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This paper proposes a three-tier algorithmic framework as the basis for the flexible design of data-driven structural health monitoring (SHM) systems. The three major functions of the SHM system, including data normalization, feature extraction, and hypothesis testing (HT), are mapped to the three layers of the framework. The first tier of the framework is devoted to data normalization. Machine learning...
Machine learning is a subfield of artificial intelligence that deals with the exploration and construction of systems that can learn from data. Machine learning trains the computers to manage the critical situations via examining, self-training, inference by observation and previous experience. This paper provides an overview of the development of an efficient classifier that represents the semantics...
Machine Learning concept offers the biomedical research field a great support. It provides many opportunities for disease discovering and related drugs revealing. The machine learning medical applications had been evolved from the physician needs and motivated by the promising results extracted from empirical studies. Medical support systems can be provided by screening, medical images, pattern classification...
Machine-learning techniques such as decision support systems (DSS) are of great help in various fields. Medicine is one of the fields that can benefit from the application of data mining and pattern recognition techniques. The evolution of computational intelligence can improve many areas in health care including diagnosis, prognosis, screening, etc. The multiclass classification problem is important...
Data mining is a fast evolving technology, is being adopted in biomedical sciences and research. Data mining in medicine is an emerging field of high importance for providing prognosis and a deeper understanding of the classification of neurodegenerative diseases. Given a data set of consists of 487 patients records collected from ADRC, USA. Around eight hundred and ninety patients were recruited...
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