In the Internet era, it is a huge challenge for users to find suitable and pertinent information out of the huge amount of online data. The challenge is particularly arduous for students searching for education information in a specific domain. To solve the problem, this paper puts forward an educational website ranking method, which applies fuzzy logic and k-means clustering in sequence. First, a fuzzy inference system (FIS) was established based on the fuzzy logic, and used to find the utility value (UV) of an educational website according to the feedback marks of each student. Then, the general utility value (GUV) of each educational website was determined through k-means clustering of all the UVs of that website. Then, the educational websites were ranked by their GUVs. The experimental results show that the proposed method ranks the educational websites clearly and correctly, enabling students to find the desired education information.

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Abstract-Skill assessment is an important but complicated task in the entire web based teaching and learning process. The learner's performance assessment has a strong influence on learners' approaches to learn and their learning outcomes like professional acceptability on desired skills. Most educators focus either on assessing a learner's technical skill set or nontechnical skill set, individually, rather than focusing on both the aspects. This paper bridges the gap by applying fuzzy logic approach to analyze a learner's joint skills incorporating both skills-set.An already proven e-commerce website's evaluation technique has been chosen and applied in two situations of learner's skill assessment through case studies namely: technical skills evaluation, and non-technical skills evaluation. Experiments show that the learner's success depends on both sets of skill attributes. This work then proposed a novel method for skill assessment considering two (instead of one) sets of skill attributes invoking parallel or joint application of the technique. This new technique has also been analysed through a case study.

Removal of random valued noisy pixel is extremely challenging when the noise density is above 50%. The existing filters are generally not capable of eliminating such noise when density is above 70%. In this paper a region wise density based detection algorithm for random valued impulse noise has been proposed. On the basis of the intensity values, the pixels of a particular window are sorted and then stored into four regions. The higher density based region is considered for stepwise detection of noisy pixels. As a result of this detection scheme a maximum of 75% of noisy pixels can be detected. For this purpose this paper proposes a unique noise removal algorithm. It was experimentally proved that the proposed algorithm not only performs exceptionally when it comes to visual qualitative judgment of standard images but also this filter combination outsmarts the existing algorithm in terms of MSE, PSNR and SSIM comparison even up to 70% noise density level.

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.