Preface

Regarded from our editorial views, this monograph has been wished to become a most valuable reference work of 2021 within the domains of modern information and decision aiding, urban and international, local and regional, ecological and spatiotemporal, industrial and natural operational research, artificial intelligence and creative arts and sciences. Proceeding diligent work from the sides of the authors and of us all, the endeavor of this work succeeded by stimulating, gathering, and compiling the newest research on the present state and inventions of electric and electronic, informational and energetic, green and recoverable, creative and re-creative means and their smart and dynamic employment, with care and responsibility. Our thus given monograph and handbook about scientific research on concerns about natural and human resources and their supply is a remarkable scholarly resource. It analyzes and discusses the efficient utilization of those resources that have a supportive impact on sustainable development and relations of, within and between us humans and our communities, cities and rural countryside, and finally migrations, social peace, and peace among our countries.

For this book's international directions, it has advanced toward a very special resource outlining the remarkable progress obtained worldwide, related to artificial intelligence, operational research, electronic, information and mobility devices and methods, renewable energy, natural resources, etc. The book is on the way to becoming respected on a global level for its broad analytic and practical contents.

"Artificial Intelligence in Industry 4.0 and 5G Technology" provides details on cutting-edge methodologies utilized in business and industrial sectors. It gives a holistic background on innovative optimization applications, focusing on main technology sectors such as 5G networks, Industry 4.0, and robotics. It discusses topics such as hyper-heuristics algorithmic enhancements and performance measurement approaches and provides keen insights into the implementation of meta-heuristic strategies to many-objectives optimization real-life problems in business, economics, and finance. With this book, the esteemed readers can learn to solve real-world sustainable optimization problems effectively using the appropriate techniques from emerging fields including artificial intelligence, hybrid evolutionary and swarm intelligence, hyper-heuristics programming, and many-objectives optimization.

"Artificial Intelligence in Industry 4.0 and 5G Technology" is a well-chosen collection of creative research about the methodologies and utilization of deep learning approaches in business, economics and finance, science and engineering, neuroscience, and medicine. While highlighting topics including intelligent optimization and computational modeling, data hybridization, and artificial intelligence, this work is ideally shaped and elaborated for high-tech experts and engineers, IT specialists and big-data analysts, data scientists and engineers, researchers and academicians, philanthropes, and political decision makers who look for contemporary studies on deep learning and its fruitful utilization in upcoming smart and green industries.

Deep Learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear; however, there is a need for research on various applications and techniques of deep learning in the areas of artificial intelligence and machine learning. Modern methodologies and tools from neuro-imaging, brain imaging, etc., today generate high-quality neurophysiological data with a resolution quality never reached before. These accelerating dynamics generate promising pathways to improve our comprehension of the nervous system and eventually of deeper learning. Computational issues occurred because of the high complexity of neuronal systems and the big number of constituents with still unknown connections between them. Highly innovative considerations and methods of computational neuroscience lead to more realistic biophysical representations which provide amazing chances for conditional behavior and links among brain regions in economic, professional, and our daily decision-making spheres.

This handbook encompasses three areas called "units:"

Unit 1 Industry 4.0:

Advanced Techniques and Technologies for Energy Saving, Artificial Intelligence in Smart Agriculture and Agroengineering and their AI-optimized Hardware, Biometrics, Big-Data, Cloud Computing, Cybersecurity, Embedded Systems, Fractional Differential Approach on Machine Learning, Graph-based Data Analysis, Grid Computing, Internet of Things (IoT), Intelligent Spindle Frameworks, Knowledge Representation and Reasoning, Smart Manufacturing, Spindle Frameworks, Manufacturing Intelligence and Informatics, Unmanned Arial Vehicles (drones technology).

Unit 2 Artificial Intelligence:

Augmented AI, Adaptive Systems, Bioinformatics, Data Mining, Deep Learning, Evolutionary Computations, Fuzzy Logic, Hybrid and Nonlinear Systems, Knowledge Representation and Reasoning, Machine Learning, Meta Heuristics, Mathematical Modelling in Artificial Intelligence, Natural Language Processing, Natured-Inspired Algorithms, Robotics Automation, Swarm Intelligence.

Unit 3 5G Technology:

Innovative Smart Cities Design and Applications, Mathematical Optimization in Engineering and Business Applications, Multi-task Learning, Radio Communications Technologies, Remote Access and Control, Robotic Process Automation, Sequential and Image Processing, Smart Communications Systems, Smart Grid, Speech Recognition, Sensors, Virtual Machines, Vehicular Networks, Wireless Sensor Networks, Wearable Technologies.

Subsequently, we provide a short introduction to the fifteen chapters of this work.

In the first chapter "Dynamic Key-based Biometric End User Authentication Proposal for IoT in Industry 4.0," the coauthors Subhash Mondal, Swapnoj Banerjee, Soumodipto Halder, and Diganta Sengupta recalls research and growth in IoT, both in the prospects of architecture as well as a voluminous increase in inter-networked devices. This chapter concentrates on securing the data acquisition at the end-user node level by means of biometric authentication. Extended AES algorithm incorporates an S-box, which defines the edge security through a nonlinear behavior. This seems to be a first attempt to combine Minutiae Extraction algorithm, Key Generation algorithm, AES, and fingerprint authentication to generate a single Edge-Framework for fingerprint authentication for the end users.

By intelligence algorithms, the second chapter "Decision Support Methodology for Scheduling Orders in Addictive Manufacturing" co-authored by Juan Jesús Tello Rodriguez and Fernando Lopez Irarragorri studies additive manufacturing, a family of manufacturing technologies where a 3-dimensional solid is manufactured, shaped from a computerized model by depositing thin layers of material. It is considered one of the most important emerging technologies because of the multiple benefits it brings for businesses, and because it is harmoniously coupled with Industry 4.0 and the digitization of manufacturing. This work addresses a variant of the job-shop re-scheduling problem in additive manufacturing. The new approach is applied to a real-world problem.

In the third chapter called "Significance in consuming 5G built Artificial Intelligence in smart cities," Y.Bevish Jinila, Cinthia Joy Godly, Joshua Thomas and S.Prayla Shyry recall that smart cities have a greater potential toward convenient, comfortable, and automated applications that could help humans make things easier. The traditional systems do not have sufficient models. AI has experienced a great "boom" in smart transportation. The advent of 5G has created a greater impact in the field of telecommunication, where the data transfer speed enormously increased. In this chapter, the significance of 5G-built AI in smart cities is presented. The 5G-built AI model for smart cities is also modeled. The proposed model highlights the importance of applying 5G with AI to improve the performance of the system.

The fourth chapter, titled "Neural network approach to segmentation of economic infrastructure objects on high-resolution satellite images," authored by V.A. Kozub, A.B. Murynin, I.S. Litvinchev, I.A. Matveev, and P. Vasant, addresses the problem of semantic segmentation of infrastructure objects in high-resolution satellite images, considered as an integral part of the method for constructing digital terrain models. Semantic segmentation involves classes such as buildings, roads, and railways. A set of labeled satellite images is collected, and neural network architecture is selected and trained. In order to reduce the imbalance of classes in the training sample, a probabilistic method of augmentation is developed and applied.

The fifth chapter "The impact of data security on the Internet of Things" written by coauthors Joshua Ebere Chukwuere and Boitumelo Molefe reminds us that the IoT is making lives easier and more productive; security on the IoT has also increased. The main purpose of this chapter is to determine the impact of data security on IoT. A quantitative methodology was used where...