Название: AI for Product Development Автор: Kanchan Naithani, Shrikant Tiwari, Shabnam Kumari Издательство: CRC Press Год: 2026 Страниц: 319 Язык: английский Формат: pdf (true), epub Размер: 33.6 MB
AI for Product Development explores the transformative role of Artificial Intelligence (AI) in reshaping modern industries. This book offers a comprehensive guide, spanning the evolution of AI in product innovation to practical applications, such as clustering techniques, human-autonomous vehicle interactions, and personalized healthcare solutions. With contributions from leading researchers, it covers explainable AI, real-world case studies, and ethical considerations in intelligent systems. The chapters delve into cutting-edge topics such as YOLO model variants, AI-driven emotion detection, and strategies for overcoming AI implementation challenges. Designed for researchers, professionals, and students, it bridges theory and practice, emphasizing AI's profound impact on product development and beyond.
In recent years, data-driven approaches have revolutionized how product teams make decisions. By leveraging vast data and advanced analytical techniques companies can uncover deep insights into customer behavior and preferences, leading to more informed and accurate prioritization. One such powerful technique is K-Means clustering, a Machine Learning (ML) algorithm that assembles data points into clusters created on their similarities. This method can be particularly useful in product development, where it can help specify patterns and trends that inform feature prioritization.
Setting up the clustering environment involves selecting and utilizing tools like Python, NumPy, Pandas, Scikit-learn, and Matplotlib for efficient data processing and visualization. Tools and Libraries: Setting up the clustering environment involves selecting appropriate tools and libraries that facilitate efficient data processing and K-Means clustering. Popular tools and libraries for implementing K-Means include:
• Python: A multipurpose programming language extensively used in data science and ML. • NumPy: A library for numerical computations, supporting large, multidimensional data. • Pandas: A data handling library offering data structures and functions to manipulate structured data. • Scikit-learn: A machine learning library that includes an implementation of the K-Means algorithm. • Matplotlib and Seaborn: Libraries for data visualization that are useful for plotting data and clusters.
Note that in this chapter, the focus is on Python programming and its associated tools due to their widespread popularity. However, other programming languages such as C, C++, and Java are equally capable of handling similar problems.
Explainable AI (XAI) refers to systems, procedures and strategies that aim to make the decision-making processes of Artificial Intelligence (AI) models clear and understandable for humans. This is critical to address the ‘black-box’ character of traditional AI models, which deliver outcomes but do not reveal the underlying logic. In contrast to these standard models, XAI seeks to ‘open the black box’ and reveal the mechanisms that convert inputs into outputs. This openness helps consumers, developers and other stakeholders understand, trust and operate AI systems more effectively.
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