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.

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.

Removal of impulse noise from corrupted digital images has been a hitch in the field of image processing. Random nature of impulse noise makes the task of noise removal more critical. Different filters have been designed for noise removal purpose and have shown formidable results mostly for low and medium level noise densities. In this paper, a new two-stage technique called k-means clustering identified fuzzy filter (KMCIFF) is proposed for de-noising gray-scale images. KMCIFF consists of a k-Means clustering-based high density impulse noise detection, followed by a fuzzy logic-oriented noise removal mechanism. In the detection process, a 5 × 5 window centering upon each pixel of the image is considered. K-Means clustering is applied on each 5 × 5 window to group the pixels into different clusters to detect whether the central pixel of each window is noisy or not. In the noise removal process, a 7 × 7 window centering upon each noisy pixel of the image, as detected by the clustering is considered. Fuzzy logic is used to find the nonnoisy pixel in each 7 × 7 window having the highest influence on the central noisy pixel of the window. Finally, that pixel is replaced by the approximated pixel intensity value calculated from the highest influencing non-noisy pixel. KMCIFF is evaluated upon seven different standard test images using peak signal to noise ratio (PSNR), structural similarity index measurement (SSIM), Percentage of actual nonnoisy pixels detected as erroneous out of the total number of pixels (PDAE) and average run time (ART). It has been observed that KMCIFF shows significantly more competitive visual and quantitative performances visa -vis most of the extant traditional filters at high noise densities of up to 90 % .

Students modeling is an integral part of any form of education and this becomes more challenging with the advent of new tools of ICT especially Intelligent Tutoring Systems (ITS). Educators must have to take help of such tools to identify the curriculum gaps towards Outcome Based Education (OBE). One of the ways to reduce such gaps is to identify the personalized requirements of tutorials for learners after going through some topics divided into subtopics. This paper proposes a technique to identify the personalized tutorial gaps by analyzing the responses provided by students against MCQ type questions. The proposed method has been implemented within a web based environment. Prototype of the tool having integrated with the proposed method shows that the students can identify their tutorial requirement without the help of human tutor and hence discovering the student's understanding.

Tutorial requirements of student modeling are traditionally identified by human tutors through crisp mental calculations, and the accuracy of this identification enhances with the experience of the tutor. These identifications become challenging when the mode of delivery is through e-learning. Automatic identification of tutorial requirements must be included in e-learning through ITS where some intelligent computational paradigms can help in automatic identification of the said requirements. This article proposes some fuzzy logic-based techniques toward the said purpose of automatic tutorial gap identification where analysis of the learner's responses to purposefully designed modular MCQ tests and verbal explanations are taken in to consideration as inputs for the purpose. Variation of the fuzzy logics was taken in to account to justify the usefulness of methods under consideration and to provide a realistic and adaptable approach. The experimental results show that the proposed methods are successful and efficient towards the said purpose.