Journal Of Artificial Intelligence Cloud Computing

Journal of Artificial Intelligence & Cloud Computing: A Deep Dive into the Synergistic Future

The convergence of artificial intelligence (AI) and cloud computing is reshaping industries and driving unprecedented technological advancements. This powerful synergy is creating innovative solutions across diverse sectors, from healthcare and finance to manufacturing and transportation. This comprehensive guide delves into the burgeoning field of AI and cloud computing, exploring its applications, challenges, and the future it promises. We'll examine the leading journals dedicated to this exciting intersection, uncover key research areas, and highlight the transformative potential of this technological partnership. Prepare to embark on a journey into the heart of a revolution.

Understanding the Synergy: AI and Cloud Computing

The relationship between AI and cloud computing is symbiotic. Cloud computing provides the scalable infrastructure, processing power, and storage capacity necessary for training and deploying complex AI models. Without the vast resources of the cloud, the development and application of advanced AI algorithms would be severely hampered. Conversely, AI is revolutionizing cloud computing itself, enabling more efficient resource allocation, enhanced security, and the creation of intelligent cloud services.

1. Scalability and Cost-Effectiveness: Cloud platforms offer the flexibility to scale resources up or down based on demand. This is crucial for AI, where training models can require immense computational power. Cloud computing significantly reduces the upfront investment in hardware and infrastructure, making AI accessible to a wider range of organizations.

2. Data Storage and Management: AI thrives on data. Cloud platforms provide secure and scalable storage solutions for massive datasets, enabling efficient data management and analysis – a cornerstone of AI development. Advanced cloud-based data lakes and warehouses allow for the streamlined integration of data from various sources, crucial for training effective AI models.

3. Enhanced Security: Cloud providers invest heavily in security infrastructure, protecting sensitive data from unauthorized access. This is critical for AI applications, particularly those handling personal or confidential information. Advanced security features like encryption and access controls are integral to safeguarding AI-powered systems in the cloud.

4. Accelerated Innovation: The accessibility of cloud resources and pre-trained models allows developers to focus on application development rather than infrastructure management. This accelerates the pace of innovation, enabling faster deployment of AI solutions across diverse industries.

Key Research Areas in the Journal of Artificial Intelligence & Cloud Computing

A dedicated journal focusing on this intersection would likely encompass a broad spectrum of research areas, including:

1. AI Model Training and Deployment on Cloud Platforms: This explores optimal strategies for training and deploying various AI models (e.g., deep learning, machine learning) on different cloud architectures (e.g., AWS, Azure, GCP). Research might focus on optimizing performance, minimizing costs, and ensuring scalability.

2. Cloud-Based AI for Specific Applications: Studies focusing on the implementation of AI in specific sectors like healthcare (e.g., medical image analysis, disease prediction), finance (e.g., fraud detection, risk assessment), and manufacturing (e.g., predictive maintenance, quality control) are essential.

3. Security and Privacy in Cloud-Based AI Systems: This research addresses crucial aspects of data security and privacy in AI systems deployed on the cloud. Topics would encompass data encryption, access control mechanisms, and mitigating vulnerabilities specific to cloud-based AI environments.

4. AI-driven Cloud Resource Management: This area focuses on leveraging AI to optimize resource allocation and management in cloud environments. AI algorithms can predict resource needs, automate provisioning, and improve overall efficiency, minimizing costs and maximizing performance.

5. Ethical Considerations of Cloud-Based AI: As AI becomes more prevalent, ethical considerations regarding bias, fairness, transparency, and accountability are paramount. Research in this area is critical for responsible AI development and deployment.

Journal Structure: "Advances in AI & Cloud Computing"

Name: Advances in AI & Cloud Computing

Contents:

Introduction: Defining the scope of AI and cloud computing convergence, highlighting the journal's aims and objectives.
Chapter 1: Foundations of AI and Cloud Computing: Explaining the fundamental concepts of AI and cloud computing, their individual strengths, and the rationale behind their integration.
Chapter 2: AI Model Deployment Strategies on Cloud Platforms: A deep dive into different deployment strategies, their advantages and disadvantages, and best practices for optimization.
Chapter 3: AI-Driven Cloud Resource Management: Exploring the use of AI for efficient resource allocation, cost optimization, and performance enhancement in cloud environments.
Chapter 4: Case Studies of AI and Cloud Computing Applications: Presenting real-world examples of successful AI and cloud computing implementations across diverse sectors.
Chapter 5: Security and Privacy Challenges in Cloud-Based AI: Addressing the security and privacy risks associated with cloud-based AI systems and proposing mitigation strategies.
Chapter 6: Ethical Considerations and Responsible AI Development: Examining the ethical implications of AI and cloud computing and promoting responsible development practices.
Chapter 7: Future Trends and Research Directions: Discussing emerging trends and potential future research directions within the field.
Conclusion: Summarizing the key findings and emphasizing the transformative potential of the synergistic relationship between AI and cloud computing.


Detailed Explanation of Each Chapter:

(The following sections expand on the outline above, providing in-depth explanations of each chapter’s content.)

Chapter 1: Foundations of AI and Cloud Computing: This chapter would lay the groundwork by defining key concepts such as machine learning, deep learning, neural networks, cloud computing architectures (IaaS, PaaS, SaaS), and virtualization. It would emphasize the unique capabilities of each and how their combination creates synergistic effects.

