Ergonomic design knowledge built in the intelligent decision support system

A SDSS combines database storage technologies, geographic information systems (GIS) and decision modeling into tools which can be used to address a wide variety of decision support areas (Eklund, Kirkby, and Pollitt, 1996). Recently, various emerging technologies in computer hardware and software such as speedy microprocessors, gigabit network connections, fast internet mapping servers along with Web-based technologies like extensible markup language (XML), Web services, etc provide promising opportunities to take the traditional spatial decision support systems one step further to provide easy-to-use, round-the-clock access to spatial data and decision support over the Web. Traditional DSS and Web-based spatial DSS can be further improved by integrating expert knowledge and utilizing intelligent software components (such as expert systems and intelligent agents) to emulate the human intelligence and decision making. These kinds of decision support systems are classified as intelligent decision support systems. The objective of this chapter is to discuss the development of an intelligent web-based spatial decision support system and demonstrate it with a case study for planning snow removal operations.

Intelligent decision support (IDS) systems leverage artificial intelligence techniques to generate recommendations that guide human users through the decision making phases of a task. However, a key challenge is that IDS systems are not perfect, and in complex real-world scenarios may produce suboptimal output or fail to work altogether. The field of explainable AI (XAI) has sought to develop techniques that improve the interpretability of black-box systems. While most XAI work has focused on single-classification tasks, the subfield of explainable AI planning (XAIP) has sought to develop techniques that make sequential decision making AI systems explainable to domain experts. Critically, prior work in applying XAIP techniques to IDS systems has assumed that the plan being proposed by the planner is always optimal, and therefore the action or plan being recommended as decision support to the user is always optimal. In this work, we examine novice user interactions with a non-robust IDS system – one that occasionally recommends suboptimal actions, and one that may become unavailable after users have become accustomed to its guidance. We introduce a new explanation type, subgoal-based explanations, for plan-based IDS systems, that supplements traditional IDS output with information about the subgoal toward which the recommended action would contribute. We demonstrate that subgoal-based explanations lead to improved user task performance in the presence of IDS recommendations, improve user ability to distinguish optimal and suboptimal IDS recommendations, and are preferred by users. Additionally, we demonstrate that subgoal-based explanations enable more robust user performance in the case of IDS failure, showing the significant benefit of training users for an underlying task with subgoal-based explanations.

The object of the study is intelligent decision support systems. The problem addressed in the research is to improve the accuracy of modeling the functioning process of intelligent decision support systems. A polymodel complex for resource management of intelligent decision support systems has been developed. The originality of the study lies in: – the comprehensive description of the functioning processes of intelligent decision support systems; – the capability to model both an individual process occurring in intelligent decision support systems and to perform comprehensive modeling of the processes taking place within them; – the establishment of conceptual dependencies in the functioning process of intelligent decision support systems. This makes it possible to describe the interaction of individual models at all stages of solving computational tasks; – the description of coordination processes in hybrid intelligent decision support systems, which ensures an increase in the reliability of managerial decision-making; – the modeling of processes for solving complex computational tasks in intelligent decision support systems through the conceptual description of the specified process; – the coordination of computational processes in intelligent decision support systems, which leads to a reduction in the number of computational resources of the systems; – the comprehensive resolution of conflicts through a set of corresponding mathematical models. The proposed polymodel complex is advisable for use in solving the problem of managing intelligent decision support systems characterized by a high degree of complexity

In the present study a novel conceptual framework for an intelligent decision support system (IDSS) is proposed, specifically designed with consideration of the unique operational requirements of small enterprises. The primary objectives of the research are twofold: first, to conduct an in-depth analysis and critical evaluation of existing theoretical approaches to decision support in the small and medium-sized enterprise segment; second, to develop a specialized IDSS model tailored to advisory services for this class of organizations. The methodological foundation of the research consisted of a scrupulous systematic review of scientific publications from the last 5 years devoted to the implementation of artificial intelligence methods in the management processes of small and medium-sized enterprises, as well as to the key directions of development in explainable AI (XAI). As a result, the principal architectural design principles and technological components ensuring the effective functioning of such systems were identified. In conclusion, illustrative case studies of the application of the developed IDSS in tasks of strategic market positioning and financial diagnostics of small businesses are presented. These examples may serve as practical guidelines for IT solution developers, consulting firms, and academic researchers oriented toward the digital transformation of small and medium-sized enterprises.

