Search Results (62)

Despite the advantages of using cloud computing, data breaches and security challenges remain, especially when dealing with sensitive information. The integration of deep learning (DL) techniques in a cloud environment ensures privacy preservation. This review paper analyzes 38 papers published from 2020 to 2025, focusing on privacy-preserving techniques in DL for cloud environments. Combining different privacy preservation technologies with DL results in improved utility for privacy protection and better security against data breaches than using individual applications such as differential privacy, homomorphic encryption, or federated learning. Further, a discussion is provided on the technical limitations when applying DL with various privacy preservation techniques, which include large communication overhead, lower model accuracy, and high computational cost. Additionally, this review paper presents the latest research in a comprehensive manner and provides directions for future research necessary to develop privacy-preserving DL models. Full article

Cloud computing (CC) represents a key digital transformation advancement, reshaping the ways in which businesses operate across diverse industries. The exponential growth in data production has created an unprecedented challenge in the ways in which data is created, processed, and managed. This study investigates the impact of cloud computing on the digital transformation of the enterprise, including issues concerning sustainability and long-term preservation and curation. While there has been a proliferation of studies concerning the adoption and implementation of cloud computing in the enterprise, there is still a gap in the literature concerning the use of cloud computing technology for long-term preservation, digital curation, and sustainability. The study employed a mixed-methods approach that utilized a systematic review of the literature and an Internet-based survey. The combination of the systematic review and survey was intended to provide insights into the key strategic factors impacting the use of cloud computing for long-term preservation and sustainability. The results of the study show that, despite the growing recognition of the benefits of cloud computing, most organizations are still concerned about issues such as security, privacy, accessibility, and cost. Concerns regarding the long-term preservation and sustainability of enterprise information are closely tied to the extent to which cloud computing services are deemed reliable and trustworthy. This study underscores the varying levels of satisfaction among users, with businesses acknowledging both the advantages and disadvantages of the current cloud solutions. Full article

A synergetic computing mechanism is proposed to authenticate the validity of event data in merchandise exchange applications. The events are handled by the proposed synergetic computing system which is composed of edge devices. Asteroid_Node_on_Duty (ANOD) acts like a supernode to take the duty of coordination. The computation performed by nodes in local area can reduce round-trip data propagation delay to distant data centers. Events with different risk levels are processed in parallel through different flows by using Chief chain (CC) and Telstar chain (TC) methods. Low-risk events are computed in edge nodes to form TC, which can be periodically integrated into CC that contains data of high-risk events. New authentication methods are proposed. The difficulty of authentication tasks is adjusted for different scenarios where lower difficulty in low-risk tasks may accelerate the process of validation. Authentication by a certain number of nodes is required so that the system may ensure the consistency of data. Participants in the system may need to register as members. The transaction processing speed on low-risk events may reach 25,000 TPS based on the assumption of certain member classes given that all of ANOD, and Asteroid_Node_of_Backup (ANB), Edge Cloud, and Core Cloud function normally. Full article

This study investigates the impact of enterprise digital transformation through information and communication technology (ICT) on auditing fees. Based on data from publicly listed companies in China and employing information asymmetry theory, the research finds that the adoption of three factors associated with digital transformation—artificial intelligence (AI), cloud computing (CC), and big data technologies (BD)—exhibits a significant inverted U-shaped effect on auditing fees. Further analysis reveals that this effect is moderated by the quality of internal controls, the level of corporate governance, and discretionary accruals. These findings underscore the necessity for a nuanced understanding of the relationship between technology and auditing, as well as the importance for audit organizations to integrate new technologies into their practices to effectively respond to the rapid adoption of digital technologies by enterprises. Full article

This paper focuses on the portability of Cloud Computing (CC) services, specifically on the problems with the portability of Infrastructure as a Service (IaaS). We analyze the current state of CC with the intention of standardizing the portability of CC solutions. CC IaaS providers often use proprietary solutions, which leads to a problem known as “vendor lock-in”. Another problem might appear during migration between two providers if huge scripts are written in a proprietary language. To solve the portability problem, we applied the Model-Driven Architecture (MDA) approach to propose the general IaaS reference architecture. Using a generic IaaS model, we are able to describe entities of the IaaS environment and then design necessary transformation rules for specific IaaS environments in a simplified but flexible way. Using this model, we continue designing transformation rules that define the transcript of IaaS services. The CC-portability problem is thus solved by transforming a specific IaaS service description from one description to another through the generic model. This approach is extensible and can be adopted for the evolution of CC services. Therefore, it can be used as a generic solution to IaaS-portability issues. Using this flexible approach, the introduction of a new CC environment requires only the design of a single transformation rule that prevents proprietary peer-to-peer full-mesh mappings. Thanks to the proposed model and the transformation rules described, we were able to experimentally confirm the functionality of the transfer of the environment description between three cloud providers. Full article