Chapter 2: AI Model Deployment Strategies on Cloud Platforms: This chapter would delve into the practical aspects of deploying AI models on different cloud platforms, including containerization (Docker, Kubernetes), serverless computing (AWS Lambda, Azure Functions), and model optimization techniques. Best practices for scaling and managing AI workloads would also be covered.

Chapter 3: AI-Driven Cloud Resource Management: This chapter would explore how AI algorithms can be used to automate resource provisioning, predict resource needs, and optimize performance. Topics such as reinforcement learning for resource allocation and anomaly detection for identifying performance bottlenecks would be included.

Chapter 4: Case Studies of AI and Cloud Computing Applications: This chapter would showcase real-world examples of successful AI and cloud computing applications across different industries. Case studies might include using AI for medical image analysis in healthcare, fraud detection in finance, or predictive maintenance in manufacturing. The success factors and lessons learned from these implementations would be analyzed.

Chapter 5: Security and Privacy Challenges in Cloud-Based AI: This chapter would address the security risks associated with storing and processing sensitive data in cloud-based AI systems. Topics such as data encryption, access control, and the protection against adversarial attacks would be covered. Compliance with relevant data privacy regulations (GDPR, CCPA) would also be discussed.

Chapter 6: Ethical Considerations and Responsible AI Development: This chapter would explore the ethical implications of using AI in various applications. Bias in algorithms, fairness, transparency, and accountability would be key themes. Guidelines and best practices for developing and deploying responsible AI systems would be presented.

Chapter 7: Future Trends and Research Directions: This chapter would examine emerging trends and future research directions in the field of AI and cloud computing. Topics such as edge AI, quantum computing, and the convergence with other technologies (e.g., blockchain, IoT) would be explored.

Frequently Asked Questions (FAQs)

1. What are the main benefits of using cloud computing for AI development? Cloud computing offers scalability, cost-effectiveness, enhanced security, and faster innovation compared to on-premise solutions.

2. What are some common challenges in deploying AI models on cloud platforms? Challenges include managing data security and privacy, optimizing model performance, and scaling resources effectively to meet fluctuating demands.

3. How can AI improve cloud resource management? AI can predict resource needs, automate provisioning, optimize performance, and reduce costs by identifying inefficiencies and anomalies.

4. What ethical considerations should be addressed when developing cloud-based AI systems? Ethical concerns include bias in algorithms, data privacy, transparency, accountability, and the potential for job displacement.

5. What are some real-world examples of successful AI and cloud computing applications? Examples include medical image analysis, fraud detection, predictive maintenance, and personalized recommendations.

6. How can we ensure the security and privacy of data in cloud-based AI systems? Robust security measures such as data encryption, access control, and regular security audits are essential.

7. What are some emerging trends in the field of AI and cloud computing? Trends include edge AI, quantum computing, and the integration with other technologies like IoT and blockchain.

8. What are the future research directions in this field? Future research will focus on developing more efficient and robust AI models, improving security and privacy, addressing ethical concerns, and exploring new applications in various sectors.

9. Where can I find more information on the intersection of AI and cloud computing? Numerous research papers, journals, and conferences are dedicated to this field. Online resources and professional organizations are also valuable sources of information.

Related Articles:

1. The Role of Cloud Computing in Deep Learning: Explores how cloud platforms facilitate the training and deployment of complex deep learning models.

2. AI-Powered Cloud Security: A Comprehensive Overview: Examines the use of AI for enhancing cloud security, including threat detection and response.

3. Ethical Frameworks for Cloud-Based AI: Discusses ethical guidelines and best practices for developing and deploying responsible AI systems in the cloud.

4. Serverless Computing for AI: Advantages and Challenges: Focuses on the benefits and drawbacks of using serverless architectures for AI applications.

5. Optimizing AI Model Performance on Cloud Platforms: Provides practical tips and techniques for improving the efficiency and accuracy of AI models deployed in the cloud.

6. Data Privacy and Security in Cloud-Based AI Systems: A deep dive into data protection strategies and compliance with relevant regulations in cloud-based AI.

7. The Future of AI and Cloud Computing: Emerging Trends and Predictions: Explores anticipated advancements and future directions in the field.

8. Case Studies: AI and Cloud Computing in Healthcare: Illustrates how AI and cloud computing are transforming healthcare through real-world examples.

9. Cost Optimization Strategies for Cloud-Based AI: Provides practical advice on minimizing the costs associated with running AI workloads in the cloud.


