Decision Making: A Computer-Science and Information-Technology Viewpoint

We address the phenomenon of decision making from the viewpoint of computer science and information technology. The basic question from this viewpoint is: what can the computer offer to decision makers and how it can support their work? Therefore, the main issue is to provide support to people who make complex decisions. In this article, we first present the taxonomy of disciplines that are concerned with methodological and operational aspects of decision support. At the main level, we distinguish between decision sciences, which are concerned with human decision making, and decision systems, which address computer decision making. This is followed by basic definitions related to decision processes and their components. We also describe properties that characterise different classes of decision problems. In the main part of the article, we present three prevailing approaches to decision support and give illustrative examples of their application: decision analysis, operational research, and decision support systems. Finally, we make a short overview of the area of decision systems and its achievements.decision making, decision sciences, decision support, decision analysis, decision systems

Business intelligence in banking: A literature analysis from 2002 to 2013 using Text Mining and latent Dirichlet allocation

telligence applications for the banking industry. Searches were performed in relevant journals resulting in 219 articles published between 2002 and 2013. To analyze such a large number of manuscripts, text mining techniques were used in pursuit for relevant terms on both business intelligence and banking domains. Moreover, the latent Dirichlet allocation modeling was used in or- der to group articles in several relevant topics. The analysis was conducted using a dictionary of terms belonging to both banking and business intelli- gence domains. Such procedure allowed for the identification of relationships between terms and topics grouping articles, enabling to emerge hypotheses regarding research directions. To confirm such hypotheses, relevant articles were collected and scrutinized, allowing to validate the text mining proce- dure. The results show that credit in banking is clearly the main application trend, particularly predicting risk and thus supporting credit approval or de- nial. There is also a relevant interest in bankruptcy and fraud prediction. Customer retention seems to be associated, although weakly, with targeting, justifying bank offers to reduce churn. In addition, a large number of ar- ticles focused more on business intelligence techniques and its applications, using the banking industry just for evaluation, thus, not clearly acclaiming for benefits in the banking business. By identifying these current research topics, this study also highlights opportunities for future research

Factors shaping the evolution of electronic documentation systems

The main goal is to prepare the space station technical and managerial structure for likely changes in the creation, capture, transfer, and utilization of knowledge. By anticipating advances, the design of Space Station Project (SSP) information systems can be tailored to facilitate a progression of increasingly sophisticated strategies as the space station evolves. Future generations of advanced information systems will use increases in power to deliver environmentally meaningful, contextually targeted, interconnected data (knowledge). The concept of a Knowledge Base Management System is emerging when the problem is focused on how information systems can perform such a conversion of raw data. Such a system would include traditional management functions for large space databases. Added artificial intelligence features might encompass co-existing knowledge representation schemes; effective control structures for deductive, plausible, and inductive reasoning; means for knowledge acquisition, refinement, and validation; explanation facilities; and dynamic human intervention. The major areas covered include: alternative knowledge representation approaches; advanced user interface capabilities; computer-supported cooperative work; the evolution of information system hardware; standardization, compatibility, and connectivity; and organizational impacts of information intensive environments

The creation of “Questions Bank" and introduction of 2.0. examination session

In the Institute of American Studies and Polish Diaspora Jagiellonian University, with the support of the Distance Learning Centre at the Jagiellonian University was introduced an innovative method of examination based on empowering the students. During the 2014 session, the students were invited to create test questions. Accepted by the lecturer questions were used on he exam. Extensive “Questions Bank” may be used in subsequent examinations 2.0. The authors of the paper present practical advice on how to prepare and carry out such an examination. They share know how of practical suggestions from pedagogical to technical aspect of moving from teaching to learning while using the idea of Questions Bank. They discuss the impact on the motivation and creativity of students, the principles of achievement and assessment, methods of verifying the content of the questions and technical measures to make questions and hindering cheating. The use of innovative methods of preparing and conducting the exam based on the Questions Bank had a positive impact on the mobilization and involvement of students, which resulted in a very good performance evaluation questionnaires of the lecture

Composite Monte Carlo Decision Making under High Uncertainty of Novel Coronavirus Epidemic Using Hybridized Deep Learning and Fuzzy Rule Induction

In the advent of the novel coronavirus epidemic since December 2019, governments and authorities have been struggling to make critical decisions under high uncertainty at their best efforts. Composite Monte-Carlo (CMC) simulation is a forecasting method which extrapolates available data which are broken down from multiple correlated/casual micro-data sources into many possible future outcomes by drawing random samples from some probability distributions. For instance, the overall trend and propagation of the infested cases in China are influenced by the temporal-spatial data of the nearby cities around the Wuhan city (where the virus is originated from), in terms of the population density, travel mobility, medical resources such as hospital beds and the timeliness of quarantine control in each city etc. Hence a CMC is reliable only up to the closeness of the underlying statistical distribution of a CMC, that is supposed to represent the behaviour of the future events, and the correctness of the composite data relationships. In this paper, a case study of using CMC that is enhanced by deep learning network and fuzzy rule induction for gaining better stochastic insights about the epidemic development is experimented. Instead of applying simplistic and uniform assumptions for a MC which is a common practice, a deep learning-based CMC is used in conjunction of fuzzy rule induction techniques. As a result, decision makers are benefited from a better fitted MC outputs complemented by min-max rules that foretell about the extreme ranges of future possibilities with respect to the epidemic.Comment: 19 page

