Cloud Computing Data Research Articles

ТРАНСФОРМАЦІЯ СТРУКТУРИ СИСТЕМИ ВАЛЮТНОГО КОНТРОЛЮ В ЦИФРОВІЙ ЕКОНОМІЦІ АЗЕРБАЙДЖАНУ

Digital transformation in the banking and financial services sector largely involves a shift towards online and digital services from top-down branch banking, integrating digital systems, customer service platforms, applications and infrastructure. The purpose of the article is characterise the transformation of the structure of the currency control system in the digital economy of Azerbaijan. The subject of the study is processes of transformation of the currency control system structure in the digital economy of Azerbaijan. The methodological basis and information base of the research are current scientific and applied research, statistical information. The article uses a number of methods, including analysis, synthesis, comparison and generalization. The main hypothesis of the study was the assumption that the efficiency of economic management and the financial condition of the public sector depend on the quality of budget forecasting and planning, because they are the key to the sustainable development of socio-economic processes. Presenting main material. Technologies being implemented include blockchain, artificial intelligence (AI), cloud computing, machine learning, and customer data collection, management and analysis. With increasing adoption of FinTech (innovative technology supporting banking and financial services) disrupting the sector, it is attracting regulatory interest and prompting stricter regulatory standards.The originality and practical significance of the research.Systemic reforms of the payment ecosystem not only affect the development of financial markets and the country’s economy as a whole, but also ensure safe and efficient payments between business entities. Conclusions. Deepening digital transformation in the banking system and expanding the use of innovative payment solutions makes electronic payments an integral part of our daily lives and at the same time increases the competitiveness of the banking system as a whole

The Impact of Fintech Innovation on Bank’s Performance: Evidence from the Kingdom of Bahrain

This study investigates the impact of bank-level Financial Technology (Fintech) innovations on banks’ performance in the Kingdom of Bahrain from 2012-2021. Annual data of banks listed in Bahrain’s Bourse has been utilized to achieve this objective. In addition, bank-level FinTech indices have been constructed by a textual analysis method that assesses input dimensions through 32 keywords under four categories, including artificial intelligence, blockchain, cloud computing, and big data; FinTech output dimension is evaluated through payment and settlement, resource allocation, risk management, and channel construction technology. Using different panel data estimators, such as pooled OLS, fixed effects, random effects, and panel-corrected standard errors linear regression, results show that FinTech innovations increase banks’ performance. In addition, the findings demonstrate that banks' capital adequacy ratio, earnings ability, total assets, and annual GDP growth rate also significantly positively impact bank performance. Years in business have a significant adverse effect on bank performance. Interestingly, conventional and state-owned banks have a higher positive impact on returns on assets (ROA) than Islamic and privately owned banks. Policymakers and investors should pay close attention to facilitating ongoing FinTech innovations in the Kingdom of Bahrain to create opportunities and build a more inclusive and efficient financial sector.

Digital transformation in food supply chains: an implementation framework

PurposeDigital transformation using Industry 4.0 technologies can address various challenges in food supply chains (FSCs). However, the integration of emerging technologies to achieve digital transformation in FSCs is unclear. This study aims to establish how the digital transformation of FSCs can be achieved by adopting key technologies such as the Internet of Things (IoTs), cloud computing (CC) and big data analytics (BDA).Design/methodology/approachA systematic literature review (SLR) resulted in 57 articles from 2008 to 2022. Following descriptive and thematic analysis, a conceptual framework based on the diffusion of innovation (DOI) theory and the context-intervention-mechanism-outcome (CIMO) logic is established, along with avenues for future research.FindingsThe combination of DOI theory and CIMO logic provides the theoretical foundation for linking the general innovation process to the digital transformation process. A novel conceptual framework for achieving digital transformation in FSCs is developed from the initiation to implementation phases. Objectives and principles for digitally transforming FSCs are identified for the initiation phase. A four-layer technology implementation architecture is developed for the implementation phase, facilitating multiple applications for FSC digital transformation.Originality/valueThe study contributes to the development of theory on digital transformation in FSCs and offers managerial guidelines for accelerating the growth of the food industry using key Industry 4.0 emerging technologies. The proposed framework brings clarity into the “neglected” intermediate stage of data management between data collection and analysis. The study highlights the need for a balanced integration of IoT, CC and BDA as key Industry 4.0 technologies to achieve digital transformation successfully.

Novel enterprises digital transformation influence empirical study.