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  journal of artificial intelligence cloud computing: Artificial Intelligence and Machine Learning for EDGE Computing Rajiv Pandey, Sunil Kumar Khatri, Neeraj Kumar Singh, Parul Verma, 2022-04-26 Artificial Intelligence and Machine Learning for Predictive and Analytical Rendering in Edge Computing focuses on the role of AI and machine learning as it impacts and works alongside Edge Computing. Sections cover the growing number of devices and applications in diversified domains of industry, including gaming, speech recognition, medical diagnostics, robotics and computer vision and how they are being driven by Big Data, Artificial Intelligence, Machine Learning and distributed computing, may it be Cloud Computing or the evolving Fog and Edge Computing paradigms. Challenges covered include remote storage and computing, bandwidth overload due to transportation of data from End nodes to Cloud leading in latency issues, security issues in transporting sensitive medical and financial information across larger gaps in points of data generation and computing, as well as design features of Edge nodes to store and run AI/ML algorithms for effective rendering. - Provides a reference handbook on the evolution of distributed systems, including Cloud, Fog and Edge Computing - Integrates the various Artificial Intelligence and Machine Learning techniques for effective predictions at Edge rather than Cloud or remote Data Centers - Provides insight into the features and constraints in Edge Computing and storage, including hardware constraints and the technological/architectural developments that shall overcome those constraints
  journal of artificial intelligence cloud computing: Machine Learning Approach for Cloud Data Analytics in IoT Sachi Nandan Mohanty, Jyotir Moy Chatterjee, Monika Mangla, Suneeta Satpathy, Sirisha Potluri, 2021-07-14 Machine Learning Approach for Cloud Data Analytics in IoT The book covers the multidimensional perspective of machine learning through the perspective of cloud computing and Internet of Things ranging from fundamentals to advanced applications Sustainable computing paradigms like cloud and fog are capable of handling issues related to performance, storage and processing, maintenance, security, efficiency, integration, cost, energy and latency in an expeditious manner. In order to expedite decision-making involved in the complex computation and processing of collected data, IoT devices are connected to the cloud or fog environment. Since machine learning as a service provides the best support in business intelligence, organizations have been making significant investments in this technology. Machine Learning Approach for Cloud Data Analytics in IoT elucidates some of the best practices and their respective outcomes in cloud and fog computing environments. It focuses on all the various research issues related to big data storage and analysis, large-scale data processing, knowledge discovery and knowledge management, computational intelligence, data security and privacy, data representation and visualization, and data analytics. The featured technologies presented in the book optimizes various industry processes using business intelligence in engineering and technology. Light is also shed on cloud-based embedded software development practices to integrate complex machines so as to increase productivity and reduce operational costs. The various practices of data science and analytics which are used in all sectors to understand big data and analyze massive data patterns are also detailed in the book.
  journal of artificial intelligence cloud computing: IoT and Cloud Computing for Societal Good Jitendra Kumar Verma, Deepak Saxena, Vicente González-Prida, 2021-12-26 This book gathers the state-of-the-art for industrial application of scientific and practical research in the Cloud and IoT paradigms to benefit society. The book first aims to discuss and outline various aspects of tackling climate change. The authors then discuss how Cloud and IoT can help for digital health and learning from industrial aspects. The next part of book discusses technical improvements in the fields of security and privacy. The book also covers Smart Homes and IoT in agriculture. The book is targeted towards advancing undergraduate, graduate, and post graduate students, researchers, academicians, policymakers, various government officials, NGOs, and industry research professionals who are currently working in the field of science and technology either directly or indirectly to benefit common masses.
  journal of artificial intelligence cloud computing: A Citizen's Guide to Artificial Intelligence John Zerilli, 2021-02-23 A concise but informative overview of AI ethics and policy. Artificial intelligence, or AI for short, has generated a staggering amount of hype in the past several years. Is it the game-changer it's been cracked up to be? If so, how is it changing the game? How is it likely to affect us as customers, tenants, aspiring home-owners, students, educators, patients, clients, prison inmates, members of ethnic and sexual minorities, voters in liberal democracies? This book offers a concise overview of moral, political, legal and economic implications of AI. It covers the basics of AI's latest permutation, machine learning, and considers issues including transparency, bias, liability, privacy, and regulation.
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  journal of artificial intelligence cloud computing: Impacts and Challenges of Cloud Business Intelligence Aljawarneh, Shadi, Malhotra, Manisha, 2020-12-18 Cloud computing provides an easier alternative for starting an IT-based business organization that requires much less of an initial investment. Cloud computing offers a significant edge of traditional computing with big data being continuously transferred to the cloud. For extraction of relevant data, cloud business intelligence must be utilized. Cloud-based tools, such as customer relationship management (CRM), Salesforce, and Dropbox are increasingly being integrated by enterprises looking to increase their agility and efficiency. Impacts and Challenges of Cloud Business Intelligence is a cutting-edge scholarly resource that provides comprehensive research on business intelligence in cloud computing and explores its applications in conjunction with other tools. Highlighting a wide range of topics including swarm intelligence, algorithms, and cloud analytics, this book is essential for entrepreneurs, IT professionals, managers, business professionals, practitioners, researchers, academicians, and students.
