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@article{143870,
        author = {B.Anjaneyulu and I. Praveen Reddy},
        title = {Analysis and Design Bloom Filters for Error Detection and Correction},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {3},
        number = {3},
        pages = {227-230},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=143870},
        abstract = {A Bloom Filter (BF) is a data structure compatible for performing set membership queries very effectively. A standard BloomFilter representing a set of n elements is generated by using an array of m bits and uses k unbiased hash functions. Bloom Filters have some attractive properties together with low storage requirement, fast membership checking and no false negatives. False positives are viable however their probability is also managed and significantly lowered depending upon the application standards. In brief, it's proven that BFs can be used to discover and correct errors in their associated data set. This allows for a synergetic reuse of existing BFs to additionally realize and correct mistakes. That is illustrated by way of an instance ofa counting BF used for IP traffic classification. The outcome exhibit that theproposed scheme can simply correct single mistakes in the related set.The proposed scheme may also be of interest in useful designs to without difficulty mitigate mistakes with a lowered overhead in terms of circuit area and power.},
        keywords = {Bloom filters (BFs), error correction, soft errors.},
        month = {},
        }