Recent Advances In Logo Detection Using Machine Learning Paradigms

This book presents recent machine learning paradigms and advances in learning analytics, an emerging research discipline concerned with the collection, advanced processing, and extraction of useful information from both educators’ and learners’ data with the goal of improving education and learning systems. In this context, internationally respected researchers present various aspects of learning analytics and selected application areas, including: • Using learning analytics to measure student engagement, to quantify the learning experience and to facilitate self-regulation; • Using learning analytics to predict student performance; • Using learning analytics to create learning materials and educational courses; and • Using learning analytics as a tool to support learners and educators in synchronous and asynchronous eLearning. The book offers a valuable asset for professors, researchers, scientists, engineers and students of all disciplines. Extensive bibliographies at the end of each chapter guide readers to probe further into their application areas of interest.

At the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in deep learning-based technological applications and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1) Deep Learning in Sensing, (2) Deep Learning in Social Media and IOT, (3) Deep Learning in the Medical Field, (4) Deep Learning in Systems Control, (5) Deep Learning in Feature Vector Processing, and (6) Evaluation of Algorithm Performance. This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest.

This book aims at updating the relevant computer science-related research communities, including professors, researchers, scientists, engineers and students, as well as the general reader from other disciplines, on the most recent advances in applications of methods based on Fusing Machine Learning Paradigms. Integrated or Hybrid Machine Learning methodologies combine together two or more Machine Learning approaches achieving higher performance and better efficiency when compared to those of their constituent components and promising major impact in science, technology and the society. The book consists of an editorial note and an additional eight chapters and is organized into two parts, namely: (i) Recent Application Areas of Fusion of Machine Learning Paradigms and (ii) Applications that can clearly benefit from Fusion of Machine Learning Paradigms. This book is directed toward professors, researchers, scientists, engineers and students in Machine Learning-related disciplines, as the hybridism presented, and the case studies described provide researchers with successful approaches and initiatives to efficiently address complex classification or regression problems. It is also directed toward readers who come from other disciplines, including Engineering, Medicine or Education Sciences, and are interested in becoming versed in some of the most recent Machine Learning-based technologies. Extensive lists of bibliographic references at the end of each chapter guide the readers to probe further into the application areas of interest to them.

This book illustrates the rapid pace of development in intelligent assistive technology in recent years, and highlights some salient examples of using modern IT&C technologies to provide devices, systems and application software for persons with certain motor or cognitive disabilities. The book proposes both theoretical and practical approaches to intelligent assistive and emergent technologies used in healthcare for the elderly and patients with chronic diseases. Intelligent assistive technology (IAT) is currently being introduced and developed worldwide as an important tool for maintaining independence and high quality of life among community-living people with certain disabilities, and as a key enabler for the aging population. The book offers a valuable resource for students at technical, medical and general universities, but also for specialists working in various fields in which emergent technologies are being used to help people enjoy optimal quality of life.

This book constitutes the proceedings of the 14th International Symposium on Recent Advances in Intrusion Detection, RAID 2011, held in Menlo Park, CA, USA in September 2011. The 20 papers presented were carefully reviewed and selected from 87 submissions. The papers are organized in topical sections on application security; malware; anomaly detection; Web security and social networks; and sandboxing and embedded environments.

This book explores some of the emerging scientific and technological areas in which the need for data analytics arises and is likely to play a significant role in the years to come. At the dawn of the 4th Industrial Revolution, data analytics is emerging as a force that drives towards dramatic changes in our daily lives, the workplace and human relationships. Synergies between physical, digital, biological and energy sciences and technologies, brought together by non-traditional data collection and analysis, drive the digital economy at all levels and offer new, previously-unavailable opportunities. The need for data analytics arises in most modern scientific disciplines, including engineering; natural-, computer- and information sciences; economics; business; commerce; environment; healthcare; and life sciences. Coming as the third volume under the general title MACHINE LEARNING PARADIGMS, the book includes an editorial note (Chapter 1) and an additional 12 chapters, and is divided into five parts: (1) Data Analytics in the Medical, Biological and Signal Sciences, (2) Data Analytics in Social Studies and Social Interactions, (3) Data Analytics in Traffic, Computer and Power Networks, (4) Data Analytics for Digital Forensics, and (5) Theoretical Advances and Tools for Data Analytics. This research book is intended for both experts/researchers in the field of data analytics, and readers working in the fields of artificial and computational intelligence as well as computer science in general who wish to learn more about the field of data analytics and its applications. An extensive list of bibliographic references at the end of each chapter guides readers to probe further into the application areas of interest to them.

This book includes original research findings in the field of memetic algorithms for image processing applications. It gathers contributions on theory, case studies, and design methods pertaining to memetic algorithms for image processing applications ranging from defence, medical image processing, and surveillance, to computer vision, robotics, etc. The content presented here provides new directions for future research from both theoretical and practical viewpoints, and will spur further advances in the field.

Artificial intelligence in all its forms is increasingly interwoven into all our lives, and remains one of the most lively areas of discussion and interest in technology today. This book presents the proceedings of the 20th International Conference of the Catalan Association for Artificial Intelligence (CCIA’2017): ‘Recent Advances in Artificial Intelligence Research and Development’, held in Deltebre, Terres de l'Ebre, Spain, in October 2017. Despite its title, this annual conference is not only for researchers from the Catalan Countries, but is an international event which attracts participants from countries all around the world. In total, 41 original contributions were submitted to CCIA’2017. Of these, 21 were accepted as long papers for oral presentation and 13 were accepted as short papers to be presented as posters. These 34 submissions appear in this book organized around a number of different topics including: agents and multi-agent systems; artificial vision and image processing; machine learning; artificial neural networks; cognitive modeling; fuzzy logic and reasoning; robotics; and AI applications. The book also includes abstracts of the 3 presentations by invited speakers. The book offers a representative sample of the current state of the art in the artificial intelligence community, and will be of interest to all those working with AI worldwide.