  journal of artificial intelligence cloud computing: Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning Nur Zincir-Heywood, Marco Mellia, Yixin Diao, 2021-10-12 COMMUNICATION NETWORKS AND SERVICE MANAGEMENT IN THE ERA OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Discover the impact that new technologies are having on communication systems with this up-to-date and one-stop resource Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning delivers a comprehensive overview of the impact of artificial intelligence (AI) and machine learning (ML) on service and network management. Beginning with a fulsome description of ML and AI, the book moves on to discuss management models, architectures, and frameworks. The authors also explore how AI and ML can be used in service management functions like the generation of workload profiles, service provisioning, and more. The book includes a handpicked selection of applications and case studies, as well as a treatment of emerging technologies the authors predict could have a significant impact on network and service management in the future. Statistical analysis and data mining are also discussed, particularly with respect to how they allow for an improvement of the management and security of IT systems and networks. Readers will also enjoy topics like: A thorough introduction to network and service management, machine learning, and artificial intelligence An exploration of artificial intelligence and machine learning for management models, including autonomic management, policy-based management, intent based ­management, and network virtualization-based management Discussions of AI and ML for architectures and frameworks, including cloud ­systems, software defined networks, 5G and 6G networks, and Edge/Fog networks An examination of AI and ML for service management, including the automatic ­generation of workload profiles using unsupervised learning Perfect for information and communications technology educators, Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning will also earn a place in the libraries of engineers and professionals who seek a structured reference on how the emergence of artificial intelligence and machine learning techniques is affecting service and network management.
  journal of artificial intelligence cloud computing: Integrating AI in IoT Analytics on the Cloud for Healthcare Applications Jeya Mala, D., 2022-01-07 Internet of things (IoT) applications employed for healthcare generate a huge amount of data that needs to be analyzed to produce the expected reports. To accomplish this task, a cloud-based analytical solution is ideal in order to generate faster reports in comparison to the traditional way. Given the current state of the world in which every day IoT devices are developed to provide healthcare solutions, it is essential to consider the mechanisms used to collect and analyze the data to provide thorough reports. Integrating AI in IoT Analytics on the Cloud for Healthcare Applications applies artificial intelligence (AI) in edge analytics for healthcare applications, analyzes the impact of tools and techniques in edge analytics for healthcare, and discusses security solutions for edge analytics in healthcare IoT. Covering topics such as data analytics and next generation healthcare systems, it is ideal for researchers, academicians, technologists, IT specialists, data scientists, healthcare industries, IoT developers, data security analysts, educators, and students.
  journal of artificial intelligence cloud computing: Recent Advances in Artificial Intelligence and Data Engineering Pushparaj Shetty D., Surendra Shetty, 2021-10-31 This book presents select proceedings of the International Conference on Artificial Intelligence and Data Engineering (AIDE 2020). Various topics covered in this book include deep learning, neural networks, machine learning, computational intelligence, cognitive computing, fuzzy logic, expert systems, brain-machine interfaces, ant colony optimization, natural language processing, bioinformatics and computational biology, cloud computing, machine vision and robotics, ambient intelligence, intelligent transportation, sensing and sensor networks, big data challenge, data science, high performance computing, data mining and knowledge discovery, and data privacy and security. The book will be a valuable reference for beginners, researchers, and professionals interested in artificial intelligence, robotics and data engineering.
  journal of artificial intelligence cloud computing: Fuzzy Modelling Witold Pedrycz, 2012-12-06 Fuzzy Modelling: Paradigms and Practice provides an up-to-date and authoritative compendium of fuzzy models, identification algorithms and applications. Chapters in this book have been written by the leading scholars and researchers in their respective subject areas. Several of these chapters include both theoretical material and applications. The editor of this volume has organized and edited the chapters into a coherent and uniform framework. The objective of this book is to provide researchers and practitioners involved in the development of models for complex systems with an understanding of fuzzy modelling, and an appreciation of what makes these models unique. The chapters are organized into three major parts covering relational models, fuzzy neural networks and rule-based models. The material on relational models includes theory along with a large number of implemented case studies, including some on speech recognition, prediction, and ecological systems. The part on fuzzy neural networks covers some fundamentals, such as neurocomputing, fuzzy neurocomputing, etc., identifies the nature of the relationship that exists between fuzzy systems and neural networks, and includes extensive coverage of their architectures. The last part addresses the main design principles governing the development of rule-based models. Fuzzy Modelling: Paradigms and Practice provides a wealth of specific fuzzy modelling paradigms, algorithms and tools used in systems modelling. Also included is a panoply of case studies from various computer, engineering and science disciplines. This should be a primary reference work for researchers and practitioners developing models of complex systems.
  journal of artificial intelligence cloud computing: Blockchain Security in Cloud Computing K.M. Baalamurugan, S. Rakesh Kumar, Abhishek Kumar, Vishal Kumar, Sanjeevikumar Padmanaban, 2021-08-12 This book explores the concepts and techniques of cloud security using blockchain. Also discussed is the possibility of applying blockchain to provide security in various domains. The authors discuss how blockchain holds the potential to significantly increase data privacy and security while boosting accuracy and integrity in cloud data. The specific highlight of this book is focused on the application of integrated technologies in enhancing cloud security models, use cases, and its challenges. The contributors, both from academia and industry, present their technical evaluation and comparison with existing technologies. This book pertains to IT professionals, researchers, and academicians towards fourth revolution technologies.
