Integrating AI and Cloud Technologies for Scalable, Low-Latency Edge Computing in Enterprise Workloads

The combination of education and artificial intelligence is the developmental direction of future educational systems. Through the participation of artificial intelligence, an educational system with sensibility and computer rationality can be created. Albeit the advantages and importance of this education system are beyond doubt, nevertheless, at present, the combination of artificial intelligence and education is still in its infancy. This is because the theoretical application, equipment research, and development are neither perfect nor up to the required standard. The research points out the reality that artificial intelligence technology has been widely used in sports and analyzes the specific application of artificial intelligence in sports. In this paper, the knowledge of artificial intelligence is combined with the physical training and teaching in colleges and universities, and an educational system is developed to guide teachers' and students' training, which improves the teaching quality and training efficiency.

Advancements in Artificial Intelligence (AI) and edge computing are reshaping what is possible in the world of hyper-personalization, currently powering customer experience in e-commerce and retail. In this study we explore when AI algorithms and edge computing frameworks enable real time, individualized customer interactions resulting in deeper engagement and higher loyalty. This study uses a quantitative research design to explore data driven personalization techniques with datasets from leading retail and ecommerce platforms. To evaluate the scalability and responsiveness of hyper-personalization technologies, a combination of machine learning models and terms edge computing infrastructures is methodologically analyzed. Through case studies of top tier e-commerce platforms, results show that integration of AI and edge computing can reduce latency in customer interactions by up to 70%, while upgrading conversion rates by roughly 30%. These findings emphasize the added value of creating real time customer experience driven sales and customer loyalty leveraging real time customer insights enabled by AI at the edge. This work presents a novel framework that enables scalable, secure hyper personalization in e-commerce to overcome existing limitations in data processing speed and privacy compliance. It offers actionable recommendations for retailers that look to adopt AI driven, edge enabled hyper personalization techniques for enhancing customer engagement and operational efficiency.

An adaptive access control scheme based on trust degrees for edge computing

The rapid growth of digital infrastructure across finance, healthcare, energy, manufacturing, education, and public services has fundamentally reshaped how data is generated, processed, and utilized. While these advancements drive operational efficiency and innovation, they also expose systems to a complex array of cybersecurity challenges. In today's hyperconnected landscape—shaped by cloud computing, the proliferation of IoT devices, edge computing, and artificial intelligence—the threat surface has become broader and more intricate. This review explores the cybersecurity risks emerging from digital transformation, such as vulnerabilities in both legacy and modern systems, sophisticated attack vectors including advanced persistent threats (APTs), ransomware, and zero-day exploits, as well as fragmented regulatory frameworks and persistent human-related risks such as insider threats, inadequate cybersecurity awareness, and social engineering. It also examines sector-specific concerns, including data privacy in healthcare, financial fraud in digital banking, cyber-physical risks in critical infrastructure, and intellectual property theft in research institutions. The chapter concludes with an assessment of evolving mitigation strategies, including trust-based architectures, AI-powered threat detection, federated learning, distributed security models, and the adoption of security-by-design approaches in agile and DevSecOps environments.

Artificial Intelligence, Machine Learning and Real-time Probabilistic Data for Cyber Risk (Super) – Forecasting

