Intelligent Decision Support Systems- A Framework

Information technology applications that support decision-making processes and problem- solving activities have thrived and evolved over the past few decades. This evolution led to many different types of Decision Support System (DSS) including Intelligent Decision Support System (IDSS). IDSS include domain knowledge, modeling, and analysis systems to provide users the capability of intelligent assistance which significantly improves the quality of decision making. IDSS includes knowledge management component which stores and manages a new class of emerging AI tools such as machine learning and case-based reasoning and learning. These tools can extract knowledge from previous data and decisions which give DSS capability to support repetitive, complex real-time decision making.  This paper attempts to assess the role of IDSS in decision making. First, it explores the definitions and understanding of DSS and IDSS. Second, this paper illustrates a framework of IDSS along with various tools and technologies that support it. Keywords: Decision Support Systems, Data Warehouse, ETL, Data Mining, OLAP, Groupware, KDD, IDS

Reasoning by Structural Analogy Taking into Account the Context for Intelligent Decision Support Systems

Development of methods and tools for modeling human reasoning (common sense reasoning) by analogy in intelligent decision support systems is considered. Special attention is drawn to modeling reasoning by structural analogy taking the context into account. The possibility of estimating the obtained analogies taking into account the context is studied. This work was supported by RFBR

An Intelligent System for Investigations and Provision of Safety for Complex Constructions

Methodology of computer-aided investigation and provision of safety for complex constructions and a prototype of the intelligent applied system, which implements it, are considered. The methodology is determined by the model of the object under scrutiny, by the structure and functions of investigation of safety as well as by a set of research methods. The methods are based on the technologies of object-oriented databases, expert systems and on the mathematical modeling. The intelligent system’s prototype represents component software, which provides for support of decision making in the process of safety investigations and investigation of the cause of failure. Support of decision making is executed by analogy, by determined search for the precedents (cases) with respect to predicted (on the stage of design) and observed (on the stage of exploitation) parameters of the damage, destruction and malfunction of a complex hazardous construction

The innovation network as a complex adaptive system: flexible multi-agent based modeling, simulation and evolutionary decision making

The literature rarely considers an innovation network as a complex adaptive system. In this paper, theories of complex adaptive systems research are employed to model and analyze intra-organization networks, inter-organization networks as well as their interaction mechanisms in the whole innovation context, with a conceptual framework proposed and presented. Flexible multi-agent based modeling, smart simulation, self-survival and adaptive intelligent software agents, expert systems, analytic hierarchy process, hybrid decision support approach, and statistical methods are integrated to deal with the innovation network problem and support evolutionary decision making in the open and dynamic environments

Data Mining Technology Used in an Internet of Things-Based Decision Support System for Information Processing Intelligent Manufacturing

In recent years, database technology has improved significantly, and database management systems have gained widespread adoption. As a result, the volume of data saved across numerous databases has increased exponentially. However, the vast majority of information is hidden beneath this mountain of data. The goal of this study is to get a comprehensive understanding of the decision information system employed in the Internet of Things for intelligent manufacturing data processing. The proposed Decision support system (DSS) information processing is accomplished through the use of an IoT-based intelligent manufacturing data mining model. Numerous DM algorithms that are frequently encountered are analyzed, including the ARS and Apriori Algorithm (AA). The Decision Tree data mining algorithm is investigated, as is the generation of several Decision Trees and the pruning algorithm for digital twins. The findings demonstrate that data mining technology is capable of analyzing statistical data from a variety of angles and perspectives by modeling, classifying, and grouping large amounts of data as well as discovering correlations between them. Additionally, statistical work involves the calculation of data and the use of their correlations to aid in decision analysis. The proposed theoretical framework demonstrates how DSS-integrated components can work cooperatively in Intelligent Manufacturing to define a stable data flow within the Internet of Things. Particular emphasis is placed on conceptualizing the decision support system's integrated performance