  journal of artificial intelligence cloud computing: Big Data and Knowledge Sharing in Virtual Organizations Gyamfi, Albert, Williams, Idongesit, 2019-01-25 Knowledge in its pure state is tacit in nature—difficult to formalize and communicate—but can be converted into codified form and shared through both social interactions and the use of IT-based applications and systems. Even though there seems to be considerable synergies between the resulting huge data and the convertible knowledge, there is still a debate on how the increasing amount of data captured by corporations could improve decision making and foster innovation through effective knowledge-sharing practices. Big Data and Knowledge Sharing in Virtual Organizations provides innovative insights into the influence of big data analytics and artificial intelligence and the tools, methods, and techniques for knowledge-sharing processes in virtual organizations. The content within this publication examines cloud computing, machine learning, and knowledge sharing. It is designed for government officials and organizations, policymakers, academicians, researchers, technology developers, and students.
  journal of artificial intelligence cloud computing: CLOUD COMPUTING UNLEASHED: Navigating the Future of Digital Infrastructure Santhosh Kumar Gopal, Anil Kumar Kommrraju, Kodanda Rami Reddy Manukonda, Pavan Nutalapati, Viswanadham Mandala, .....
  journal of artificial intelligence cloud computing: Artificial Intelligence and Machine Learning for COVID-19 Fadi Al-Turjman, 2021-02-17 This book is dedicated to addressing the major challenges in fighting COVID-19 using artificial intelligence (AI) and machine learning (ML) – from cost and complexity to availability and accuracy. The aim of this book is to focus on both the design and implementation of AI-based approaches in proposed COVID-19 solutions that are enabled and supported by sensor networks, cloud computing, and 5G and beyond. This book presents research that contributes to the application of ML techniques to the problem of computer communication-assisted diagnosis of COVID-19 and similar diseases. The authors present the latest theoretical developments, real-world applications, and future perspectives on this topic. This book brings together a broad multidisciplinary community, aiming to integrate ideas, theories, models, and techniques from across different disciplines on intelligent solutions/systems, and to inform how cognitive systems in Next Generation Networks (NGN) should be designed, developed, and evaluated while exchanging and processing critical health information. Targeted readers are from varying disciplines who are interested in implementing the smart planet/environments vision via wireless/wired enabling technologies.
  journal of artificial intelligence cloud computing: Building Machine Learning and Deep Learning Models on Google Cloud Platform Ekaba Bisong, 2019-09-27 Take a systematic approach to understanding the fundamentals of machine learning and deep learning from the ground up and how they are applied in practice. You will use this comprehensive guide for building and deploying learning models to address complex use cases while leveraging the computational resources of Google Cloud Platform. Author Ekaba Bisong shows you how machine learning tools and techniques are used to predict or classify events based on a set of interactions between variables known as features or attributes in a particular dataset. He teaches you how deep learning extends the machine learning algorithm of neural networks to learn complex tasks that are difficult for computers to perform, such as recognizing faces and understanding languages. And you will know how to leverage cloud computing to accelerate data science and machine learning deployments. Building Machine Learning and Deep Learning Models on Google Cloud Platform is divided into eight parts that cover the fundamentals of machine learning and deep learning, the concept of data science and cloud services, programming for data science using the Python stack, Google Cloud Platform (GCP) infrastructure and products, advanced analytics on GCP, and deploying end-to-end machine learning solution pipelines on GCP. What You’ll Learn Understand the principles and fundamentals of machine learning and deep learning, the algorithms, how to use them, when to use them, and how to interpret your resultsKnow the programming concepts relevant to machine and deep learning design and development using the Python stack Build and interpret machine and deep learning models Use Google Cloud Platform tools and services to develop and deploy large-scale machine learning and deep learning products Be aware of the different facets and design choices to consider when modeling a learning problem Productionalize machine learning models into software products Who This Book Is For Beginners to the practice of data science and applied machine learning, data scientists at all levels, machine learning engineers, Google Cloud Platform data engineers/architects, and software developers
  journal of artificial intelligence cloud computing: Artificial Intelligence Harvard Business Review, 2019 Companies that don't use AI to their advantage will soon be left behind. Artificial intelligence and machine learning will drive a massive reshaping of the economy and society. What should you and your company be doing right now to ensure that your business is poised for success? These articles by AI experts and consultants will help you understand today's essential thinking on what AI is capable of now, how to adopt it in your organization, and how the technology is likely to evolve in the near future. Artificial Intelligence: The Insights You Need from Harvard Business Review will help you spearhead important conversations, get going on the right AI initiatives for your company, and capitalize on the opportunity of the machine intelligence revolution. Catch up on current topics and deepen your understanding of them with the Insights You Need series from Harvard Business Review. Featuring some of HBR's best and most recent thinking, Insights You Need titles are both a primer on today's most pressing issues and an extension of the conversation, with interesting research, interviews, case studies, and practical ideas to help you explore how a particular issue will impact your company and what it will mean for you and your business.
  journal of artificial intelligence cloud computing: The Economics of Artificial Intelligence Ajay Agrawal, Joshua Gans, Avi Goldfarb, Catherine Tucker, 2024-03-05 A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.
  journal of artificial intelligence cloud computing: Convergence of Cloud with AI for Big Data Analytics Danda B. Rawat, Lalit K. Awasthi, Valentina Emilia Balas, Mohit Kumar, Jitendra Kumar Samriya, 2023-03-21 CONVERGENCE of CLOUD with AI for BIG DATA ANALYTICS This book covers the foundations and applications of cloud computing, AI, and Big Data and analyses their convergence for improved development and services. The 17 chapters of the book masterfully and comprehensively cover the intertwining concepts of artificial intelligence, cloud computing, and big data, all of which have recently emerged as the next-generation paradigms. There has been rigorous growth in their applications and the hybrid blend of AI Cloud and IoT (Ambient-intelligence technology) also relies on input from wireless devices. Despite the multitude of applications and advancements, there are still some limitations and challenges to overcome, such as security, latency, energy consumption, service allocation, healthcare services, network lifetime, etc. Convergence of Cloud with AI for Big Data Analytics: Foundations and Innovation details all these technologies and how they are related to state-of-the-art applications, and provides a comprehensive overview for readers interested in advanced technologies, identifying the challenges, proposed solutions, as well as how to enhance the framework. Audience Researchers and post-graduate students in computing as well as engineers and practitioners in software engineering, electrical engineers, data analysts, and cyber security professionals.
