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Author: Ankur Choudhary
Publisher: Springer Nature
ISBN: 9811630674
Category : Computers
Languages : en
Pages : 738
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Book Description
The book presents a collection of peer-reviewed articles from the International Conference on Advances and Applications of Artificial Intelligence and Machine Learning - ICAAAIML 2020. The book covers research in artificial intelligence, machine learning, and deep learning applications in healthcare, agriculture, business, and security. This volume contains research papers from academicians, researchers as well as students. There are also papers on core concepts of computer networks, intelligent system design and deployment, real-time systems, wireless sensor networks, sensors and sensor nodes, software engineering, and image processing. This book will be a valuable resource for students, academics, and practitioners in the industry working on AI applications.
Author: Ankur Choudhary
Publisher: Springer Nature
ISBN: 9811630674
Category : Computers
Languages : en
Pages : 738
Get Book
Book Description
The book presents a collection of peer-reviewed articles from the International Conference on Advances and Applications of Artificial Intelligence and Machine Learning - ICAAAIML 2020. The book covers research in artificial intelligence, machine learning, and deep learning applications in healthcare, agriculture, business, and security. This volume contains research papers from academicians, researchers as well as students. There are also papers on core concepts of computer networks, intelligent system design and deployment, real-time systems, wireless sensor networks, sensors and sensor nodes, software engineering, and image processing. This book will be a valuable resource for students, academics, and practitioners in the industry working on AI applications.
Author: Jingzheng Ren
Publisher: Elsevier
ISBN: 012821743X
Category : Technology & Engineering
Languages : en
Pages : 542
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Book Description
Applications of Artificial Intelligence in Process Systems Engineering offers a broad perspective on the issues related to artificial intelligence technologies and their applications in chemical and process engineering. The book comprehensively introduces the methodology and applications of AI technologies in process systems engineering, making it an indispensable reference for researchers and students. As chemical processes and systems are usually non-linear and complex, thus making it challenging to apply AI methods and technologies, this book is an ideal resource on emerging areas such as cloud computing, big data, the industrial Internet of Things and deep learning. With process systems engineering's potential to become one of the driving forces for the development of AI technologies, this book covers all the right bases. Explains the concept of machine learning, deep learning and state-of-the-art intelligent algorithms Discusses AI-based applications in process modeling and simulation, process integration and optimization, process control, and fault detection and diagnosis Gives direction to future development trends of AI technologies in chemical and process engineering
Author:
Publisher:
ISBN: 9788981194833
Category : Artificial intelligence
Languages : en
Pages : 0
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Book Description
The book presents a collection of peer-reviewed articles from the International Conference on Advances and Applications of Artificial Intelligence and Machine Learning ICAAAIML 2021. The book covers research in the areas of artificial intelligence, machine learning, and deep learning applications in health care, agriculture, business, and security. This book contains research papers from academicians, researchers as well as students. There are also papers on core concepts of computer networks, intelligent system design and deployment, real-time systems, wireless sensor networks, sensors and sensor nodes, software engineering, and image processing. This book is a valuable resource for students, academics, and practitioners in the industry working on AI applications.