Decision making tasks are subject to limitation and also depends on human knowledge, experiences, judgments and preferences. In this case, Intelligent Decision System (IDS) technologies can be used to provide realistic and consistent decisions, besides to improve the effectiveness of decision making processes. Intelligent Decision Support System (IDSS) is a contributory of IDS technology to assist decision makers in high level phases of decision making by integrating human knowledge with modeling tools. Nowadays, data mining (DM) techniques is also can be used to support Knowledge Management (KM) tasks especially for knowledge discovery and knowledge engineering. DM is emerging data analysis tool and widely used in order to produce valuable knowledge for decision making as knowledge modeling task. In Human Resource (HR), managing talent is among the challenges of HR professionals which can be handle by using IDSS and data mining technologies. For that reason, in this article, we discussed the potential to uses IDSS approach for talent management using DM techniques by proposing IDSS architecture and a case study on talent classification. This study consists of three parts; the first part is to know the previous works on IDSS, talent management and DM. The second part is discussion on proposed IDSS architecture for talent management. Finally, the third part is a case study on the use of DM classification method for talent forecasting, especially for employee's job promotion.

The object of the study is the clinical decision-making process for selecting of hepatocellular carcinoma (HCC) treatment method based on the patient's medical data. The process of the HCC treatment method selection remains poorly formalized and is characterized by multi-criteria and the presence of numerous clinical situations, for each of which it is necessary to promptly identify the most accurate therapeutic solution. This study develops an intelligent medical decision support system for HCC treatment method selection based on knowledge applicable in clinical practice. It offers architectural and functional principles of the intelligent decision support system, classifying clinical situations by HCC treatment method, consisting of multiple possible combinations of 44 informative parameters. Based on the current values of these parameters, expert knowledge is transformed into production rules identifying HCC treatment methods. A heuristic procedure treatment selection is developed based on production rule analysis in accordance with current parameter values, reproducing the reasoning patterns of participants in a multidisciplinary council during their consensus decision-making process regarding HCC treatment appointment. A software implementation of a decision-making model for HCC treatment selection, implemented in C# using the Visual Study 2019 platform, enabled the integration of an intelligent system with web technologies. The intelligent medical decision support system automates the unique experience of professionals and helps physicians in a multidisciplinary consultation make prompt and informed decisions online regarding the appointment of personalized therapy. The system was piloted with expert physicians in several iterations until complete match between the consensus decision of the multidisciplinary council and the decision made by the developed system in accordance with clinical recommendations was achieved

In order to improve the effect of precision intelligent agricultural decision support system, this paper combines big data technology to carry out precision mining of agricultural data, and combines decision tree algorithm to carry out data classification processing. Moreover, this paper obtains the most effective agricultural decision reference data through data mining, combines the agricultural decision support process to set the functional modules of the decision system, and analyzes the implementation process of each functional module. In addition, this paper studies the theoretical basis and key technologies of the agricultural production structure optimisation decision support system, and builds a precision and intelligent agricultural decision support system based on big data analysis. The system mainly performs accurate processing of agricultural data and makes effective predictions, and then makes scientific decision results. Finally, this paper verifies the structure of the model in this paper combined with experimental analysis. From the comparison of experiments, it can be seen that the precision and intelligent agricultural decision support system constructed in this paper has significant effects.

Introduction: Unprecedented opportunities and difficulties have arisen as a result of the quick expansion of digital environments, especially in the fields of cybersecurity and decision support systems. More clever and flexible strategies are required because traditional security measures frequently can't keep up with new threats. In intelligent decision support systems (IDSS), cognitive computing—which combines artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and sophisticated data analytics—has become a disruptive force. These systems can process enormous volumes of structured and unstructured data, identify patterns, and offer proactive insights for improved decision-making in security-critical circumstances by simulating human cognitive processes. The reconfiguration of IDSS by cognitive computing to improve the security and resilience of digital ecosystems is examined in this chapter. In order to reduce cyber risks, we go over the use of AI-driven threat intelligence, real-time anomaly detection, and predictive risk assessment. Furthermore, we investigate how adaptive and self-learning algorithms in cognitive computing improve fraud detection, network security, and access control. By integrating explainable AI (XAI), security decision-making becomes transparent, resolving issues with trust and accountability in automated security systems. The chapter also explores new developments that will influence cognitive-driven IDSS in the future, such as federated learning for decentralised threat intelligence sharing, autonomous security agents, and the possible effects of quantum computing on cryptographic security. We also draw attention to the difficulties in putting cognitive security solutions into practice, including computational complexity, hostile AI model assaults, and moral dilemmas in automated decision-making. Objectives: This study paper's main goal is to examine current developments, new trends, and potential paths for digital environment security. Modern cyber risks are addressed by this study's analysis of cutting-edge technology, developing cybersecurity frameworks, and clever defence measures. Additionally, the study looks into how security methods are changing as a result of developments in blockchain, zero-trust architecture, quantum cryptography, artificial intelligence, and cybersecurity automation. In order to improve the resilience, privacy, and integrity of digital systems in a world that is becoming more linked, the study also aims to highlight current issues and suggest possible solutions. Methods: The qualitative research methodology used in this study includes a review of recent cybersecurity innovations-related literature, research articles, and case studies. Information was gathered from reliable sources, including industry publications, scholarly journals, and professional comments. In order to secure digital environments, the study compares traditional and modern security methods, analyses emerging technologies, and looks for future trends and solutions. Results: According to the survey, cutting-edge technologies like blockchain, artificial intelligence (AI), quantum cryptography, and zero-trust architecture are all significantly improving cybersecurity. Blockchain guarantees safe data exchanges, while AI aids in danger

