Explainable Ai Xai Making Machine Learning Models Interpretable And Trustworthy Cloud Computing
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Explainable Ai Xai Making Machine Learning Models Interpretable And Trustworthy Cloud Computing
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Author : Amit Vyas
language : en
Publisher: Xoffencer international book publication house
Release Date : 2024-05-30
Explainable Ai Xai Making Machine Learning Models Interpretable And Trustworthy Cloud Computing written by Amit Vyas and has been published by Xoffencer international book publication house this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-30 with Computers categories.
Both explainable artificial intelligence (XAI) and cloud computing are vital components because they both play a significant part in the creation of the landscape of artificial intelligence (AI) and computing infrastructure. XAI and cloud computing are two of the most important pillars in the world of current technology. The purpose of this introduction is to provide an overview of the fundamental concepts behind both Explainable AI and cloud computing. In this section, we will study the relevance of these notions, as well as their applications and the synergies that they offer. A solution that satisfies the critical requirement for interpretability and transparency in artificial intelligence systems is referred to as explainable artificial intelligence, or XAI for short. Understanding the method by which artificial intelligence algorithms arrive at conclusions is of the highest significance, particularly in sensitive industries such as healthcare, finance, and law. This is because the algorithms are growing more intricate and prevalent, and it is becoming increasingly important to understand how they arrive at their results. XAI techniques are intended to give insights into the inner workings and reasoning processes of artificial intelligence models, with the purpose of demystifying the "black box" nature of these models. XAI approaches are aimed to deliver these insights. In addition to allowing stakeholders to detect biases or mistakes and ensure compliance with regulations, increasing the interpretability of artificial intelligence systems enables stakeholders to have a greater degree of trust in these systems. The provisioning, administration, and distribution of computer resources are all fundamentally transformed by cloud computing, which is regarded to be a breakthrough technology. Cloud computing is also known as utility computing. The term "cloud computing" refers to the practice of storing, managing, and processing data through the utilization of a network of distant servers that are located on the Internet. This is in contrast to the conventional method of computing, which is dependent on the infrastructure and servers located locally. This technology offers organizations unrivaled scalability, flexibility, and cost-efficiency, making it possible for them to use computer resources on demand without the trouble of managing physical infrastructure.
Interpretable Machine Learning
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Author : Christoph Molnar
language : en
Publisher: Lulu.com
Release Date : 2020
Interpretable Machine Learning written by Christoph Molnar and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computers categories.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Multidisciplinary Research Area In Arts Science Commerce Volume 4
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Author :
language : en
Publisher: The Hill Publication
Release Date : 2025-07-18
Multidisciplinary Research Area In Arts Science Commerce Volume 4 written by and has been published by The Hill Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-18 with Antiques & Collectibles categories.
Ai Driven Audit And Compliance Intelligence Integrating Machine Learning Cloud Computing And Big Data For Risk Aware Finance And Smart Manufacturing Ecosystems
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Author : DWARAKA NATH KUMMARI
language : en
Publisher: JEC PUBLICATION
Release Date :
Ai Driven Audit And Compliance Intelligence Integrating Machine Learning Cloud Computing And Big Data For Risk Aware Finance And Smart Manufacturing Ecosystems written by DWARAKA NATH KUMMARI and has been published by JEC PUBLICATION this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
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The Role Of Artificial Intelligence In Advancing Applied Life Sciences
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Author : Emara, Tamer
language : en
Publisher: IGI Global
Release Date : 2025-04-29
The Role Of Artificial Intelligence In Advancing Applied Life Sciences written by Emara, Tamer and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-29 with Science categories.
The transformative role of artificial intelligence (AI) is revolutionizing the life sciences sector. AI is being used to accelerate drug discovery, personalize treatments, and improve patient outcomes. AI has demonstrated its potential in optimizing crop yields, enhancing food safety, and addressing global food security challenges. Additionally, AI has applications in climate modeling, species conservation, and pollution monitoring. Discussion of AI implementation in life sciences may stimulate further research and development in AI-driven life science solutions. The Role of Artificial Intelligence in Advancing Applied Life Sciences equips readers with a solid understanding of technology's potential to address complex life science problems. It also discusses the ethical implications and challenges associated with AI implementation in this field. Covering topics such as biomanufacturing, disease identification, and climate change patters, this book is an excellent resource for life scientists, computer scientists, healthcare practitioners, environmentalists, agriculturalists, professionals, researchers, scholars, academicians, and more.
Introduction To Explainable Ai Xai
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Author : Robert Johnson
language : en
Publisher: HiTeX Press
Release Date : 2024-10-27
Introduction To Explainable Ai Xai written by Robert Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-27 with Computers categories.
"Introduction to Explainable AI (XAI): Making AI Understandable" is an essential resource for anyone seeking to understand the burgeoning field of explainable artificial intelligence. As AI systems become integral to critical decision-making processes across industries, the ability to interpret and comprehend their outputs becomes increasingly vital. This book offers a comprehensive exploration of XAI, delving into its foundational concepts, diverse techniques, and pivotal applications. It strives to demystify complex AI behaviors, ensuring that stakeholders across sectors can engage with AI technologies confidently and responsibly. Structured to cater to both beginners and those with an existing interest in AI, this book covers the spectrum of XAI topics, from model-specific approaches and interpretable machine learning to the ethical and societal implications of AI transparency. Readers will be equipped with practical insights into the tools and frameworks available for developing explainable models, alongside an understanding of the challenges and limitations inherent in the field. As we look toward the future, the book also addresses emerging trends and research directions, positioning itself as a definitive guide to navigating the evolving landscape of XAI. This book stands as an invaluable reference for students, practitioners, and policy makers alike, offering a balanced blend of theory and practical guidance. By focusing on the synergy between humans and machines through explainability, it underscores the importance of building AI systems that are not only powerful but also trustworthy and aligned with societal values.
