Integration Of Cloud Computing Research Articles

Optimizing scope 3 emissions in the automotive manufacturing industry: a multidisciplinary approach

The acceleration of global climate change poses enormous challenges to the automotive manufacturing industry, a key sector in reducing greenhouse gas (GHG) emissions. Particularly, Scope 3 emissions, encompassing indirect emissions, often constitute the largest carbon footprint component in this sector, yet their quantification and management remain challenging. This paper proposes an interdisciplinary approach that integrates cloud computing, text analysis, and machine learning, and systematically details its implementation, key benefits, and potential applications. Through this methodology, the paper seeks to provide the automotive industry with innovative and actionable insights to tackle the complexities of Scope 3 emissions. It focuses on enhancing the accuracy of emission quantification and optimizing supply chains, aiming to reduce the overall carbon footprint. Moreover, this paper outlines the future challenges and directions for these technologies and methodologies in sustainable development and environmental management. This work underlines the critical role of interdisciplinary approaches in resolving environmental challenges, setting the stage for the automotive industry to forge paths towards a greener future.Graphical

Innovative Cloud Computing Techniques for Big Data Administration Getting Past Roadblocks and Developing New Paths

The digital era's introduction of big data has fundamentally altered the landscape of information management, providing organizations across a wide range of industries with unprecedented opportunities and difficulties. This scientific inquiry digs into the convergence of cloud computing and big data, presenting a thorough history of their evolution and underlining their critical roles in modern data governance. Through a meticulous examination of the critical role of effective big data management across diverse industries, the paper emphasizes the critical importance of cloud computing solutions in addressing the multifaceted complexities arising from data volume, velocity, variety, veracity, and security. Conventional big data handling approaches inside cloud computing frameworks, such as batch processing, stream processing, data warehousing, and virtualization, are thoroughly evaluated in light of their inherent constraints. The paper then examines modern cloud computing solutions that have been precisely tuned to handle the issues of scalability, real-time processing, and security inherent in large data management. The investigation is complemented by the inclusion of informative case studies and real-world examples that demonstrate the practical applicability of these pioneering ideas across a wide range of sectors, from entertainment to manufacturing. Furthermore, the paper defines emerging technologies and trends in cloud computing, highlights persistent obstacles, and outlines prospects for innovation and future research in the field. Ethical and regulatory aspects such as data privacy, fairness, openness, and compliance are extensively reviewed and emphasized. Finally, the article recommends for ongoing innovation, academic investigation, and ethical discernment to fully realize the promise of big data while limiting related hazards, emphasizing the dynamic nature of big data governance within the context of cloud computing. Key Words: Cloud computing, Big data, Hadoop ,Spark, AI and machine learning integration Blockchain

Online Auction System Using Python, Blockchain and Cloud Computing

Abstract: The "Advanced Online Auction System Using Python, Cloud Computing, and Block chain Integration” is a pioneering project designed to transform the landscape of online auctions. This project leverages the synergistic power of Python programming, cloud computing infrastructure, and Block chain technology to create an innovative online auction platform. Our system offers a secure and transparent environment for auction participants, ensuring trust and credibility throughout the auction process. Through the integration of cutting-edge technologies, it addresses the evolving needs and challenges of online auctions, providing an efficient and user- friendly experience. With a focus on security, transparency, scalability, and user engagement, this project aims to revolutionize the online auction industry, setting new standards for efficiency and trustworthiness. By combining these technologies, it represents a significant step forward in the world of online auctions and promise store shape the way auctions are conducted and experienced.

A benchmark dataset with Knowledge Graph generation for Industry 4.0 production lines

Industry 4.0 (I4.0) is a new era in the industrial revolution that emphasizes machine connectivity, automation, and data analytics. The I4.0 pillars such as autonomous robots, cloud computing, horizontal and vertical system integration, and the industrial internet of things have increased the performance and efficiency of production lines in the manufacturing industry. Over the past years, efforts have been made to propose semantic models to represent the manufacturing domain knowledge, one such model is Reference Generalized Ontological Model (RGOM).11 https://w3id.org/rgom However, its adaptability like other models is not ensured due to the lack of manufacturing data. In this paper, we aim to develop a benchmark dataset for knowledge graph generation in Industry 4.0 production lines and to show the benefits of using ontologies and semantic annotations of data to showcase how the I4.0 industry can benefit from KGs and semantic datasets. This work is the result of collaboration with the production line managers, supervisors, and engineers in the football industry to acquire realistic production line data22 https://github.com/MuhammadYahta/ManufacturingProductionLineDataSetGeneration-Football,.33 https://zenodo.org/record/7779522 Knowledge Graphs (KGs) or Knowledge Graph (KG) have emerged as a significant technology to store the semantics of the domain entities. KGs have been used in a variety of industries, including banking, the automobile industry, oil and gas, pharmaceutical and health care, publishing, media, etc. The data is mapped and populated to the RGOM classes and relationships using an automated solution based on JenaAPI, producing an I4.0 KG. It contains more than 2.5 million axioms and about 1 million instances. This KG enables us to demonstrate the adaptability and usefulness of the RGOM. Our research helps the production line staff to take timely decisions by exploiting the information embedded in the KG. In relation to this, the RGOM adaptability is demonstrated with the help of a use case scenario to discover required information such as current temperature at a particular time, the status of the motor, tools deployed on the machine, etc.

