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Asprise C# .NET OCR ( optical character recognition ) and barcode recognition SDK offers a high performance API library for you to equip your C# . neural network This is a perfect example of why and how executives need to adopt the smart data paradigm for developing an enterprise smart data strategy, for building an enterprise leadership, for sustaining integration architecture, for following the enterprise process models, and for ensuring enterprise data accountability and resource commitment to implementation To construct the features, a feature construction algorithm called FC was used FC uses the original data but builds new representations of that data Training data that is dif cult for a neural network to learn characteristically exhibits a high degree of dispersion Feature construction can lead to a reduced degree of dispersion in the search space in which learning occurs Feature construction was combined with backpropagation and then compared to back-propagation alone to determine deferences in learning performance The feature construction/back-propagation hybrid performed faster than back-propagation alone The increase in speed is due to the fact that feature construction effectively reduces the size of the search space The result is that instead of improving the backpropagation algorithm itself, scalability is made to be less of an issue Also, by choosing features carefully, the number of peaks in the search space can be reduced These peaks are often a source of dif culty for back-propagation In addition to the increased speed of the feature construction/back ropagation hybrid, the accuracy of the hybrid was higher as well Limits of Tenability There are several important elements in determining capacity for change and improvement in management of data These include executive and staff time, operations discretionary time/surge capacity, cash ow, access to capital, customer/constituent situation, executive performance optimization infrastructure, planning, budgeting, funding cycle, number and criticality of de ciencies (de ciency pro le), competitor/threat situation, and schedule and event drivers The proper use of arti cial intelligence (AI) methods such as GA and ANN can help managers make optimal use of resources as well as managing these elements of change management In this case we will compare various AI methods and show how they can be applied to managerial tasks such as forecasting and classi cation [4] GAs are based on Charles Darwin s biological theory of survival of the ttest, which means that, in a species, the characteristics of the ttest members of the population will be passed to future generations Over time, the species will grow to be increasingly well adapted for survival This analogy can be applied to computer search problems When trying to solve a mathematical problem or function, there may not be one correct answer The problem then becomes one of nding an optimal solution A GA will nd a solution set of increasingly optimal solutions to the problem The rst step in a GA is to randomly generate a population of numbers or binary strings, which are analogous to chromosomes in a biological connotation Each chromosome is a vector of parameters, or genes, each of which represents a partial solution to the problem The possible values of each gene are analogous to alleles The chromosomes. open source ocr api c#: Find out most popular NuGet barcode Packages. ... NET library based on the open source Barcode Library: ZXing (Zebra ... ... ocr sdk open source c# C# OCR Algorithm or Open - source Library - CodeProject
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