Author: Adam Bohr
Publisher: Academic Press
ISBN: 0128184396
Category : Computers
Languages : en
Pages : 385
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Book Description
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. Highlights different data techniques in healthcare data analysis, including machine learning and data mining Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks Includes applications and case studies across all areas of AI in healthcare data
Author: Blake Tech
Publisher: Independently Published
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Applications of Artificial Intelligence (AI) and Machine Learning (ML) Artificial Intelligence (AI) and Machine Learning (ML) are two of the most popular and talked about topics in the tech world today. Both have the potential to revolutionize the way we live and work, and there is a lot of excitement around what they can achieve. The book covers the following: Chapter 1: Introduction to AI and ML 1.1 Definition and overview of Artificial Intelligence (AI) and Machine Learning (ML) 1.2 Historical background and evolution of AI and ML 1.3 Key concepts and terminology in AI and ML 1.4 Importance and impact of AI and ML in various industries 1.5 Ethical considerations and challenges in AI and ML development Chapter 2: Foundations of Machine Learning 2.1 Introduction to machine learning algorithms and techniques 2.2 Supervised, unsupervised, and reinforcement learning in Machine Learning 2.3 Data preprocessing and feature engineering in Machine Learning 2.4 Evaluation metrics for machine learning models 2.5 Model selection and hyperparameter tuning in Machine Learning Chapter 3: Natural Language Processing (NLP) Applications 3.1 Introduction to NLP and its applications 3.2 Text classification and sentiment analysis 3.3 Named Entity Recognition (NER) and information extraction 3.4 Language translation and generation 3.5 Chatbots and virtual assistants Chapter 4: Computer Vision Applications 4.1 Overview of computer vision and its applications 4.2 Image classification and object detection using AI 4.3 Facial recognition and emotion detection using AI 4.4 Image segmentation and scene understanding using AI 4.5 Video analysis and action recognition using AI Chapter 5: Recommender Systems 5.1 Introduction to recommender systems 5.2 Collaborative filtering techniques using AI 5.3 Content-based filtering and hybrid approaches using AI 5.4 Evaluation and optimization of recommender systems using AI 5.5 Personalization and adaptive systems using AI Chapter 6: AI in Healthcare 6.1 Applications of AI in healthcare and medicine 6.2 Medical image analysis and diagnostics using AI 6.3 Electronic health records and patient monitoring using AI 6.4 Drug discovery and personalized medicine using AI 6.5 Ethical considerations and challenges in AI healthcare applications Chapter 7: AI in Finance 7.1 Overview of AI applications in the finance industry 7.2 Fraud detection and cybersecurity using AI 7.3 Algorithmic trading and risk management using AI 7.4 Credit scoring and loan approvals using AI 7.5 Customer service and chatbots in finance using AI Chapter 8: AI in Manufacturing and Robotics 8.1 Industrial automation and robotics using AI 8.2 Predictive maintenance and quality control using AI 8.3 Supply chain optimization and demand forecasting using AI 8.4 Autonomous vehicles and drones using AI 8.5 Human-robot collaboration and safety considerations using AI Chapter 9: AI in Marketing and Customer Analytics 9.1 AI-driven customer segmentation and targeting 9.2 Sentiment analysis and social media analytics using AI 9.3 Personalized marketing and recommendation engines using AI 9.4 Customer churn prediction and retention strategies using AI 9.5 AI-powered marketing automation tools Chapter 10: AI in Agriculture and Environmental Sustainability 10.1 AI applications in precision agriculture 10.2 Crop yield prediction and disease detection using AI 10.3 Smart irrigation and resource management using AI 10.4 Environmental monitoring and conservation using AI 10.5 Climate change modeling and prediction using AI Chapter 11: Developing AI and ML Models Chapter 12: Ethical and Legal Considerations in AI Development
Author: Seda Khadimally
Publisher: Information Science Reference
ISBN: 9781799877769
Category : Artificial intelligence
Languages : en
Pages : 300
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Book Description
Focuses on the parameters of remote learning, machine learning, deep learning, and artificial intelligence under 21st-century learning and instructional contexts. Topics covered include data coding and social networking technology.
Author: Zoltán Somogyi
Publisher: Springer Nature
ISBN: 3030600327
Category : Computers
Languages : en
Pages : 448
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Book Description
This book presents a unique, understandable view of machine learning using many practical examples and access to free professional software and open source code. The user-friendly software can immediately be used to apply everything you learn in the book without the need for programming. After an introduction to machine learning and artificial intelligence, the chapters in Part II present deeper explanations of machine learning algorithms, performance evaluation of machine learning models, and how to consider data in machine learning environments. In Part III the author explains automatic speech recognition, and in Part IV biometrics recognition, face- and speaker-recognition. By Part V the author can then explain machine learning by example, he offers cases from real-world applications, problems, and techniques, such as anomaly detection and root cause analyses, business process improvement, detecting and predicting diseases, recommendation AI, several engineering applications, predictive maintenance, automatically classifying datasets, dimensionality reduction, and image recognition. Finally, in Part VI he offers a detailed explanation of the AI-TOOLKIT, software he developed that allows the reader to test and study the examples in the book and the application of machine learning in professional environments. The author introduces core machine learning concepts and supports these with practical examples of their use, so professionals will appreciate his approach and use the book for self-study. It will also be useful as a supplementary resource for advanced undergraduate and graduate courses on machine learning and artificial intelligence.
