| Abstract |
The rapid advancement of artificial intelligence (AI) and machine learning (ML) has transformed the technological innovation landscape, presenting incredible opportunities across diverse domains. This chapter thoroughly explores the current state-of-the-art modelling AI and ML techniques, emphasizing a robust framework and elucidating key classifications to enhance our comprehension of these transformative technologies. The framework encompasses a systematic and holistic approach to modelling AI and ML techniques, highlighting the integration of theoretical foundations, algorithmic methodologies, and practical applications. Through an in-depth analysis, this chapter seeks to elucidate the intricate interplay between various components within the framework, providing a roadmap for researchers, practitioners, and policymakers to navigate the complexities of AI and ML development. The chapter begins by exploring the foundational principles underpinning AI and ML, encompassing mathematical concepts, statistical methods, and computational algorithms. Subsequently, the chapter delves into a comprehensive classification of AI and ML techniques based on their inherent characteristics, learning paradigms, and application domains. This classification system is a valuable resource for researchers and practitioners seeking to identify the most suitable techniques for specific tasks or challenges. There are several key classifications in ML, including supervised, unsupervised, reinforcement, and hybrid models. Each of these classifications has distinct applications and advantages. The chapter further explores the evolving landscape of deep learning, a subset of ML that has garnered immense attention for its ability to learn hierarchical representations from data automatically. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are scrutinized as prominent deep learning architectures, focusing on their applications in image recognition, natural language processing, and sequential data analysis. Additionally, the chapter investigates the intersection of AI and ML with other emerging technologies, such as the Internet of Things (IoT) and blockchain. The synergistic integration of these technologies holds the potential to create innovative solutions and address complex challenges across diverse domains, including healthcare, finance, and smart cities. In conclusion, this chapter provides a comprehensive framework and classification system for modelling AI and ML techniques, offering a roadmap for navigating the complexities of these transformative technologies. This chapter seeks to contribute to the ongoing progress and responsible utilization of AI and ML in diverse domains by clarifying the theoretical foundations, examining key classifications, and addressing ethical considerations. The proposed framework and classifications are intended to be a valuable resource for researchers, practitioners, and policymakers aiming to leverage AI and ML's complete potential for society's betterment. © 2025 selection and editorial matter, A. J. Singh, Nikita Gupta, Sanjay Kumar, Sumit Sharma, Subho Upadhyay, and Sandeep Kumar. |
