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In this book, innovative research using artificial neural networks (ANNs) is conducted to automate the sizing task of RF IC design, which is used in two different steps of the automatic design process. The advances in telecommunications, such as the 5th generation broadband or 5G for short, open doors to advances in areas such as health care, education, resource management, transportation, agriculture and many other areas. Consequently, there is high pressure in today¿s market for significant communication rates, extensive bandwidths and ultralow-power consumption. This is where radiofrequency (RF) integrated circuits (ICs) come in hand, playing a crucial role. This demand stresses out the problem which resides in the remarkable difficulty of RF IC design in deep nanometric integration technologies due to their high complexity and stringent performances. Given the economic pressure for high quality yet cheap electronics and challenging time-to-market constraints, there is an urgent need for electronic design automation (EDA) tools to increase the RF designers¿ productivity and improve the quality of resulting ICs. In the last years, the automatic sizing of RF IC blocks in deep nanometer technologies has moved toward process, voltage and temperature (PVT)-inclusive optimizations to ensure their robustness. Each sizing solution is exhaustively simulated in a set of PVT corners, thus pushing modern workstations¿ capabilities to their limits.Standard ANNs applications usually exploit the model¿s capability of describing a complex, harder to describe, relation between input and target data. For that purpose, ANNs are a mechanism to bypass the process of describing the complex underlying relations between data by feeding it a significant number of previously acquired input/output data pairs that the model attempts to copy. Here, and firstly, the ANNs disrupt from the most recent trials of replacing the simulator in the simulation-based sizing with a machine/deep learning model, by proposing two different ANNs, the first classifies the convergence of the circuit for nominal and PVT corners, and the second predicts the oscillating frequencies for each case. The convergence classifier (CCANN) and frequency guess predictor (FGPANN) are seamlessly integrated into the simulation-based sizing loop, accelerating the overall optimization process. Secondly, a PVT regressor that inputs the circuit¿s sizing and the nominal performances to estimate the PVT corner performances via multiple parallel artificial neural networks is proposed. Two control phases prevent the optimization process from being misled by inaccurate performance estimates. As such, this book details the optimal description of the input/output data relation that should be fulfilled. The developed description is mainly reflected in two of the system¿s characteristics, the shape of the input data and its incorporation in the sizing optimization loop. An optimal description of thesecomponents should be such that the model should produce output data that fulfills the desired relation for the given training data once fully trained. Additionally, the model should be capable of efficiently generalizing the acquired knowledge in newer examples, i.e., never-seen input circuit topologies.

The book focuses on mathematical modelling of COVID-19 pandemic using the Susceptible, Infectious, and Recovered (SIR) model. The predictive modelling of the disease, with the exact facts and figures, provides a ground to reason about growing trends and its future trajectory. The book emphasizes on how the pandemic actually spreads out, lockdown impact analysis, and future course of actions based on mathematical calculations. Moreover, since COVID-19 spread outburst has been twice, the intensity studies and comparative analysis of the two waves of COVID-19 are another interesting feature of the book content. The book is a knowledge base for various researchers and academicians to dive into the detailing of the COVID spread (mathematical) model and understand how it could be explored to draw outcomes. To represent the factual information and analytical results effectively, graphical and diagrammatic representations have been appended at appropriate places. To keep the explanation simple and yet concrete, mathematical concepts have also been introduced; to carry out analysis to generate results for understanding the viral dynamics.

This book presents theory and applications of recently introduced butterfly optimization algorithm (BOA). It also highlights hybridization process in the basic structure of BOA with in-depth analysis of complexity. This book also describes the constraint handling process. The newly introduced variant is implemented and validated on a set of linear and nonlinear real works problems of engineering and pulp and paper industry. The simulated results are compared with most of the basic algorithms. Comparative and nonparametric statistical result analysis illustrates the efficacy of the algorithm. 

As a client or a developer, what do you need to consider when developing an age-appropriate website or mobile application? This book explains how age-related limitations affect the use of websites and mobile applications. If gerontological aspects are not taken into account in the design of digital channels, older people tend to do without them. Therefore, websites and mobile applications need to take into account the needs of older people. In the book, you will find useful recommendations on how to do this so that your websites and mobile apps are easy to use for everyone.

This open access book serves as a compact source of information on sine cosine algorithm (SCA) and a foundation for developing and advancing SCA and its applications. SCA is an easy, user-friendly, and strong candidate in the field of metaheuristics algorithms. Despite being a relatively new metaheuristic algorithm, it has achieved widespread acceptance among researchers due to its easy implementation and robust optimization capabilities. Its effectiveness and advantages have been demonstrated in various applications ranging from machine learning, engineering design, and wireless sensor network to environmental modeling. The book provides a comprehensive account of the SCA, including details of the underlying ideas, the modified versions, various applications, and a working MATLAB code for the basic SCA.

This book aims to critically assess the Cartesian stance that affirms robotic and AI technologies as candidates to supplant human capacities in non-routine intellectual nature activities based on social power relations. In the argumentation listed, the contradictions that this hypothesis approaches will be exposed, mainly in a historical and social analysis based on a complex and uncertain sociological perspective, added by a discussion within the scope of the Chaos theory. Finally, an outline of the current era, fostered by extremisms and extremists, will be described in terms of its contradictions in an attempt at sociological analysis. For this, some crucial and unsettling questions will be asked, and an effort to come up with answers will be made for some.

It also helps find ways in which we can measure possible consequences and societal impacts by taking the following factors into account: open public health issues and common AI solutions (with multiple case studies, such as TB and SARS: COVID-19), AI in sustainable health care, AI in precision medicine and data privacy issues.

