The exponential growth of Internet of Things (IoT) devices integrated with fifth-generation (5G) wireless networks has created unprecedented opportunities for ultra-low-latency applications while introducing complex security vulnerabilities and computational challenges. This paper presents a comprehensive framework for deploying adaptive lightweight Convolutional Neural Networks (CNNs) in 5G-enabled IoT environments to address intrusion detection, intelligent traffic classification, and dynamic resource optimization. We propose a novel multi-objective optimization approach that integrates Adaptive Depthwise Separable Convolutions (ADSC), Dynamic Quantization-Aware Training (DQAT), and Real time Pruning Strategy (RPS) specifically designed for 5G network slicing architectures. Our methodology incorporates federated learning principles, edge-cloud collaboration, and context-aware adaptation mechanisms. Comprehensive evaluation on multiple datasets, including NF-ToN-IoT-v2, NSL-KDD, and CICIDS-2017, demonstrates superior performance with 97.8% accuracy in multi-class attack detection, 76% reduction in computational overhead, 71% decrease in energy consumption, and 42% improvement in network throughput. The framework achieves inference times under 8.5ms on edge devices while maintaining robust security postures across heterogeneous IoT deployments. Statistical significance testing and large-scale ablation studies verify the effectiveness of each of the suggested elements.

This study presents a bibliometric analysis of scholarly research on deepfake crimes in the age of AI, examining emerging risks and research trends between 2019 and 2025. Drawing on 349 publications from the Web of Science Core Collection, the study uses VOSviewer to map publication patterns, collaboration networks, and thematic clusters. The results reveal that research on deepfake crimes is very interdisciplinary, as AI-based detection studies are increasingly intersecting with legal, social, and criminal discussions. The cluster analysis highlights that while significant progress has been made in technical detection methods, there are still serious gaps in addressing the societal harms of misinformation and non-consensual content. These results indicate that the field is not only growing rapidly, but is also moving towards a more integrated agenda that combines technological innovation with ethical and regulatory considerations. The study contributes theoretically by framing deep counterfeiting as technological and criminal phenomena, and practically by providing insights to policy makers, researchers and practitioners seeking to mitigate the societal and security risks of artificial media.

This research examines how artificial intelligence (AI) integration has affected employment in China’s construction industry. This research builds on the theories of skill-biased technological change and creative destruction to study how AI influences both positive and negative employment effects that later influence overall employment. The report confirms, based on the survey data and by using PLS-SEM, that AI introduction results in both job growth and job losses for managerial-level employees. In addition, whether an organization is ready greatly affects how these relationships play out, improving good outcomes and reducing the bad ones. It is clear from the findings that preparing a strategy helps make the most of AI and alleviate its risks. The study contributes to a more detailed view of AI’s effects on jobs and supplies ideas for sustaining both innovation and employment.

Current network security technologies face new threats from determined attackers employing advanced evasion techniques such as IP spoofing, tiny fragment attacks, tunneling, and HTML smuggling. Conventional intrusion detection and prevention systems (IDS/IPS) have significant limitations in detecting zero-day attacks and sophisticated threats that can continuously alter their attack vectors. This paper presents a novel deep learning-driven, multilayer intrusion detection and prevention framework that integrates network-based IDS/IPS, host-based intrusion detection systems (HIDS), and honeypot technologies with advanced machine learning models, including graph neural networks (GNNs), autoencoders, and transformers. The framework employs adaptive learning mechanisms to enhance resilience against evasion techniques while maintaining low false positive rates. Experimental evaluation using diverse attack datasets demonstrates superior performance, achieving 97.3% detection accuracy for zero-day attacks and 94.8% resilience against advanced evasion techniques, significantly outperforming existing state-of-the-art solutions. The proposed framework contributes to cybersecurity research by introducing innovative multilayer correlation mechanisms, adaptive threat modeling, and evasion-resilient detection algorithms.

This study aims to explore the impact of Big Data Analytics (BDA) on Customer Happiness (CH) in Marketing 4.0 (M4.0) Era in the Saudi healthcare sector. The purpose of the study is to examine how the integration of data-driven decision making and modern marketing strategies can enhance patient happiness. The sample consisted of 450 employees from various levels within healthcare organizations across Saudi Arabia. A quantitative research approach was used, using a structured survey to collect data on perceptions of BDA and M4.0 and their impact on CH. Statistical analyses were conducted to test the proposed hypotheses. The results indicate that both BDA and M4.0 have a statistically significant positive impact on customer happiness, with BDA enhancing personalized healthcare services and M4.0 improving patient happiness. Based on these findings, healthcare organizations are encouraged to invest in Big Data analytics tools and adopt Marketing 4.0 strategies, such as personalized marketing and digital patient engagement, to enhance patient experiences and happiness. It is also recommended that future studies explore patient happiness through big data analytics, and to expand understanding of these technologies in diverse healthcare settings.