Factors Influencing the Integration of Cloud Computing in Modern Accounting Practices in the Malaysian Accounting Sector: A Conceptual Study

In the dynamic landscape of contemporary business, cloud computing has become a prevalent tool for organizations to manage and store data efficiently as more organizations adopt cloud-based solutions for their IT needs. In this case, the adoption of cloud computing is beneficial not only for managing and storing data but also for implementing effective cloud accounting systems. However, amidst the transformation and benefits, adopting and integrating cloud computing in accounting practices are not without challenges. This study aims to delve deeper into these issues to investigate the key factors influencing the adoption of cloud computing in accounting, particularly in the Malaysian business landscape. The factors explore the distinctive benefits like security, cost-effectiveness and flexibility while also shedding light on the associated challenges, with a particular emphasis on security. Despite these challenges, such as security vulnerabilities, cost overruns and potential for data loss, the study asserts that the benefits of integrating cloud computing into accounting practices substantially outweigh the hurdles. Consequently, it recommends strategic steps to ensure a smooth transition to cloud-based accounting systems. These measures are critical in aiding businesses in navigating through the challenges while capitalizing on the transformative potential of cloud computing, thereby staying competitive and agile in the rapidly evolving digital era.

Integration of Cloud Computing and Wearable Technology for Enhanced Interactivity

The emergence of wearable computing has revolutionized the way we interact with technology, blurring the lines between the physical and digital worlds. In this research, we explore the dynamic interaction between cloud technology and wearable computing, a synergy that is reshaping the landscape of personal technology and data management. The study delves into how cloud comput-ing provides a powerful platform for wearable devices, enabling enhanced data processing capabili-ties, storage, and ubiquitous access to information. We investigate the various applications of this interaction, ranging from health monitoring to augmented reality, emphasizing the transformative impact on everyday life and various industries. The research also addresses the challenges inherent in this integration, such as data security, privacy concerns, and the need for robust, low-latency communication networks. Through a comprehensive analysis of current trends and future prospects, this study highlights the potential of cloud-assisted wearable technology in creating more personal-ized, efficient, and interconnected experiences. The findings suggest that the convergence of cloud technology and wearable computing not only offers significant benefits in terms of functionality and user experience but also poses critical considerations for data governance and ethical implications in an increasingly connected world.

CONCEPTUALIZING CLOUD COMPUTING IN FINANCIAL SERVICES: OPPORTUNITIES AND CHALLENGES IN AFRICA-US CONTEXTS

This review paper examines the transformative role of cloud computing in financial services, with a focus on the comparative analysis of opportunities and challenges in Africa and the US. It explores how cloud computing enhances operational efficiency, drives innovation, and facilitates cross-border collaboration. Despite the significant potential, the paper identifies regulatory compliance, infrastructure limitations, and data security concerns as major barriers to adoption. Recommendations for policymakers, financial institutions, and technology providers are provided to address these challenges and support the integration of cloud computing. The paper underscores the importance of harmonized regulations, investment in infrastructure, and robust cybersecurity measures to unlock the full potential of cloud computing in financial services. Future research directions are suggested to further investigate cloud technology's impact on financial inclusion and regulatory frameworks.
 Keywords: Cloud Computing, Financial Services, Regulatory Challenges, Africa-US Comparison

Blockchain for Distributed Systems Security in Cloud Computing: A Review of Applications and Challenges

