Adaptive Decision Support System Research Articles

Data stream solution for decision-making processes: a general and adaptive system for decision support

ABSTRACT With the rise of new data generation processes and availability rates, new challenges appear linked with the information and pattern extraction for decisional processes. In these contexts, decision support systems (DSS) need to incrementally integrate the data flows to provide readable, understandable and meaningful conclusions. This research presents a novel adaptive DSS that offers updated and explainable knowledge for decision makers, especially in uncertain decision scenarios. Also, this work explores applications of this technology in some social fields, aiming to analyse the potential contribution of machine learning, combined with adaptive decision systems, in society.

Complex-Valued GMDH-Based Data Characteristic-Driven Adaptive Decision Support System for Customer Classification

Complex-Valued GMDH-Based Data Characteristic-Driven Adaptive Decision Support System for Customer Classification

A comparative study of classification techniques in data mining algorithms used for medical diagnosis based on DSS

A significant amount of data is gathered by the healthcare sector, but it is not appropriately mined and utilized. Finding these hidden links and patterns is frequently underutilized. Our study focuses on this element of medical diagnostics by identifying patterns in the information gathered about kidney illness, liver disease, and chronic pancreatitis (CP) and designing adaptive medical decision support systems (MDSS) to assist doctors. This research compares a variety of data mining (DM) techniques, knowledge extraction tools, and software platforms for usage in a DSS for analysis using the Waikato environment for knowledge analysis (WEKA) mining tool (decision tree (DT)). The objective is to determine the most significant risk factors based on the extraction of the categorization criteria. The datasets used for this work are illustrates how successfully DM and DSS are integrated. In this research, we suggest using the C4.5 DT algorithm, Naïve Bayes (NB) algorithm, and the logistic regression (LR) algorithm to categorize these diseases and evaluate their performance and accuracy rates. It inferred that the C4.5 algorithm accuracy is 0.873% which is better than the other two algorithms in terms of rule generation and accuracy.

Effectiveness of Adaptive Operating Rules for Reservoirs

In this paper, a methodological proposal is made to develop an adaptive decision support system for reservoir management. The system is based on an optimization model that determines operating rules that meet certain optimality conditions based on the state of the reservoir at a certain time and on a streamflow forecast, if available. The model is based on Spiliotis et al. in Water Resour Manage 30:5759-5778, where a general methodology was developed to specify static operating rules for reservoir systems. The proposed methodology consists of modifying the optimization procedure developed in the previous work to dynamically update operating rules to adapt management to the changing situation. To evaluate its effectiveness, the methodology was applied to the Pisuerga-Carrión reservoir system, in the Spanish part of the Duero basin. The results obtained with adaptive rules were compared with those obtained applying the same static operating rule for the entire analysis period. Adaptive rules were found to lead to better operating results, particularly if a successful streamflow forecast method is available.

Towards a Technology-Driven Adaptive Decision Support System for Integrated Pavement and Maintenance strategies (TDADSS-IPM): focus on risk assessment framework for climate change adaptation

Decision Support Systems for pavement and maintenance strategies have traditionally been designed as silos led to local optimum systems. Moreover, since big data usage didn't exist as result of Industry 4.0 as of today, DSSs were not initially designed adaptive to the sources of uncertainties led to rigid decisions. Motivated by the vulnerability of the road assets to the climate phenomena, this paper takes a visionary step towards introducing a Technology-Driven Adaptive Decision Support System for Integrated Pavement and Maintenance activities called TDADSS-IPM. As part of such DSS, a bottom-up risk assessment model is met via Bayesian Belief Networks (BBN) to realize the actual condition of the Danish roads due to weather condition. Such model fills the gaps in the knowledge domain and develops a platform that can be trained over time, and applied in real-time to the actual event.