Artificial Intelligence Towards Future Industrial Opportunities and Challenges

The industry 4.0 will bring reflective changes to our society, including an important digital shift in the manufacturing sector. At present, several manufacturing firms are trying to adopt the practices of industry 4.0 throughout their supply chain. The Fourth Industrial Revolution and the artificial intelligence at its core are fundamentally changing the way we live, work and interact as citizens. The complexity of this transformation may look overwhelming and to many threatening. Recently, the dramatic growth of new generation information technologies has prompted several countries to seek new strategies for industrial revolution. The globalization and the competitiveness are forcing companies to rethink and to innovate their production processes following the so-called Industry 4.0 paradigm. It represents the integration of tools already used in the past (big data, cloud, robot, 3D printing, simulation, etc.) that are now connected into a global network by transmitting digital data. Digitization and intelligentization of manufacturing process is the need for today’s industry. The manufacturing industries are currently changing from mass production to customized production. The rapid advancements in manufacturing technologies and applications in the industries help in increasing productivity. The term Industry 4.0 stands for the fourth industrial revolution which is defined as a new level of organization and control over the entire value chain of the life cycle of products; it is geared towards increasingly individualized customer requirements. Industry 4.0 is still visionary but a realistic concept which includes Internet of Things, Industrial Internet, Smart Manufacturing and Cloud based Manufacturing. Industry 4.0 concerns the strict integration of human in the manufacturing process so as to have continuous improvement and focus on value adding activities and avoiding wastes. The objective of this work is to provide an overview of Industry 4.0 and understanding of the pillars of Industry 4.0 with its applications and identifying the challenges and issues occurring with implementation the Industry 4.0 and to study the new trends and streams related to Industry 4.0 with artificial intelligence by using flexible intelligent approach. Based on intelligent and flexible AI methods and the complex safety relations in the process industry, we identify and discuss several technical challenges associated with process safety: knowledge acquisition with scarce labels for process safety; knowledge-based reasoning for process safety; accurate fusion of heterogeneous data from various sources; and effective learning for dynamic risk assessment and aided decision-making

Neuro-fuzzy resource forecast in site suitability assessment for wind and solar energy: a mini review

Abstract:Site suitability problems in renewable energy studies have taken a new turn since the advent of geographical information system (GIS). GIS has been used for site suitability analysis for renewable energy due to its prowess in processing and analyzing attributes with geospatial components. Multi-criteria decision making (MCDM) tools are further used for criteria ranking in the order of influence on the study. Upon location of most appropriate sites, the need for intelligent resource forecast to aid in strategic and operational planning becomes necessary if viability of the investment will be enhanced and resource variability will be better understood. One of such intelligent models is the adaptive neuro-fuzzy inference system (ANFIS) and its variants. This study presents a mini-review of GIS-based MCDM facility location problems in wind and solar resource site suitability analysis and resource forecast using ANFIS-based models. We further present a framework for the integration of the two concepts in wind and solar energy studies. Various MCDM techniques for decision making with their strengths and weaknesses were presented. Country specific studies which apply GIS-based method in site suitability were presented with criteria considered. Similarly, country-specific studies in ANFIS-based resource forecasts for wind and solar energy were also presented. From our findings, there has been no technically valid range of values for spatial criteria and the analytical hierarchical process (AHP) has been commonly used for criteria ranking leaving other techniques less explored. Also, hybrid ANFIS models are more effective compared to standalone ANFIS models in resource forecast, and ANFIS optimized with population-based models has been mostly used. Finally, we present a roadmap for integrating GIS-MCDM site suitability studies with ANFIS-based modeling for improved strategic and operational planning

Digitalization of Offshore Wind Farm Systems

Master's thesis in Offshore Technology: Industrial asset managementThis thesis investigates how new digital technologies and digitalization can help further evolve the offshore wind industry using the Industry 4.0 concept as a basis and explores how technologies within this concept can contribute to an offshore wind farm that overcomes some of these challenges. The study focuses on an offshore wind farm from a systems perspective, including respective modules, and where the Industry 4.0 technologies can be applied. Following this is the establishment of a systematic digitalization framework and a proposal on how to cope with increased volumes of data, connectivity, and complexity.publishedVersio

Analysis of existing decision support systems to provide current baseline applications, implementation methodologies, problem areas, hardware and communication complexities

Issued as Monthly report and Final report, Project no. G-36-66

Risks and Prospects of Smart Electric Grids Systems measured with Real Options

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