The blockchain is a technology that utilizes a decentralized and distributed ledger system to enhance security in cloud computing for distributed systems. It has gained significant attention in various applications, including the Internet of Things (IoT) and cloud computing. However, the blockchain has scalability limitations that restrict its ability to handle different types of transactions effectively. On the other hand, cloud computing provides the availability of shared computer system resources on demand, but it faces challenges related to automation, process management, policy, and others. By combining blockchain technology with cloud computing in a unified system, it is possible to improve data integrity, resource management, pricing, fair compensation, and resource allocation. This article examines the applications and challenges of blockchain, emphasizing how it ensures data integrity, transparency, and resistance to tampering. It also explores various use cases to address obstacles like scalability issues and interoperability concerns, providing a comprehensive overview of the intersection between blockchain, distributed systems, and cloud computing security. The integration of cloud computing and blockchain is important for business applications because it offers advantages in terms of privacy, security, and service support. This review provides an extensive and up-to-date summary of the integration of cloud computing and blockchain, highlighting its significance in business contexts.

Application of cloud computing platform in industrial big data processing

With the rapid growth and increasing complexity of industrial big data, traditional data processing methods are facing many challenges. This article takes an in-depth look at the application of cloud computing technology in industrial big data processing and explores its potential impact on improving data processing efficiency, security, and cost-effectiveness. The article first reviews the basic principles and key characteristics of cloud computing technology, and then analyzes the characteristics and processing requirements of industrial big data. In particular, this study focuses on the application of cloud computing in real-time data processing, predictive maintenance, and optimization, and demonstrates its practical effects through case studies. At the same time, this article also discusses the main challenges encountered during the implementation process, such as data security, privacy protection, performance and scalability issues, and proposes corresponding solution strategies. Finally, this article looks forward to the future trends of the integration of cloud computing and industrial big data, as well as the application prospects of emerging technologies such as artificial intelligence and machine learning in this field. The results of this study not only provide practical guidance for cloud computing applications in the industry, but also provide a basis for further research in academia.

Fog Cloud Computing and IoT Integration for AI enabled Autonomous Systems in Robotics

Fog Cloud Computing and the Internet of Things are transforming robotics by empowering AI-enabled autonomous systems. This study analyzes the benefits, drawbacks, and uses of this integration. AI-enabled autonomous robots can use edge computing and cloud resources for real-time data processing and decision-making, improving their performance and adaptability. Communication protocols, data management, security, and scalability are examined in the ecosystem. Case studies reveal how this confluence affects robotics applications. This research shows how FCC, IoT, and AI may improve robotic systems' efficiency, intelligence, and autonomy. The article covers AI-enabled autonomous systems in transportation, manufacturing, healthcare, agriculture, and smart cities. These technologies can improve productivity and safety in many fields, from self-driving automobiles to surgical robots. Integrating these technologies raises safety, ethical decision-making, data privacy, and security concerns. The report emphasizes transparent and ethical AI algorithms, unbiased decision-making, and regulatory frameworks to enable responsible integration and mitigate dangers. In the future, AI-enabled autonomous systems will be shaped by improved AI algorithms, multi-modal sensing, human-robot collaboration, and edge intelligence. It emphasizes the necessity of interdisciplinary collaboration and ethical considerations in responsible technology development. This study concludes with a detailed analysis of fog/cloud computing, IoT, and AI in robotics, revealing the immense promise and problems of AI-enabled autonomous systems. Responsible development and collaboration can help us negotiate this transformational frontier and create a safer, more efficient, and innovative society with AI-driven autonomous systems.

An Ensemble-Based Framework for Sophisticated Crop Classification Exploiting Google Earth Engine

Corn and soybeans play pivotal roles in the agricultural landscape of the United States, and accurately delineating their cultivation areas is indispensable for ensuring food security and addressing hunger-related challenges. Traditional methods for crop mapping are both labor-intensive and time-consuming. Fortunately, the advent of high-resolution imagery, exemplified by Sentinel-2A (S2A), has opened avenues for precise identification of these crops at a field scale, with the added advantage of cloud computing. This paper presents an innovative algorithm designed for large-scale mapping of corn and soybean planting areas on the Google Cloud Engine, drawing inspiration from symmetrical theory. The proposed methodology encompasses several sequential steps. First, S2A data undergo processing incorporating phenological information and spectral characteristics. Subsequently, texture features derived from the grayscale matrix are synergistically integrated with spectral features in the first step. To enhance algorithmic efficiency, the third step involves a feature importance analysis, facilitating the retention of influential bands while eliminating redundant features. The ensuing phase employs three base classifiers for feature training, and the final result maps are generated through a collective voting mechanism based on the classification results from the three classifiers. Validation of the proposed algorithm was conducted in two distinct research areas: Ford in Illinois and White in Indiana, showcasing its commendable classification capabilities for these crops. The experiments underscore the potential of this method for large-scale mapping of crop areas through the integration of cloud computing and high-resolution imagery.

