On The Impact Of Missing Data On Machine Learning Algorithms And Sensitivity Reduction To Missing Data By Dynamic Allocation Of Neighbors

On the Impact of Missing Data on Machine Learning Algorithms and Sensitivity Reduction to Missing Data by Dynamic Allocation of Neighbors

Hybrid and Advanced Technologies

The proceedings of the International Conference on Hybrid and Advanced Technologies (ICHAT 2024) present a rich repository of cutting-edge research on the various applications of machine learning, deep learning, and AI in cybersecurity, healthcare, agriculture and communication systems. It highlights the revolutionary potential of data science in transforming traditional practices, improving efficiency and accuracy across diverse domains and addressing complex real-world challenges. These proceedings contains innovative neural-network models for agriculture that can predict tractor fuel consumption and optimize smart irrigation, besides suggesting greenhouse automation for enhanced agricultural productivity. It also provides a roadmap for IoT-based monitoring systems for asthma patients and machine learning approaches for early detection of diabetes, cancer and aquatic plant ailments. Through an array of practical examples and comparative studies, the book further highlights advancements in machine learning for enhancing palm vein authentication, combating fake news, keeping data safe and improving customer segmentation in e-commerce. The findings would be instrumental in combating critical global issues and foster a deeper understanding of the role of AI in image processing, cybersecurity, medical diagnostics, and intelligent systems in the future. This will be a highly interesting guide to researchers, data scientists, and practicing professionals in the fields of artificial intelligence, machine learning, and cybersecurity. It will also be of interest to healthcare professionals, agricultural scientists, and technology enthusiasts in fostering global collaborations, exploring future challenges and opportunities and introducing state-of-the-art technologies to streamline processes.

Nonlinear Dimensionality Reduction

Methods of dimensionality reduction provide a way to understand and visualize the structure of complex data sets. Traditional methods like principal component analysis and classical metric multidimensional scaling suffer from being based on linear models. Until recently, very few methods were able to reduce the data dimensionality in a nonlinear way. However, since the late nineties, many new methods have been developed and nonlinear dimensionality reduction, also called manifold learning, has become a hot topic. New advances that account for this rapid growth are, e.g. the use of graphs to represent the manifold topology, and the use of new metrics like the geodesic distance. In addition, new optimization schemes, based on kernel techniques and spectral decomposition, have lead to spectral embedding, which encompasses many of the recently developed methods. This book describes existing and advanced methods to reduce the dimensionality of numerical databases. For each method, the description starts from intuitive ideas, develops the necessary mathematical details, and ends by outlining the algorithmic implementation. Methods are compared with each other with the help of different illustrative examples. The purpose of the book is to summarize clear facts and ideas about well-known methods as well as recent developments in the topic of nonlinear dimensionality reduction. With this goal in mind, methods are all described from a unifying point of view, in order to highlight their respective strengths and shortcomings. The book is primarily intended for statisticians, computer scientists and data analysts. It is also accessible to other practitioners having a basic background in statistics and/or computational learning, like psychologists (in psychometry) and economists.