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Augmented Intelligence Deep Learning Machine Learning Cognitive Computing Educational Data Mining


Augmented Intelligence Deep Learning Machine Learning Cognitive Computing Educational Data Mining
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Augmented Intelligence Deep Learning Machine Learning Cognitive Computing Educational Data Mining


Augmented Intelligence Deep Learning Machine Learning Cognitive Computing Educational Data Mining
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Author : Om Prakash Jena
language : en
Publisher: Bentham Science Publishers
Release Date : 2022-07-29

Augmented Intelligence Deep Learning Machine Learning Cognitive Computing Educational Data Mining written by Om Prakash Jena and has been published by Bentham Science Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-29 with Computers categories.


Augmented intelligence is an alternate approach of artificial intelligence (AI), which emphasizes AI’s assistive role. Augmented intelligence enhances human skills of reasoning in a robotic system or software by simulating expectancy, educational mining, problem solving, recollection, sequencing, and decision-making capabilities. It is based on a combination of techniques such as machine learning, deep learning and cognitive computing. This book explains artificial intelligence models that support assistive processes in different situations. The contributors aim to provide information to a diverse audience with groundbreaking developments in mathematical computing. The book presents 8 chapters on these topics: - Educational data mining in augmented reality virtual learning environment - Brain and computer interfaces - Tree-based tools for chemometric analysis of infrared spectra - Applications of deep learning in medical engineering - Bankruptcy prediction model using an enhanced boosting classifier - Reputation systems for mobile agent security - The crow search algorithm - COVID-19 diagnosis and treatment The contents attempt to integrate various facets of augmented Intelligence, by describing recent research developments and advanced topics of interest to academicians and researchers working on machine learning problems and AI.



Bridging Human Intelligence And Artificial Intelligence


Bridging Human Intelligence And Artificial Intelligence
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Author : Mark V. Albert
language : en
Publisher: Springer Nature
Release Date : 2022-02-24

Bridging Human Intelligence And Artificial Intelligence written by Mark V. Albert and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-24 with Education categories.


This edited volume is based on contributions from the TCET-AECT “Human-Technology Frontier: Understanding the Learning of Now to Prepare for the Work of the Future Symposium” held in Denton, Texas on May 16-18, sponsored by AECT. The authors embrace an integrative approach to designing and implementing advances technologies in learning and instruction, and focus on the emerging themes of artificial intelligence, human-computer interactions, and the resulting instructional design. The volume will be divided into four parts: (1) Trends and future in learning and learning technologies expected in the next 10 years; (2) Technologies likely to have a significant impact on learning in the next 10 years; (3) Challenges that will need to be addressed and resolved in order to achieve significant and sustained improvement in learning; and (4) Reflections and insights from the Symposium that should be pursued and that can form the basis for productive research collaborations. The primary audience for this volume is academics and researchers in disciplines such as artificial intelligence, cognitive science, computer science, educational psychology, instructional design, human-computer interactions, information science, library science, and technology integration.



Advances In Artificial Intelligence And Data Engineering


Advances In Artificial Intelligence And Data Engineering
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Author : Niranjan N. Chiplunkar
language : en
Publisher: Springer Nature
Release Date : 2020-08-13

Advances In Artificial Intelligence And Data Engineering written by Niranjan N. Chiplunkar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-13 with Technology & Engineering categories.


This book presents selected peer-reviewed papers from the International Conference on Artificial Intelligence and Data Engineering (AIDE 2019). The topics covered are broadly divided into four groups: artificial intelligence, machine vision and robotics, ambient intelligence, and data engineering. The book discusses recent technological advances in the emerging fields of artificial intelligence, machine learning, robotics, virtual reality, augmented reality, bioinformatics, intelligent systems, cognitive systems, computational intelligence, neural networks, evolutionary computation, speech processing, Internet of Things, big data challenges, data mining, information retrieval, and natural language processing. Given its scope, this book can be useful for students, researchers, and professionals interested in the growing applications of artificial intelligence and data engineering.



Human Computer Interaction And Augmented Intelligence


Human Computer Interaction And Augmented Intelligence
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Author : Christos Troussas
language : en
Publisher: Springer Nature
Release Date : 2025-03-13

Human Computer Interaction And Augmented Intelligence written by Christos Troussas and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-13 with Computers categories.


