Distributed Denial of services is one of the most dangerously planned attacks in cloud computing, resulting in huge losses of data and money for both the cloud services providers and the users of these services. Many efforts have been performed to help protect the cloud from these attacks using machine learning techniques. This study focuses on enhancing the efficiency of the Gaussian Naïve Bayes classifier, considered one of the cheapest and fastest classifiers. Still, it has some problems resulting from its equation’s statistical nature. The nature of this classifier is based on multiplication, resulting in inaccurate classification due to the zero-frequency issue and the fact that it assumes that features are independent. This research proposed a framework handling the selection of a set of highly independent features following an iterative feature selection approach using the Pearson Correlation Coefficient, Mutual Information, and Chi-squared and then selecting other subsets of features from these sets to reach a set of highly independent features. After that, we used a specific algorithm handling the data pre-processing to handle the zero-frequency problem where we used the Mode to replace the missing values, and if the mode was zero, it used the mean instead. Still, if the record’s label is zero, we get the value of the previous record with zero labels. After that, we handled the data imbalances using SMOTE. These enhancements increased both accuracy for the mutual information model by 2% and the average overall accuracy and precision by 1.5%.

Nowadays, Learning Management Systems (LMS) have become increasingly popular, particularly due to the COVID-19 pandemic, offering enhanced effectiveness and efficiency. Online exams have emerged as a critical feature within LMS, serving as a means to evaluate students’ performance and assess their understanding of the course material. These exams play a crucial role in determining students’ eligibility for progression to the next grade or level of study. It is imperative that online exam results meet the standards of reliability and transparency. Any lack of reliability, such as the vulnerability to hacking, can have detrimental effects on students’ overall grades. Traditional online exam systems typically store data in centralized locations like MySQL databases, leaving them susceptible to unauthorized access by malicious individuals who may alter students’ exam results. This paper aims to propose a blockchain-based framework that facilitates the secure and peer-to-peer conduct and evaluation of academic exams. The framework employs hashing techniques to ensure the integrity of the data and utilizes proof of stake mechanisms to enhance security. Blockchain technology has proven to be effective in safeguarding data integrity by virtue of its decentralized data storage approach and the use of cryptographic hashing for every block within the chain. This paper demonstrates how online exams can be developed using blockchain technology, with each question asked and answered being directly stored on the blockchain. To achieve this, we have developed a module that integrates with the Moodle learning management system. Through a comparative analysis of the default centralized storage approach in Moodle, our module modifies the exam results’ storage method, ensuring secure and tamper-proof data storage on the blockchain network. By leveraging the blockchain network, the data associated with exam results is reliably secured, ensuring its integrity, and making it immune to manipulation. Our results indicate that the data stored through the blockchain achieved complete accuracy, with no discrepancies observed when compared to the standard approach employed by the Moodle LMS for storing results. The blockchain network provides a reliable and immutable platform that prevents unauthorized alterations or manipulations of student data. In conclusion, our blockchain-based framework offers a robust solution for enhancing the security and reliability of online exam results. By leveraging the decentralized and tamper-proof nature of blockchain technology, we can ensure the integrity and transparency of student data, ultimately providing a more trustworthy and accurate assessment of their academic performance.

In the past few years, web documents, blogs, and reviews have played an important role in many fields as organizations always aim to find consumer or public opinions about their products and services. On the other hand, individual consumers also seek the opinions or emotions of existing users of a certain product before purchasing it. This method is currently one of the most vital methods for adapting the organizations' plans. In this article, the authors provide a survey for different techniques and approaches for emotion analysis from the text. They also demonstrate the techniques and the methods that have been proposed by different researchers with criticizing many of these methods according to the applied approach and the accuracy level.

In the educational field, the system performance, as well as the stakeholders’ satisfaction, are considered a bottleneck in the e-learning system due to the high number of users who are represented in the educational system’s stakeholders including instructors and students. On the other hand, successful resource utilization in cloud systems is one of the key factors for increasing system performance which is strongly related to the ability for the optimal load distribution. In this study, a novel load-balancing algorithm is proposed. The proposed algorithm aims to optimize the educational system’s performance and, consequently, the users’ satisfaction in the educational field represented by the students. The proposed enhancement in the e-learning system has been evaluated by two methods, first, a simulation experiment for confirming the applicability of the proposed algorithm. Then a real-case experiment has been applied to the e-learning system at Helwan University. The results revealed the advantages of the proposed algorithm over other well-known load balancing algorithms. A questionnaire was also developed to measure the users’ satisfaction with the system’s performance. A total of 3,670 thousand out of 5,000 students have responded, and the results have revealed a satisfaction percentage of 95.4% in the e-learning field represented by the students.

This research proposes a multi-level predictive algorithm based on the k-means algorithm with multiple adaptations. The research highlights the main limitations of k-means and proposes a set of adaptations that enhance the clustering task results in accuracy with minimal centroid distance error. The study proposes three enhancements in selecting the number of clusters, identifying the initial point, and exploring the contributing features set. Moreover, prediction is performed through a multi-level paradigm targeting performance enhancement by following the partitioning approach. The research experimental study focused on applying the proposed algorithm to the healthcare supply chain as it is one of the most influential factors for health services delivery. The proposed algorithm is applied to a dataset that is offered by USAID which includes 31622 records with 104 attributes. The proposed algorithm aims to predict the valued information in the supply chain progress such as predicted delivery time, predicted delay time, and other vital attributes. The data source is the USAID dataset; however, the research utilized the dataset for multiple objectives in vital aspects prediction of the healthcare supply chain with an average accuracy of 97.45%.