2828 research outputs found
Sort by
A Decision Modelling Approach for Security Modules of Delegation Methods in Mobile Cloud Computing using Probabilistic Interval Neutrosophic Hesitant Fuzzy Set
No full textMobile Cloud Computing (MCC) has become a pervasive technology that offers on-demand, flexible, and scalable computing resources to mobile devices. However, the security issues associated with MCC have become a major concern for users and organizations, leading to the development of various Security Modules. These modules typically use delegation methods that involve the transfer of data or operations from mobile devices to the cloud to perform the task at the best performance and security levels.
Despite extensive attempts to design secure security modules of delegation in Mobile Cloud Computing (MCC), none of the existing modules possess all the necessary development attributes. Our analysis indicates that previous studies have not used security development attributes as evaluation criteria to compare and assess the available Security modules of delegation methods in MCC. However, Modeling these modules is critical and poses significant challenges in selecting the most secure security module. du to multicriteria, importance of data and data variation. To address this issue, this study proposes a Decision Modelling Approach for Security Modules of Delegation Methods in Mobile Cloud Computing using multi-criteria decision-making (MCDM) methods. The proposed approach involves the integration of Evaluation based on Distance from Average Solution (EDAS) method with fuzzy weighted with zero inconsistency (FWZIC) under Probabilistic Interval Neutrosophic Hesitant Fuzzy Set (PINHFS) environment. .
The framework presented in this study involves two primary stages : the construction of decision matrices for Security Modules of delegation methods in MCC and the application of the PINHFS-FWZIC method to determine the weight of the security evaluation criteria. The EDAS method is then employed to modeling the Security Modules of delegation methods in MCC based on the formulated decision matrices and criteria weight. The validation and evaluation of the proposed framework were conducted through model validation and decision evaluation procedures. Model validation involved sensitivity analysis and systematic ranking procedures . The benchmarking checklist was used to compare the results of the proposed framework with the existing approaches.
Based on the findings, it can be concluded that the proposed framework can efficiently weight the security criteria and successfully rank Security Modules of delegation methods in MCC. The PINHFS-FWZIC method effectively handled the uncertainty and hesitancy of the decision-makers in assigning weights to the evaluation criteria. Overall, the proposed framework provides a useful benchmark for evaluating other Security Modules of delegation methods in MCC. It can aid decision-makers in selecting the most secure MCC delegation method system by providing a comprehensive evaluation and Modeling of the available Security Modules
Smart Technology Improves TPM on Material Handling System in Freight Facilities
No full textTotal Productive Maintenance (TPM) in material handling systems stands to benefit greatly from the introduction of smart technology into goods facilities. Manual labour-intensive conventional practises typically result in mistakes and higher overall expenses. Focusing on automation, data analysis, cost-effectiveness, security, and scalability, this study investigates the influence of smart technology adoption on TPM in goods facilities. This study takes a holistic look at the link between TPM practises, smart technology integration, and the perceived effects of each. The findings show that there is a strong correlation between the use of smart technology, improved TPM practises, and the perceived effects on TPM. This dissertation uses rigorous statistical analysis to offer useful insights that will help logistics firms improve their material-handling operations in the age of smart technologies
The Digitalization of the UAE Construction Industry and its Impact on Dispute Resolution
No full textDisputes are an unavoidable aspect of the construction sector, despite employing various mechanisms for dispute avoidance and resolution. These conflicts consume a significant portion of stakeholders' time, effort and resources. This is primarily due to the construction industry's dependence on conventional record-keeping and process methods.
This dissertation focuses on outlining dispute resolution methodologies – with a focus on alternative dispute resolution - that prevail in UAE Architecture, Engineering and Construction (AEC) industry and how the latter can be improved by digitalizing of standard working practices in the industry and the dispute resolution process. The target is to bring in picture the opportunities and challenges posed by the digitalization of the AEC.
