Nowadays, in many modern societies, the industry of E-commerce (EC) is becoming more popular and creates tremendous business opportunities for many firms and people. This huge development in EC happened due to the fact that people in these modern societies are gradually tending to prefer online shopping that results in saving their time, effort and money. However, due to the information overload on the internet generally and EC in particular, where hundreds to thousands of products and items are added and/or sold every day. This rapid growth in the volume of the data put users in a big challenge to find and purchase the products that best meet their preferences. Furthermore, it is also difficult for users to find and purchase relevant items that are compatible with each other. For instance, when a user buys new shirt, he might also need to buy new trousers that best suit with the shirt. Therefore, a smart recommendation system needs to be incorporated in these EC platforms that help users to find the preferred products. Recommendation systems incorporate software tools and techniques that enable thorough access to a large number of items aiming at retrieving the most relevant items that match the user given preferences. Various studies on recommendation systems have been reported in the literature concentrating on the issues of cold-start and data sparsity, which are among the most common challenges in recommendation systems. Different clustering techniques have been exploited in recommendation systems to resolve these issues. This study attempts to examine and evaluate a new clustering technique named Ordered Clustering (OC) with the aim of reducing the impact of the cold-start and the data sparsity problems in EC recommendation systems. Resolving these two issues leads to increasing the accuracy of the results obtained in these recommendation systems and ensure providing the most relevant products that best fit with the user preferences. Several experiments have been conducted over various real datasets. The results of the experiments confirmed that our proposed approach outperforms the most recent previous approach in terms of Precision (P), Recall (R), and F-measure (F).

While the some weaknesses have appeared in many hash functions such as MD4, MD5, RIPEMD-family and the series of Secure Hash Algorithm (SHA) families such as, SHA-0, SHA-1, SHA-2, with a number of SHA-3. Thus, National Institute of Standard and Technology (NIST) has suggested different principles to avoid the blunders, and to choice the ideal quality of hash function, that to be a measurement in future of hash function generations. Therefore, the goal of the NIST contenders in SHA-3 between the hash functions is to be chosen as the winner in the end of 2012, or the beginning of 2013. Therefore, for this reason the research addresses the comparative study of the finalist SHA-3 candidates in: design and structure, complexity, with performance and cost, to measure the robustness of the algorithms through the Fundamentals Security Measurement Factors of Hash Function (FSMFHF) of Secure Hash Algorithm (SHA). As a result, from this comprehensive comparison study between the finalist SHA-3 candidates such as; BLAKE, Grostl, JH, Keccak, and Skein, a choice can be made as to the ideal security algorithm against different attacks in suitable structure design of high utilizing of message distribution with high trade-off of speed/ area performance in low cost. Therefore main idea in this research is classify, comparison, and analysis for all five SHA-3 candidates of finalist, and studying the details of these five candidates that in Round 3 by using different structures and different design approaches, and identifying whether the complexity for each design. Thus, this research found a JH hash function is the faster in implementation followed by Grostl hash function, after that Keccak hash function, which gets best security of the hash function applications for different industry structures, as well as supporting a security of different size of data in computer science.

This research proposes an algorithm for Shariah Screening Platform to assist Shariah decision making process in the Islamic Financial Industry. It is designed based on mixed methodology approach which involves both quantitative as well as qualitative method for data collection and analysis. Qualitative approach is first done by collecting sample of bank product proposal and guidelines from the Central Bank of Malaysia for similarity algorithm development. The first test of similarity is done using Levenshtein Distance. Given the algorithm written in Python, this research tests two different sets of text data where the first one is sample of Tawarruq product proposal (query) with Tawarruq policy document (corpus) and the second set is the same Tawarruq proposal (query) with Shariah Governance Policy Document as corpus. A suitable algorithm is built for document similarity analysis using Python programming language. Two measures of similarity is adopted which are Levenshtein Distance and FuzzyWuzzy Package to compare two strings document. These algorithms then are used to develop a Shariah Screening Platform named as SHARSP. The second stage is quantitative approach where data is collected through online structured survey distributed among industry practitioners to observe their view on the proposed Shariah Screening Platform. Based on Technology Acceptance Model (TAM) and Technology Readiness Index (TRI) as a measurement tool and multiple regression analysis, majority of the respondents were in view that the proposed SHARSP is useful, and they were prone towards the industry innovation. Overall, outputs of this research are significant to Islamic financial industry in proposing new technology for document screening. It would also add to the existing literatures on Islamic finance especially in the adoption of NLP technology. Keywords: Shariah Screening Platform, Islamic Financial Industry, TAM, TRI, NLP