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Model of Self-organizing Knowledge Representation and Organizational Knowledge Transformation
Issue:
Volume 4, Issue 1, June 2020
Pages:
1-19
Received:
24 February 2020
Accepted:
16 March 2020
Published:
24 March 2020
Abstract: The purpose of the paper is development of a conceptual model for the representation of knowledge as an active intellectual substance and, on this basis, study of metaphysics of knowledge transformation process being produced both individually and collectively in the practice of organizations. The first principle of knowledge engineering, as Edward Albert Feigenbaum noted, says that the power in solving problems that an intellectual subject (person or machine) manifests in the process of activity depends primarily on its knowledge base, and only secondly on the methods of inference used. Strength is hidden in knowledge. The process of producing knowledge is permanent and does not depend on whether an individual is going to use this knowledge or not. Knowledge constantly produces new knowledge regardless of the owner's desire. Besides that, knowledge can’t arise from nothing, but always – from some knowledge obtained earlier. As well as the intelligence, knowledge is an emergent instance arising from the collective interaction of a lot of intellectual atomic elements of knowledge (knowledge quanta). Idiosyncrasy of this interaction is expressed precisely in the creation of new knowledge. Due to postulating the knowledge self-organizing, the hierarchical knowledge structures in memory and the process of thinking as a kind of syntax for the procedure of new knowledge generation are described. This is an effort towards understanding the memory mechanisms, the process of thinking, the sources of heuristic knowledge just through the inner nature of knowledge. Also, based on the knowledge self-organization principle, an archetype of the appropriate knowledge-based system architecture is presented too. As an implementation of the concept, the perceptual act model is described, and on its base, a possible scenario for the behavior of a robot meeting an obstacle in its path is considered. As the mutual transformation of tacit and explicit knowledge makes new knowledge, the impact of the self-organization of knowledge on the transformation process as well as conditions of self-organization of both individual knowledge and organizational knowledge are analyzed in detail. Finally, modification of the known model of knowledge dimensions by Nonaka and Takeuchi is proposed. Because of the native activity of knowledge, it is impossible to build a knowledge management system without considering the internal structure of knowledge and its emergent ability to self-organize. Ensuring the natural process of knowledge development at all ontological levels in an organization is an essential prerequisite for the evolution of values in this organization.
Abstract: The purpose of the paper is development of a conceptual model for the representation of knowledge as an active intellectual substance and, on this basis, study of metaphysics of knowledge transformation process being produced both individually and collectively in the practice of organizations. The first principle of knowledge engineering, as Edward...
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A Comprehensive Review on Heart Disease Prediction Using Data Mining and Machine Learning Techniques
Lamido Yahaya,
Nathaniel David Oye,
Etemi Joshua Garba
Issue:
Volume 4, Issue 1, June 2020
Pages:
20-29
Received:
12 March 2020
Accepted:
2 April 2020
Published:
23 April 2020
Abstract: Heart disease is one of the major causes of life complicacies and subsequently leading to death. The heart disease diagnosis and treatment are very complex, especially in the developing countries, due to the rare availability of efficient diagnostic tools and shortage of medical professionals and other resources which affect proper prediction and treatment of patients. Inadequate preventive measures, lack of experienced or unskilled medical professionals in the field are the leading contributing factors. Although, large proportion of heart diseases is preventable but they continue to rise mainly because preventive measures are inadequate. In today’s digital world, several clinical decision support systems on heart disease prediction have been developed by different scholars to simplify and ensure efficient diagnosis. This paper investigates the state of the art of various clinical decision support systems for heart disease prediction, proposed by various researchers using data mining and machine learning techniques. Classification algorithms such as the Naïve Bayes (NB), Decision Tree (DT), and Artificial Neural Network (ANN) have been widely employed to predict heart diseases, where various accuracies were obtained. Hence, only a marginal success is achieved in the creation of such predictive models for heart disease patients therefore, there is need for more complex models that incorporate multiple geographically diverse data sources to increase the accuracy of predicting the early onset of the disease.
Abstract: Heart disease is one of the major causes of life complicacies and subsequently leading to death. The heart disease diagnosis and treatment are very complex, especially in the developing countries, due to the rare availability of efficient diagnostic tools and shortage of medical professionals and other resources which affect proper prediction and t...
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Autonomous Systems and Reliability Assessment: A Systematic Review
Kalesanwo Olamide,
Kuyoro ‘Shade,
Eze Monday,
Awodele Oludele
Issue:
Volume 4, Issue 1, June 2020
Pages:
30-35
Received:
14 March 2020
Accepted:
25 March 2020
Published:
30 April 2020
Abstract: The advancement of technology has heralded novel computing devices and gadgets like self-driving cars, IoT devices, and autonomous systems. These advancements required high computational demand in achieving its goals. In matching the high computational demand of these new technologies, machine learning, parallelism, multicore processing and scaling are some of the approaches and techniques put in place. However, there is a pressure on the architectural development of recent computing devices as the traditional transistors seem to be fast outgrown. This article examines the reliability of autonomous systems using the PRISMA approach. Autonomous systems are systems that can fully operate and perform operations (computational or otherwise) with minimal human intervention. They are also capable of evaluating their performance. Thus, there is a need for a high degree of reliability. Several existing autonomous systems were reviewed and reliability issues of these systems were discussed. It was discovered that the reliability of a complex system is dependent on the reliability of underlying individual components and compromise of any of the underlying components of the autonomous system can affect the overall reliability of the entire system. The effort to enhance the reliability of these components will, in turn, improve the reliability of the entire system.
Abstract: The advancement of technology has heralded novel computing devices and gadgets like self-driving cars, IoT devices, and autonomous systems. These advancements required high computational demand in achieving its goals. In matching the high computational demand of these new technologies, machine learning, parallelism, multicore processing and scaling...
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Research on the Framework of Smart City Operating System Based on New ICTs
Issue:
Volume 4, Issue 1, June 2020
Pages:
36-41
Received:
17 April 2020
Accepted:
18 May 2020
Published:
29 May 2020
Abstract: With the deepening of Chinses national strategies such as Digital China and Artificial Intelligence, urban informatization is evolving from a smart city to a more advanced and super-intelligent system integrating urban governance with new information and communication technologies (ICTs) represented by artificial intelligence, big data, cloud computing. How to use the new ICTs to build the "smart city operating system" and other smart platforms to realize urban smartness has become the current research focus. First, this paper analyzes the four stages of the evolution of smart cities and the new ICTs that support urban agents. Second, taking the smart city planning of Wuhan Yangtze New City as an example, this paper introduces vision of Wuhan Yangtze New City, and analyzes the overall structure and smart infrastructure architecture of Wuhan Yangtze new smart city, and deeply studies the framework of the new ICTs-based Yangtze River Smart City operating system, including its compositions, integrated data architecture and application domain architecture. The third, how the new ICTs-based urban operating system can help cities achieve super-intelligence is also discussed. In addition, the paper suggested that Wuhan Yangtze New City should further improve its resilient urban planning for the future.
Abstract: With the deepening of Chinses national strategies such as Digital China and Artificial Intelligence, urban informatization is evolving from a smart city to a more advanced and super-intelligent system integrating urban governance with new information and communication technologies (ICTs) represented by artificial intelligence, big data, cloud compu...
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