Nature-inspired adaptive decision support system for secured clustering in cyber networks

Nature-inspired adaptive decision support system for secured clustering in cyber networks

Data-driven decision making: new opportunities for DSS in data stream contexts

ABSTRACT Traditionally, Decision Support Systems (DSS) data were stored statically and persistently in a database. Increasing volume and intensity of information and data streams create new opportunities and challenges for DSS experts, data scientists, and decision makers. Novel data stream contexts require that we move beyond static DSS modelling techniques to support data-driven decision-making. Implementing incremental and/or adaptive algorithms may help to solve some of the challenges arising from data streams. This research investigates the use of these algorithms to better understand how their performance compares with more traditional approaches. We show that an adaptive DSS engine has the potential to identify errors and improve the accuracy of the model. We briefly identify how this approach could be applied to unexpected highly uncertain decision scenarios. Future research considers new opportunities to pursue a multidisciplinary approach to adaptive DSS design, development, and implementation leveraging emerging machine learning techniques in tackling complex decision problems.

Adaptive Decision Support System for On-Line Multi-Class Learning and Object Detection

A new online multi-class learning algorithm is proposed with three main characteristics. First, in order to make the feature pool fitter for the pattern pool, the adaptive feature pool is proposed to dynamically combine the three general features, Haar-like, Histogram of Oriented Gradient (HOG), and Local Binary Patterns (LBP). Second, the external model is integrated into the proposed model without re-training to enhance the efficacy of the model. Third, a new multi-class learning and updating mechanism are proposed that help to find unsuitable decisions and adjust them automatically. The performance of the proposed model is validated with multi-class detection and online learning system. The proposed model achieves a better score than other non-deep learning algorithms used in public pedestrian and multi-class databases. The multi-class databases contain data for pedestrians, faces, vehicles, motorcycles, bicycles, and aircraft.

Mathematical model of an intelligent decision support system for road quality management

The experience of operation of decision support systems is analyzed, it is shown that the existing principles of creating these systems do not take into account the failure and change in the performance of individual service element, which depend on the current load on the system. The definition of an adaptive decision support system as an intelligent system that works in an automated mode and is able to function in a changing environment, its internal structure, knowledge base, regulatory framework for management, support tools, system users is given. The tasks of construction a mathematical model of an adaptive decision support system and determining its objective function are set. A distributed decision support system, which receives information containing data from various input sources was studied. For construction a mathematical model of the system under investigation, the procedures for selecting mathematical models and methods of queuing theory were used. To assess the quality and effectiveness of an adaptive decision support system, simulation modeling is proposed.

NBIC-technology and strategic management tools in the organization of business and production

The paper is devoted to the analysis of the development of industrial production organizations, using focal points for the development and implementation of a production management strategy. It also solves the problems associated with the development of mathematical and algorithmic support for adaptive decision support systems in situational centres, taking into account expert options for institutional conditions in information systems for operational monitoring, situation analysis and assessment of production situations.

Adaptive forecasting and financial risk estimation

The study is directed towards development of an adaptive decision support system for modeling and forecasting nonlinear nonstationary processes in economy, finances and other areas of human activities. The structure and parameter adaptation procedures for the regression and probabilistic models are proposed as well as the respective information system architecture and functional layout are developed. The system development is based on the system analysis principles such as adaptive model structure estimation, optimization of model parameter estimation procedures, identification and taking into consideration of possible uncertainties met in the process of data processing and mathematical model development. The uncertainties are inherent to data collecting, model constructing and forecasting procedures and play a role of negative influence factors to the information system computational procedures. Reduction of their influence is favourable for enhancing the quality of intermediate and final results of computations. The illustrative examples of practical application of the system developed proving the system functionality are provided.

Coaching in energy efficiency control of manufacturing

New trends in the management of manufacturing efficiency are associated with the development of such areas of Data Science as Big Data and Data Analytics, taking into account the human factor. With asymmetric awareness of management and personnel about the potential for efficiency, undesirable activity of personnel is possible. In this case, personnel as the active element selects its state in such a way as to maximize its own objective function. For this reason the energy efficiency of manufacturing can be not equal to the potential. Therefore, traditional methods and algorithms of Data Science may be ineffective. To solve this problem in the face of uncertainty, adaptive decision support system with coacher’s instructions is proposed. In this system, the manager is considered a disciple of the coach, even if both of them are not aware of the potential of the energy efficiency. In order to ensure the effectiveness of traditional learning algorithm of Data Science, that system includes rating and stimulation procedures. Sufficient conditions have been found for the synthesis of the decision support system which is learning with coacher’s instructions, in which the active element uses the potential of energy efficiency. The use of such system is illustrated by the example of energy efficiency of a wagon repair company.