With the rapid development of technologies such as cloud computing and big data, various levels of government departments in the country have successively introduced digital subsidy policies to promote enterprises' digital transformation. However, the effectiveness of these policies and their ability to truly achieve policy objectives have become pressing concerns across society. Against this backdrop, this paper employs a moderated mediation effects model to empirically analyze the incentive effects of financial subsidies on the digital transformation of A-share listed manufacturing companies in the Shanghai and Shenzhen stock markets from 2013 to 2022. The research findings indicate a significant promotion effect of financial subsidies on the digital transformation of manufacturing enterprises, especially demonstrating a notable incentive impact on the digital transformation of large enterprises, non-asset-intensive enterprises, technology-intensive enterprises, and non-labor-intensive enterprises. However, the incentive effect on the digital transformation of small and medium-sized enterprises (SMEs), asset-intensive enterprises, non-technology-intensive enterprises, and labor-intensive enterprises is not significant. Notably, the expansion of financial subsidies positively influences the augmentation of R&D investment within manufacturing enterprises, subsequently providing indirect encouragement for their digital transformation. Additionally, the incorporation of the degree of marketization implies its potential to moderate both the direct and indirect impacts of financial subsidies on enterprise digital transformation. This study enriches the research on the mechanism of the role of financial subsidies in digital transformation and provides empirical evidence on how market participation influences the effects of financial subsidies, thereby assisting policymakers in comprehensively understanding the impact of financial subsidy policies on different types of enterprises.

Forest Disturbance Monitoring Using Cloud-Based Sentinel-2 Satellite Imagery and Machine Learning.

Forest damage has become more frequent in Hungary in the last decades, and remote sensing offers a powerful tool for monitoring them rapidly and cost-effectively. A combined approach was developed to utilise high-resolution ESA Sentinel-2 satellite imagery and Google Earth Engine cloud computing and field-based forest inventory data. Maps and charts were derived from vegetation indices (NDVI and Z∙NDVI) of satellite images to detect forest disturbances in the Hungarian study site for the period of 2017-2020. The NDVI maps were classified to reveal forest disturbances, and the cloud-based method successfully showed drought and frost damage in the oak-dominated Nagyerdő forest of Debrecen. Differences in the reactions to damage between tree species were visible on the index maps; therefore, a random forest machine learning classifier was applied to show the spatial distribution of dominant species. An accuracy assessment was accomplished with confusion matrices that compared classified index maps to field-surveyed data, demonstrating 99.1% producer, 71% user, and 71% total accuracies for forest damage and 81.9% for tree species. Based on the results of this study and the resilience of Google Earth Engine, the presented method has the potential to be extended to monitor all of Hungary in a faster, more accurate way using systematically collected field-data, the latest satellite imagery, and artificial intelligence.

Curriculum Teaching and Research Based on Cloud Computing and Big Data

This study investigates the integrated application of cloud computing and big data in the field of education and its impact on curriculum teaching. Through a comprehensive review of the fundamental principles, current applications, and relevant literature, the study reveals the advantages and challenges of cloud computing and big data in aspects such as curriculum design, learning analytic, and personalized teaching. It also explores their influence on aspects like the sharing of teaching resources, the construction of online learning environments, and the evaluation of student learning outcomes. Furthermore, through case analysis and empirical studies, this paper provides a more in-depth validation of the practical effects of cloud computing and big data in improving teaching effectiveness, promoting educational innovation, and cultivating students’ information literacy. In summary, the integration of cloud computing and big data provides new perspectives and solutions for building smart education, while also laying a solid foundation for nurturing talents capable of meeting the demands of future society.

Cloud Computing Based Data Processing and Automated Management of Power Dispatch

Abstract As smart grids increase, the scope of power dispatch systems has broadened, encompassing not just traditional dispatch services but also multifaceted information services, encompassing data fusion, heterogeneous system integration, and big data analytics. However, managing intricate information businesses like big data analysis poses numerous challenges for the current power dispatch system, hindering its ability to keep up with escalating business demands. We introduce a cloud computing and big data analytics-based design scheme for a power dispatch data processing and automation management system to address this. Leveraging the distributed computing prowess of cloud computing platforms, the system ensures swift processing and analysis of vast power dispatch data. Additionally, by harnessing big data analysis techniques, the system delves into historical data, uncovers potential operational patterns and risk areas, and offers insights for optimizing the Electric Power System’s (EPS) operations. Experimental outcomes demonstrate the system’s remarkable proficiency in enhancing data processing, real-time control, and automation management capabilities, thereby bolstering the growth of intelligent power dispatch systems with robust technical backing.

