Factors influencing cloud computing adoption in Yemen higher education institutions

Cloud-based technology, which is now well established, helps reducing costs and providing accessibility, reliability and flexibility. However, the Yemen Higher educational system has not yet embraced cloud computing due to security and privacy concerns, lack of trust, negative cultural attitudes (i.e. tribalism), and most importantly, little digital devices experience in educational settings as well as lack of knowledge and technical know-how. Thus, this study proposes a conceptual model of cloud computing (CC) adoption in Yemen HEIs by investigating the influence of Technology, Organization and Environment (TOE) factors. In addition, this study investigates the moderating effect of tribalism culture in the relationships between the identified factors and CC adoption. The study employed both qualitative and quantitative approaches. A preliminary study through semi-structured interviews with ten (10) participants from top management of HEIs to refine and confirm the proposed model. The quantitative approach was used to determine the factors that influence CC adoption in Yemen HEIs through a questionnaire survey. Data were collected from 328 respondents in 38 HEIs and analyzed using Partial Least Square (PLS) Structural Equation Modelling (SEM). The results showed that relative advantage, reliability, compatibility, security, technology readiness, top management support, regulatory policy and competitive pressure have a positive significant impact on CC adoption. However, tribalism culture has a negative significant impact towards CC adoption. The study also found that tribalism culture moderates the relationship between compatibility, reliability, security, relative advantage, regulatory policy and CC adoption. This study contributes to TOE model adoption by including the cultural factor as a moderator towards CC adoption in Yemen HEIs. The study also provides a model and insights for HEIs, technology consultants, vendors and policy makers in better understanding of the factors that influence CC adoption in least developed countries (LDCs), specifically, Yemen

Selection of access network using cost function method in heterogeneous wireless network

The challenge for future generation of wireless environment is on how to choose the appropriate wireless access network connection when there are several different wireless networks are existed. Hence, a vertical handover network selection (VHONS) being used to aid in term of service quality for user's satisfaction of the mobile terminal.This article intends to propose a new method of choosing the vertical handover network. This method covers the weight distribution and cost factor techniques.The weight distribution is used to measure different weights for existing wireless network based on the user's preference and mobile terminal power.The cost factor technique is also used to identify the cost for performing handover target by considering every network parameters and its weight.Results obtained showed that the algorithm has the ability to increase user's satisfaction compared to other algorithms, which consistently choose one accessible network

Analysis of single sign-on protocols from the perspective of architecture deployment, security and usability

Single Sign-On (SSO) requires one time authentication with a set of username and password which then allows an authorized user to enter all resources.This scheme was introduced to overcome the issue of memorability load among users who own several accounts.Currently, there are four main SSO protocols; 1) Security Assertion Markup Language (SAML), 2) OpenID, 3) Info Card and 4) OAuth.These protocols were sh~died separately and they have different architecture deployment and implementation wise.It was found from the literature, that many users were not aware of the existence of those protocols which probably explain the slow adoption.Thus, this paper seeks to study the four protocols together by making further analysis and then compare them in terms of its architecture deployment and implementation wise focusing on security and usability perspective.It is much in hope that this paper will be beneficial in giving a better understanding of the SSO protocols, and contributes to better improvement in its implementation

Web information gathering processes for gold and silver price information

This paper presents a research on product-specific web information gathering concept that intends to improve web searching through conventional search engines.Search engines are useful for searching general information; however, the results are not always accurate because they return the results based on key-words occurrences, but not accurateness.Further, users must filter and organize the information accordingly to make the information meaningful.Users must also visit multiple websites independently to check whether the information is relevant.These processes are tedious and very time consuming especially for users who search for specific products information Hence, we propose a web information gathering model that aims to provide a standard for developing product-specific web searching tools. It was applied in a mobile application called Gold-Trader. The mobile application provides gold and silver prices information to individual or personal traders.It can help individual or personal traders to monitor and compare gold and silver prices from multiple websites without the needs to visit them individually

Deep Learning-based Face Mask Usage Detection on Low Compute Resource Devices

The advent of the COVID-19 pandemic has brought several never-before-seen changes in the daily lives of people around the globe. As a way to curb the spreading of the disease, wearing face masks has become mandatory in the majority of public places. To solve the necessity of face mask detection in such situations, there have been only a handful of research endeavors up to this date. Computer vision has advanced multi-fold with the advent of AlexNet architecture. With a motivation to go deeper with the neural network architecture, the concept of Depthwise Separable Convolutions and projection layer was developed in MobileNetV1. In this work, a novel lightweight deep learning model based on Single Shot Detector (SSD) MobileNetV2 architecture is proposed for face mask detection using images and video streams of crowds aiming its utilization on low compute re-source environment. An open benchmark face mask dataset, with 4095 images including masked and no mask images, is utilized to train the model for detection. The model is initialized using transfer learning with the freezing of base layers. The proposed methodology can efficiently aid in tracking and enforcing social distancing rules in crowded places with the use of surveillance cameras. On the different benchmarks that we have tested, the model proved to be highly successful and has achieved an accuracy rate of 99.39% and an F1 score of 0.995

Towards heat tolerant metagenome functional prediction, coral microbial community composition, and enrichment analysis

Coral reefs represent one of the most biodiverse marine ecosystems on our planet. These consist of colonies of very small sea animals belonging to the phylum named Cnidaria, and of more complex, yet not so well-known microbial communities. Despite the fact that they occupy only a tiny portion of the oceans\u27 surface, coral reefs are swarming with life, providing food and shelter to a wide number of marine species, ranging from mollusks to numerous fish species. There is a number of factors that can affect their sustainability and likelihood of developing diseases, including increased seawater temperature, acidity, salinity, and human impact. It is crucial to study the relationship between corals and microbial communities linked to them. This work analyzes the overall microbial community composition of the different coral species found in the Australian waters and identifies the most abundant Operational Taxonomic Units (OTUs) on different taxonomic levels. Additionally, heat specific coral core microbiome found across at least 20% of the investigated coral host species was identified and thoroughly analyzed. Lastly, metagenome functional prediction was carried out and the most abundant heat tolerance related genes were highlighted

A secure edge computing model using machine learning and IDS to detect and isolate intruders.

The article presents a secure edge computing model that utilizes machine learning for intrusion detection and isolation. It addresses the security challenges arising from the rapid expansion of IoT and edge computing. The proposed Intrusion Detection System (IDS) combines Linear Discriminant Analysis (LDA) and Logistic Regression (LR) to swiftly and accurately identify intrusions without alerting neighboring devices. The model outperforms existing solutions with an accuracy of 96.56%, precision of 95.78%, and quick training time (0.04 s). It is effective against various types of attacks, enhancing the security of edge networks for IoT applications. •The methodology employs a hybrid model that combines LDA and LR for intrusion detection.•Machine learning techniques are used to analyze and identify intrusive activities during data acquisition by edge nodes.•The methodology includes a mechanism to isolate suspected devices and data without notifying neighboring edge nodes to prevent intruders from gaining control over the edge network. [Abstract copyright: © 2024 The Author(s).