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Nowadays e-commerce environment plays an important role to exchange commodity knowledge between consumers commonly with others. Accurately predicting customer purchase patterns in the e-commerce market is one of the critical applications of data mining. In order to achieve high profit in e-commerce, the relationship between customer and merchandise are very important. Moreover, many e-commerce websites increase rapidly and instantly and competition has become just a mouse-click away. That is why the importance of staying in the business, and improving the profit needs to accurately predict purchase behavior and target their customers with personalized services according to their preferences. In this paper, a data mining model has been proposed to enhance the accuracy of predicting and to find association rules for frequent item sets. Also, K-means clustering algorithm has been used to reduce the size of the dataset in order to enhance the runtime for the proposed model. The proposed model has used four different classifiers which are C4.5, J48, CS-MC4, and MLR. Also, Apriori algorithm to provide recommendations for items based on previous purchases. The proposed model has been tested on Northwind trader's dataset and the results archives accuracy equal 95.2% when the number of clusters were 8.

This study suggests a digital assessment tool - Smart Equation Exam System (SEED) - as a substitute for traditional multiple-choice and paper and pen exams. SEED includes a question bank, an answer platform, mathematical equations, and physics formulae. The novelty of SEED is that it allows all students with or without physical disabilities via creation of an equal and non-discriminatory platform to solve the physics questions step by step and submit all the solution steps as well as the final answer. The study population was composed of students enrolled in the General Physics (II) course at university level. The sample consisted of fifty students who were selected randomly. The results showed that more than eighty presents of the participants had a positive perception of SEED compared to traditional exams, and they found it more flexible in reviewing solution steps, and getting feedback, especially for the student with handwriting disabilities.         Keywords: Assistive technology; computer software; disabilities education; physics education; digital assessment

The use of Voice over Internet Protocol (VoIP) innovation is rising due to its various merits. Nevertheless, the ineffective use of bandwidth is a key dilemma that restricts the fast-rising use of VoIP innovation. The main factor behind this ineffective use of the bandwidth is the sizable VoIP packet preamble. This research creates a technique to address this dilemma of packet preamble. The created technique is known as payload elimination (PldE). The fundamental concept of the PldE technique is to exploit the information (elements) of the VoIP packet preamble that is superfluous for point-to-point calls. In general, these elements are utilized to transport the payload of VoIP packets. Consequently, the payload size of VoIP packet will be lowered or removed, preserving the available bandwidth. The performance test of the PldE technique indicated an improvement of up to 41.6% in the exploitation of IP network bandwidth. So, the PldE technique is showing signs that it could help solve the problem of the IP network's inefficient use of bandwidth.