Interpretable Machine Learning With Python
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Author : Serg Masís
language : en
Publisher:
Release Date : 2021-03-26
Interpretable Machine Learning With Python written by Serg Masís and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-26 with categories.
Understand the key aspects and challenges of machine learning interpretability, learn how to overcome them with interpretation methods, and leverage them to build fairer, safer, and more reliable models Key Features: Learn how to extract easy-to-understand insights from any machine learning model Become well-versed with interpretability techniques to build fairer, safer, and more reliable models Mitigate risks in AI systems before they have broader implications by learning how to debug black-box models Book Description: Do you want to understand your models and mitigate risks associated with poor predictions using machine learning (ML) interpretation? Interpretable Machine Learning with Python can help you work effectively with ML models. The first section of the book is a beginner's guide to interpretability, covering its relevance in business and exploring its key aspects and challenges. You'll focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. The second section will get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, the book also helps the reader to interpret model outcomes using examples. In the third section, you'll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you'll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning. What You Will Learn: Recognize the importance of interpretability in business Study models that are intrinsically interpretable such as linear models, decision trees, and Naïve Bayes Become well-versed in interpreting models with model-agnostic methods Visualize how an image classifier works and what it learns Understand how to mitigate the influence of bias in datasets Discover how to make models more reliable with adversarial robustness Use monotonic constraints to make fairer and safer models Who this book is for: This book is for data scientists, machine learning developers, and data stewards who have an increasingly critical responsibility to explain how the AI systems they develop work, their impact on decision making, and how they identify and manage bias. Working knowledge of machine learning and the Python programming language is expected.
Establishing Ai Specific Cloud Computing Infrastructure
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Author : Sharma, Avinash Kumar
language : en
Publisher: IGI Global
Release Date : 2025-04-08
Establishing Ai Specific Cloud Computing Infrastructure written by Sharma, Avinash Kumar and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-08 with Computers categories.
As artificial intelligence (AI) continues to drive innovation across industries, the need for specialized cloud computing infrastructure to support AI workloads is critical. Traditional cloud platforms often struggle to meet the high computational demands and storage requirements of AI models, especially as they grow in complexity and scale. Establishing AI-specific cloud computing infrastructure involves designing systems optimized for the needs of AI, such as powerful processing capabilities, massive data storage, and real-time processing. With advancements in hardware like graphics processing units and tensor processing units, along with sophisticated data management solutions, businesses can better harness the full potential of AI technologies. This specialized infrastructure enhances the performance and scalability of AI applications while enabling faster innovation and more efficient deployment of AI-driven solutions across sectors. Establishing AI-Specific Cloud Computing Infrastructure explores how AI has evolved as a transformative new technology, capable of delivering large incremental value to a wide range of sectors. It examines recent advances in innovation, specifically how computing power, data storage, and digitized data have led to AI-based applications for business and governance. This book covers topics such as digital technology, sustainable development, and artificial intelligence, and is a useful resource for computer engineers, business owners, academicians, data scientists, and researchers.
Computational Intelligence Techniques For 5g Enabled Iot Networks
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Author : Mohit Kumar
language : en
Publisher: Springer Nature
Release Date : 2025-07-26
Computational Intelligence Techniques For 5g Enabled Iot Networks written by Mohit Kumar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-26 with Computers categories.
This book explores emerging interdisciplinary themes and applications reflecting advancements in Computational Intelligence (CI) for IoT and 5G networks. It is divided into four sections: Section 1 introduces Computational Intelligence and Sustainability Solutions for Next-Gen IoT Networks. Section 2 covers Optimization and Resilience Strategies for 5G-Enabled IoT Networks. Section 3 delves Intelligent Resource Allocation and Service Optimization in 5G IoT Networks. Section 4 presents Case studies on Applied Computational Intelligence and 5G IoT Innovations for Industry 4.0. This comprehensive work is essential for researchers and professionals interested in leveraging CI, IoT, and 5G technologies across diverse applications.
Current And Future Prospects Of Deep Learning Models For Smart Agriculture
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Author : Rajneesh Kumar Patel
language : en
Publisher: Cambridge Scholars Publishing
Release Date : 2025-03-21
Current And Future Prospects Of Deep Learning Models For Smart Agriculture written by Rajneesh Kumar Patel and has been published by Cambridge Scholars Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-21 with Computers categories.
Examine the cutting edge of agriculture and technology in "Current and Future Prospects of Deep Learning Models for Smart Agriculture". This enlightening book explores how agricultural yields, sustainability, and deep learning are revolutionising farming techniques. The revolutionary potential of Artificial Intelligence (AI) in agriculture will be shown to readers via a thorough examination of current uses, ranging from soil management and crop monitoring to precision farming and insect detection. In order to provide farmers, academics, and tech enthusiasts with the knowledge necessary to utilise deep learning for a more intelligent and effective agricultural landscape, the book also looks ahead, imagining future developments and difficulties. Come along on this trip with us as we grow food production's future!