In this review, the EMI shielding properties of the various carbonaceous fillers are thoroughly reviewed. Electromagnetic interference (EMI) had been a cause of major concern in the live broadcasting, entertainment, aviation and defense industries since vital radio signals could create more interference, which could lead to poor performance. To reduce the effect of EMI, the organic polymeric composites along with the carbonaceous fillers are mostly used since they are flexible, low denser, high mechanical strength, high thermo-stability, high electrical and thermal conductivity, excellent fracture toughness, and high friction/wear resistance. There are lot of carbon based materials are being used as EMI shielding material in mono and compound form. This review gives a broad understanding of the utilization of carbonaceous fillers in polymer matrixes. Thus, the overall coverage on this carbon based materials and their effectiveness could help the researchers to find right carbon material for suitable application. According to this review, the absorption mechanism is vital to achieve high EMI shielding effect. The fillers such as graphene and CNTs are most preferable EMI shielding filler, according to the vast coverage of previous articles. However, there are more magnetoelectric materials also evolved recently, having combined properties of both conductive and magnetic, yielding high SE at elevated frequencies.
In recent years, discovery of association rules among itemsets in a large database has been described as an important database-mining problem. The problem of discovering association rules has received considerable research attention and several algorithms for mining frequent itemsets have been developed. Many algorithms have been proposed to discover rules at single concept level. However, mining association rules at multiple concept levels may lead to the discovery of more specific and concrete knowledge from data. The discovery of multiple level association rules is very much useful in many applications. In most of the studies for multiple level association rule mining, the database is scanned repeatedly which affects the efficiency of mining process. In this research paper, a new method for discovering multilevel association rules is proposed.It is based on FP-tree structure and uses cooccurrence frequent item tree to find frequent items in multilevel concept hierarchy.
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