In this paper mixed sampling plans for acceptance sampling of second quality lots are proposed. Mixed sampling plans combine process control and lot control aiming at increasing the reliability and/or the efficiency of acceptance sampling plans. The process control part refers to a measurable process characteristic and is based on the normal approximation with known standard deviation. The lot control part is performed by an attribute sampling plan based on the truncated Poisson distribution. The operating characteristic function and associated measures are derived and tables are constructed and presented for an easy selection of the plans.

Life testing for very high priced products with least of sample size can be done using the procedure of sampling plan designed in this paper. The required sample size for various of operating characteristic function using new design procedure is obtained using program in OCTAVE based on Lomaxdistribution and is compared with sample size obtained based on exponential distribution.

In this article a design procedure of attribute chain sampling plan for variable fraction defective is presented using stochastic differential equations. An iterative procedure for finding the parameters of the plan satisfying the given conditions with respect to producer quality level is given. Tables are constructed for easy selection of the parameters which are readily available to apply in the shaft floors of production process. The performance of the chain sampling plan for variable fraction defective is also discussed by determining the new operating characteristic function.

One of the extremely deliberated data mining processes is HUIM (High Utility Itemset Mining). Its applications include text mining, e-learning bioinformatics, product recommendation, online click stream analysis, and market basket analysis. Likewise lot of potential applications availed in the HUIM. However, HUIM techniques could find erroneous patterns because they don’t look at the correlation of the retrieved patterns. Numerous approaches for mining related HUIs have been presented as an outcome. The computational expense of these methods continues to be problematic, both in terms of time and memory utilization. A technique for extracting weighted temporal designs is therefore suggested to rectify the identified issue in HUIM. Preprocessing of time series-based information into fuzzy item sets is the first step of the suggested technique. These feed the Graph Based Ant Colony Optimization (GACO) and Fuzzy C Means (FCM) clustering methodologies used in the Improvised Adaptable FCM (IAFCM) method. The suggested IAFCM technique achieves two objectives: optimal item placement in clusters using GACO; and ii) IAFCM clustering and information decrease in FCM cluster. The proposed technique yields high-quality clusters by GACO. Weighted sequential pattern mining, which considers facts of patterns with the highest weight and low frequency in a repository that is updated over a period, is used to locate the sequential patterns in these clusters. The outcomes of this methodology make evident that the IAFCM with GACO improves execution time when compared to other conventional approaches. Additionally, it enhances information representation by enhancing accuracy while using a smaller amount of memory.

Reliability of engineering system is very much essential in production industries. However accurate Reliability of a system or device is tedious if the number of components or items is large. But the decision has to be made whether to accept or reject a batch of items based on the reliability of the components and hence Reliability-based Sampling Plans indexed by operating reliability rate at the deflection point (RSP-RR) are developed using Exponentiated Loglogistic model. Tables and illustration are also provided for easy selection of the plan. The minimum sample size required for testing the sample units can be obtained using RSP-RR sampling plans.