This book covers all small details about Underwater Sensor Networks (UWSN). Researchers can use this book as a prerequisite before starting any research on underwater networks or underwater applications.

This book presents different experimental results as evidence of the good results obtained compared with respect to conventional approaches and literature references based on fuzzy logic. The main objective of the present work is the generation of fuzzy diagnosis systems that offer competitive classifiers to be applied in diagnosis systems.

A new method for resampling financial data is presented as alternative to the classical time sampled data commonly used in financial market trading. The new resampling method uses a closing value threshold to resample the data creating a signal better suited for financial trading, thus achieving higher returns without increased risk.

This book presents a methodology for forecasting events and phenomena occurring in technology and natural environments. The methodology helps determining the time of the onset of a destructive earthquake, its strength and the coordinates of the epicentre, predicting the time of the descent of glaciers and landslides long before the event.

This book addresses the issue of cognitive semantics' aspects that cannot be represented by traditional digital and logical means. The electromagnetic waves, quantum fields, beam of light, chaos control, relativistic theory, cosmic string recognition, category theory, group theory, and so on can be used for this aim.

This book offers a basic understanding of the Internet of Things (IoT), its design issues and challenges for healthcare applications. It also provides details of the challenges of healthcare big data, role of big data in healthcare and techniques, and tools for IoT in healthcare.

This book emphasizes that learning efficiency of the learners can be increased by providing personalized course materials and guiding them to attune with suitable learning paths based on their characteristics such as learning style, knowledge level, emotion, motivation, self-efficacy and many more learning ability factors in e-learning system.

This book deals with network representation learning. It deals with embedding nodes, edges, subgraphs and graphs. There is a growing interest in understanding complex systems in different domains including health, education, agriculture and transportation. Such complex systems are analyzed by modeling, using networks that are aptly called complex networks. Networks are becoming ubiquitous as they can represent many real-world relational data, for instance, information networks, molecular structures, telecommunication networks and protein¿protein interaction networks. Analysis of these networks provides advantages in many fields such as recommendation (recommending friends in a social network), biological field (deducing connections between proteins for treating new diseases) and community detection (grouping users of a social network according to their interests) by leveraging the latent information of networks. An active and important area ofcurrent interest is to come out with algorithms that learn features by embedding nodes or (sub)graphs into a vector space. These tasks come under the broad umbrella of representation learning. A representation learning model learns a mapping function that transforms the graphs' structure information to a low-/high-dimension vector space maintaining all the relevant properties.

This book focuses on the fields of fuzzy logic, bio-inspired algorithm, especially the differential evolution algorithm and also considering the fuzzy control area.

This book is focused on the use of intelligent techniques, such as fuzzy logic, neural networks and bio-inspired algorithms, and their application in medical diagnosis. The main idea is that the proposed method may be able to adapt to medical diagnosis problems in different possible areas of the medicine and help to have an improvement in diagnosis accuracy considering a clinical monitoring of 24 hours or more of the patient.In this book, tests were made with different architectures proposed in the different modules of the proposed model. First, it was possible to obtain the architecture of the fuzzy classifiers for the level of blood pressure and for the pressure load, and these were optimized with the different bio-inspired algorithms (Genetic Algorithm and Chicken Swarm Optimization). Secondly, we tested with a local database of 300 patients and good results were obtained. It is worth mentioning that this book is an important part of the proposed generalmodel; for this reason, we consider that these modules have a good performance in a particular way, but it is advisable to perform more tests once the general model is completed.

Also investigating data-analytics-based tools for projections using time series data, pattern analysis tools for unusual pattern discovery (anomaly detection) in image data, as well as AI-enabled robotics and its possible uses, the book will appeal to a broad readership, including academics, researchers and industry professionals.

This book examines various models/solutions in areas, such as individuals, home, work and society, where IoT and AI are being utilized to mitigate the Covid-19 pandemic.

This book investigates the application of promising machine learning techniques to address two problems: (i) how to find profitable pairs while constraining the search space and (ii) how to avoid long decline periods due to prolonged divergent pairs.

This book discusses the use of converged technology, a rapidly growing area that enhancements smart devices, communication, Internet of things (IoT), and augmented reality (AR).

This book examines how the wonders of AI have contributed to the battle against COVID-19.

This book discusses in detail the latest trends in sentiment analysis,focusing on "how online reviews and feedback reflect the opinions of users and have led to a major shift in the decision-making process at organizations." Social networking has become essential in today's society.

The book describes various texture feature extraction approaches and texture analysis applications. It introduces and discusses the importance of texture features, and describes various types of texture features like statistical, structural, signal-processed and model-based.

The book presents compression techniques for digital video stream, describing their design using various image transforms, such as discrete cosine transform (DCT), discrete wavelet transform (DWT), and singular value decomposition (SVD).

This book offers an overview of audio processing, including the latest advances in the methodologies used in audio processing and speech recognition. It then offers brief insights into the human speech production system and its modeling, which are required to produce artificial speech.

A combination of several technological, healthcare and financial factors are driving this trend to create a new healthcare model that stresses preventative 'health-care' rather than 'sick-care', and a shift from volume to value.

The language of business is the language of dreams, but the language of war is the language of nightmare made real.

In this book a new model for data classification was developed. Some architectures were developed in order to work mainly with two datasets, an arrhythmia dataset (using ECG signals) for classifying 15 different types of arrhythmias, and a satellite images segments dataset used for classifying six different types of soil.