The AEC industry has lagged in digitization compared to other sectors. However, given the advent of new technologies and its integration into dispute prevention and resolution processes in AEC has introduced a new area of digital engineering, extending beyond design. Tools like Building Information Modelling (BIM), Digital Twin (DT), and Smart Contracts (SC) are among the most promising of the vast information technology tools that are available to facilitate fair risk distribution, enhance transparency, efficiency, and foster collaboration. Nevertheless, it is essential to emphasize that these tools should not supplant any human involvement in the project; rather, they should augment human efficiency in conflict prevention and dispute resolution.
Digitalization of AEC industry has the potential to revolutionize the real estate sector, but harnessing its benefits necessitates a transformative shift within the industry itself. To unlock the full potential of AEC digitalization, professionals must adapt to its capabilities and integrate them into their operations. To that end, this dissertation makes a number of recommendations for companies and policy-makers to foster a culture of efficiency through innovation. Embracing this technological evolution is essential for the UAE construction industry and individual companies to stay competitive and to meet the evolving demands of the modern AEC industry landscape
An investigation of the United Arab Emirates’ Performance and Progress on PISA Assessments: A Comparative Analysis of Multiple Case Studies
No full textThe participation of nations in the Programme for International Student Assessment (PISA) has seen a significant surge in numbers throughout the last decade. These large-scale assessments contribute to the acquisition of a profound comprehension of the similarities and differences across international teaching and learning environments. PISA has emerged as a prominent tool for assessing the equity, quality, and effectiveness of educational systems. The purpose of this study is to carry out a comparative analysis of UAE performance in PISA assessments in relation to the performance of UK, Singapore, and Finland, identify the variables that contribute to favourable PISA scores, and finally provide the UAE with a set of recommendations and suggestions to promote the achievement of higher results in future PISA rounds, ultimately aiming to improve the country's ranking among other countries.
This study employs a comparative approach to analyse multiple case studies, using quantitative methodologies and relying on secondary data sources. The study examines the results of PISA assessments of UAE, UK, Singapore, and Finland across the domains of Mathematics, Reading, and Science throughout multiple rounds.
The research emphasizes that the implementation of appropriate reforms in educational systems and promoting educational initiatives, might have a substantial impact on enhancing the performance of UAE in PISA assessments. These findings are drawn from the analysis of experiences in other countries that were included in the study.
The study provides significant benefits for policy makers in UAE when they consider incorporating the research suggestions into the development of educational policies, as well as their subsequent implementation. Additionally, the outcomes of this study will serve as a valuable resource for future academics, enabling them to undertake other investigations in a similar vein
Monitoring and evaluating instruction in an adaptive multicultural classroom in a private American elementary school in Sharjah from the perspective of headteachers and teachers
No full textThe study focuses on the tension between recognizing the significance of ethnicity and culture for individual and group identities while avoiding essentializing them in multicultural education theory and practice. The aim is to highlight the importance of a critical approach that combines sociological understandings of identity with an analysis of structural inequalities and power differentials faced by minority groups. The research employs a critical analysis of existing multicultural and intercultural education programs over the past two decades, emphasizing their focus on cultural knowledge acquisition and harmonious relationships. The study reveals that these programs often neglect to address power inequalities related to cultural diversity, race, and racism, and that they can reinforce prejudices without proper critical frameworks. The implications are significant, as these programs often lack consideration of the local context, relying on migrating models. The study underscores the need for more comprehensive, context-specific, and critical approaches in multicultural education to ensure a deeper understanding of cultural diversity, racism, and power dynamics, ultimately leading to more effective and inclusive educational outcomes
The Effect of Using Multiple Digital Platforms to Enhance Formative Assessment and Teacher Feedback in UAE Schools