This study addresses the critical challenge of low 5G user retention in China, despite extensive infrastructure development. Moving beyond traditional adoption models, we investigate post-adoption continuance intention using a novel integration of the Expectation-Confirmation Model (ECM) and the Value-Based Adoption Model (VAM). Uniquely, we explore the impact of telecom investment strategies—specifically, co-investing vs. independently investing firms—on user perceptions and loyalty. Based on 508 valid responses and multi-group structural equation modeling, we find that while satisfaction and perceived value are key drivers of continuance intention, users’ value perception is shaped differently depending on the investment model. This study is among the first to empirically link telecom infrastructure investment models with user continuance intention. The results provide actionable insights for telecommunications operators and governments seeking to improve long-term 5G adoption and retention.

Sentiment analysis, a key component of natural language processing and data mining, plays a pivotal role in extracting subjective insights from textual data, particularly on social media platforms. In response to the growing importance of digital engagement, understanding public sentiment has become essential for formulating effective marketing strategies. This study aims to enhance the marketing strategy of energy products in subsidiaries of State-Owned Enterprises (SOEs) by employing a hybrid sentiment analysis model that integrates the Valence Aware Dictionary and Sentiment Reasoner (VADER) with Long Short-Term Memory (LSTM) neural networks. Utilizing a mixed-method approach that combines both quantitative and qualitative analyses, the study collects and processes data from multiple social media sources to identify and classify consumer sentiment. The results demonstrate that the hybrid VADER-LSTM model achieves an accuracy rate of up to 84%, enabling a more nuanced interpretation of consumer opinions. These insights inform the development of data-driven, responsive, and targeted marketing strategies. Furthermore, the study highlights the significance of fostering interactive communication between companies and consumers to enhance the impact of digital marketing efforts. Theoretical implications include a contribution to the academic discourse on information systems and digital marketing, while practical outcomes offer valuable guidance for SOEs in adopting adaptive, sentiment-informed marketing approaches within the energy sector.

This study investigates the social and technical factors influencing the adoption of Artificial Intelligence (AI) technologies within organizations and examines how these factors impact innovative workplace behaviour. Drawing on a combination of organizational culture, leader humility, work relationships, and AI-related technical skills, the study presents a comprehensive framework for understanding the integration of AI. Data were collected through an online survey from employees in the government, semi-government, banking, healthcare, and private sectors in the United Arab Emirates (UAE). 441 professional respondents from multiple sectors. The study’s findings reveal that social factors, such as organizational culture and leader humility, and technical factors, including managerial and employee AI skills, significantly contribute to the successful adoption and integration of AI. This study contributes to the literature by integrating both social and technical dimensions into a unified model. In addition, the study highlighted that AI adoption succeeds when technological readiness is matched with strong workplace relationships, supportive culture, and leader humility creating the conditions for sustained innovation. Finally, the findings provide practical implications for managers aiming to promote a supportive environment for AI adoption and innovation.

We examine the asymmetric effects of national technology-driven diversification policies on firm-level profitability in the Gulf Cooperation Council (GCC), addressing a critical gap in the microeconomic literature on the region's transition from hydrocarbons. Using a dynamic panel dataset of 63 strategically important firms across all six GCC countries from 2016 to 2025, we employ a Difference-in-Differences (DiD) approach, complemented by System Generalized Method of Moments (SGMM) estimation, to establish causal relationships while rigorously addressing endogeneity concerns. The results reveal that technology-focused policies have significantly boosted profitability and total factor productivity in firms that actively invest in digital technologies, with policy milestones increasing asset-based returns by 2.1% and equity-based returns by 2.8%. Government subsidies specifically targeted toward technology adoption amplified these effects by an additional 4.5% and 6.5%, respectively, with these impacts intensifying post-2020 to gains of 8.8% on assets and 13.1% on equity for technology-intensive firms. Conversely, firms in traditional sectors with minimal technology adoption showed no statistically significant response to these policy interventions. The findings underscore the efficacy of precisely targeted fiscal incentives and selective policy support for technology sectors in driving successful economic diversification, offering valuable insights for policymakers in resource-rich economies seeking to engineer sustainable, technology-enabled post-oil transitions through firm-level interventions.

The purpose of this study is to analyze the relationship between e-CRM (Electronic Customer Relationship Management) variables on e-Loyalty of online shop customers, e-WOM (electronic word-of-mouth) on e-Loyalty of online shop customers, and e-service quality on e-Loyalty of online shop customers. This study uses a quantitative approach. The population consists of all online shop consumers, and the sample of this study is 765 online shop consumers. The sampling technique used is simple random sampling. The research instrument is a questionnaire with a 7-point Likert scale. The research variables include e-CRM (Electronic Customer Relationship Management), e-WOM (electronic word-of-mouth), e-service quality, and e-Loyalty. Data were analyzed using Partial Least Square – Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0. The analysis consists of two stages: Outer Model (Measurement Model): Testing convergent validity, discriminant validity, and reliability. Inner Model (Structural Model): Testing path coefficients, R² values, and direct effects or hypothesis testing. The results of this study are E-CRM (Electronic Customer Relationship Management) has a positive relationship on e-Loyalty of online shop Customers, e-WOM (electronic word-of-mouth) has a positive relationship on e-Loyalty of online shop Customers, E-service quality has a positive relationship on e-Loyalty of online shop Customers. Optimal implementation of E-CRM, e-WOM and E-service quality through applications or websites can improve the overall user experience, which will ultimately encourage e-loyalty.