Innovative Applications of Artificial Neural Networks to Data Analytics and Signal Processing 

 Studies in Computational Intelligence 1171

This book deals with the application of ANNs in real-world problems requiring data analysis and signal processing. Artificial neural networks (ANNs) have emerged in society thanks to the large number of applications that have been used in an awe-inspiring way. These networks offer effective solutions to practical, real-world problems. 

The wide variety of application fields of the studies in the book is remarkable; these are related to sensorization, agriculture, healthcare, air pollution, video games, and cybersecurity, among others. To organize this variety, the chapters have been grouped into three sections related to (1) Forecasting and Prediction, (2) Knowledge Discovery and Knowledge Management, and (3) Signal Processing. This book aims to reach readers interested in ANNs and their applications in different fields, so it is interesting not only for computer science but also for other related disciplines.

Rivera, G., Pedrycz, W., Moreno-Garcia, J., & Sánchez-Solís, J.P. (Eds.) (2024). "Innovative Applications of Artificial Neural Networks to Data Analytics and Signal Processing". Studies in Computational Intelligence 1171, Springer Cham.

  Artificial Intelligence in Prescriptive Analytics: Innovations in Decision Analysis, Intelligent Optimization, and Data-driven Decisions

 Intelligent Systems Reference Library 260

Considering the advances of the different approaches and applications in the last years, and even in the last months, this is a particular moment in history to transform every data-driven decision-making process with the power of Artificial Intelligence (AI). 

This book reveals, through concrete case studies and original application ideas, how cutting-edge AI techniques are revolutionizing industries such as finance, health care, and manufacturing. It invites us to discover how machine learning, decision analysis, and intelligent optimization are changing, directly or indirectly, almost all aspects of our daily lives. 

This comprehensive book offers practical insights and real-world applications for professionals, researchers, and students alike. It helps to learn how to apply AI for smarter, data-driven decisions in areas like supply chain management, risk assessment, and even personalized medicine. Be inspired by the chapters of this book and unlock the full potential of AI in your field!

Pedrycz, W., Rivera, G., Fernandez, E., & Meschino, G. J. (Eds.) (2024). "Artificial Intelligence in Prescriptive Analytics: Innovations in Decision Analysis, Intelligent Optimization, and Data-driven Decisions". Intelligent Systems Reference Library 260, Springer Cham.

 Innovations in Machine and Deep Learning: Case Studies and Applications

Studies in Big Data 134

In recent years, significant progress has been made in achieving artificial intelligence (AI) with an impact on students, managers, scientists, health personnel, technical roles, investors, teachers, and leaders. This book presents numerous successful applications of AI in various contexts.

The innovative implications covered fall under the general field of machine learning (ML), including deep learning, decision-making, forecasting, pattern recognition, information retrieval, and interpretable AI. Decision-makers and entrepreneurs will find numerous successful applications in health care, sustainability, risk management, human activity recognition, logistics, and Industry 4.0.

This book is an essential resource for anyone interested in challenges, opportunities, and the latest developments and real-world applications of ML. Whether you are a student, researcher, practitioner, or simply curious about AI, this book provides valuable insights and inspiration for your work and learning.

Rivera, G., Rosete, A., Dorronsoro, B., & Rangel-Valdez, N. (Eds.) (2023). "Innovations in Machine and Deep Learning: Case Studies and Applications". Studies in Big Data 134, Springer Cham.

 Data Analytics and Computational Intelligence: Novel Models, Algorithms and Applications

Studies in Big Data 132

In the age of transformative artificial intelligence (AI), which has the potential to revolutionize our lives, this book provides a comprehensive exploration of successful research and applications in AI and data analytics.

Covering innovative approaches, advanced algorithms, and data analysis methodologies, this book addresses complex problems across topics such as machine learning, pattern recognition, data mining, optimization, and predictive modeling.

With clear explanations, practical examples, and cutting-edge research, this book seeks to expand the understanding of a wide readership, including students, researchers, practitioners, and technology enthusiasts eager to explore these exciting fields.

Featuring real-world applications in education, health care, climate modeling, cybersecurity, smart transportation, conversational systems, and material analysis, among others, this book highlights how these technologies can drive innovation and generate competitive advantages.

Rivera, G., Cruz-Reyes, L., Dorronsoro, B., & Rosete, A. (Eds.) (2023). "Data Analytics and Computational Intelligence: Novel Models, Algorithms and Applications". Studies in Big Data 132, Springer Cham.

Computational Intelligence for Business Analytics

Studies in Computational Intelligence 953

Corporate success has been changed by the importance of new developments in Business Analytics (BA) and, furthermore, by the support of computational intelligence-based techniques. This book opens a new avenue in these subjects and identifies key developments and opportunities. The book will be of interest to students, researchers, and professionals to identify innovative ways delivered by Business Analytics based on computational intelligence solutions. They help elicit information, handle knowledge, and support decision-making for more informed and reliable decisions even under high-uncertainty environments.

Computational Intelligence for Business Analytics has collected the latest technological innovations in the field of BA to improve business models related to Group Decision-Making, Forecasting, Risk Management, Knowledge Discovery, Data Breach Detection Social Well-Being, among other key topics related to this field.

Pedrycz, W., Martínez, L., Espin-Andrade, R. A., Rivera, G., & Marx-Gómez, J. (Eds.) (2021). "Computational Intelligence for Business Analytics". Studies in Computational Intelligence 953, Springer Cham.

Soft Computing for Business Intelligence

Studies in Computational Intelligence 537

The book Soft Computing for Business Intelligence is the remarkable output of a program based on the idea of joint trans-disciplinary research as supported by the Eureka Iberoamerica Network and the University of Oldenburg.

It contains twenty-seven papers allocated to three sections: Soft Computing, Business Intelligence and Knowledge Discovery, and Knowledge Management and Decision Making. Although the contents touch different domains, they are similar in so far as they follow the BI principle of “Observation and Analysis” while keeping a practical-oriented theoretical eye on sound methodologies, like Fuzzy Logic, Compensatory Fuzzy Logic (CFL), Rough Sets, and other soft computing elements.

The book tears down the traditional focus on business and extends Business Intelligence techniques in an impressive way to a broad range of fields like medicine, environment, wind farming, social collaboration and interaction, car sharing, and sustainability.

Espin-Andrade, R. A., Bello Pérez, R, Cobo, A., Marx-Gómez, J, & Racet-Valdés, A. (Eds.) (2014). "Soft Computing for Business Intelligence". Studies in Computational Intelligence 537Springer Verlag. 

Studies on Knowledge Discovery, Knowledge Management and Decision Making

Advances in Intelligent Systems Research 51

Eureka-2013 is the fourth international workshop on Knowledge Discovery, Knowledge Management and Decision Support. Eureka-2013 aims to build on the success of the 2011 meeting in Santander, Spain, in bringing together the Knowledge Discovery (KD), Knowledge Management (KM), and Decision Analysis (DA) communities, and will also stimulate a new focus on the application of KD, KM and DA research to support solutions of real problems in government, business, and industry. 

Leyva López, J. C., Espin Andrade, R.A., Bello Pérez, R. & Álvarez Carrillo, P.A. (Eds.) (2013). Proceedings of the Fourth International Workshop on Knowledge Discovery, Knowledge Management and Decision Support. Atlantis Press.