No full textFormative assessments and teacher feedback are essential elements that support the learning process, and using technologies is a demand nowadays. The present study utilized mixed methodology to investigate the teacher's perception of using multiple educational platforms in formative assessment, the quality of feedback, and the opportunities and challenges of using these platforms in public schools in Abu Dhabi. In recent years, technology in the classroom has changed how teachers manage assessments and give student feedback. Due to the development of several digital educational platforms, educators can access various tools and materials that help improve formative assessment techniques and feedback. Both quantitative (Teachers Questionnaire) and qualitative (Teachers Interviews) instruments were used in the study simultaneously. However, the data analysis maintained the two threads apart. After that, a thorough grasp of teacher beliefs about formative assessment and feedback using platforms was developed using the two sets of results derived from the questionnaire and interviews. The results of this research showed how using multi-platform for formative assessment allows teachers to design dynamic learning environments that engage students, encourage active learning, and support continual development by leveraging various digital tools and platforms. These platforms provide higher productivity, better lesson planning, and improved learning process. Teachers can save time and effort when using the ready assessments, and they can use the analysis and track students' learning progress through the platforms. Feedback could be provided to students employing platforms focusing on specific areas of improvement and offering constructive suggestions for enhancement based on students' level in different types of feedback. On the other hand, Teachers must be updated and investigate the available digital resources to improve teaching methods and deliver direct individualized feedback. Find alternative ways to complete assessments virtually to overcome exam reliability issues and test characteristics problems
الكشف عن تغريدات التنمر الإلكتروني باللغة العربية على مواقع التواصل الاجتماعي العربية باستخدام التعلم العميق
No full textThe widespread engagement with social media platforms in recent years has made cyberbullying a significant concern. Individuals may have catastrophic side effects from that as well, including despair, anxiety, and even suicide. Due to the difficulty of manually detecting and categorizing vast volumes of electronic text data, conventional methods for recognizing and combating cyberbullying have not proven successful. As a consequence, deep learning methods have become a potential solution for this situation. Artificial neural networks and other deep learning approaches can automatically identify patterns and features from a massive quantity of data. These methods may be applied to electronic text data analysis to spot cyberbullying-related trends. Techniques for natural language processing may be used to text data to extract useful features like sentiment, emotion, and subjectivity. A sizable dataset of electronic text data was gathered from multiple social media platforms like Twitter, Instagram, YouTube, and many more sites in order to examine cyberbullying in social media using machine learning and deep learning techniques. The data needs to be initially prepared so that deep learning algorithms may be trained on it before cyberbullying analysis can be done. Manually annotated data from a corpus collection was used to label the information for deep learning purposes. Pre-processing is a vital part of the data preparation process for cyberbullying detection. There are several varieties of Arabic, but the three most common are dialect Arabic , Modern Standard Arabic, and Classical Arabic. Because of its widespread use on social media, DA Arabic is the subject of this essay. Based on the existence of cyberbullying, the data was then preprocessed and classified. In this work, two cases of classification were adapted. The first case was 2-classes classification where the data labeled as either cyberbullying or not cyberbullying. The second case was 6-classes classification which consists of six different cyberbullying types. To categorize electronic text in these two cases, deep learning models such as convolutional neural networks and recurrent neural networks and a combination of CNN-RNN were trained on this data. In an independent test set, the trained models were assessed, and they showed promise in identifying cyberbullying via social media. The results that obtained from 2-classes classification showed a superiority of LSTM in terms of accuracy with 95.59%, while the best accuracy in the 6-classes classification gained from implementing CNN with 78.75%. Meanwhile the f1-score results were the highest in LSTM for the 2-lasses and 6-classes classifications with 96.73% , and 89%, respectively. These findings emphasize the potential for deep learning techniques to be applied in the development of automated systems for identifying and combating cyberbullying on social media and show how well they work in detecting cyberbullying
The Development of a Quality Function Deployment (QFD) for the Implementation of a Reverse Engineering Department – UAE
No full textThe purpose of the research is to investigate the impact of applying Quality function deployment in the reverse engineering department. Total quality management tools are important in reverse engineering because there is a lot of competition. It’s important for the department to make sure they are doing a good job and always improving. Moreover, Quality Function Deployment (QFD) helps make sure reverse engineering is giving customers what they need. This paper talks about using QFD to make the department better. It looks at how QFD can be used and how to change it to work better for engineering. A Quality Function Deployment is a tool that helps make sure things are good and people are satisfied. QFD allows companies to prioritize customer feedback and understand which areas of their business can be improved upon. By understanding customer wants and needs, companies can focus on making products and services that are tailored to meet customer expectations and make them satisfy.
