Recent Advances in Logo Detection Using Machine Learning Paradigms
Title | Recent Advances in Logo Detection Using Machine Learning Paradigms PDF eBook |
Author | Yen-Wei Chen |
Publisher | Springer Nature |
Pages | 128 |
Release | |
Genre | |
ISBN | 3031598113 |
Machine Learning Paradigms
Title | Machine Learning Paradigms PDF eBook |
Author | Maria Virvou |
Publisher | Springer |
Pages | 230 |
Release | 2019-03-16 |
Genre | Technology & Engineering |
ISBN | 3030137430 |
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.
Leveraging AI for Effective Digital Relationship Marketing
Title | Leveraging AI for Effective Digital Relationship Marketing PDF eBook |
Author | Santos, José Duarte |
Publisher | IGI Global |
Pages | 634 |
Release | 2024-10-11 |
Genre | Business & Economics |
ISBN |
Todays businesses face the pressing challenge of how to effectively engage and build lasting relationships with customers in an increasingly crowded and competitive online space. Traditional marketing tactics are no longer sufficient to capture the attention and loyalty of modern consumers who demand personalized experiences and sustainable practices from the brands they support. This shifting paradigm necessitates innovative solutions that leverage cutting-edge technologies to enhance customer engagement and foster meaningful connections. Leveraging AI for Effective Digital Relationship Marketing addresses this critical dilemma by exploring the transformative potential of artificial intelligence (AI) in revolutionizing customer relationships. By harnessing the power of AI-driven strategies, businesses can gain deeper insights into individual customer behaviors and preferences, enabling them to deliver personalized interactions and anticipate customer needs with unparalleled accuracy. Through the implementation of AI-powered solutions, companies can navigate the complexities of digital marketing with confidence, positioning themselves as leaders in building sustainable and mutually beneficial relationships with their customers.
Machine Learning Paradigms
Title | Machine Learning Paradigms PDF eBook |
Author | George A. Tsihrintzis |
Publisher | Springer Nature |
Pages | 429 |
Release | 2020-07-23 |
Genre | Computers |
ISBN | 3030497240 |
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.
Fusion of Machine Learning Paradigms
Title | Fusion of Machine Learning Paradigms PDF eBook |
Author | Ioannis K. Hatzilygeroudis |
Publisher | Springer Nature |
Pages | 204 |
Release | 2023-02-06 |
Genre | Technology & Engineering |
ISBN | 3031223713 |
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.
Recent Advances in Big Data, Machine, and Deep Learning for Precision Agriculture
Title | Recent Advances in Big Data, Machine, and Deep Learning for Precision Agriculture PDF eBook |
Author | Muhammad Fazal Ijaz |
Publisher | Frontiers Media SA |
Pages | 379 |
Release | 2024-02-19 |
Genre | Science |
ISBN | 2832544959 |
Recent Advances in Intelligent Assistive Technologies: Paradigms and Applications
Title | Recent Advances in Intelligent Assistive Technologies: Paradigms and Applications PDF eBook |
Author | Hariton Costin |
Publisher | Springer Nature |
Pages | 220 |
Release | 2019-11-07 |
Genre | Technology & Engineering |
ISBN | 3030308170 |
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.