Mobile phones are one of the highly used gadgets now a day. These handheld devices serve multiple purposes through different available functionalities. Demand of services and functionalities vary with time and person concern. Before purchasing a new mobile phone, one has to judge specifications like functionalities, hardware capabilities and efficiencies available with the particular model of the device. We often find it difficult to identify or decide the best model among the available multiple alternatives by heuristics quick analysis of the specifications and prices.
This paper proposes a method for ranking mobile phone models based on Analytical Hierarchical Process (AHP), one of the typically used mathematical models for Multi Criteria Decision Making (MCDM) problems. The effectiveness of the proposed method is analyzed through a case study consisting of various sophisticated approaches based on AHP. A novice mobile phone buyer will be benefitted by the use of the proposed method incorporated through e-commerce sites.

Salt and pepper noise is a type of impulse noise, where certain amount of black and white dots appear in the image. The intensity is accumulated in 8 bit integer, giving 256 possible gray levels in the range (0 – 255).In this range salt and pepper noise takes either minimum or maximum intensity. Positive impulse appears as white (salt) points with intensity '255' and negative impulse appears as black (pepper) points with intensity '0' respectively. Salt and pepper noise removal is not an easy task mostly when noise density in the contaminated image is high and restoration of image quality is essential. Different filters like MF, SMF, AMF, PSMF, DBA, DBUTMF, and MDBUTMF and so on are noticed useful for taking away low, moderate and high density salt and pepper noise. The purpose of this paper is to present these filters first and then revise their art to enhance their performances and usefulness. The comparison shows that some of these filters are very fruitful in some particular noise density levels and hence classified applications on these situations are recommended based on the output of investigations.