The integration of Artificial Intelligence (AI) and cloud computing has revolutionized enterprise systems, particularly in predictive marketing. AI-powered enterprise solutions enable businesses to analyze vast amounts of data in real-time, enhancing decision-making, customer engagement, and operational efficiency. Predictive analytics allows companies to anticipate consumer behavior, refine marketing strategies, and optimize customer interactions. Cloud computing further supports AI-driven predictive marketing by providing scalable and cost-effective solutions that enhance data processing capabilities and business intelligence. AI-integrated enterprise resource planning (ERP) and customer relationship management (CRM) systems facilitate automated decision-making, improving supply chain management and personalized marketing campaigns. Despite its advantages, AI adoption in enterprise systems and predictive marketing presents challenges such as data privacy concerns, cybersecurity risks, and ethical considerations. The complexity of AI integration requires substantial investment in infrastructure and regulatory compliance to mitigate biases and ensure transparency in AI-driven decisions. Explainable AI (XAI) is increasingly necessary to build trust and accountability in enterprise applications. Future advancements in AI, including blockchain, augmented reality (AR), and quantum computing, will enhance predictive analytics and business intelligence, further transforming marketing automation and decision-making processes. The convergence of AI and blockchain is particularly promising in securing digital transactions and improving data transparency in enterprise operations. As AI continues to reshape enterprise systems and predictive marketing, businesses must adopt responsible AI practices, strengthen cybersecurity measures, and comply with global regulations to maximize its benefits. Companies that leverage AI-driven insights will gain a competitive edge by improving customer engagement, optimizing marketing strategies, and driving sustainable growth in the evolving digital economy.

Recent years have witnessed the widespread popularity of Internet of things (IoT). By providing sufficient data for model training and inference, IoT has promoted the development of artificial intelligence (AI) to a great extent. Under this background and trend, the traditional cloud computing model may nevertheless encounter many problems in independently tackling the massive data generated by IoT and meeting corresponding practical needs. In response, a new computing model called edge computing (EC) has drawn extensive attention from both industry and academia. With the continuous deepening of the research on EC, however, scholars have found that traditional (non-AI) methods have their limitations in enhancing the performance of EC. Seeing the successful application of AI in various fields, EC researchers start to set their sights on AI, especially from a perspective of machine learning, a branch of AI that has gained increased popularity in the past decades. In this article, we first explain the formal definition of EC and the reasons why EC has become a favorable computing model. Then, we discuss the problems of interest in EC. We summarize the traditional solutions and hightlight their limitations. By explaining the research results of using AI to optimize EC and applying AI to other fields under the EC architecture, this article can serve as a guide to explore new research ideas in these two aspects while enjoying the mutually beneficial relationship between AI and EC.

The high-paced development of wireless communication technologies brought about the growing need of smart, autonomous, and ultra-high-performance network structures, and the combination of Artificial Intelligence and Machine Learning in the 6G wireless communication networks is a major research issue. The conventional network management techniques cannot support the new demands that include ultra-low latency communications, intelligent spectrum management, mass connectivity, and real-time dynamic resource provision. This paper is a systematically-conducted literature review on the recent research on AI-driven networks, edge AI, federated learning, intelligent radio access network design, and AI-native architecture in next-generation networks based on PRISMA. The review focuses on the possibilities of self-organizing networks, digital twin networks, and smart connectivity that machine learning, deep learning, and autonomous wireless systems provide to the next generation of wireless networks working in terahertz communication ranges and integrated sensing and communication landscape. The findings indicate that AI based resource allocation, intelligent surfaces of reconfigurability, massive mimo optimization and network automation are all effective towards improving spectral efficiency, reliability and energy efficiency of 6G wireless communication. Moreover, the paper has also noted the increased importance of smart security mechanisms, distributed learning, and edge computing in enabling scalable and secure AI-native architectures. The results also indicate that the next generation wireless networks will be based upon intelligent spectrum management, autonomous control, and real-time data analytics to accommodate the new applications in holographic communication, smart cities, extended reality, and connected autonomous systems.

Influenced by the great success of deep learning via cloud computing and the rapid development of edge chips, research in artificial intelligence (AI) has shifted to both of the computing paradigms, i.e., cloud computing and edge computing. In recent years, we have witnessed significant progress in developing more advanced AI models on cloud servers that surpass traditional deep learning models owing to model innovations (e.g., Transformers, Pretrained families), explosion of training data and soaring computing capabilities. However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed. In this survey, we conduct a systematic review for both cloud and edge AI. Specifically, we are the first to set up the collaborative learning mechanism for cloud and edge modeling with a thorough review of the architectures that enable such mechanism. We also discuss potentials and practical experiences of some on-going advanced edge AI topics including pretraining models, graph neural networks and reinforcement learning. Finally, we discuss the promising directions and challenges in this field.