Decision Support Research in Warehousing and Distribution: A Systematic Literature Review

The increase in e-commerce and omnichannel commerce is having a significant impact on the supply chain sector and its warehouses. Fluctuations in demand and priorities, the requirement for value-added service, government regulations and other factors put pressure on the operational decision makers on the warehouse floor and the systems that support them. The increasing complexity of daily warehouse operations means that decision support systems will need to become more sophisticated and intelligent to assist decision makers in real-time. The aim of this literature review is to investigate how decision support in warehousing and distribution operations is examined in the research literature. The objective of this review is to understand how this decision support research can assist operational decision makers to manage and complete the daily volume of work through the warehouse. Fifty-one articles were obtained by the literature search. Articles were categorized by type of warehouse, decision support target, operational task and problem type, research article methodology, architecture and technology. Decision support is examined in almost all areas of warehousing operations with the use of a variety of methods and technologies within the research literature. Most “daily warehouse operational” decision support deals with expertise transfer and reacting to real-time events. This paper highlights the lack of research into human–machine collaboration in adaptive decision support systems to assist warehouse operational decision makers.

Models, Algorithms, and Architecture for Generating Adaptive Decision Support Systems

A method for generating adaptive decision support systems is examined in this paper. Models, algorithms, and programming technology making possible the use of knowledge of distributed experts for system adaptation to environmental variation that increases its life-cycle and the efficiency of decision-making processes are presented. The multicomponent adaptive model of a subject domain for the problem on decision-making and algorithms of structure and information adaptation of the model are given. The open architecture of the adaptive decision support system in federation form is also presented. The architecture is developed according to the concept of distributed simulation standard High Level Architecture and it secures component compatibility, interaction possibility, and reuse.

ADAPTIVE MODELING AND FORECASTING OF NONLINEAR NONSTATIONARY PROCESSES

The study is directed towards development of adaptive decision support system for modeling and forecasting nonlinear nonstationary processes in economy, finances and other areas of human activities. The structure and parameter adaptation procedures for the regression and probabilistic models are proposed as well as respective information system architecture and functional layout are developed. The system development is based on the system analysis principles such as adaptive model structure estimation, optimization of model parameter estimation procedures, identification and taking into consideration of possible uncertainties met in the process of data processing and mathematical model development. The uncertainties are inherent to data collecting, model constructing and forecasting procedures and play a role of negative influence factors to the information system computational procedures. Reduction of their influence is favorable for enhancing the quality of intermediate and final results of computations. The illustrative examples of practical application of the system developed proving the system functionality are provided.

Методика расчета параметров адаптивных систем поддержки принятия решений с элементами обслуживания

Проанализированы компьютерные средства для реализации адаптивной системы поддержки принятия решений; показано, что в качестве основных элементов обслуживания используются контролируемые серверы, в качестве основных инструментов обработки информации - запросы. Сформулирована целевая функция адаптивной системы поддержки принятия решений, направленная на максимизацию количества обрабатываемых в единицу времени запросов при различных меняющихся внешних и внутренних воздействиях. Методика расчета параметров исследуемой системы строится на основе предложенной математической модели. Computer tools are analyzed for implementing an adaptive decision support system; It is shown that controlled servers are used as the main service elements, and requests are used as the main information processing tools. The objective function of the adaptive decision support system is formulated, aimed at maximizing the number of requests processed per unit time under various changing external and internal influences. The methodology for calculating the parameters of the studied system is based on the proposed mathematical model.

