Gheorghita Ghinea – författare

The book IoT and Big Data Analytics (IoT-BDA) for Smart Cities – A Global Perspective, emphasizes the challenges, architectural models, and intelligent frameworks with smart decisionmaking systems using Big Data and IoT with case studies. The book illustrates the benefits of Big Data and IoT methods in framing smart systems for smart applications. The text is a coordinated amalgamation of research contributions and industrial applications in the field of smart cities.

Features:

This book is primarily aimed at IT professionals. Undergraduates, graduates, and researchers in the area of computer science and information technology will also find this book useful.

The book IoT and Big Data Analytics (IoT-BDA) for Smart Cities – A Global Perspective, emphasizes the challenges, architectural models, and intelligent frameworks with smart decisionmaking systems using Big Data and IoT with case studies. The book illustrates the benefits of Big Data and IoT methods in framing smart systems for smart applications. The text is a coordinated amalgamation of research contributions and industrial applications in the field of smart cities.

Features:

This book is primarily aimed at IT professionals. Undergraduates, graduates, and researchers in the area of computer science and information technology will also find this book useful.

The importance of addressing security issues in communication devices, networks, and computing models in Industry 5.0 cannot be overstated. Industry 5.0 represents the next phase in the evolution of manufacturing and industrial processes, characterized by increased connectivity, automation, and the integration of smart technologies. Here are several reasons why security is crucial in this context: Industry 5.0 involves the convergence of information technology (IT) and operational technology (OT), making industrial control systems susceptible to cyber threats. A breach in security could compromise critical infrastructure such as power grids, transportation systems, and water treatment plants. Securing computing models and networks is vital for protecting critical infrastructure and ensuring the safety and stability of essential services.

Industry 5.0 encourages the use of advanced technologies such as the Industrial Internet of Things (IIoT) and edge computing, leading to increased data exchange and collaboration. Security issues could result in the theft or manipulation of intellectual property, proprietary designs, and sensitive business information. Robust security measures are necessary to safeguard intellectual property, maintain a competitive edge, and foster innovation within Industry 5.0 ecosystems. Communication devices and networks in Industry 5.0 transmit vast amounts of sensitive data, including production data, supply chain information, and operational metrics. Ensuring the integrity and confidentiality of this data is crucial for informed decision-making and maintaining a competitive advantage. Security breaches could lead to data manipulation, unauthorized access, and exposure of sensitive information, jeopardizing the trust of stakeholders and partners. Industry 5.0 involves interconnected supply chains, where multiple entities collaborate and share data. Weaknesses in communication devices and networks can be exploited to compromise the integrity of the entire supply chain, impacting product quality and safety. Securing communication channels and computing models is vital for maintaining the trustworthiness of the supply chain, ensuring product quality, and minimizing the risk of counterfeit components.

In summary, addressing security issues in communication devices, networks, and computing models is fundamental to the successful implementation of Industry 5.0. It not only protects the assets and operations of organizations but also contributes to the overall safety, reliability, and sustainability of advanced industrial systems.

The importance of addressing security issues in communication devices, networks, and computing models in Industry 5.0 cannot be overstated. Industry 5.0 represents the next phase in the evolution of manufacturing and industrial processes, characterized by increased connectivity, automation, and the integration of smart technologies. Here are several reasons why security is crucial in this context: Industry 5.0 involves the convergence of information technology (IT) and operational technology (OT), making industrial control systems susceptible to cyber threats. A breach in security could compromise critical infrastructure such as power grids, transportation systems, and water treatment plants. Securing computing models and networks is vital for protecting critical infrastructure and ensuring the safety and stability of essential services.

Industry 5.0 encourages the use of advanced technologies such as the Industrial Internet of Things (IIoT) and edge computing, leading to increased data exchange and collaboration. Security issues could result in the theft or manipulation of intellectual property, proprietary designs, and sensitive business information. Robust security measures are necessary to safeguard intellectual property, maintain a competitive edge, and foster innovation within Industry 5.0 ecosystems. Communication devices and networks in Industry 5.0 transmit vast amounts of sensitive data, including production data, supply chain information, and operational metrics. Ensuring the integrity and confidentiality of this data is crucial for informed decision-making and maintaining a competitive advantage. Security breaches could lead to data manipulation, unauthorized access, and exposure of sensitive information, jeopardizing the trust of stakeholders and partners. Industry 5.0 involves interconnected supply chains, where multiple entities collaborate and share data. Weaknesses in communication devices and networks can be exploited to compromise the integrity of the entire supply chain, impacting product quality and safety. Securing communication channels and computing models is vital for maintaining the trustworthiness of the supply chain, ensuring product quality, and minimizing the risk of counterfeit components.

In summary, addressing security issues in communication devices, networks, and computing models is fundamental to the successful implementation of Industry 5.0. It not only protects the assets and operations of organizations but also contributes to the overall safety, reliability, and sustainability of advanced industrial systems.