  journal of artificial intelligence cloud computing: Artificial Intelligence in Healthcare Adam Bohr, Kaveh Memarzadeh, 2020-06-21 Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data
  journal of artificial intelligence cloud computing: Research Anthology on Artificial Intelligence Applications in Security Management Association, Information Resources, 2020-11-27 As industries are rapidly being digitalized and information is being more heavily stored and transmitted online, the security of information has become a top priority in securing the use of online networks as a safe and effective platform. With the vast and diverse potential of artificial intelligence (AI) applications, it has become easier than ever to identify cyber vulnerabilities, potential threats, and the identification of solutions to these unique problems. The latest tools and technologies for AI applications have untapped potential that conventional systems and human security systems cannot meet, leading AI to be a frontrunner in the fight against malware, cyber-attacks, and various security issues. However, even with the tremendous progress AI has made within the sphere of security, it’s important to understand the impacts, implications, and critical issues and challenges of AI applications along with the many benefits and emerging trends in this essential field of security-based research. Research Anthology on Artificial Intelligence Applications in Security seeks to address the fundamental advancements and technologies being used in AI applications for the security of digital data and information. The included chapters cover a wide range of topics related to AI in security stemming from the development and design of these applications, the latest tools and technologies, as well as the utilization of AI and what challenges and impacts have been discovered along the way. This resource work is a critical exploration of the latest research on security and an overview of how AI has impacted the field and will continue to advance as an essential tool for security, safety, and privacy online. This book is ideally intended for cyber security analysts, computer engineers, IT specialists, practitioners, stakeholders, researchers, academicians, and students interested in AI applications in the realm of security research.
  journal of artificial intelligence cloud computing: Applications of Artificial Intelligence for Smart Technology Swarnalatha, P., Prabu, S., 2020-10-30 As global communities are attempting to transform into more efficient and technologically-advanced metropolises, artificial intelligence (AI) has taken a firm grasp on various professional fields. Technology used in these industries is transforming by introducing intelligent techniques including machine learning, cognitive computing, and computer vision. This has raised significant attention among researchers and practitioners on the specific impact that these smart technologies have and what challenges remain. Applications of Artificial Intelligence for Smart Technology is a pivotal reference source that provides vital research on the implementation of advanced technological techniques in professional industries through the use of AI. While highlighting topics such as pattern recognition, computational imaging, and machine learning, this publication explores challenges that various fields currently face when applying these technologies and examines the future uses of AI. This book is ideally designed for researchers, developers, managers, academicians, analysts, students, and practitioners seeking current research on the involvement of AI in professional practices.
  journal of artificial intelligence cloud computing: Cloud Computing Dan C. Marinescu, 2013-05-30 Cloud Computing: Theory and Practice provides students and IT professionals with an in-depth analysis of the cloud from the ground up. Beginning with a discussion of parallel computing and architectures and distributed systems, the book turns to contemporary cloud infrastructures, how they are being deployed at leading companies such as Amazon, Google and Apple, and how they can be applied in fields such as healthcare, banking and science. The volume also examines how to successfully deploy a cloud application across the enterprise using virtualization, resource management and the right amount of networking support, including content delivery networks and storage area networks. Developers will find a complete introduction to application development provided on a variety of platforms. - Learn about recent trends in cloud computing in critical areas such as: resource management, security, energy consumption, ethics, and complex systems - Get a detailed hands-on set of practical recipes that help simplify the deployment of a cloud based system for practical use of computing clouds along with an in-depth discussion of several projects - Understand the evolution of cloud computing and why the cloud computing paradigm has a better chance to succeed than previous efforts in large-scale distributed computing
  journal of artificial intelligence cloud computing: Convergence of Cloud Computing, AI, and Agricultural Science Sharma, Avinash Kumar, Chanderwal, Nitin, Khan, Rijwan, 2023-08-18 Convergence of Cloud Computing, AI, and Agricultural Science explores the transformative potential of integrating cutting-edge technologies into the field of agriculture. With the rapid advancements in cloud computing, Artificial Intelligence (AI), and the Internet of Things (IoT), this research presents a comprehensive framework for monitoring agriculture farms remotely using a smart cloud-based system. The book delves into the application of AI-based machine learning models, such as the Support Vector Machine (SVM), to accurately classify and process the collected data. This advanced research reference book also explores how digital information can provide farmers with information about international markets, enabling them to make informed decisions regarding their crops. With its academic tone and in-depth exploration of cloud computing in smart agriculture, this book serves as an essential resource for researchers, academics, and professionals in the fields of agriculture, computer science, and environmental science. By examining the convergence of cloud computing, AI, and agricultural science, it provides a roadmap for harnessing technology to revolutionize farming practices and ensure sustainable agri-food systems in the digital era.