Background: Virtual reality (VR), widely used in the medical field, may affect future medical training and treatment. Therefore, this study examined VR’s potential uses and research directions in medicine. Methods: Citation data were downloaded from the Web of Science Core Collection database (WoSCC) to evaluate VR in medicine in articles published between 1 January 2012 and 31 December 2023. These data were analyzed using CiteSpace 6.2. R2 software. Present limitations and future opportunities were summarized based on the data. Results: A total of 2143 related publications from 86 countries and regions were analyzed. The country with the highest number of publications is the USA, with 461 articles. The University of London has the most publications among institutions, with 43 articles. The burst keywords represent the research frontier from 2020 to 2023, such as “task analysis”, “deep learning”, and “machine learning”. Conclusion: The number of publications on VR applications in the medical field has been steadily increasing year by year. The USA is the leading country in this area, while the University of London stands out as the most published, and most influential institution. Currently, there is a strong focus on integrating VR and AI to address complex issues such as medical education and training, rehabilitation, and surgical navigation. Looking ahead, the future trend involves integrating VR, augmented reality (AR), and mixed reality (MR) with the Internet of Things (IoT), wireless sensor networks (WSNs), big data analysis (BDA), and cloud computing (CC) technologies to develop intelligent healthcare systems within hospitals or medical centers. Full article

The recent tailings storage facility (TSF) dam failures recorded around the world have concerned society in general, forcing the mining industry to improve its operating standards, invest greater economic resources, and implement the best available technologies (BATs) to control TSFs for safety purposes and avoid spills, accidents, and collapses. In this context, and as the era of digitalization and Industry 4.0 continues, monitoring technologies based on sensors have become increasingly common in the mining industry. This article studies the state of the art of implementing sensor technologies to monitor structural health and safety management issues in TSFs, highlighting advances and experiences through a review of the scientific literature on the topic. The methodology applied in this article adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and utilizes scientific maps for data visualization. To do so, three steps were implemented: (i) a quantitative bibliometric analysis, (ii) a qualitative systematic review of the literature, and (iii) a mixed review to integrate the findings from (i) and (ii). As a result, this article presents the main advances, gaps, and future trends regarding the main characteristics of the sensor technologies applied to monitor TSF structural health and safety management in the era of digitalization. According to the results, the existing research predominantly investigates certain TSF sensor technologies, such as wireless real-time monitoring, remote sensors (RS), unmanned aerial vehicles (UAVs), unmanned survey vessels (USVs), artificial intelligence (AI), cloud computing (CC), and Internet of Things (IoT) approaches, among others. These technologies stand out for their potential to improve the safety management monitoring of mine tailings, which is particularly significant in the context of climate change-related hazards, and to reduce the risk of TSF failures. They are recognized as emerging smart mining solutions with reliable, simple, scalable, secure, and competitive characteristics. Full article

Even though the field of medicine has made great strides in recent years, infectious diseases caused by novel viruses that damage the respiratory system continue to plague people all over the world. This type of virus is very dangerous, especially for people with serious long-term breathing problems like asthma, pneumonia, or bronchitis infections. Thus, this paper demonstrates a new secure machine learning monitoring system for a model for virus detection. Our proposed model makes use of four basic emerging technologies, the Internet of Things (IoT), Wireless Sensor Networks (WSN), Cloud Computing (CC), and Machine Learning (ML), to detect dangerous types of viruses that infect people or animals causing panic worldwide and deregulating human daily life. The proposed system is a robust system that could be established in various buildings, like hospitals, entertainment halls, universities, etc., and will provide accuracy, speed, and privacy for data collected in the detection of viruses. Full article