The importance of addressing security issues in communication devices, networks, and computing models in Industry 5.0 cannot be overstated. Industry 5.0 represents the next phase in the evolution of manufacturing and industrial processes, characterized by increased connectivity, automation, and the integration of smart technologies. Here are several reasons why security is crucial in this context: Industry 5.0 involves the convergence of information technology (IT) and operational technology (OT), making industrial control systems susceptible to cyber threats. A breach in security could compromise critical infrastructure such as power grids, transportation systems, and water treatment plants. Securing computing models and networks is vital for protecting critical infrastructure and ensuring the safety and stability of essential services.

Industry 5.0 encourages the use of advanced technologies such as the Industrial Internet of Things (IIoT) and edge computing, leading to increased data exchange and collaboration. Security issues could result in the theft or manipulation of intellectual property, proprietary designs, and sensitive business information. Robust security measures are necessary to safeguard intellectual property, maintain a competitive edge, and foster innovation within Industry 5.0 ecosystems. Communication devices and networks in Industry 5.0 transmit vast amounts of sensitive data, including production data, supply chain information, and operational metrics. Ensuring the integrity and confidentiality of this data is crucial for informed decision-making and maintaining a competitive advantage. Security breaches could lead to data manipulation, unauthorized access, and exposure of sensitive information, jeopardizing the trust of stakeholders and partners. Industry 5.0 involves interconnected supply chains, where multiple entities collaborate and share data. Weaknesses in communication devices and networks can be exploited to compromise the integrity of the entire supply chain, impacting product quality and safety. Securing communication channels and computing models is vital for maintaining the trustworthiness of the supply chain, ensuring product quality, and minimizing the risk of counterfeit components.

In summary, addressing security issues in communication devices, networks, and computing models is fundamental to the successful implementation of Industry 5.0. It not only protects the assets and operations of organizations but also contributes to the overall safety, reliability, and sustainability of advanced industrial systems.

The importance of addressing security issues in communication devices, networks, and computing models in Industry 5.0 cannot be overstated. Industry 5.0 represents the next phase in the evolution of manufacturing and industrial processes, characterized by increased connectivity, automation, and the integration of smart technologies. Here are several reasons why security is crucial in this context: Industry 5.0 involves the convergence of information technology (IT) and operational technology (OT), making industrial control systems susceptible to cyber threats. A breach in security could compromise critical infrastructure such as power grids, transportation systems, and water treatment plants. Securing computing models and networks is vital for protecting critical infrastructure and ensuring the safety and stability of essential services.

Industry 5.0 encourages the use of advanced technologies such as the Industrial Internet of Things (IIoT) and edge computing, leading to increased data exchange and collaboration. Security issues could result in the theft or manipulation of intellectual property, proprietary designs, and sensitive business information. Robust security measures are necessary to safeguard intellectual property, maintain a competitive edge, and foster innovation within Industry 5.0 ecosystems. Communication devices and networks in Industry 5.0 transmit vast amounts of sensitive data, including production data, supply chain information, and operational metrics. Ensuring the integrity and confidentiality of this data is crucial for informed decision-making and maintaining a competitive advantage. Security breaches could lead to data manipulation, unauthorized access, and exposure of sensitive information, jeopardizing the trust of stakeholders and partners. Industry 5.0 involves interconnected supply chains, where multiple entities collaborate and share data. Weaknesses in communication devices and networks can be exploited to compromise the integrity of the entire supply chain, impacting product quality and safety. Securing communication channels and computing models is vital for maintaining the trustworthiness of the supply chain, ensuring product quality, and minimizing the risk of counterfeit components.

In summary, addressing security issues in communication devices, networks, and computing models is fundamental to the successful implementation of Industry 5.0. It not only protects the assets and operations of organizations but also contributes to the overall safety, reliability, and sustainability of advanced industrial systems.

The book provides the direction for future research in deep learning in terms of its role in targeted treatment, biological systems, site-specific drug delivery, risk assessment in therapy, etc.

Deep Learning for Targeted Treatments describes the importance of the deep learning framework for patient care, disease imaging/detection, and health management. Since deep learning can and does play a major role in a patient’s healthcare management by controlling drug delivery to targeted tissues or organs, the main focus of the book is to leverage the various prospects of the DL framework for targeted therapy of various diseases. In terms of its industrial significance, this general-purpose automatic learning procedure is being widely implemented in pharmaceutical healthcare.

Audience The book will be immensely interesting and useful to researchers and those working in the areas of clinical research, disease management, pharmaceuticals, R&D formulation, deep learning analytics, remote healthcare management, healthcare analytics, and deep learning in the healthcare industry.

The book provides the direction for future research in deep learning in terms of its role in targeted treatment, biological systems, site-specific drug delivery, risk assessment in therapy, etc.