Leveraging Fog Computing, Blockchain, and IoT for Enhanced Healthcare Monitoring Services

Abstract: With the rapid evolution of technology, smart cities have emerged as geographic areas powered by advanced information and communication technologies. These advancements have been significantly influenced by integrating smart technologies, such as the Internet of Things (IoT), blockchain, and fog computing. This paper aims to explore the impact and potential of these technologies on healthcare services within the context of smart cities. Fog computing, as a solution to the challenges faced by integrated Cloud Computing (CC) in IoT applications, offers a promising approach by bringing computation closer to the edge devices. Unlike traditional cloud-based approaches, fog computing enables local data processing and storage, resulting in improved performance, security, and reduced latency. Furthermore, fog computing acts as a middle layer between IoT sensors/devices and cloud data centers, facilitating efficient resource provisioning and service administration. In smart cities, IoT, blockchain, and fog computing are pivotal in enhancing healthcare services. While IoT is widely integrated into healthcare systems, it faces challenges such as cost efficiency, data privacy, and data interoperability. In contrast, blockchain technology offers solutions for safeguarding private data, establishing decentralized databases, and enhancing data interoperability.

Unified ensemble federated learning with cloud computing for online anomaly detection in energy-efficient wireless sensor networks

Anomaly detection in Wireless Sensor Networks (WSNs) is critical for their reliable and secure operation. Optimizing resource efficiency is crucial for reducing energy consumption. Two new algorithms developed for anomaly detection in WSNs—Ensemble Federated Learning (EFL) with Cloud Integration and Online Anomaly Detection with Energy-Efficient Techniques (OAD-EE) with Cloud-based Model Aggregation. EFL with Cloud Integration uses ensemble methods and federated learning to enhance detection accuracy and data privacy. OAD-EE with Cloud-based Model Aggregation uses online learning and energy-efficient techniques to conserve energy on resource-constrained sensor nodes. By combining EFL and OAD-EE, a comprehensive and efficient framework for anomaly detection in WSNs can be created. Experimental results show that EFL with Cloud Integration achieves the highest detection accuracy, while OAD-EE with Cloud-based Model Aggregation has the lowest energy consumption and fastest detection time among all algorithms, making it suitable for real-time applications. The unified algorithm contributes to the system's overall efficiency, scalability, and real-time response. By integrating cloud computing, this algorithm opens new avenues for advanced WSN applications. These promising approaches for anomaly detection in resource constrained and large-scale WSNs are beneficial for industrial applications.

Earthquake Early Warning Systems by Using Cloud and IoT

Earthquake early warning systems play a crucial role in minimizing the impact of seismic events on human life and infrastructure. This research paper explores the integration of Cloud Computing and Internet of Things (IoT) technologies to enhance the effectiveness of earthquake early warning systems. The proposed system aims to provide real-time data collection, analysis, and dissemination of alerts through a distributed network, leveraging the scalability and flexibility of cloud-based platforms. This paper discusses the architecture, components, and benefits of the proposed system, along with a case study to illustrate its potential in earthquake-prone regions.

Navigating Digital Transformation and Knowledge Structures: Insights for Small and Medium-Sized Enterprises

AbstractIn today’s rapidly evolving business landscape, digital transformation has become synonymous with the strategic integration of cloud computing, mobile internet, social media, and big data. This paradigm shift has reshaped traditional business models, presenting organizational challenges and opportunities. Small and medium-sized enterprises (SMEs), in particular, face a unique set of obstacles as they navigate the complexities of this digital revolution. This research paper delves into the intricacies of digital transformation within the SME context. It acknowledges the significance of this transformation for enhancing core competencies and sustainable development, emphasizing the convergence of business strategy and technological advancement. To provide a comprehensive understanding, this study employs bibliometric analysis using CiteSpace, shedding light on underappreciated procedures, steps, and data related to SMEs’ digital transformation. A systematic literature review extracts a theoretical framework, offering valuable insights and guidance for SMEs operating in an increasingly digitalized business environment. The research contributes to the ongoing global discussion on digital transformation and highlights differences and similarities across various research domains. It identifies thematic clusters such as “Digital transformation of SMEs,” “Industry 4.0,” “Adoption paths,” and “Business model,” revealing trends, themes, and shifts in studying digital transformation in SMEs worldwide. This paper provides a valuable synthesis of previous research accomplishments, offering theoretical implications for academia and practical managerial insights. It emphasizes the importance of collaborative mechanisms and research networks, fostering knowledge exchange and driving innovation in the field of digital transformation for SMEs, both nationally and internationally.