The papers provide guidelines on how developers can effectively utilize QFD to ensure their work is of high quality. This research was carried out using a quantitative approach, enabling to gathering of valuable data from a representative sample of 71 individuals. To obtain this data, a total of 71 surveys were distributed to customers, and in addition, four interviews were conducted with industry experts. Additionally, by using QFD, companies can also plan for the future and anticipate customer needs before they arise. A two-part survey was conducted. The first survey was about what are the customer requirements of the company. The second survey involves identifying the company's strongest competitors. Based on the result, using Quality function deployment helps to guide the improvement of the reverse engineering department in manufacturing the printed circuit board (PCB).
Keywords: Quality Function Deployment, House of Quality, Reverse Engineering, Total Quality Management
Artificial Intelligence Chatbots: A Survey of Classical versus Deep Machine Learning Techniques
No full text: Artificial Intelligence (AI) enables machines to be intelligent, most importantly using Machine Learning (ML) in which
machines are trained to be able to make better decisions and predictions. In particular, ML-based chatbot systems have been developed
to simulate chats with people using Natural Language Processing (NLP) techniques. The adoption of chatbots has increased rapidly in
many sectors, including, Education, Health Care, Cultural Heritage, Supporting Systems and Marketing, and Entertainment. Chatbots
have the potential to improve human interaction with machines, and NLP helps them understand human language more clearly and thus
create proper and intelligent responses. In addition to classical ML techniques, Deep Learning (DL) has attracted many researchers to
develop chatbots using more sophisticated and accurate techniques. However, research has paid chatbots have widely been developed
for English, there is relatively less research on Arabic, which is mainly due to its complexity and lack of proper corpora compared
to English. Though there have been several survey studies that reviewed the state-of-the-art of chatbot systems, these studies (a) did
not give a comprehensive overview of how different the techniques used for Arabic chatbots in comparison with English chatbots; and
(b) paid little attention to the application of ANN for developing chatbots. Therefore, in this paper, we conduct a literature survey of
chatbot studies to highlight differences between (1) classical and deep ML techniques for chatbots; and (2) techniques employed for
Arabic chatbots versus those for other languages. To this end, we propose various comparison criteria of the techniques, extract data
from collected studies accordingly, and provide insights on the progress of chatbot development for Arabic and what still needs to be
done in the future
Project leader’s interactive use of controls, team learning behaviour and IT project performance: the moderating role of process accountability
No full textPurpose– This study aims to understand how project leaders’ interactive use of the project management
control systems (MCS) impact IT project performance, by examining the mechanisms through which this
relationship is enacted.
Design/methodology/approach– Data were collected from a cross-sectional survey of 109 IT project
managersworkinginCanadianandUSA-basedorganizations.Amoderatedmediationmodelwasanalysedby
hierarchical component reflective-formative measurement modelling using PLS-SEM.
Findings– Results suggest that the leader’s interactive use of project MCS is associated with IT project
performance, and this relationship is partially mediated by team learning behaviour. In addition, the
relationship between the interactive use of project MCS and team learning behaviour is moderated by the
organization’s emphasis on process accountability, with the effect being stronger under the conditions of
higher emphasis on process accountability.
Originality/value– This study contributes to the literature on the use of controls in the IT project-based
business environments by explaining how the project leader’s style of use of controls influences project team
learning behaviour that in turn impacts project performance. Additionally, this study extends the project
governance and accountability literature by identifying and empirically examining how the perceptions of project
leader’s institutionalized organizational accountability arrangements moderate the impact of the interactive use of
control systems on team learning behaviour. A methodological contribution of the study is the scale development
to measure leader’s perceptions about the organization’s emphasis on process accountability.
Keywords Team development, Management information system, Leaders managerial skills,
Business leadership, Effective leader behaviours, Organization and behaviour management