Cloud computing (CC) is an internet-enabled environment that provides computing services such as networking, databases, and servers to clients and organizations in a cost-effective manner. Despite the benefits rendered by CC, its security remains a prominent concern to overcome. An intrusion detection system (IDS) is generally used to detect both normal and anomalous behavior in networks. The design of IDS using a machine learning (ML) technique comprises a series of methods that can learn patterns from data and forecast the outcomes consequently. In this background, the current study designs a novel multi-objective seagull optimization algorithm with a deep learning-enabled vulnerability detection (MOSOA-DLVD) technique to secure the cloud platform. The MOSOA-DLVD technique uses the feature selection (FS) method and hyperparameter tuning strategy to identify the presence of vulnerabilities or attacks in the cloud infrastructure. Primarily, the FS method is implemented using the MOSOA technique. Furthermore, the MOSOA-DLVD technique uses a deep belief network (DBN) method for intrusion detection and its classification. In order to improve the detection outcomes of the DBN algorithm, the sooty tern optimization algorithm (STOA) is applied for the hyperparameter tuning process. The performance of the proposed MOSOA-DLVD system was validated with extensive simulations upon a benchmark IDS dataset. The improved intrusion detection results of the MOSOA-DLVD approach with a maximum accuracy of 99.34% establish the proficiency of the model compared with recent methods. Full article

With the proliferation of IoT devices, there has been exponential growth in data generation, placing substantial demands on both cloud computing (CC) and internet infrastructure. CC, renowned for its scalability and virtual resource provisioning, is of paramount importance in e-commerce applications. However, the dynamic nature of IoT and cloud services introduces unique challenges, notably in the establishment of service-level agreements (SLAs) and the continuous monitoring of compliance. This paper presents a versatile framework for the adaptation of e-commerce applications to IoT and CC environments. It introduces a comprehensive set of metrics designed to support SLAs by enabling periodic resource assessments, ensuring alignment with service-level objectives (SLOs). This policy-driven approach seeks to automate resource management in the era of CC, thereby reducing the dependency on extensive human intervention in e-commerce applications. This paper culminates with a case study that demonstrates the practical utilization of metrics and policies in the management of cloud resources. Furthermore, it provides valuable insights into the resource requisites for deploying e-commerce applications within the realms of the IoT and CC. This holistic approach holds the potential to streamline the monitoring and administration of CC services, ultimately enhancing their efficiency and reliability. Full article

The fourth industrial revolution brought a paradigm shift in the present manufacturing system and its supply chain management (SCM). The evolution of Industry 4.0 (I4.0) brought several disruptive technologies like cloud computing (CC), blockchain, the Internet of Things (IoT), cyber-physical systems (CPS), etc. These disruptive technologies have changed the face of the modern manufacturing system and its manufacturing supply chain (SC). Several changes in manufacturing in terms of lead time, cost reduction, agility, flexibility, and response to market sensitivity are seen in almost all types of manufacturing. I4.0’s disruptive technologies influence lean SC, agile SC, leagile SC, and green SC. The current study examines how I4.0 technologies affect society on such supply chains (SCs), which leads to enhanced performance of the manufacturing SC. The effect of process innovation (PI) resulting from I4.0 innovations is also investigated. SEM-PLS-based modeling is constructed based on 195 responses received from manufacturing enterprises implementing various SC practices in managing their manufacturing SCs. The findings demonstrate a favorable correlation between I4.0 technology and the enhancement of various SCs. The result also revealed that there is a positive impact of I4.0 technologies on PI, which leads to manufacturing SC performance improvements. Full article

Several sectors have embraced Cloud Computing (CC) due to its inherent characteristics, such as scalability and flexibility. However, despite these advantages, security concerns remain a significant challenge for cloud providers. CC introduces new vulnerabilities, including unauthorized access, data breaches, and insider threats. The shared infrastructure of cloud systems makes them attractive targets for attackers. The integration of robust security mechanisms becomes crucial to address these security challenges. One such mechanism is an Intrusion Detection System (IDS), which is fundamental in safeguarding networks and cloud environments. An IDS monitors network traffic and system activities. In recent years, researchers have explored the use of Machine Learning (ML) and Deep Learning (DL) approaches to enhance the performance of IDS. ML and DL algorithms have demonstrated their ability to analyze large volumes of data and make accurate predictions. By leveraging these techniques, IDSs can adapt to evolving threats, detect previous attacks, and reduce false positives. This article proposes a novel IDS model based on DL algorithms like the Radial Basis Function Neural Network (RBFNN) and Random Forest (RF). The RF classifier is used for feature selection, and the RBFNN algorithm is used to detect intrusion in CC environments. Moreover, the datasets Bot-IoT and NSL-KDD have been utilized to validate our suggested approach. To evaluate the impact of our approach on an imbalanced dataset, we relied on Matthew’s Correlation Coefficient (MCC) as a normalized measure. Our method achieves accuracy (ACC) higher than 92% using the minimum features, and we managed to increase the MCC from 28% to 93%. The contributions of this study are twofold. Firstly, it presents a novel IDS model that leverages DL algorithms, demonstrating an improved ACC higher than 92% using minimal features and a substantial increase in MCC from 28% to 93%. Secondly, it addresses the security challenges specific to CC environments, offering a promising solution to enhance security in cloud systems. By integrating the proposed IDS model into cloud environments, cloud providers can benefit from enhanced security measures, effectively mitigating unauthorized access and potential data breaches. The utilization of DL algorithms, RBFNN, and RF has shown remarkable potential in detecting intrusions and strengthening the overall security posture of CC. Full article