  journal of artificial intelligence cloud computing: Web, Artificial Intelligence and Network Applications Leonard Barolli, Makoto Takizawa, Fatos Xhafa, Tomoya Enokido, 2019-03-14 The aim of the book is to provide latest research findings, innovative research results, methods and development techniques from both theoretical and practical perspectives related to the emerging areas of Web Computing, Intelligent Systems and Internet Computing. As the Web has become a major source of information, techniques and methodologies that extract quality information are of paramount importance for many Web and Internet applications. Data mining and knowledge discovery play key roles in many of today’s prominent Web applications such as e-commerce and computer security. Moreover, the outcome of Web services delivers a new platform for enabling service-oriented systems. The emergence of large scale distributed computing paradigms, such as Cloud Computing and Mobile Computing Systems, has opened many opportunities for collaboration services, which are at the core of any Information System. Artificial Intelligence (AI) is an area of computer science that build intelligent systems and algorithms that work and react like humans. The AI techniques and computational intelligence are powerful tools for learning, adaptation, reasoning and planning. They have the potential to become enabling technologies for the future intelligent networks. Recent research in the field of intelligent systems, robotics, neuroscience, artificial intelligence and cognitive sciences are very important for the future development and innovation of Web and Internet applications.
  journal of artificial intelligence cloud computing: Machine Learning and Cognitive Computing for Mobile Communications and Wireless Networks Krishna Kant Singh, Akansha Singh, Korhan Cengiz, Dac-Nhuong Le, 2020-07-08 Communication and network technology has witnessed recent rapid development and numerous information services and applications have been developed globally. These technologies have high impact on society and the way people are leading their lives. The advancement in technology has undoubtedly improved the quality of service and user experience yet a lot needs to be still done. Some areas that still need improvement include seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability and so on. Thus, it is highly desirable to develop smart technologies for communication to improve the overall services and management of wireless communication. Machine learning and cognitive computing have converged to give some groundbreaking solutions for smart machines. With these two technologies coming together, the machines can acquire the ability to reason similar to the human brain. The research area of machine learning and cognitive computing cover many fields like psychology, biology, signal processing, physics, information theory, mathematics, and statistics that can be used effectively for topology management. Therefore, the utilization of machine learning techniques like data analytics and cognitive power will lead to better performance of communication and wireless systems.
  journal of artificial intelligence cloud computing: Applications of Computational Science in Artificial Intelligence Nayyar, Anand, Kumar, Sandeep, Agrawal, Akshat, 2022-04-22 Computational science, in collaboration with engineering, acts as a bridge between hypothesis and experimentation. It is essential to use computational methods and their applications in order to automate processes as many major industries rely on advanced modeling and simulation. Computational science is inherently interdisciplinary and can be used to identify and evaluate complicated systems, foresee their performance, and enhance procedures and strategies. Applications of Computational Science in Artificial Intelligence delivers technological solutions to improve smart technologies architecture, healthcare, and environmental sustainability. It also provides background on key aspects such as computational solutions, computation framework, smart prediction, and healthcare solutions. Covering a range of topics such as high-performance computing and software infrastructure, this reference work is ideal for software engineers, practitioners, researchers, scholars, academicians, instructors, and students.
  journal of artificial intelligence cloud computing: Computational Intelligence for Green Cloud Computing and Digital Waste Management Kumar, K. Dinesh, Varadarajan, Vijayakumar, Nasser, Nidal, Poluru, Ravi Kumar, 2024-02-27 In the digital age, the relentless growth of data centers and cloud computing has given rise to a pressing dilemma. The power consumption of these facilities is spiraling out of control, emitting massive amounts of carbon dioxide, and contributing to the ever-increasing threat of global warming. Studies show that data centers alone are responsible for nearly eighty million metric tons of CO2 emissions worldwide, and this figure is poised to skyrocket to a staggering 8000 TWh by 2030 unless we revolutionize our approach to computing resource management. The root of this problem lies in inefficient resource allocation within cloud environments, as service providers often over-provision computing resources to avoid Service Level Agreement (SLA) violations, leading to both underutilization of resources and a significant increase in energy consumption. Computational Intelligence for Green Cloud Computing and Digital Waste Management stands as a beacon of hope in the face of the environmental and technological challenges we face. It introduces the concept of green computing, dedicated to creating an eco-friendly computing environment. The book explores innovative, intelligent resource management methods that can significantly reduce the power consumption of data centers. From machine learning and deep learning solutions to green virtualization technologies, this comprehensive guide explores innovative approaches to address the pressing challenges of green computing. Whether you are an educator teaching about green computing, an environmentalist seeking sustainability solutions, an industry professional navigating the digital landscape, a resolute researcher, or simply someone intrigued by the intersection of technology and sustainability, this book offers an indispensable resource.
  journal of artificial intelligence cloud computing: Applications of Cloud Computing Prerna Sharma, Moolchand Sharma, Mohamed Elhoseny, 2020-11-12 In the era of the Internet of Things and with the explosive worldwide growth of electronic data volume, and associated need of processing, analysis, and storage of such a humongous amount of data, it has now become mandatory to exploit the power of massively parallel architecture for fast computation. Cloud computing provides a cheap source of such a computing framework for a large volume of data for real-time applications. It is, therefore, not surprising to see that cloud computing has become a buzzword in the computing fraternity over the last decade. Applications of Cloud Computing: Approaches and Practices lays a good foundation for the core concepts and principles of cloud computing applications, walking the reader through the fundamental ideas with expert ease. The book progresses on the topics in a step-by-step manner. It reinforces theory with a full-fledged pedagogy designed to enhance students' understanding and offer them a practical insight into the applications of it. It is a valuable source of knowledge for researchers, engineers, practitioners, and graduate and doctoral students working in the field of cloud computing. It will also be useful for faculty members of graduate schools and universities.
