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This paper mainly focuses on incorporating the idea of đť’©*ga continuous functions in neutrosophic topological spaces. We are also studying their features and looking at their properties.
In this paper, the notions of continuous and irresolute functions in neutrosophic topological spaces are given. Furthermore, we analyze their characterizations and investigate their properties.
This book presents the advancements and applications of neutrosophics, which are generalizations of fuzzy logic, fuzzy set, and imprecise probability. The neutrosophic logic, neutrosophic set, neutrosophic probability, and neutrosophic statistics are increasingly used in engineering applications (especially for software and information fusion), medicine, military, cybernetics, physics.In the last chapter a soft semantic Web Services agent framework is proposed to facilitate the registration and discovery of high quality semantic Web Services agent. The intelligent inference engine module of soft semantic Web Services agent is implemented using interval neutrosophic logic.
This book presents the proceedings of International Conference on Knowledge Society: Technology, Sustainability and Educational Innovation (TSIE 2019). The conference, which was held at UTN in Ibarra, Ecuador, on 3–5 July 2019, allowed participants and speakers to share their research and findings on emerging and innovative global issues. The conference was organized in collaboration with a number of research groups: Group for the Scientific Research Network (e-CIER); Research Group in Educational Innovation and Technology, University of Salamanca, Spain(GITE-USAL); International Research Group for Heritage and Sustainability (GIIPS), and the Social Science Research Group (GICS). In addition, it had the endorsement of the RedCLARA, e-science, Fidal Foundation, Red CEDIA, IEEE, Microsoft, Business IT, Adobe, and Argo Systems. The term “knowledge society” can be understood as the management, understanding and co-creation of knowledge oriented toward the sustainable development and positive transformation of society. In this context and on the occasion of the XXXIII anniversary of the Universidad Técnica del Norte (UTN), the Postgraduate Institute through its Master of Technology and Educational Innovation held the I International Congress on Knowledge Society: Technology, Sustainability and Educational Innovation – TSIE 2019, which brought together educators, researchers, academics, students, managers, and professionals, from both the public and private sectors to share knowledge and technological developments. The book covers the following topics: 1. curriculum, technology and educational innovation; 2. media and education; 3. applied computing; 4. educational robotics. 5. technology, culture, heritage, and tourism development perspectives; and 6. biodiversity and sustainability.
In this study, we give some concepts concerning the neutrosophic sets, single valued neutrosophic sets, interval-valued neutrosophic sets, bipolar neutrosophic sets, neutrosophic hesitant fuzzy sets, inter-valued neutrosophic hesitant fuzzy sets, refined neutrosophic sets, bipolar neutrosophic refined sets, multi-valued neutrosophic sets, simplified neutrosophic linguistic sets, neutrosophic over/off/under sets, rough neutrosophic sets, rough bipolar neutrosophic sets, rough neutrosophic hyper-complex set, and their basic operations.
Can you tell the difference between talking to a human and talking to a machine? Or, is it possible to create a machine which is able to converse like a human? In fact, what is it that even makes us human? Turing's Imitation Game, commonly known as the Turing Test, is fundamental to the science of artificial intelligence. Involving an interrogator conversing with hidden identities, both human and machine, the test strikes at the heart of any questions about the capacity of machines to behave as humans. While this subject area has shifted dramatically in the last few years, this book offers an up-to-date assessment of Turing's Imitation Game, its history, context and implications, all illustrated with practical Turing tests. The contemporary relevance of this topic and the strong emphasis on example transcripts makes this book an ideal companion for undergraduate courses in artificial intelligence, engineering or computer science.
Hybrid Computational Intelligence: Challenges and Utilities is a comprehensive resource that begins with the basics and main components of computational intelligence. It brings together many different aspects of the current research on HCI technologies, such as neural networks, support vector machines, fuzzy logic and evolutionary computation, while also covering a wide range of applications and implementation issues, from pattern recognition and system modeling, to intelligent control problems and biomedical applications. The book also explores the most widely used applications of hybrid computation as well as the history of their development. Each individual methodology provides hybrid systems with complementary reasoning and searching methods which allow the use of domain knowledge and empirical data to solve complex problems. - Provides insights into the latest research trends in hybrid intelligent algorithms and architectures - Focuses on the application of hybrid intelligent techniques for pattern mining and recognition, in big data analytics, and in human-computer interaction - Features hybrid intelligent applications in biomedical engineering and healthcare informatics
In this paper, we introduce and study the concept of "neutrosophic closed set "and "neutrosophic continuous function". Possible application to GIS topology rules are touched upon.
Deep Learning Models for Medical Imaging explains the concepts of Deep Learning (DL) and its importance in medical imaging and/or healthcare using two different case studies: a) cytology image analysis and b) coronavirus (COVID-19) prediction, screening, and decision-making, using publicly available datasets in their respective experiments. Of many DL models, custom Convolutional Neural Network (CNN), ResNet, InceptionNet and DenseNet are used. The results follow 'with' and 'without' transfer learning (including different optimization solutions), in addition to the use of data augmentation and ensemble networks. DL models for medical imaging are suitable for a wide range of readers starting from early career research scholars, professors/scientists to industrialists. - Provides a step-by-step approach to develop deep learning models - Presents case studies showing end-to-end implementation (source codes: available upon request)