2828 research outputs found
Sort by
Detection of Depression in Arabic Social Media: A Comparison of Traditional and Modern Machine Learning Algorithms
No full textThis study aims to address the research gap in detecting depression from Arabic tweets using the PHQ-9 scale as a framework. The dataset collected was a set of 200,000 tweets from around 20,000 users. A team of psychologists and assistants used a user-based approach to label users as either depressed or not. The data labelling and annotation process involved a user-based evaluation of the tweets to label users as either depressed or not, based on the two target variables of depressed_binary and depressed_multi. Users with scores between 0 and 6 were categorized as not depressed in the depressed_binary variable, while those with scores above six were classified as depressed. For the depressed_multi variable, users with scores ranging from 0 to 2 were labelled as not depressed, scores from 3 to 6 indicated mild depression, scores from 7 to 9 indicated moderate depression and scores of 10 or above represented high depression. Four machine learning models were employed in this study: HGB (Histogram Gradient Boost), GRU (Gated Recurrent Units), LSTM (Long Short-Term Memory), and SVM (Support Vector Machines). The findings revealed that the older models exhibited strong performance in binary classification, while the new models demonstrated competitive results. Future research should focus on exploring and developing newer deep learning models, such as HGB and GRU models, to enhance the accuracy and performance of depression detection in Arabic tweets. Future studies should also investigate strategies to account for the influence of different Arabic dialects and incorporate Arabic colloquialisms in depression detection models
Breach of Due Process as a Ground for Non-Recognition and Non-Enforcement of Foreign Arbitral Award Under the New York Convention
No full textVarious arbitrators and legal scholars worldwide have repeatedly used the term “due process” either during their legal practice or in efforts to help their clients obtain justice. Most attorneys regard It as established accounts of legal procedures based on a system that adheres to the legal knowledge, enhancing and protecting the individuals' rights.
However, its context in the New York Convention award was different. Despite its failure to appear in the report that was made effective in June 1959, it's obvious to note that the Convention's top objective was to promote the importance of acknowledging foreign arbitrations and using them effectively to settle disputes in a way that each part reaps a mutual benefit. Also, the need to make the foreign Arbitral award globally recognized since it steered for standard legislation would promote fairness during the court or arbitration processes. However, even after the agreement to start enforcing the Convention's report, the Foreign Arbitral Award has yet to be fully recognized internationally.
The dissertation will analyze and discuss the reasons for refusing to recognize and implement a foreign arbitral award under the New York Convention, as well as how it is applied by different jurisdictions, with a focus on due process, in terms of its concept, the consequences of its breach, how the courts interpret it and how the parties bring it before the competent court, and finally the conclusion and recommendation
indoor air quality, employee's health, United Arab Emirates (UAE)
No full textEmployee performance and health are significantly impacted by the work environment. Indoor air quality (IAQ) has a significant impact on employees' health and productivity in the office setting. Due to the lack of studies done to evaluate IAQ influence inside low and high-cost companies in UAE, this study was motivated to fill this gap and to evaluate the effects of IAQ within two different levels of working environments. Poor Indoor Air Quality is a relevant area of concern which has a direct impact on an employee's performance (Azuma et al., 2017). The purpose of this study was to examine the relationship between IAQ exposure and associated risks on employee health and performance in two different companies’ levels. Two different levels of companies were chosen as a case study where the best methodology applied. Data were collected using quantitative method (Field measurement) where we utilized three devices in order to reach the problem causes and to know more about our research question like (4 in 1 Environmental Meter, Air quality CO2 meter, and a sound level alert), a survey questionnaire utilized to know employee’s satisfaction and reaction with surrounding working environment, and finally a employees interview were conduct to elaborate more on their perspective regarding indoor air quality. The survey questionnaire which was distributed to all employees who were working in those companies revealed multiple evidence and facts. The questioning where mainly focusing on employee’s perspective to multiple aspects like: the amount of time that employees spend in their workplace, glare presence, windows availability, presence of printers/photocopiers, and scanners, wet ceilings, ventilation system, type of furniture, thermal comfort, as well as flooring materials. Respondents from low cost company showed that there was evidence of inadequate indoor quality from different aspects, while high-cost company showed that there was sufficient indoor quality from many manners perspective. Field measurements shed light on specific areas like: the levels of carbon dioxide (CO2) in the workplace, humidity levels, air velocity, temperature, and noise source. These measurements discovered the presence of multi factors in low cost companies associated in causing poor indoor quality. Moving forward the results revealed that there are many different problems derived from working environments. This paper correspondingly presents some new ideas and scopes that will help engineering companies to recognize the impact of poor IAQ on their employees and analyze the reasons that lead to weak IAQ in their companies as well as to know this issue could be addressed in order to maintain positive atmosphere that enhance employee’s performance and health
Linkage Between Maintenance management, Enterprise Risk Management and Organizational Performance
