ABSTRACT

Improving the performance of e-learning services to provide a scalable and effective e-learning system is a big challenge for the educational organizations. The e-learning system faces many challenges in pedagogical services (e.g. learning design and learning content problems), and technical services (e.g. resource provisioning and financial cost). This article presents an e-learning environment based on cloud computing (PCLE) in attempt to enhance the e-learning services' by customizing the contents of the course's material depending on the students' knowledge, experiences, and requirements. Also, the system focuses on supporting and achieving reusability, interoperability, adaptation and personalization in order to overcome the passive role of the student and transfer it into an interactive and useful participation. On the other hand, PCLE is built over the cloud computing environment trying to overcome the traditional web-hosting challenges as scalability, availability, and cost.

Reliance on deep learning techniques has become an important trend in several science domains including biological science, due to its proven efficiency in manipulating big data that are often characterized by their non-linear processes and complicated relationships. In this study, Convolutional Neural Networks (CNN) has been recruited, as one of the deep learning techniques, to be used in classifying and predicting the biological activities of the essential oil-producing plant/s through their chemical compositions. The model is established based on the available chemical composition’s information of a set of endemic Egyptian plants and their biological activities. Another type of machine learning algorithms, Multiclass Neural Network (MNN), has been applied on the same Essential Oils (EO) dataset. This aims to fairly evaluate the performance of the proposed CNN model. The recorded accuracy in the testing process for both CNN and MNN is 98.13% and 81.88%, respectively. Finally, the CNN technique has been adopted as a reliable model for classifying and predicting the bioactivities of the Egyptian EO-containing plants. The overall accuracy for the final prediction process is reported as approximately 97%. Hereby, the proposed deep learning model could be utilized as an efficient model in predicting the bioactivities of, at least Egyptian, EOs-producing plants.

Day after day, the importance of relying on nature in many fields such as food, medical, pharmaceutical industries, and others is increasing. Essential oils (EOs) are considered as one of the most significant natural products for use as antimicrobials, antioxidants, antitumorals, and anti-inflammatories. Optimizing the usage of EOs is a big challenge faced by the scientific researchers because of the complexity of chemical composition of every EO, in addition to the difficulties to determine the best in inhibiting the bacterial activity. The goal of this article is to present a new computational tool based on two methodologies: reduction by using rough sets and optimization with particle swarm optimization. The developed tool dubbed as Essential Oil Reduction and Optimization Tool is applied on 24 types of EOs that have been tested toward 17 different species of bacteria.

The increasing volume of unsolicited bulk e-mail (also known as spam) has generated a need for reliable antispam filters. Using a classifier based on machine learning techniques to automatically filter out spam email has drawn many researchers attention. In this paper we review some of the most popular machine learning methods (Bayesian classification, k-NN, ANNs, SVMs, Artificial immune system and Rough sets) and of their applicability to the problem of spam Email classification. Descriptions of the algorithms are presented, and the comparison of their performance on the SpamAssassin spam corpus is presented.

Abstract-The Service Level Agreement (SLA) becomes an important issue especially over the Cloud Computing and online services that based on the 'pay-as-you-use' fashion. Establishing the Service level agreements (SLAs), which can be defined as a negotiation between the service provider and the user, is needed for many types of current applications as the E-Learning systems. The work in this paper presents an idea of optimizing the SLA parameters to serve any E-Learning system over the Cloud Computing platform, with defining the negotiation process, the suitable frame work, and the sequence diagram to accommodate the E-Learning systems.

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