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.

Security aspects in IoT based cloud computing

Cloud computing offers a flexible framework in which data and resources are spread across different locations and can be accessed from various industrial environments. This technology has revolutionized the way resources such as data, services, and applications are used, stored, and shared in industrial applications. Over the past decade, industries have rapidly embraced cloud computing due to its advantages of enhanced accessibility, cost reduction, and improved performance. Moreover, the integration of cloud computing has led to significant advancements in the field of the Internet of Things (IoT). However, this quick shift to the cloud has also introduced various security concerns and challenges. Traditional security solutions are not always suitable or effective for cloud-based systems. Despite the continuous use of complex cyber weapons, efforts have been made in recent years to address the security issues and concerns associated with cloud platforms. The rapid progress of deep learning (DL) in the field of artificial intelligence (AI) has provided opportunities to tackle these security challenges in the cloud. The research presented in this study encompasses a comprehensive survey of the enabling architecture, services, configurations, and security models for cloud-based IoT. It also categorizes the security concerns in IoT within four major categories (data, network and service, applications, and people-related security issues) and provides a detailed discussion on each category. Furthermore, the study examines the latest advancements in cloud-based IoT attacks, analyzes significant security issues within each category, and presents the limitations from a broader perspective encompassing general, artificial intelligence, and deep learning aspects.

Integration of cloud computing: a new transition for Bangladesh power grid empowerment from reliability to grid resiliency

Integration of cloud computing: a new transition for Bangladesh power grid empowerment from reliability to grid resiliency

Optimization and Scalability of Educational Platforms: Integration of Artificial Intelligence and Cloud Computing

The intersection between technology and education has taken on unprecedented relevance, driven by the promise of transforming teaching and learning through advanced digital tools. This study proposes a comprehensive exploration of how cloud computing and artificial intelligence converge to impact education, focusing on accessibility, efficiency, and quality of learning. A mixed-research design identified a 25% improvement in the personalization of educational content thanks to AI and a 60% increase in simultaneous user capacity through cloud computing. Additionally, a significant reduction in administrative errors and improvements in scalability were observed without sacrificing quality. The results demonstrate that these technologies not only improve efficiency and accessibility in education but also enrich the learning experience. By comparing these findings with previous research, this study highlights the synergistic value of these technologies and positions itself as a critical resource to guide future developments and improvements in the education sector in a digitally advanced world.

Location Based Power Reduction Cloud Integrated Social Sensor Network

It is great to hear about the advancements in wireless sensor networks and their applications, as well as the integration of cloud computing to enhance data analysis and storage capabilities. Indeed, these technologies have opened up numerous possibilities across various fields, including infrastructure tracking, environmental monitoring, healthcare, and more. The concept of a social sensor cloud, as you mentioned, brings an interesting dimension to this technology landscape by focusing on knowledge-sharing and connecting like-minded individuals or organizations. This could potentially lead to more collaborative and efficient solutions across a wide range of domains. Energy efficiency is a critical consideration in the design and operation of wireless sensor networks and the cloud infrastructure that supports them. The limited battery life of sensors necessitates careful management of energy consumption to ensure optimal functionality and longevity. Sleep scheduling methods are a common technique used to manage energy consumption in these networks. By coordinating when sensors are active and when they are in a low-power sleep mode, energy consumption can be significantly reduced without compromising the network's overall effectiveness. In the context of the Social Sensor Cloud, managing energy efficiency becomes even more crucial due to the shorter battery life of the sensors involved. This is particularly relevant given the growing concerns about environmental sustainability and the need to reduce energy consumption across technological systems. It's clear that your research paper addresses these challenges head-on, by exploring energy-efficient techniques for the Social Sensor Cloud. Sleep scheduling is just one of the many strategies that researchers and engineers are working on to strike a balance between functionality and energy consumption. Other methods might include optimizing data transfer protocols, developing energy-harvesting mechanisms, and enhancing sensor hardware efficiency. As technology continues to evolve, the integration of wireless sensor networks, cloud computing, and social networks will likely pave the way for innovative solutions and transformative applications. Addressing energy efficiency concerns will undoubtedly play a crucial role in ensuring the long-term viability and positive impact of these technologies.