The COVID-19 pandemic has highlighted the need for a robust medical infrastructure and crisis management strategy as part of smart-city applications, with technology playing a crucial role. The Internet of Things (IoT) has emerged as a promising solution, leveraging sensor arrays, wireless communication networks, and artificial intelligence (AI)-driven decision-making. Advancements in edge computing (EC), deep learning (DL), and deep transfer learning (DTL) have made IoT more effective in healthcare and pandemic-resilient infrastructures. DL architectures are particularly suitable for integration into a pandemic-compliant medical infrastructures when combined with medically oriented IoT setups. The development of an intelligent pandemic-compliant infrastructure requires combining IoT, edge and cloud computing, image processing, and AI tools to monitor adherence to social distancing norms, mask-wearing protocols, and contact tracing. The proliferation of 4G and beyond systems including 5G wireless communication has enabled ultra-wide broadband data-transfer and efficient information processing, with high reliability and low latency, thereby enabling seamless medical support as part of smart-city applications. Such setups are designed to be ever-ready to deal with virus-triggered pandemic-like medical emergencies. This study presents a pandemic-compliant mechanism leveraging IoT optimized for healthcare applications, edge and cloud computing frameworks, and a suite of DL tools. The framework uses a composite attention-driven framework incorporating various DL pre-trained models (DPTMs) for protocol adherence and contact tracing, and can detect certain cyber-attacks when interfaced with public networks. The results confirm the effectiveness of the proposed methodologies.

Aim/Purpose: This study carried out a systematic review of the literature on artificial intelligence (AI) in English language teaching (ELT). The objective was to delineate the current research progress in the field and to further understand the challenges. Background: The study analyzed articles published between 2015 and 2021. Methodology: The qualitative research method was employed. Five steps were taken to steer the review. 200 articles were scrutinized; 64 were retained. Contribution: Prior research on AI in ELT has not investigated how the literature is progressing or what areas of AI are being covered. Without a holistic picture, some important research findings could be missed. Understanding how studies on AI in ELT are designed and implemented will contribute to a greater understanding of the existing state of research. Findings: Findings show that there is a promising future for AI in ELT. AI in ELT yielded positive results in terms of optimizing the English language skills, translation, assessment, recognition, attitude, satisfaction, etc. It was also found out that more and more articles on the topic are being published; the mixed research method is the most commonly used, higher education level is the most sampled, students as participants are the most sampled, and most studies developed novel AI-based systems. Various AI approaches have been identified in the reviewed studies, including machine learning, neural network, support vector machine, genetic algorithms, deep learning, decision tree, expert system, natural language processing, data mining, cloud computing, and edge computing. However, AI in ELT is still in its infancy, where little research has been conducted and gaps in the literature are still present, especially in terms of inherent issues related to body language, gestures, expressions, emotions, translation, lack of elaborate description of teaching material used for learning driven by AI, uncertainties and vagueness with regards to what can be considered under the realm of AI, and most authors being outside of the ELT discipline. Recommendations for Practitioners: This literature review is likely to provide practitioners with an overview of the current adopted technology, research method, instruments and/or tools, educational level, language skill, and the effects reported by the AI-based studies for designing effective systems for the use of AI in their ELT classrooms. Recommendation for Researchers: Researchers need to conduct research on AI in ELT along with a detailed in-depth description of the methodology, research design, and the proposed systems used to achieve AI in ELT. Furthermore, it is recommended that researchers explore the efficiency of AI-based systems used in previous research and ensure their relevance and functionality. They are also required to provide in-depth analysis of the challenges inherent to systems that have been highlighted in the literature, which will maximize the potentials of these AI-based technologies. Impact on Society: The findings of this paper can provide visualization of research findings that could particularly benefit researchers, educators, and AI specialists who are involved in the study of the applications of AI in ELT. Future Research: Future AI research needs to seriously include more detailed descriptions of the method in further research.