No full textThe purpose of this study was to investigate the impact of maintenance management on organizational performance, with the mediation effect of enterprise risk management. The research problem addressed was the need to understand how maintenance management and enterprise risk management are interrelated and how they affect organizational performance. The basic design of the study involved a literature review and empirical research using survey data. Major findings indicated that maintenance management has a positive impact on organizational performance, with almost 46% of the variation predicted. Preventive maintenance was found to have a positive impact on company efficiency and competitiveness. Enterprise risk management was found to mediate the relationship between maintenance management and organizational performance, with more than 58% of variation in organizational performance being recorded. The study concluded that maintenance management plays a crucial role in improving organizational performance, and that enterprise risk management is dependent on maintenance management. This study provides evidence for the validation of scales related to maintenance and enterprise risk management, and organizational performance, contributing to the literature on organizational performance
Blockchain-Powered NFTs: A Paradigm Shift in Carbon Credit Transactions for Traceability, Transparency, and Accountability
No full textThe adoption of carbon credit systems has emerged as a pivotal strat egy in addressing climate change and promoting sustainable environmental prac tices. This research paper delves into the multifaceted landscape of carbon credit
systems, specifically focusing on the challenges inherent in their design and im plementation. We investigate the innovative use of Non-Fungible Tokens (NFTs)
as a transformative approach to carbon credits, exploring the intricacies of NFT
structures tailored for carbon credit trading. Furthermore, the paper presents a
comprehensive examination of the system architecture underpinning a block chain-enabled NFT trading platform for carbon credits. The architectural frame work's design and components are meticulously scrutinized to ensure transpar ency, security, and efficiency in the trading ecosystem. The research also dissects
the intricate functionalities that empower the blockchain-NFT trading system, fa cilitating seamless transactions and traceability. A pivotal facet of this study is a
compelling case study spotlighting the application of a Blockchain-NFT trading
system for Agricultural Carbon Credits within a rural farming community. The
case study highlights the tangible benefits reaped by farmers through the innova tive system, shedding light on how the technology incentivizes sustainable farm ing practices and offers an additional revenue stream
Cyberbullying Detection Model for Arabic Text Using Deep Learning
No full text. In the new era of digital communications, cyberbullying is a significant concern for society.
Cyberbullying can negatively impact stakeholders and can vary from psychological to pathological,
such as self-isolation, depression and anxiety potentially leading to suicide. Hence, detecting any act
of cyberbullying in an automated manner will be helpful for stakeholders to prevent any unfortunate
results from the victim’s perspective. Data-driven approaches, such as machine learning (ML), par ticularly deep learning (DL), have shown promising results. However, the meta-analysis shows that
ML approaches, particularly DL, have not been extensively studied for the Arabic text classification
of cyberbullying. Therefore, in this study, we conduct a performance evaluation and comparison for
various DL algorithms (LSTM, GRU, LSTM-ATT, CNN-BLSTM, CNN-LSTM and LSTM-TCN) on
different datasets of Arabic cyberbullying to obtain more precise and dependable findings. As a result
of the models’ evaluation, a hybrid DL model is proposed that combines the best characteristics of the
baseline models CNN, BLSTM and GRU for identifying cyberbullying. The proposed hybrid model
improves the accuracy of all the studied datasets and can be integrated into different social media sites
to automatically detect cyberbullying from Arabic social datasets. It has the potential to significantly
reduce cyberbullying. The application of DL to cyberbullying detection problems within Arabic text
classification can be considered a novel approach due to the complexity of the problem and the tedious
process involved, besides the scarcity of relevant research studies
Investigating the Impact of Digital Leadership on Technology Use in Elementary Schools in UAE
No full textThis research paper explores the impact of digital leadership on technology use in elementary schools within the United Arab Emirates (UAE), focusing on three major cities: Abu Dhabi, Sharjah, and Dubai. Using a mixed-methods approach, the study integrates qualitative and quantitative data collection, offering a broad perspective. Participants were selected through convenience sampling. The study pursues three key objectives. First, it investigates existing technology policies in UAE elementary schools, examining the guidelines, regulations, and national initiatives that govern technology integration. Second, it identifies leadership practices and competencies that support digital transformation in schools, including leadership styles, strategic approaches, and the role of school leaders. Third, the research explores factors that either facilitate or hinder the effectiveness of digital leadership in elementary education, such as insufficient training, lack of resources, or resistance to change. The findings highlight that while UAE schools have made significant efforts in drafting technology policies, the success of these initiatives largely depends on strong digital leadership. Effective strategies include collaborative decision-making, ongoing professional development, and modeling of technology use by school leaders. Conversely, challenges like limited infrastructure and gaps in leadership training negatively affect implementation. This study offers practical recommendations for school leaders, educators, and policymakers to enhance digital learning environments and emphasizes the urgent need to develop digital leadership capacities and address systemic challenges for sustainable technology integration
Cybersecurity effectiveness in UK construction firms: an extended McKinsey 7S model approach
No full textPurpose–Thisstudyaimstoassesstheessentialelementsofinternalorganisationalcapabilitythatinfluence
the cybersecurity effectiveness of a construction firm. An extended McKinsey 7S model is used to analyse the
relationship between a construction firm’s cybersecurity effectiveness and nine internal capability elements:
shared values, strategy, structure, systems, staff, style, skills, relationships with third parties and regulatory
compliance.