The node–edge–cloud collaborative computation paradigm has introduced new security challenges to data sharing. Existing data-sharing schemes suffer from limitations such as low efficiency and inflexibility and are not easily integrated with the node–edge–cloud environment. Additionally, they do not provide hierarchical access control or dynamic changes to access policies for data privacy preservation, leading to a poor user experience and lower security. To address these issues, we propose a data-sharing scheme using attribute-based encryption (ABE) that supports node–edge–cloud collaborative computation (DS-ABE-CC). Our scheme incorporates access policies into ciphertext, achieving fine-grained access control and data privacy preservation. Firstly, considering node–edge–cloud collaborative computation, it outsources the significant computational overhead of data sharing from the owner and user to the edge nodes and the cloud. Secondly, integrating deeply with the “node–edge–cloud” scenario, the key distribution and agreement between all entities embedded in the encryption and decryption process, with a data privacy-preserving mechanism, improve the efficiency and security. Finally, our scheme supports flexible and dynamic access control policies and realizes hierarchical access control, thereby enhancing the user experience of data sharing. The theoretical analysis confirmed the security of our scheme, while the comparison experiments with other schemes demonstrated the practical feasibility and efficiency of our approach in node–edge–cloud collaborative computation. Full article

The paper presents analytical formulas for computation in the time domain of electromagnetic (EM) fields generated by tortuous cloud-to-cloud (CC) lightning channels over a perfectly conducting ground. For the first time, the study was not limited to a horizontal lightning path but was extended to take into account the natural, tortuous geometry of the lightning channel. After the calculation of the step response, a convolution integration was applied for the computation of the fields generated by an arbitrary current source. The produced electric and magnetic fields were then compared with the fields generated by a horizontal channel. The method can be of primary importance to evaluating the hazards for electric and electronic systems of flying aircraft, estimating the voltages induced on overhead transmission lines by CC lightning, and, in general, evaluating the induced effects on sensitive electric and electronic components. Moreover, it may represent a simple, robust, and time-saving tool for estimating important physical parameters that characterize lightning phenomena. Full article

The term “Resilient Supply Chain 4.0” incorporates two research areas: Industry 4.0 and Supply Chain Resilience (SCRes). Industry 4.0 technologies include innovations such as the Internet of Things (IoT), Cyber-Physical Systems (CPS), Augmented Reality (AR), Cloud Computing (CC), the Internet of Services (IoS), Big Data Analytics (BDA), Artificial Intelligence (AI), Digital Twins (DT), Blockchain (BC), Industrial Robotics (IR), and Additive Manufacturing (AM). Industry 4.0 technologies do not have a direct impact on SCRes, but on resilience elements such as flexibility, redundancy, visibility, agility, collaboration, robustness, and information sharing. This paper aims to investigate which of the Industry 4.0 technologies can help improve the Key Performance Indicators (KPIs) that are used for creating a Resilient Supply Chain 4.0. A non-systematic literature review has been conducted for the identification of (a) the most important constituent elements of SCRes, (b) the Industry 4.0 technologies that improve the SCRes elements, and (c) the KPIs that enhance SCRes. A systematic literature review has been conducted to identify which of the Industry 4.0 technologies have an impact on the KPIs that enhance SCRes. The findings of this work demonstrate that Industry 4.0 technologies can help improve the KPIs for a Resilient Supply Chain 4.0. Full article