  journal of artificial intelligence cloud computing: Proceedings of the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023) Pushpendu Kar, Jiayang Li, Yuhang Qiu, 2023-11-25 This is an open access book. Scope of Conference 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI2023), which will be held from August 11 to August 13 in Singapore provides a forum for researchers and experts in different but related fields to discuss research findings. The scope of ICIAAI 2023 covers research areas such as imaging, algorithms and artificial intelligence. Related fields of research include computer software, programming languages, software engineering, computer science applications, artificial intelligence, Intelligent data analysis, deep learning, high-performance computing, signal processing, information systems, computer graphics, computer-aided design, Computer vision, etc. The objectives of the conference are: The conference aims to provide a platform for experts, scholars, engineers and technicians engaged in the research of image, algorithm and artificial intelligence to share scientific research results and cutting-edge technologies. The conference will discuss the academic trends and development trends of the related research fields of image, algorithm and artificial intelligence together, carry out discussions on current hot issues, and broaden research ideas. It will be a perfect gathering to strengthen academic research and discussion, promote the development and progress of relevant research and application, and promote the development of disciplines and promote talent training.
  journal of artificial intelligence cloud computing: Developing AI, IoT and Cloud Computing-based Tools and Applications for Women’s Safety Parul Dubey, Gurpreet Singh Chhabra, Bui Thanh Hung, Umashankar Ghugar, 2024-12-05 In a world increasingly driven by technology, this book explores the intersection of artificial intelligence (AI), IoT, and Cloud Computing and women's safety, highlighting the transformative potential of technology in safeguarding women's well-being in the physical and the digital world. As the safety and security industry embraces technological advancements, the need for inclusive and gender-centric solutions has become increasingly evident. This reference book delves into this critical area, showcasing the development of AI, IoT, and Cloud applications specifically tailored to address the unique safety challenges faced by women. • Provides a comprehensive exploration of how AI and related technologies are reshaping the future of women's safety. • Emphases the utilisation of AI to tackle the specific challenges women encounter in various contexts. • Introduces innovative solutions such as wearable technology, AI-powered surveillance systems, and mobile applications designed for emergency responses. • Discusses ethical implications of deploying technology for personal security and navigates the evolving legal landscape surrounding data privacy. • Bridges the gap between theoretical discussions and practical implementations, offering a guide to developing technology for the improvement of women's safety. It is an invaluable resource for professionals and researchers interested in the transformative role of AI, IoT, and Cloud in shaping the future of women's safety.
  journal of artificial intelligence cloud computing: Security, Trust, and Regulatory Aspects of Cloud Computing in Business Environments Srinivasan, S., 2014-03-31 Emerging as an effective alternative to organization-based information systems, cloud computing has been adopted by many businesses around the world. Despite the increased popularity, there remain concerns about the security of data in the cloud since users have become accustomed to having control over their hardware and software. Security, Trust, and Regulatory Aspects of Cloud Computing in Business Environments compiles the research and views of cloud computing from various individuals around the world. Detailing cloud security, regulatory and industry compliance, and trust building in the cloud, this book is an essential reference source for practitioners, professionals, and researchers worldwide, as well as business managers interested in an assembled collection of solutions provided by a variety of cloud users.
  journal of artificial intelligence cloud computing: Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance El Bachir Boukherouaa, Mr. Ghiath Shabsigh, Khaled AlAjmi, Jose Deodoro, Aquiles Farias, Ebru S Iskender, Mr. Alin T Mirestean, Rangachary Ravikumar, 2021-10-22 This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.
  journal of artificial intelligence cloud computing: Confluence of AI, Machine, and Deep Learning in Cyber Forensics Misra, Sanjay, Arumugam, Chamundeswari, Jaganathan, Suresh, S., Saraswathi, 2020-12-18 Developing a knowledge model helps to formalize the difficult task of analyzing crime incidents in addition to preserving and presenting the digital evidence for legal processing. The use of data analytics techniques to collect evidence assists forensic investigators in following the standard set of forensic procedures, techniques, and methods used for evidence collection and extraction. Varieties of data sources and information can be uniquely identified, physically isolated from the crime scene, protected, stored, and transmitted for investigation using AI techniques. With such large volumes of forensic data being processed, different deep learning techniques may be employed. Confluence of AI, Machine, and Deep Learning in Cyber Forensics contains cutting-edge research on the latest AI techniques being used to design and build solutions that address prevailing issues in cyber forensics and that will support efficient and effective investigations. This book seeks to understand the value of the deep learning algorithm to handle evidence data as well as the usage of neural networks to analyze investigation data. Other themes that are explored include machine learning algorithms that allow machines to interact with the evidence, deep learning algorithms that can handle evidence acquisition and preservation, and techniques in both fields that allow for the analysis of huge amounts of data collected during a forensic investigation. This book is ideally intended for forensics experts, forensic investigators, cyber forensic practitioners, researchers, academicians, and students interested in cyber forensics, computer science and engineering, information technology, and electronics and communication.