Besides structured data, patient care information in Electronic Health Records (EHRs) comprises data in unstructured form (i.e., clinical notes). While EHR information may overlap between structured and unstructured sections, some crucial information remains only in the unstructured sections [1-5]. Relevant information for clinical decisions can easily be overlooked when dealing with large amounts of notes. Previous investigations have already found that clinical text alone can often provide su!cient information for decisions [2, 6, 7]. However, extracting actionable information from clinical text remains di!cult due to language variability, inconsistent use of medical terminology, and lack of standardised formatting [8]. In addition, the lack of structure introduces ambiguities and inconsistencies in the text. Thus, it is still di!cult to accurately interpret and analyse clinical notes for decision support systems. As a result, the development of advanced natural language processing (NLP) models that can extract and represent information from clinical narrative has become a focal point of research in digital medicine [2, 9, 10]. Contextual embedding models have emerged as a powerful tool for transforming unstructured texts into dense vector representations to encode rich semantic information [11, 12].

Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not conform to the norms are an indication that the rules of science governing the data are incomplete, and something new needs to be present to explain these unexpected outliers. The challenge of finding anomalies can be confounding since it requires codifying a complete knowledge of the known scientific behaviors and then projecting these known behaviors on the data to look for deviations. When utilizing machine learning, this presents a particular challenge since we require that the model not only understands scientific data perfectly but also recognizes when the data is inconsistent and out of the scope of its trained behavior. In this paper, we present three datasets aimed at developing machine learning-based anomaly detection for disparate scientific domains covering astrophysics, genomics, and polar science. We present the different datasets along with a scheme to make machine learning challenges around the three datasets findable, accessible, interoperable, and reusable (FAIR). Furthermore, we present an approach that generalizes to future machine learning challenges, enabling the possibility of large, more compute-intensive challenges that can ultimately lead to scientific discovery.

This chapter aims to provide next level understanding of the problems of the world and the solutions available to those problems, which lie very well within the domain of neural computing, and at the same time are intelligent in their approach, to invoke a sense of innovation among the educationalists, researchers, academic professionals, students and people concerned, by highlighting the work done by major researchers and innovators in this field and thus, encouraging the readers to develop newer and more advanced techniques for the same. By means of this chapter, the societal problems are discussed and various solutions are also given by means of the theories presented and researches done so far. Different types of neural networks discovered so far and applications of some of those neural networks are focused on, apart from their theoretical understanding, the working and core concepts involved in the applications.