The Sentimental Analysis (SA) is a widely known and used technique in the natural language processing realm. It is often used in determining the sentiment of a text. It can be used to perform social media analytics. This study sought to compare two algorithms; Logistic Regression, and Support Vector Machine (SVM) using Microsoft Azure Machine Learning. This was demonstrated by performing a series of experiments on three Twitter datasets (TD). Accordingly, data was sourced from Twitter a microblogging platform. Data were obtained in the form of individuals’ opinions, image, views, and twits from Twitter. Azure cloud-based sentiment analytics models were created based on the two algorithms. This work was extended with more in-depth analysis from another Master research conducted lately. Results confirmed that Microsoft Azure ML platform can be used to build effective SA models that can be used to perform data analytics.

Lately, skeleton-based action recognition has drawn remarkable attention to graph convolutional networks (GCNs). Recent methods have focused on graph learning because graph topology is the key to GCNs. We propose to align graph learning on the channel level by introducing graph convolution with enriched topology based on careful channel-wise correlations, namely the attentive channel-wise correlation graph convolution (ACC-GC). For the model to learn channel-wise enriched topologies, ACC-GC learns a shared graph topology spanning many channels and enhances it with careful channel-wise correlations. Encoding the intra-correlation between various nodes within each channel, boosting informative channel-wise correlations, and suppressing trivial ones generates attentive channel-wise correlations. Our enhanced ACC-GCN is created by substituting our ACC-GC for the GC in a standard GCN. Extensive experiments on NTURGB60 and Northwestern-UCLA datasets demonstrate that our proposed ACC-GCN performs comparably to state-of-the-art methods while reducing the computational cost.

The purpose of this study is to evaluate the effectiveness of blended learning on the academic achievement of students in the University of Jordan. To gain in depth understanding of the phenomena under investigation, survey method is employed to collect natural data. For the sake of respondent convince all the questions asked in this survey are directed in Arabic language. Conventional sampling technique is employed due to the subjectivity of the issue. A sample of (427) students from King Abdulla II School for Information Technology at Jordan University are randomly selected. SPSS10 software is used to make statistical analysis. The robust checks of the result are made through arithmetic average, standard deviation statistics and Pearson correlation matrix. Statistical results of the study report that there is a significant and positive impact of blended learning on academic achievement of the students in university of Jordan.

Due to the accelerated growth of symmetrical sentiment data across different platforms, experimenting with different sentiment analysis (SA) techniques allows for better decision-making and strategic planning for different sectors. Specifically, the emergence of COVID-19 has enriched the data of people’s opinions and feelings about medical products. In this paper, we analyze people’s sentiments about the products of a well-known e-commerce website named Alibaba.com. People’s sentiments are experimented with using a novel evolutionary approach by applying advanced pre-trained word embedding for word presentations and combining them with an evolutionary feature selection mechanism to classify these opinions into different levels of ratings. The proposed approach is based on harmony search algorithm and different classification techniques including random forest, k-nearest neighbor, AdaBoost, bagging, SVM, and REPtree to achieve competitive results with the least possible features. The experiments are conducted on five different datasets including medical gloves, hand sanitizer, medical oxygen, face masks, and a combination of all these datasets. The results show that the harmony search algorithm successfully reduced the number of features by 94.25%, 89.5%, 89.25%, 92.5%, and 84.25% for the medical glove, hand sanitizer, medical oxygen, face masks, and whole datasets, respectively, while keeping a competitive performance in terms of accuracy and root mean square error (RMSE) for the classification techniques and decreasing the computational time required for classification.

Cloud computing is the delivery of computing resources over the Internet. Examples include, among others, servers, storage, big data, databases, networking, software, and analytics. Institutes that provide cloud computing services are called providers. Cloud computing services were primarily developed to help IT professionals through application development, big data storage and recovery, website hosting, on-demand software delivery, and analysis of significant data patterns that could compromise a system’s security. Given the widespread availability of cloud computing, many companies have begun to implement the system because it is cost-efficient, reliable, scalable, and can be accessed from anywhere at any time. The most demanding feature of a cloud computing system is its security platform, which uses cryptographic algorithm levels to enhance protection of unauthorized access, modification, and denial of services. For the most part, cloud security uses algorithms to ensure the preservation of big data stored on remote servers. This study proposes a methodology to reduce concerns about data privacy by using cloud computing cryptography algorithms to improve the security of various platforms and to ensure customer satisfaction.

Beside the increasing trend of cloud computing and mobile applications, the use of cloud based mobile learning applications is also mounting. Almost every e-commerce service provider offers cloud based mobile learning applications so that they can target more visitors and ultimately increase their sales. The usability of cloud based mobile applications is not only grounded in e-commerce platforms but it also ease out mobile learning processes. Most of the educational institutes are now offering cloud based mobile applications so their students can navigate to their knowledge portal more easily and download relevant material or submit assignments respectively. The main research area of this article is to explore how cloud based mobile learning applications can be utilized more effectively and what impact they imply on its users. Also, this research compares the mobile learning methods versus traditional learning methods. The study is evidence from Jordan and the major part of the research will be carried out through surveying literature, reports, content, and national and international databases in order to critically discuss the interactions between clouds based mobile learning application and user experiences. Published researches, published reports, books and articles has been included in the review. Review of literature shows that mobile cloud computing is rising in Jordan and have significant impact on mobile learning of Jordanian students. Further, M-learning indeed an innovative tool for learning and it helps the users in many ways. In traditional learning, students used to spend their money on books and other written content. Findings of this study are helpful for the educational institution so they will come to know about user experiences of utilizing these cloud based mobile applications.