Preface

Implementing AI applications can be highly complex due to various factors. Firstly, acquiring and preprocessing large volumes of high-quality data necessary for training AI models is a significant challenge. Additionally, selecting the appropriate algorithms, architectures, and frameworks demands expertise as each application may require a different approach. Balancing computational resources, such as processing power and memory, is crucial for efficient model training and deployment. Moreover, ensuring ethical considerations, addressing bias, and maintaining transparency and interpretability in AI systems add another layer of complexity.

The ability of machines to demonstrate advanced cognitive skills in making decisions, learning, perceiving the environment, predicting certain behaviors, or processing written or spoken languages, among other skills, makes this discipline of paramount importance in today's world.

Deploying AI applications poses several challenges. One significant hurdle is understanding how AI systems arrive at decisions or predictions. Especially in critical domains like healthcare or finance, this understanding remains a challenge. Addressing these challenges requires a multidisciplinary approach involving expertise in data science, domain knowledge, ethics, and ongoing regulatory adaptation.

This book compiles a range of applications in Machine Learning in two sections: 'Machine Learning and Data Mining' and 'Current Applications and Future Trends'.

I hope that this work will be of interest to students and researchers alike, as I did my best to compose quality research contributions with a number of different applications.

> **Marco Antonio Aceves-Fernández, Ph.D.** Faculty of Engineering, Universidad Autónoma de Querétaro, Querétaro, México

Section 1
