A book that can turn failure into a success for a 'C' student. This book underlines the vital importance of becoming a systems thinker.

This book constitutes selected and revised papers presented at the First International Conference on Optimization, Learning Algorithms and Applications, OL2A 2021, held in Bragança, Portugal, in July 2021. Due to the COVID-19 pandemic the conference was held online. The 39 full papers and 13 short papers were thoroughly reviewed and selected from 134 submissions. They are organized in the topical sections on optimization theory; robotics; measurements with the internet of things; optimization in control systems design; deep learning; data visualization and virtual reality; health informatics; data analysis; trends in engineering education.

This book and its sister volume collect refereed papers presented at the 7th Inter- tional Symposium on Neural Networks (ISNN 2010), held in Shanghai, China, June 6-9, 2010. Building on the success of the previous six successive ISNN symposiums, ISNN has become a well-established series of popular and high-quality conferences on neural computation and its applications. ISNN aims at providing a platform for scientists, researchers, engineers, as well as students to gather together to present and discuss the latest progresses in neural networks, and applications in diverse areas. Nowadays, the field of neural networks has been fostered far beyond the traditional artificial neural networks. This year, ISNN 2010 received 591 submissions from more than 40 countries and regions. Based on rigorous reviews, 170 papers were selected for publication in the proceedings. The papers collected in the proceedings cover a broad spectrum of fields, ranging from neurophysiological experiments, neural modeling to extensions and applications of neural networks. We have organized the papers into two volumes based on their topics. The first volume, entitled “Advances in Neural Networks- ISNN 2010, Part 1,” covers the following topics: neurophysiological foundation, theory and models, learning and inference, neurodynamics. The second volume en- tled “Advance in Neural Networks ISNN 2010, Part 2” covers the following five topics: SVM and kernel methods, vision and image, data mining and text analysis, BCI and brain imaging, and applications.

Multi-Chaos, Fractal and Multi-Fractional Artificial Intelligence of Different Complex Systems addresses different uncertain processes inherent in the complex systems, attempting to provide global and robust optimized solutions distinctively through multifarious methods, technical analyses, modeling, optimization processes, numerical simulations, case studies as well as applications including theoretical aspects of complexity. Foregrounding Multi-chaos, Fractal and Multi-fractional in the era of Artificial Intelligence (AI), the edited book deals with multi- chaos, fractal, multifractional, fractional calculus, fractional operators, quantum, wavelet, entropy-based applications, artificial intelligence, mathematics-informed and data driven processes aside from the means of modelling, and simulations for the solution of multifaceted problems characterized by nonlinearity, non-regularity and self-similarity, frequently encountered in different complex systems. The fundamental interacting components underlying complexity, complexity thinking, processes and theory along with computational processes and technologies, with machine learning as the core component of AI demonstrate the enabling of complex data to augment some critical human skills. Appealing to an interdisciplinary network of scientists and researchers to disseminate the theory and application in medicine, neurology, mathematics, physics, biology, chemistry, information theory, engineering, computer science, social sciences and other far-reaching domains, the overarching aim is to empower out-of-the-box thinking through multifarious methods, directed towards paradoxical situations, uncertain processes, chaotic, transient and nonlinear dynamics of complex systems. - Constructs and presents a multifarious approach for critical decision-making processes embodying paradoxes and uncertainty. - Includes a combination of theory and applications with regard to multi-chaos, fractal and multi-fractional as well as AI of different complex systems and many-body systems. - Provides readers with a bridge between application of advanced computational mathematical methods and AI based on comprehensive analyses and broad theories.

What learning, teaching, and education will be in the next future is an open question. Nevertheless, believing that an increasing prevalence of AI may not influence the education field seems objectively unlikely. In recent years, the new renaissance of AI has stimulated discussion on how advances in AI can influence the educational sector and the future educational policies and the impact of AI on Technology-Enhanced Learning (TEL). On the other side, the attention of the education sector in artificial intelligence is complemented by the consideration that, since the early days of AI, researchers have shown for the education sector, which has often seen education as one of the preferred application areas. The interaction between the AI and TEL research fields led to the investigation of how the advance in AI could support the development of flexible, inclusive, personalized, engaging, and effective learning tools. Besides, research in this area could be a powerful tool to open the "learning black box" by providing a deeper understanding of how learning occurs. The proposed Research Topic aims to gather contributions that provide a comprehensive picture of how AI is changing educational practices and how the key stakeholders in the educational community (i.e., students, teachers, faculty, and families) perceive this ongoing change. Relevant topics include (but are not limited to): ● AI applications in real-world educational settings ● Intelligent Tutoring Systems ● Adaptive learning environments ● Learning design and AI ● Students profiling: definition of the student model and ethical implications ● Intelligent techniques for objective and integrated students evaluation in TEL ● Teachers' competencies for effective integration of AI into Education ● Teachers’ perceptions of AI: prejudices and attitudes ● The role of cognitive architectures in Education ● Serious games and AI ● Social robotics in Education

Man-Machine Interaction is an interdisciplinary field of research that covers many aspects of science focused on a human and machine in conjunction. Basic goal of the study is to improve and invent new ways of communication between users and computers, and many different subjects are involved to reach the long-term research objective of an intuitive, natural and multimodal way of interaction with machines. The rapid evolution of the methods by which humans interact with computers is observed nowadays and new approaches allow using computing technologies to support people on the daily basis, making computers more usable and receptive to the user's needs. This monograph is the third edition in the series and presents important ideas, current trends and innovations in the man-machine interactions area. The aim of this book is to introduce not only hardware and software interfacing concepts, but also to give insights into the related theoretical background. Reader is provided with a compilation of high-quality original papers covering a wide scope of research topics divided into eleven sections, namely: human-computer interactions, robot control, embedded and navigation systems, bio data analysis and mining, biomedical signal processing, image and sound processing, decision support and expert systems, rough and fuzzy systems, pattern recognition, algorithms and optimization, computer networks and mobile technologies and data management systems.