ICT, Manufacturing and Industrial Automation of Biological Processes

The presence of wireless and mobile technologies in developing countries, the availability of low-cost, miniaturized wireless sensors, as well as the cost-efficient services provided by new hardware infrastructures have enabled new healthcare services, or new levels of quality and cost-efficiency in established ones. One crucial aspect of Healthcare 4.0 is the concept of P4 in Medicine: predictive, preventive, personalized and participatory. This approach, based on a comprehensive understanding of each patient biology, opposite to clustering patients into treatment groups, is being applied to reduce global health budgets by minimizing unnecessary use of drugs and procedures. These innovations come from the broad set of ICTs, and among them, the pillars of HC4.0 for their importance: IoT, Cloud Computing, and Big Data. Health 4.0 is used to improve the efficiency of physicians by enabling them to optimize resources and data availability. The future of health management will become timesaving and personalized as new technologies will empower individuals to conduct their health monitoring by using cyber–physical systems. Thanks to IoT, Cloud and Fog, as well as Big Data, researchers are allowed to design novel solutions, which are able to efficiently and effectively renew consolidated healthcare practices.

Sports Prediction Model through Cloud Computing and Big Data Based on Artificial Intelligence Method

Sports Prediction Model through Cloud Computing and Big Data Based on Artificial Intelligence Method

Enriched Cloud Computing Data Security through a Triple Level Encryption Model for Healthcare Systems

Enriched Cloud Computing Data Security through a Triple Level Encryption Model for Healthcare Systems

Ensemble Learning Framework for DDoS Detection in SDN-Based SCADA Systems.

Supervisory Control and Data Acquisition (SCADA) systems play a crucial role in overseeing and controlling renewable energy sources like solar, wind, hydro, and geothermal resources. Nevertheless, with the expansion of conventional SCADA network infrastructures, there arise significant challenges in managing and scaling due to increased size, complexity, and device diversity. Using Software Defined Networking (SDN) technology in traditional SCADA network infrastructure offers management, scaling and flexibility benefits. However, as the integration of SDN-based SCADA systems with modern technologies such as the Internet of Things, cloud computing, and big data analytics increases, cybersecurity becomes a major concern for these systems. Therefore, cyber-physical energy systems (CPES) should be considered together with all energy systems. One of the most dangerous types of cyber-attacks against SDN-based SCADA systems is Distributed Denial of Service (DDoS) attacks. DDoS attacks disrupt the management of energy resources, causing service interruptions and increasing operational costs. Therefore, the first step to protect against DDoS attacks in SDN-based SCADA systems is to develop an effective intrusion detection system. This paper proposes a Decision Tree-based Ensemble Learning technique to detect DDoS attacks in SDN-based SCADA systems by accurately distinguishing between normal and DDoS attack traffic. For training and testing the ensemble learning models, normal and DDoS attack traffic data are obtained over a specific simulated experimental network topology. Techniques based on feature selection and hyperparameter tuning are used to optimize the performance of the decision tree ensemble models. Experimental results show that feature selection, combination of different decision tree ensemble models, and hyperparameter tuning can lead to a more accurate machine learning model with better performance detecting DDoS attacks against SDN-based SCADA systems.

Digital risks and Islamic FinTech: a road map to social justice and financial inclusion