Author: Zsolt Nagy
Publisher: Packt Publishing Ltd
ISBN: 1789809207
Category : Computers
Languages : en
Pages : 330
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Book Description
Create AI applications in Python and lay the foundations for your career in data science Key FeaturesPractical examples that explain key machine learning algorithmsExplore neural networks in detail with interesting examplesMaster core AI concepts with engaging activitiesBook Description Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples. As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore's law. By the end of this book, you will be confident when it comes to building your own AI applications with your newly acquired skills! What you will learnUnderstand the importance, principles, and fields of AIImplement basic artificial intelligence concepts with PythonApply regression and classification concepts to real-world problemsPerform predictive analysis using decision trees and random forestsCarry out clustering using the k-means and mean shift algorithmsUnderstand the fundamentals of deep learning via practical examplesWho this book is for Artificial Intelligence and Machine Learning Fundamentals is for software developers and data scientists who want to enrich their projects with machine learning. You do not need any prior experience in AI. However, it’s recommended that you have knowledge of high school-level mathematics and at least one programming language (preferably Python).
Author: Abdolhossein Hemmati-Sarapardeh
Publisher: Gulf Professional Publishing
ISBN: 0128223855
Category : Science
Languages : en
Pages : 324
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Book Description
Applications of Artificial Intelligence Techniques in the Petroleum Industry gives engineers a critical resource to help them understand the machine learning that will solve specific engineering challenges. The reference begins with fundamentals, covering preprocessing of data, types of intelligent models, and training and optimization algorithms. The book moves on to methodically address artificial intelligence technology and applications by the upstream sector, covering exploration, drilling, reservoir and production engineering. Final sections cover current gaps and future challenges. Teaches how to apply machine learning algorithms that work best in exploration, drilling, reservoir or production engineering Helps readers increase their existing knowledge on intelligent data modeling, machine learning and artificial intelligence, with foundational chapters covering the preprocessing of data and training on algorithms Provides tactics on how to cover complex projects such as shale gas, tight oils, and other types of unconventional reservoirs with more advanced model input
Author: William F. Lawless
Publisher: Springer Nature
ISBN: 3030772837
Category : Computers
Languages : en
Pages : 566
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Book Description
This book provides a broad overview of the benefits from a Systems Engineering design philosophy in architecting complex systems composed of artificial intelligence (AI), machine learning (ML) and humans situated in chaotic environments. The major topics include emergence, verification and validation of systems using AI/ML and human systems integration to develop robust and effective human-machine teams—where the machines may have varying degrees of autonomy due to the sophistication of their embedded AI/ML. The chapters not only describe what has been learned, but also raise questions that must be answered to further advance the general Science of Autonomy. The science of how humans and machines operate as a team requires insights from, among others, disciplines such as the social sciences, national and international jurisprudence, ethics and policy, and sociology and psychology. The social sciences inform how context is constructed, how trust is affected when humans and machines depend upon each other and how human-machine teams need a shared language of explanation. National and international jurisprudence determine legal responsibilities of non-trivial human-machine failures, ethical standards shape global policy, and sociology provides a basis for understanding team norms across cultures. Insights from psychology may help us to understand the negative impact on humans if AI/ML based machines begin to outperform their human teammates and consequently diminish their value or importance. This book invites professionals and the curious alike to witness a new frontier open as the Science of Autonomy emerges.
