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Public Perception toward AI-Driven Healthcare and the Way Forward in the Post-Pandemic Era
Published in Chinmay Chakraborty, Digital Health Transformation with Blockchain and Artificial Intelligence, 2022
Spandan Datta, Nilesh Tejrao Kate, Abhishek Srivastava
The article ‘The rise of artificial intelligence in healthcare applications’ was reviewed and analyzed. The article was published in Artificial Intelligence in Healthcare in 2020. The constructs identified are AI, healthcare applications, machine learning, precision medicine, environmental assisted living, natural language programming and machine vision. It is commonly anticipated, according to the authors, that AI technologies would help, and augment human labour rather than fully substitute the job of doctors and other medical staff. AI can help healthcare personnel with a variety of activities, such as admin work, patient records and patient engagement, as well as specialized assistance in areas like image processing, medical device control, and monitoring patients [35].
Semantics-based Decision Support - An Introduction
Published in Sarika Jain, Understanding Semantics-Based Decision Support, 2021
Most of these categories may be considered not as decision-oriented but rather as DSS tools. Based on the purpose of the DSS, they have been classified differently by different people, e.g., personal DSS (a DSS which focuses on and supports individuals), group DSS (a DSS which facilitates a group of people in reaching to a joint decision), negotiation DSS (a DSS in which negotiating is allowed on certain intermediate decisions), and business intelligence (a DSS performing data analysis of business information to convert it into actionable knowledge) [Arnott and Pervan 2005]. With the inclusion of Artificial Intelligence (AI) methods and techniques recently (fuzzy logic, knowledge bases, natural language programming (NLP), neural networks, genetic algorithms, and so forth), a lot of improvements can be seen in the working of DSSs. The new terminology thus common for DSSs is “Intelligent decision support systems.” An Intelligent DSS is able to mimic human intelligence, performcommon-sense reasoning, and context-sensitive reasoning, hence improving the ability of decision-makers.
Semantic Interoperability of Long-Tail Geoscience Resources over the Web
Published in Ashok N. Srivastava, Ramakrishna Nemani, Karsten Steinhaeuser, Large-Scale Machine Learning in the Earth Sciences, 2017
Mostafa M. Elag, Praveen Kumar, Luigi Marini, Scott D. Peckham, Rui Liu
Semantic heterogeneity occurs when there is a disagreement about the meaning of the label used to describe a resource or one of its parts, ambiguity about the interpretation of the content of a resource, or its usage pattern among related parties. Among the scientific communities, four sources of semantic heterogeneity can be identified: (1) Structural—that is, the syntax of the language used to describe and code the resources, including the natural language, programming languages, and data encoding languages; (2) Content—that is, the method and vocabularies used to describe the elements within each resource, including the naming process and missing information inside a file; (3) Conceptual—that is, the heterogeneity between related domains in the conceptualization of their information system, including heterogeneity in the structure of concepts, their level of granularity and relationships, and the terminologies that are used to describe these concepts; and (4) Contextual—that is, the mismatch between schemas used to map resources between information systems [38].
LIA: A Virtual Assistant that Can Be Taught New Commands by Speech
Published in International Journal of Human–Computer Interaction, 2019
In natural language programming (Biermann, 1983), a programmer can use natural language to develop software. In Inform 7 (Reed, 2010), for example, a programmer can create an interactive fiction program using English sentences. For example, the programmer can say “The kitchen is a room,” “The kitchen has a stove,” and “The description of the stove is: ‘very dirty’”. However, despite being in natural language, statements in Inform 7 are required to be in a specific form. Needless to say that neither of these programming languages support teaching an agent new commands based on speech. In (Chkroun & Azaria, 2018) we allowed users to teach a chatbot new responses in natural language (by using a specific form). Quirk et al. (Quirk, Mooney, & Galley, 2015b) have converted “recipes” written in natural language to if-then statements, in which the “if” part is a condition on a sensor (e.g. phone camera, mic. etc) or a cyber-sensor (e.g. weather, Twitter etc.), and the “then” part is bounded to a command (e.g. opening the camera app, or making a sound) (Quirk, Mooney, & Galley, 2015a). Huang, Azaria, & Bigham, (2016) have developed a system that uses the crowd to compose if-then recipes for mobile phones.