Effect of Yarn Type, Sett and Kind of Huck-a-back Weave on Some Characteristics of Towelling Fabrics

1-5<span style="font-size:11.0pt;line-height:115%; font-family:" calibri","sans-serif";mso-ascii-theme-font:minor-latin;mso-fareast-font-family:="" "times="" new="" roman";mso-fareast-theme-font:minor-fareast;mso-hansi-theme-font:="" minor-latin;mso-bidi-font-family:"times="" roman";mso-ansi-language:en-us;="" mso-fareast-language:en-us;mso-bidi-language:ar-sa"="">The effect of type of weft yarn, weft sett and kind of huck-a-back weave on water absorbance, abrasion resistance in dry and wet states, and tensile and te.ar strengths of towelling fabrics has been investigated. It has been observed that if the final count of weft yarn is the same, the use of single yarn associated with lower sell leads to a highly absorbent towelling fabric. Huck-a-back weave having relatively longer warp floats than weft floats, using the same yarns and fabric construction but higher ends then picks per inch, produces a fabric with improved strength and absorbance.</span

One Time Mining by Multi-Core Preprocessing on Generalized Dataset

One of the important problems in data mining is discovering association rules from databases of transactions where each transaction consists of a set of items. Many industries are interested in developing the association rules from their databases due to continuous retrieval and storage of huge amount of data. The discovery of interesting association relationship among business transaction records in many business decision making process such as catalog decision, cross-marketing, and loss-leader analysis. The enormity and high dimensionality of datasets typically available as input to problem of association rule discovery, and the time consuming operation in this discovery process is the computation of the frequency of interesting subset of items (called candidates) in the database of transactions. Hence, it is has become vital to develop a method that will make speedup the preprocessing computation. In this paper, We have proposed An Integrated approach of Parallel Computing and ARM for mining Association Rules in Generalized data set that is fundamentally different from all the previous algorithms in that multi-core preprocessing is done and by avoiding recurring scan of dataset number of passes required is reduced. The response time is calculated on space delimited text dataset