Intelligent Systems

This book is dedicated to intelligent systems of broad-spectrum application, such as personal and social biosafety or use of intelligent sensory micro-nanosystems such as "e-nose", "e-tongue" and "e-eye". In addition to that, effective acquiring information, knowledge management and improved knowledge transfer in any media, as well as modeling its information content using meta-and hyper heuristics and semantic reasoning all benefit from the systems covered in this book. Intelligent systems can also be applied in education and generating the intelligent distributed eLearning architecture, as well as in a large number of technical fields, such as industrial design, manufacturing and utilization, e.g., in precision agriculture, cartography, electric power distribution systems, intelligent building management systems, drilling operations etc. Furthermore, decision making using fuzzy logic models, computational recognition of comprehension uncertainty and the joint synthesis of goals and means of intelligent behavior biosystems, as well as diagnostic and human support in the healthcare environment have also been made easier

Biomass Gasification and Applied Intelligent Retrieval in Modeling

Gasification technology often requires the use of modeling approaches to incorporate several intermediate reactions in a complex nature. These traditional models are occasionally impractical and often challenging to bring reliable relations between performing parameters. Hence, this study outlined the solutions to overcome the challenges in modeling approaches. The use of machine learning (ML) methods is essential and a promising integration to add intelligent retrieval to traditional modeling approaches of gasification technology. Regarding this, this study charted applied ML-based artificial intelligence in the field of gasification research. This study includes a summary of applied ML algorithms, including neural network, support vector, decision tree, random forest, and gradient boosting, and their performance evaluations for gasification technologies

Decision Support System for Urbanization of the Northern Part of the Volga-Akhtuba Floodplain (Russia) on the Basis of Interdisciplinary Computer Modeling

There is a computer decision support system (CDSS) for urbanization of the northern part of the Volga-Akhtuba floodplain. This system includes subsystems of cognitive and game-theoretic analysis, geoinformation and hydrodynamic simulations. The paper presents the cognitive graph, two-level and three-level models of hierarchical games for the cases of uncontrolled and controlled development of the problem situation. We described the quantitative analysis of the effects of different strategies for the spatial distribution of the urbanized territories. For this reason we conducted the territory zoning according to the level of negative consequences of urbanization for various agents. In addition, we found an analytical solution for games with the linear dependence of the average flooded area on the urbanized area. We numerically computed a game equilibrium for dependences derived from the imitational geoinformation and hydrodynamic modeling of flooding. As the result, we showed that the transition to the three-level management system and the implementation of an optimal urbanization strategy minimize its negative consequences.Comment: 14 pages, 5 figures; Conference: Creativity in Intelligent Technologies and Data Science. CIT&DS 201

Quantitative modeling of reliability and survivability for cyber-physical power systems

Critical infrastructure systems are increasingly reliant on cyber infrastructure that enables intelligent real-time control of physical components. This cyber infrastructure utilizes environmental and operational data to provide decision support intended to increase the efficacy and reliability of the system and facilitate mitigation of failure. Realistic imperfections, such as corrupt sensor data, software errors, or failed communication links can cause failure in a functional physical infrastructure, defying the purpose of intelligent control. As such, justifiable reliance on cyber-physical critical infrastructure is contingent on rigorous investigation of the effect of intelligent control, including modeling and simulation of failure propagation within the cyber-physical infrastructure. To this end, this thesis investigates the reliability and survivability of a cyber-physical power grid based on the IEEE 9-bus test system. The research contributions include quantitative modeling of both non-functional attributes, based on data from N-1 contingency analysis that considers failures in physical and cyber components of the system. The resulting survivability model is utilized in determining the importance of each transmission line. The final research contribution is identification of optimal recovery strategies for the system, where the objective is to maintain the highest possible survivability in the course of recovery. --Abstract, page iii

WSN Configuration using Agent Modeling and Hybrid Intelligent Decision Support System

A conceptual multi-agent framework based on a knowledge-based collaborative decision suppor