the fuel. The measurements are conducted for the first time at high pressures. The study utilizes beam splitting technology to simultaneously view C2* and CH* as a line of sight, global measurement at the cost of resolution. A heavily instrumented constant volume combustor, with optical access, was employed to acquire the data. The ratio of C2* and CH* has been proven to be a good index of the equivalence ratio of premixed laminar flames. This index is attained, quite simply, by filtering each at their respected emissive peaks and taking the ratio of C2* over CH*. This technique shows great promise for use in turbomachinery as it will allow for identification of rich and lean locations in a combustor. By knowing the fuel-air field, combustor inefficiencies can be addressed to allow for greater energy release in combustion. The issue lies with the application of the indexing technique. Presented data to date has been performed on laboratory based diffusion flames exhausting to atmosphere, or premixed, steady, combustor type flames at low pressure (1atm) conditions. These types of flames are not relevant for engine combustor conditions. Understanding the fuel distribution at relevant regimes will reveal where inefficiencies may lie in injector or combustor design. Propagating flame kernels pose a problem in that they do not produce as much light as a steady flame, this makes spectral data difficult to obtain. Steady flames also do not address the effects that pressure may have on the index of C2* and CH*. The authors of this work seek to address three main issues associated with the indexing technique: The feasibility of its application to combustors (hardware design), The ability to operate at low-light ignition events, and the effects pressure may have on the correlation of intensity ratio to the fuel-air measurement.


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