Deep Learning for Targeted Treatments describes the importance of the deep learning framework for patient care, disease imaging/detection, and health management. Since deep learning can and does play a major role in a patient’s healthcare management by controlling drug delivery to targeted tissues or organs, the main focus of the book is to leverage the various prospects of the DL framework for targeted therapy of various diseases. In terms of its industrial significance, this general-purpose automatic learning procedure is being widely implemented in pharmaceutical healthcare.

Audience The book will be immensely interesting and useful to researchers and those working in the areas of clinical research, disease management, pharmaceuticals, R&D formulation, deep learning analytics, remote healthcare management, healthcare analytics, and deep learning in the healthcare industry.

Revolutionize your machine learning practice with this essential book that provides expert insights into leveraging Graph Convolutional Networks (GCNNs) to overcome the limitations of traditional CNNs.

In the last decade, computer vision has become a major focus for addressing the world''s growing processing needs. Many existing deep learning architectures for computer vision challenges are based on convolutional neural networks (CNNs). Despite their great achievements, CNNs struggle to encode the intrinsic graph patterns in specific learning tasks. In contrast, graph convolutional networks have been used to address several computer vision issues with equivalent or superior results. The use of GCNNs has shown significant achievement in image classifications, video understanding, point clouds, meshes, and other applications in natural language processing. This book focuses on the applications of graph convolutional networks in computer vision. Through expert insights, it explores how researchers are finding ways to perform convolution algorithms on graphs to improve the way we use machine learning.

Revolutionize your machine learning practice with this essential book that provides expert insights into leveraging Graph Convolutional Networks (GCNNs) to overcome the limitations of traditional CNNs.

In the last decade, computer vision has become a major focus for addressing the world''s growing processing needs. Many existing deep learning architectures for computer vision challenges are based on convolutional neural networks (CNNs). Despite their great achievements, CNNs struggle to encode the intrinsic graph patterns in specific learning tasks. In contrast, graph convolutional networks have been used to address several computer vision issues with equivalent or superior results. The use of GCNNs has shown significant achievement in image classifications, video understanding, point clouds, meshes, and other applications in natural language processing. This book focuses on the applications of graph convolutional networks in computer vision. Through expert insights, it explores how researchers are finding ways to perform convolution algorithms on graphs to improve the way we use machine learning.

This book aimed at bringing an insight to the ICN network, particularly various architectures, issues and challenges in the new networking paradigm. The book starts with an introduction to the new promising concept of ICN and its origin along with the reason behind this interesting innovation. Different architectures proposed so far in support of implementing the ICN is also discussed in details. Few of the challenges of ICN implementation are enlisted as caching, naming, routing, and security. Each of these challenges with recent development is covered in individual chapters. Moreover, integration of current trends in communication and computing like software defined networking and machine learning approach are another area that this book is focusing.  All these chapters highlight the recent developments reported in the area and also discusses the future trends. The book provides an overview of the recent developments in future internet technologies, bringing together the advancements that have been made in ICN. 

The book includes three unique chapters in the field of ICN research. The first, is the SDN framework for implementing ICN by decoupling data and control plan. The machine learning models for predicting future trends in network traffic and other management activities is another important chapter. This chapter includes the possibilities of using machine learning models for trend prediction to help network administrators and service providers to take care of unexpected sudden change traffic pattern and user behaviour. The third most vital chapter is the security issues in ICN. This chapter includes various facts that influences the security of ICN. Issues involved in naming, caching and routing are discussed separately along with few recent works in these areas. Various types of attacks in ICN are also part of the discussion. 

The stated book would be useful for researchers in this area and will work as a reference for future work. Moreover, the content of the book would also be suitable as a supporting material for undergraduate and graduate level courses in computer science and electrical engineering.

This book presents original, peer-reviewed select articles from the International Conference on Cognitive & Intelligent Computing  (ICCIC – 2021), held on December 11–12, 2021, at Hyderabad, India. The proceedings has cutting edge Research outcome related to Machine learning in control applications, Soft computing, Pattern Recognition, Decision Support Systems, Text analytics and NLP, Statistical Learning, Neural Network Learning, Learning Through Fuzzy Logic, Learning Through Evolution (Evolutionary Algorithms), Reinforcement Learning, Multi-Strategy Learning, Cooperative Learning, Planning And Learning, Multi-Agent Learning, Online And Incremental Learning, Scalability Of Learning Algorithms, Inductive Learning, Inductive Logic Programming, Bayesian Networks, Support Vector Machines, Case-Based Reasoning, Multi-Agent Systems, Human–Computer Interaction, Data Mining and Knowledge Discovery, Knowledge Management and Networks, Data Intensive Computing Architecture, Medicine, Health, Bioinformatics, and Systems Biology, Industrial and Engineering Applications, Security Applications, Smart Cities, Game Playing and Problem Solving, Intelligent Virtual Environments, Economics, Business, And Forecasting Applications. Articles in the book are carefully selected  on the basis of their application orientation. The content is expected to be especially useful for Professionals, Researchers, Research students working in the area of cognitive and intelligent computing.