Purpose This study aims to map the digital risks for the Islamic finance industry. Since 2010, the financial space has largely shifted from being banking-centric to the entrepreneurship spectrum, benefiting from groundbreaking innovations in computer technology. The problem of Islamic Finance is that it is still within its banking-centric moment that is risk averse leading to financial exclusion. As with all innovations, there are associated risks that require careful consideration to ensure the reaping of the benefits of these technologies while controlling the risks at its lowest. In this context, the aim of this study is to highlight the risks associated with financial technologies (FinTech) to prepare the Islamic finance sector to serve the economic ideals of Maqāṣid al-Shariah in financial inclusion and profit and loss sharing. The main research question is as follows: What do Islamic Finance industry need to do to manage the digital risks for financial inclusion? Design/methodology/approach This study uses narrative review method in analysing the discourse of financial technology literature using qualitative data collected from the literature on the topic. It aimed to problematise associated digital risks from the Shariah compliance and Maqā¸ṣid al-Shariah critical viewpoints. Considering the nature of this conceptual study, it adopts a qualitative methodology by using discourse and thematic analysis of the literature that can lay the foundation for future empirical testing on the topic. Findings The study found that managing risks faced by the Islamic financial sector while adapting to the digital era can be divided into two main clusters: risk mitigation for Shariah-compliant FinTech and risk avoidance for Shariah non-compliant innovations. The high level of gharar associated with current practices in both cryptocurrencies and smart contracts needs additional regulation and simulation before they can be reconsidered for market-wide application. Cloud computing, crowdfunding and big data have promising applications that can address the limitations of the Islamic finance industry, particularly in terms of reducing transactional costs. Research limitations/implications This conceptual article offers some insights into the subject; nevertheless, it does not attempt to establish causation or generalise the results. Additional statistical testing is required prior to generalising the results. Practical implications Due to the difficulties experienced since its inception, the Islamic financial industry is in urgent need of the cutting-edge solutions required to gain a competitive edge in the market and get over the limits that came with its late entry into the financial sector. Mapping digital risks is imperative for the development of comprehensive prudential risk management strategies for the Islamic finance industry that can fix its problems and enable it to deliver the more favourable Shariah-based solutions, rather than remaining in the lower bands of Shariah compliance. Originality/value Findings of the study lay the foundation for empirical testing the volatility of FinTech innovations for the Islamic finance industry to reduce uncertainties and generate reliable forecasts. Scholarship on managing digital risks for Islamic financial institutions is still developing due to the covid global lockdown and the looming recession, and this study will help enhance theorisation necessary that can aspire economic recovery after current challenges.

Big data research in nursing: A bibliometric exploration of themes and publications.

To comprehend the current research hotspots and emerging trends in big data research within the global nursing domain. Bibliometric analysis. The quality articles for analysis indexed by the science core collection were obtained from the Web of Science database as of February 10, 2023.The descriptive, visual analysis and text mining were realized by CiteSpace and VOSviewer. The research on big data in the nursing field has experienced steady growth over the past decade. A total of 45 core authors and 17 core journals around the world have contributed to this field. The author's keyword analysis has revealed five distinct clusters of research focus. These encompass machine/deep learning and artificial intelligence, natural language processing, big data analytics and data science, IoT and cloud computing, and the development of prediction models through data mining. Furthermore, a comparative examination was conducted with data spanning from 1980 to 2016, and an extended analysis was performed covering the years from 1980 to 2019. This bibliometric mapping comparison allowed for the identification of prevailing research trends and the pinpointing of potential future research hotspots within the field. The fusion of data mining and nursing research has steadily advanced and become more refined over time. Technologically, it has expanded from initial natural language processing to encompass machine learning, deep learning, artificial intelligence, and data mining approach that amalgamates multiple technologies. Professionally, it has progressed from addressing patient safety and pressure ulcers to encompassing chronic diseases, critical care, emergency response, community and nursing home settings, and specific diseases (Cardiovascular diseases, diabetes, stroke, etc.). The convergence of IoT, cloud computing, fog computing, and big data processing has opened new avenues for research in geriatric nursing management and community care. However, a global imbalance exists in utilizing big data in nursing research, emphasizing the need to enhance data science literacy among clinical staff worldwide to advance this field. This study focused on the thematic trends and evolution of research on the big data in nursing research. Moreover, this study may contribute to the understanding of researchers, journals, and countries around the world and generate the possible collaborations of them to promote the development of big data in nursing science.

Does digital transformation curb the formation of zombie firms? A machine learning approach

ABSTRACT Based on the dynamic capability view theory, we examined the impact of digital transformation (DT) on zombie firm formation using a sample of listed Chinese firms. Through textual analysis of firms’ annual reports, we categorized DT into three dimensions: strategy, technology (i.e. artificial intelligence, blockchain, cloud computing, and big data), and application. Then, we employed 11 machine learning algorithms to detect zombie firms, compared the prediction performance, and calculated the contribution of each DT indicator. The results show that DT can effectively curb the formation of zombie firms. Specifically, big data contributes the most to suppressing the prevalence of zombie firms, followed by artificial intelligence, cloud computing, and DT application. Nevertheless, DT strategy and blockchain cannot reduce zombie likelihood. Finally, our research offers valuable insights for policymakers to address the issues of zombie firms.