Design/methodology/approach– Based on a quantitative research strategy, this study collected data
through a cross-sectional survey of professionals working in the construction sector in the United Kingdom
(UK). The collected data was analysed using descriptive and inferential statistical methods.
Findings–Thefindingsunderlinedsystems,regulatorycompliance,staffandthird-partyrelationships asthe
most significant elements of internal organisational capability influencing a construction firm’s cybersecurity
effectiveness, organised in order of importance.
Researchlimitations/implications– Future research possibilities are proposed including the extension of
the proposed diagnostic model to consider additional external factors, examining it under varying industrial
relationship conditions and developing a dynamic framework that helps improve cybersecurity capability
levels while overseeing execution outcomes to ensure success.
Practical implications– The extended McKinsey 7S model can be used as a diagnostic tool to assess the
organisation’s internal capabilities and evaluate the effectiveness of implemented changes. This can provide
specific ways for construction firms to enhance their cybersecurity effectiveness.
Originality/value– This study contributes to the field of cybersecurity in the construction industry by
empirically assessing the effectiveness of cybersecurity in UK construction firms using an extended McKinsey
7S model. The study highlights the importance of two additional elements, third-party relationships and
construction firm regulatory compliance, which were overlooked in the original McKinsey 7S model. By
utilising this model,thestudydevelopsaconciseresearchmodelofessentialelementsofinternalorganisational
capability that influence cybersecurity effectiveness in construction firms
Embedding Evidence-Based Practice in Teaching-Learning Strategies in Undergraduate Physiotherapy Clinical Training in Abu Dhabi: Clinical Educators’ Perspective
No full textBackground: Evidence-based practice (EBP) is an essential attribute in the healthcare
setting. Prior to conducting this study, there was a lack of contextualized information as
to how introduction and enhancement of EBP among physiotherapy students are carried
over into undergraduate clinical training.
Purpose: The aim of this study was to understand teaching-learning strategies that
enhance physiotherapy undergraduates’ evidence-based practice during clinical training
from the perspective of clinical educators.
Methods: A qualitative approach was used which involved key-informant in-depth
interviews with physiotherapy clinical educators of physiotherapy students in Abu Dhabi
(n=12). Clinical educator participants are physiotherapy clinicians in private hospitals and
rehabilitation centres in Abu Dhabi. The audio-recorded interviews were transcribed and
underwent thematic analysis using NVivo 12.
Results: Thematic analysis revealed that clinical educators recognized seven strategies
aimed at enhancing students’ evidence-based practice during clinical placement as: (1)
case presentation, (2) problem-based learning, (3) skills demonstration, (4) shadowing, (5)
following patient’s progress, (6) exposure to a variety of patient cases, and (7) exposure
to different clinical educators.
Conclusion: Clinical educators of physiotherapy students in Abu Dhabi employ a variety
of teaching-learning strategies that enhance the adoption of an evidence-based
physiotherapy practice by students during undergraduate clinical training
A Deep Neural Network Chatbot for the Gulf Arabic Dialect: A Hybrid BiLSTM-Transformer Approach
No full textArtificial Intelligence (AI) is a technology that enables machines to mimic human intelligence, with core fields including Natural Language Processing (NLP) and Machine Learning (ML). Chatbot is a prominent AI application, that uses NLP techniques to engage in human-like conversations, enhancing human-machine interactions. This thesis explores the development of a chatbot that can automatically answer natural language questions, a key goal in AI. It provides a historical overview of chatbot evolution, generic workflow, and applications across various sectors. However, Chatbots are widely used, but there is a significant gap in systems specifically designed for the Gulf Emirati Arabic dialect, particularly in educational institutions and public sector universities, Existing systems are often trained on general corpora or other languages, highlighting a research gap in this area. Our proposed model addresses this gap by combining two advanced approaches in NLP. First, we employ Bidirectional Long Short-Term Memory (BiLSTM) networks for text generation, leveraging their ability to grasp contextual information and model long-term dependencies. Second, we integrate the Transformer model for both the encoding and decoding processes. This dual architecture enables our model to generate responses in Modern Standard Arabic (MSA) even when questions are posed in Gulf Arabic Dialect (GAD). The Transformer model was developed to handle MSA and GAD inputs in mixed-language environments. Three models were developed: BiLSTM, BiLSTM with Farasa Segmentation, and Hybrid BiLSTM-Transformer. After extensive experimentation, the Hybrid BiLSTM-Transformer model was the best-performing, achieving a BLEU score of 0.8674, 83% accuracy, and an F1 score of 0.86