This book explores the transformative roles of human-computer interaction (HCI) and augmented intelligence (AI) in shaping intelligent systems. HCI focuses on designing interactive systems that enhance human-technology relationships, while AI empowers users with adaptive, data-driven tools that complement decision-making. Together, these fields drive innovation, creating systems that are efficient, intuitive, and inclusive, addressing diverse user needs across various domains. Central to this work is the paradigm of interactive machine learning (IML), which builds on HCI and AI principles to create adaptive systems capable of evolving in real-time. The book highlights the application of IML in educational software, demonstrating how dynamic, personalized, and responsive learning environments can enhance student engagement and success. It provides detailed case studies and practical examples that showcase how IML aligns educational content, feedback, and interactions with learner behaviors and preferences. Additionally, it includes numerous Python code implementations and actionable design strategies, making these concepts accessible to practitioners and researchers alike. Key topics include leveraging cognitive and communication styles to shape adaptive systems, integrating learning models to enhance personalization, and addressing ethical considerations such as data privacy and algorithmic fairness. Readers will also discover discussions on creating personalized tutoring systems, collaborative platforms, and immersive environments that redefine educational technology. This book is a valuable resource for researchers, software developers, educators, instructional designers, and technologists at the intersection of human-computer interaction, augmented intelligence, and educational innovation. With its comprehensive framework and practical insights, it offers the tools to design adaptive, inclusive, and impactful learning systems for the future.



The Ultimate Modern Guide To Artificial Intelligence


The Ultimate Modern Guide To Artificial Intelligence
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Author : Enamul Haque
language : en
Publisher: Enamul Haque
Release Date : 2020-07-21

The Ultimate Modern Guide To Artificial Intelligence written by Enamul Haque and has been published by Enamul Haque this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-21 with Computers categories.


The era of artificial intelligence has arrived. You, who only felt far from artificial intelligence, and the growing dream trees, are now inseparable from artificial intelligence. What does AI have to do with me? Isn't it a distant future that has nothing to do with me, not a scientist, a technician, or a computer programmer? Well, Artificial intelligence is not a story of someone who has nothing to do with it, but the fact is, it is now everyone's story. AI is already deeply infiltrating everyone's life. The question is no longer whether we use technology or not; it's about working together in a better way. Surrounding technologies like Siri, Alexa, or Cortana are seamlessly integrated into our interactions. We walk into the room, turn on the lights, play songs, change the room temperature, keep track of shopping lists, book a ride at the airport, or remind ourselves to take the proper medication on time. It is now necessary to look at artificial intelligence from a broader and larger perspective. You should not just hang on to complex deep learning algorithms and think only through science and technology but through the eyes of emotions and humanities. These days, elementary school students learn English and coding at school. Tomorrow's elementary school students will learn AI. Of course, not everyone needs to be an AI expert. But if you don't understand AI, you will be left out of the trend of changing times. AI comes before English and coding. This is because artificial intelligence is the language and tool of the future. This book opens your door to the most critical understanding needed of AI and other relevant disruptive technologies. Artificial intelligence will significantly change societal structures and the operations of companies. The next generation of employees needs to be trained as a workforce before entering the job market, and the existing workforce is regularly recharged and skilled. There is plenty on this for reskilling too. This is the most definitive compendium of AI, The Internet of Things, Machine Learning, Deep Learning, Data Science, Big Data, Cloud Computing, Neural networks, Robotics, the future of work and the future of intelligent industries.



Deep Learning For Cognitive Computing Systems


Deep Learning For Cognitive Computing Systems
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Author : M.G. Sumithra
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2022-12-31

Deep Learning For Cognitive Computing Systems written by M.G. Sumithra and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-31 with Computers categories.


Cognitive computing simulates human thought processes with self-learning algorithms that utilize data mining, pattern recognition, and natural language processing. The integration of deep learning improves the performance of Cognitive computing systems in many applications, helping in utilizing heterogeneous data sets and generating meaningful insights.



Recent Advances In Artificial Intelligence And Data Engineering


Recent Advances In Artificial Intelligence And Data Engineering
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Author : Pushparaj Shetty D.
language : en
Publisher: Springer
Release Date : 2022-11-02

Recent Advances In Artificial Intelligence And Data Engineering written by Pushparaj Shetty D. and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-02 with Computers categories.