Analysing College Students' Understanding Towards Technology Acceptance: Investigating Role of Industry 4.0 in Education Sector

Purpose: Industry 4.0 is represented by advanced technology like automation, IOT, cloud computing, and big data permeating every field nowadays, exacerbating the change that varies from generation to generation. As high technology emerges the working criteria are also expected to adopt. Updating technical skills among the students of every field is a very important part of technical development to enhance the existing work system. The purpose of this paper is to identify the understanding of Industry 4.0 in the education sector. Further, how students can contribute towards learning a new phase of technology.

Retracted: A Study on Information Classification and Storage in Cloud Computing Data Centers Based on Group Collaborative Intelligent Clustering

Retracted: A Study on Information Classification and Storage in Cloud Computing Data Centers Based on Group Collaborative Intelligent Clustering

Enabling Access Control for Encrypted Multi-Dimensional Data in Cloud Computing through Range Search

Enabling Access Control for Encrypted Multi-Dimensional Data in Cloud Computing through Range Search

Efficient and privacy-preserving image classification using homomorphic encryption and chunk-based convolutional neural network

Image feature categorization has emerged as a crucial component in many domains, including computer vision, machine learning, and biometrics, in the dynamic environment of big data and cloud computing. It is extremely difficult to guarantee image data security, privacy, and computing efficiency while also lowering storage and transmission costs. This paper introduces a novel method for classifying image features that combines multilevel homomorphic encryption and image data partitioning in an integrated manner. We employ a novel partitioning strategy to reduce computational complexity, significantly reducing computational load and improving classification accuracy. In the quest for increased data security and privacy, we introduce a novel, fully homomorphic encryption approach specialized to partitioned images. To counter the inherent complexity of encryption, we devise a compound encryption strategy that exploits the full potential of homomorphic computation, with an explicit objective to curtail computational and storage overheads. Evidently superior to conventional methods, our methodology showcases pronounced benefits in computational efficiency, storage and transmission cost reduction, and robust security and privacy preservation. Hence, the methodology put forth in this paper presents a pioneering and efficacious resolution to the multifaceted challenges of image feature classification within the intricate milieu of cloud computing and big data.

Predicting Groundwater Level using Temporal Attention Enhanced Graph Neural Network

A revolutionary approach for groundwater management is essential. To predicting the water levels using for integrating IoT sensors, cloud computing, and advanced data analysis methods. IoT sensors are employed for real-time measurement of groundwater levels, creating a robust dataset. The paper focuses on predicting future groundwater levels, a crucial aspect of sustainable resource management. To enhance predictive accuracy, a preprocessing algorithm, such as Min-Max normalization, is introduced to clean and normalize the collected data, ensuring its reliability. Additionally, a feature extraction algorithm, such as Principal Component Analysis (PCA), is implemented to identify relevant patterns and trends within the dataset, enhancing the efficiency of subsequent analysis. A novel classification algorithm, Spatial-Temporal Graph Convolutional Network, is introduced, enabling the identification of potential groundwater recharge areas. This classification algorithm leverages historical data and extracted features to categorize regions based on their suitability for groundwater renewal. Finally, this research uses a Temporal attention-enhanced graph Neural Network machine learning algorithm to predict groundwater levels in the next few years. This algorithm utilizes the preprocessed data and extracted features, identifying intricate patterns and trends in historical data to generate precise predictions for groundwater levels in the upcoming years.

Transforming Financial Decision-Making: The Interplay of AI, Cloud Computing and Advanced Data Management Technologies

Financial institutions face many challenges in managing modern financial transactions and vast data volumes. To overcome these challenges, they are increasingly harnessing advanced data man- agement technologies such as artificial intelligence and cloud computing. This paper presents a com- prehensive review of how these tools transform financial decision-making in various domains and ap- plications. We analyzed both foundational and recent advancements using a rigorous methodology based on the PRISMA 2020 guideline. Our findings indicate that many major financial institutions are adopting AI-driven solutions to potentially enhance real-time risk assessment, transactional efficiency, and predictive analytics. While they bring benefits like faster decision-making and reduced operational costs, they also pose challenges like data security and integration complexities that require further research and development. Looking ahead, we envision a more integrated, responsive, and secure financial ecosystem that leverages the convergence of AI, cloud computing, and advanced data storage. This synthesis underscores the significance of contemporary data management solutions in shaping the future of data-driven financial services, offering a guideline for stakeholders in this evolving domain.