Adaptive Alert Management for Balancing Optimal Performance among Distributed CSOCs using Reinforcement Learning

Large organizations typically have Cybersecurity Operations Centers (CSOCs) distributed at multiple locations that are independently managed, and they have their own cybersecurity analyst workforce. Under normal operating conditions, the CSOC locations are ideally staffed such that the alerts generated from the sensors in a work-shift are thoroughly investigated by the scheduled analysts in a timely manner. Unfortunately, when adverse events such as increase in alert arrival rates or alert investigation rates occur, alerts have to wait for a longer duration for analyst investigation, which poses a direct risk to organizations. Hence, our research objective is to mitigate the impact of the adverse events by dynamically and autonomously re-allocating alerts to other location(s) such that the performances of all the CSOC locations remain balanced. This is achieved through the development of a novel centralized adaptive decision support system whose task is to re-allocate alerts from the affected locations to other locations. This re-allocation decision is non-trivial because the following must be determined: (1) timing of a re-allocation decision, (2) number of alerts to be re-allocated, and (3) selection of the locations to which the alerts must be distributed. The centralized decision-maker (henceforth referred to as agent) continuously monitors and controls the level of operational effectiveness-LOE (a quantified performance metric) of all the locations. The agent's decision-making framework is based on the principles of stochastic dynamic programming and is solved using reinforcement learning (RL). In the experiments, the RL approach is compared with both rule-based and load balancing strategies. By simulating real-world scenarios, learning the best decisions for the agent, and applying the decisions on sample realizations of the CSOC's daily operation, the results show that the RL agent outperforms both approaches by generating (near-) optimal decisions that maintain a balanced LOE among the CSOC locations. Furthermore, the scalability experiments highlight the practicality of adapting the method to a large number of CSOC locations.

An integrated strategic-performative planning methodology towards enhancing the sustainable decisional regeneration of fragile territories

The paper outlines in depth an integrated strategic and performance-based planning approach based on the multi-scalar dimensions of different decision-making levels; action programs; governance and social involvement, applied to a fragile territory. The methodological framework proposed is based on different methodological evaluation tools in order to support an adaptive and inclusive decisional process. The aim of this study is to highlight how a flexible, dynamic, adaptive, context-aware and site-specific decision support system can contribute to the definition of alternative scenarios and lead public authorities to opt for the most performing one in terms of environmental, social and economic sustainability. The tool implemented was tested for the case study of the regeneration of a naturalist site on the island of Pantelleria in the south of Italy.

Research on Fuzzy Adaptive Intelligent Decision-making in Complex Environment

Decision-making is an extremely complex proposition. Especially with the development of economy, the progress of science and technology and the acceleration of global integration. All walks of life are facing an increasingly complex and changing environment, and decision-making problems are becoming more and more complex. In complex environments, decision-making is made by multiple stakeholders on the basis of increasingly updated self-confidence and repeated discussions. Enterprise-level decision support system is much more complex than individual decision in decision environment, decision process and decision support technology. Multi-objective intelligent decision making is a difference and multi-objective optimization problem. Based on quantitative mathematical models, intelligent decision-making systems lack corresponding support methods for qualitative, fuzzy and uncertain problems in decision-making. Based on the complex adaptation theory, this paper constructs an adaptive intelligent decision support system. The adaptive decision process is analyzed. It is proved that the decision-making platform based on adaptive intelligent decision theory can help decision makers to provide effective decision-making in complex environments.

Intellectual Control of Dangerous States of the Facilities of Main Hydrocarbon Transport Systems in Adaptive Expert Systems

The work deals with the development of methods for assessing the reliability of oil and gas equipment and systems for adaptive decision support systems. At the Department of Hydrocarbon Transportation, studies are underway to create decision support systems with predictive, warning and reliability assessment functions. The proposed methodology can be used for adaptive expert systems for monitoring reliability in real time. The technique allows identifying the state of the technological facility and predicting the probabilities of parametric failure by the given criteria. The proposed method has been tested and can be used when estimating the reliability of oil and gas pipeline technical systems.