Technologies such as cloud, fog, and edge computing offer exceptional solutions for many technical problems in the Internet of Things (IoT). Cloud computing offers enormous flexibility in terms of resource availability and demand. In this paper, the role of cloud, edge, and fog computing in IoT was discussed along with the emergence of numerous IoT smart scenarios which are difficult to implement. This research is important in guiding future network paradigms that require faster processing with less delay and disruption, and the advanced technologies in them can address the challenges of IoT. It also reviews cloud, fog, and edge computing and their relevance to the IoT.

This article is aimed at studying the legal regulation of artificial intelligence and edge computing automated decision-making risks in wireless network communications. The data under artificial intelligence is full of flexibility and vitality, which has changed the way of data existence in the whole society. Its core is various algorithm programs, which determine the existence of artificial intelligence. In this environment, society develops rapidly with unstoppable momentum. However, from a legal perspective, artificial intelligence has algorithmic discrimination, such as gender discrimination, clothing discrimination, and racial discrimination. It does not possess openness, objectivity, and accountability. The consequences are sometimes serious enough to endanger the public interest of the entire society, leading to market disorder, etc. Therefore, the problem of artificial intelligence algorithm discrimination remains to be solved. This article uses algorithms to adjust algorithm discrimination to reduce the harm caused by artificial intelligence algorithm discrimination to a certain extent. First of all, this article introduces a regulatory-based edge cloud computing architecture model. It is mentioned that distributed cloud computing can use subsystems to calculate various resources and storage resources and can make automated decisions when calculating certain data. In order to reduce the impact of algorithm discrimination and trigger data diversification to reduce the probability of discrimination, an edge computing network data capture system is designed. And this article mentions the BP neural network model. The BP neural network model is divided into input layer, output layer, and hidden layer. The training samples are passed from the input layer to the output layer through the hidden layer. If the output information does not meet expectations, the error will be back-propagated, and the connection weight will be adjusted continuously. This paper proposes a deep learning system model in real-time artificial intelligence driven by edge computing. When this model is applied to legal regulations, it can cooperate with edge computing and artificial intelligence algorithms to provide high-precision automated decision-making. Finally, this paper designs an artificial intelligence-assisted automated decision-making experiment based on the theory of legal computing. This paper proposes a Bayesian algorithm that uses edge algorithms to merge into artificial intelligence and verifies the feasibility of this hypothesis through experiments. The experimental results show that it has a certain ability to regulate algorithmic discrimination caused by artificial intelligence in legal regulations. It can improve the regulatory effects of laws and regulations to a certain extent, and the improved artificial intelligence Bayesian algorithm clustering effect of edge computing is increased by about 7.2%.

Intelligent task prediction and computation offloading based on mobile-edge cloud computing

The rapid evolution of the e-commerce industry over the past decade has been significantly driven by digital transformation, encompassing the adoption of innovative technologies and process overhauls. This research paper investigates the pivotal role digital transformation plays in propelling e-commerce growth by integrating a detailed literature review and case study analysis. Technologies such as artificial intelligence (AI), cloud computing, and automation, which are known to be key, are explored for their influence on enhancing customer experiences and operational efficiencies. Through the synthesis of peer-reviewed sources and case studies of industry leaders like Amazon, Alibaba, and Walmart, this study identifies best practices and the most impactful technological strategies. Findings indicate that although digital transformation provides immense advantages like personalized marketing, seamless supply chain functioning, and scalability, organizations are confronted with enormous challenges. These encompass the high cost of implementation, resistance to change, and cybersecurity risks, particularly for SMEs- Small and Medium-Sized Enterprises. The study highlights the requirement for a phased, strategic adoption of digital integration backed by sound change management and sustained investment in digital capabilities. The insights obtained are intended to guide business practitioners and policymakers in utilizing digital transformation to maintain and improve the growth of e-commerce.