This book presents select proceedings of the International Conference on Artificial Intelligence and Data Engineering (AIDE 2020). Various topics covered in this book include deep learning, neural networks, machine learning, computational intelligence, cognitive computing, fuzzy logic, expert systems, brain-machine interfaces, ant colony optimization, natural language processing, bioinformatics and computational biology, cloud computing, machine vision and robotics, ambient intelligence, intelligent transportation, sensing and sensor networks, big data challenge, data science, high performance computing, data mining and knowledge discovery, and data privacy and security. The book will be a valuable reference for beginners, researchers, and professionals interested in artificial intelligence, robotics and data engineering.



Artificial Intelligence Trends For Data Analytics Using Machine Learning And Deep Learning Approaches


Artificial Intelligence Trends For Data Analytics Using Machine Learning And Deep Learning Approaches
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Author : K. Gayathri Devi
language : en
Publisher: CRC Press
Release Date : 2020-10-07

Artificial Intelligence Trends For Data Analytics Using Machine Learning And Deep Learning Approaches written by K. Gayathri Devi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-07 with Computers categories.


Artificial Intelligence (AI), when incorporated with machine learning and deep learning algorithms, has a wide variety of applications today. This book focuses on the implementation of various elementary and advanced approaches in AI that can be used in various domains to solve real-time decision-making problems. The book focuses on concepts and techniques used to run tasks in an automated manner. It discusses computational intelligence in the detection and diagnosis of clinical and biomedical images, covers the automation of a system through machine learning and deep learning approaches, presents data analytics and mining for decision-support applications, and includes case-based reasoning, natural language processing, computer vision, and AI approaches in real-time applications. Academic scientists, researchers, and students in the various domains of computer science engineering, electronics and communication engineering, and information technology, as well as industrial engineers, biomedical engineers, and management, will find this book useful. By the end of this book, you will understand the fundamentals of AI. Various case studies will develop your adaptive thinking to solve real-time AI problems. Features Includes AI-based decision-making approaches Discusses computational intelligence in the detection and diagnosis of clinical and biomedical images Covers automation of systems through machine learning and deep learning approaches and its implications to the real world Presents data analytics and mining for decision-support applications Offers case-based reasoning



Advanced Analytics And Deep Learning Models


Advanced Analytics And Deep Learning Models
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Author : Archana Mire
language : en
Publisher: John Wiley & Sons
Release Date : 2022-06-01

Advanced Analytics And Deep Learning Models written by Archana Mire and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.


Advanced Analytics and Deep Learning Models The book provides readers with an in-depth understanding of concepts and technologies related to the importance of analytics and deep learning in many useful real-world applications such as e-healthcare, transportation, agriculture, stock market, etc. Advanced analytics is a mixture of machine learning, artificial intelligence, graphs, text mining, data mining, semantic analysis. It is an approach to data analysis. Beyond the traditional business intelligence, it is a semi and autonomous analysis of data by using different techniques and tools. However, deep learning and data analysis both are high centers of data science. Almost all the private and public organizations collect heavy amounts of data, i.e., domain-specific data. Many small/large companies are exploring large amounts of data for existing and future technology. Deep learning is also exploring large amounts of unsupervised data making it beneficial and effective for big data. Deep learning can be used to deal with all kinds of problems and challenges that include collecting unlabeled and uncategorized raw data, extracting complex patterns from a large amount of data, retrieving fast information, tagging data, etc. This book contains 16 chapters on artificial intelligence, machine learning, deep learning, and their uses in many useful sectors like stock market prediction, a recommendation system for better service selection, e-healthcare, telemedicine, transportation. There are also chapters on innovations and future opportunities with fog computing/cloud computing and artificial intelligence. Audience Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in healthcare, telemedicine, transportation, and the financial sector. The book will also be a great source for software engineers and advanced students who are beginners in the field of advanced analytics in deep learning.



Machine Learning Paradigms


Machine Learning Paradigms
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Author : George A. Tsihrintzis
language : en
Publisher: Springer Nature
Release Date : 2020-07-23

Machine Learning Paradigms written by George A. Tsihrintzis and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-23 with Computers categories.


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.