Employees are the fundamental source of a sustainable competitive advantage. Without the high quality of human capital, organisations cannot attain a competitive advantage that can be sustained over time. Employer branding is a strategy that focuses on engaging and attracting the best talent from the job market, which is crucial for sectors known for high employee turnover rates. In recent years, digital platforms and information technology systems have revolutionised employer branding by helping organisations connect with talent in more personal and innovative ways. In this study, we sought to explore and understand the role of LinkedIn in employer branding efforts and evaluate the benefits of using LinkedIn to brand an employer as the first choice for prospective employees. Additionally, the research proposes a LinkedIn-Integrated Employer Branding Model (LIEBM) that incorporates LinkedIn strategies. Qualitative data were collated from the recruiting heads of the leading four- and five-star hotels in Al Ain, United Arab Emirates. The findings indicate that LinkedIn is widely used to improve an organisation’s employer brand image through various strategies. The results also demonstrate that employing LinkedIn strategies enhances the benefits of employer branding, contributing to actionable insights in the hotel industry to make excellent decisions at pre-recruitment, recruitment and selection stages.

The Advanced Encryption Standard (AES) remains a foundational component of modern cryptography, securing vast volumes of digital communication and data storage. Despite its robust design and widespread adoption, AES continues to be the subject of intensive cryptanalytic research. This paper presents a review of recent advances in attacks against AES, categorizing them into four domains: side-channel attacks, fault injection attacks, machine learning and AI-based attacks, and quantum computing threats. For each category, representative studies published between 2021 and 2025 are analyzed with respect to methodology, data requirements, attack complexity, and practical applicability. The review highlights both vulnerabilities exposed in specific AES implementations and the evolution of attack methodologies, thereby providing a comprehensive perspective on the contemporary threat landscape. The findings underscore the need for continuous evaluation and adaptation of AES-based systems to ensure cryptographic resilience in the face of advancing adversarial capabilities.

The development of technology and the availability of patient data have been increasing the leveraging of a data-driven approach to improve diagnostic accuracy. This research introduces a virtual diagnosis program that employs neural networks to predict diseases based on a dataset of 4,920 patients and 132 symptoms. Through exploratory data analysis and correlation analysis, significant associations between symptoms and diseases are identified. The developed system achieves an impressive accuracy rate of 95.6% in diagnosing diseases by utilizing advanced optimization techniques for training the neural network model. This accuracy demonstrates the potential of the program to assist healthcare professionals in making accurate diagnoses, enhancing the precision and efficiency of disease identification. The data-driven approach of this virtual diagnosis tool complements medical expertise, offering valuable support for timely and accurate diagnoses.

This study examines the determinants of cybersecurity behaviour among employees in higher education institutions (HEIs) in the United Arab Emirates (UAE) through an integrated framework combining the Protection Motivation Theory (PMT), the Theory of Planned Behaviour (TPB), and the COM-B model. Using a convergent mixed-methods design, survey data from 250 participants and interviews with 18 staff members were analysed to identify behavioural drivers and construct a predictive model. Results showed that policy familiarity, security culture, and intrinsic motivation were the strongest predictors of secure behaviour, collectively explaining nearly 70% of variance. The predictive neural network model achieved high accuracy (F1 = 91.99%, R² = 0.94), with interpretability analysis ranking policy familiarity as the top determinant. Qualitative insights reinforced these findings, revealing that policy clarity, leadership engagement, and supportive culture enable consistent compliance. The study contributes a validated, theory-driven predictive framework for HEIs, bridging behavioural science and cybersecurity governance. The findings are based on self-reported cybersecurity behaviour and should be interpreted accordingly. It concludes that effective institutional cybersecurity depends on clear policies, inclusive culture, and sustained motivation rather than fear-based enforcement.

Cybersecurity in higher education institutions (HEIs) is increasingly recognized as a behavioral and organizational challenge, not solely a technical one. This study investigates the perceptions, awareness, and attitudes of faculty and staff in UAE HEIs toward cybersecurity policies and practices, with a focus on how institutional communication, policy frameworks, and cultural dynamics shape behaviors. Semi-structured interviews were conducted with academic and administrative staff, generating rich qualitative data that were analyzed thematically. The findings reveal three dominant themes. First, cybersecurity training and communication are often symbolic, generic, and disconnected from daily professional contexts, limiting their ability to enhance coping appraisal. Second, misalignment between policy and practice results in frustration and circumvention, as rigid protocols undermine perceived behavioral control and conflict with academic autonomy. Third, organizational culture and peer dynamics play a decisive role in shaping norms of compliance or non-compliance, with weak cultural embedding leaving security viewed as an IT department responsibility rather than a shared institutional value. These findings were integrated into a conceptual model grounded in Protection Motivation Theory (PMT), the Theory of Planned Behavior (TPB), and the COM-B framework. The model demonstrates how institutional mechanisms and cultural factors converge on employee attitudes, which in turn drive secure or insecure behaviors. The study advances theoretical understanding of cybersecurity as a behavioral phenomenon and offers practical recommendations for HEIs, including role-specific training, participatory policy design, and cultural embedding. By addressing attitudinal drivers, HEIs in the UAE can build more resilient cybersecurity cultures in increasingly complex threat landscapes.