Computer-based support for decision-making, with the use of Decision Support Systems (DSS), has focused mainly on primary decision processes, i.e. data and model manipulation, and failed to take explicitly into account the need to model the secondary decision processes, i.e. support on how to decide about the use of specific data and models. The main premise of the present paper is that effective, intelligent support of the secondary decision processes could be developed by exploiting the possibilities of coupling DSS and expert system (ES) approaches. Although such an integration has been flourishing in the literature, with the advent of Intelligent Decision Support Systems (IDSS), no efforts have been made to examine its potential for enhancing the support to secondary decision processes. The paper presents an IDSS architecture that models in a systematic way the decision—making process and illustrates the use of the architecture for the development of an intelligent system for business forecasting.

Introduction. Based on the analysis, it was found that there is a need to develop tools for managing conflicts between stakeholders in projects for the creation and development of medical facilities. These projects are characterized by complex relationships between government agencies, medical staff, NGOs, patients, and investors, that have their own interests, contradicting each other. The main scientific and applied challenge is the lack of a structured approach to reconciling the interests of stakeholders, high level of conflict, lack of transparency in decision-making processes, and insufficient use of quantitative methods for assessing management effectiveness. The lack of tools for managing conflicts leads to delays in project implementation, inefficient use of resources, and dissatisfaction of stakeholders.Purpose. To substantiate the structure of the process of managing conflicts between stakeholders in projects for the creation and development of medical institutions, which will allow timely alignment of the interests of project stakeholders, reduce the level of conflicts, and ensure effective project implementation, taking into account the specifics of the medical sector.Methods. The study uses a systematic approach, which includes an analysis of scientific research on project management and conflicts in the medical field. Based on the methods of classification and generalization, the main indicators of the effectiveness of conflict management are identified, and approaches to planning and implementing conflict management processes are summarized. To analyze the relationships between the indicators, correlation analysis, construction of dependency graphs, and regression analysis was used. The practical part of the study is based on the example of projects of medical institutions in Lviv region, for which a quantitative assessment of the effectiveness of managing conflicts between stakeholders was carried out.Results. The main stakeholders of projects for the creation and development of medical institutions are identified. Seven main types of stakeholders were identified. Based on the analysis of their interests and contradictions, the structure of the process of managing contradictions and conflicts was substantiated, which consists of five management operations providing analysis of stakeholder interests, identification of potential contradictions and conflicts, planning of the process of managing contradictions and conflicts, implementation of the plan for managing contradictions and conflicts and determining the effectiveness of the process of managing contradictions and conflicts. The proposed structure enables reconciliation of the interests of stakeholders, reduce the number and level of conflicts, and ensure the effective implementation of projects for the creation and development of medical institutions. For the practical implementation of the substantiated process, the author quantifies eight indicators that provide an objective assessment of the effectiveness of managing conflicts between stakeholders in projects for the creation and development of medical institutions and the validity of decision-making. Based on the use of the developed toolkit, a quantitative assessment of medical projects implemented in the Lviv region was carried out. It was found that the conflict index of the projects ranged from 2.0 to 2.5, the level of risks - from 15% to 40%, the consolidated efficiency index - from 0.5 to 0.826, and the level of goal achievement - from 66.6% to 83.3%. The highest performance indicators were achieved in the outpatient clinic opening project, which indicates the effectiveness of stakeholder alignment.Conclusions and proposals. The obtained results indicate that the proposed structure of the conflict management process is an effective tool for aligning the interests of stakeholders and ensuring the successful implementation of projects for the creation and development of medical institutions. Implementation of this process structure will reduce the number of contradictions and the level of conflicts between stakeholders, optimize the use of resources, and increase their satisfaction. Further research is needed to integrate modern information technologies, such as artificial intelligence and intelligent decision support systems, into the process of managing stakeholder conflicts in healthcare facility creation and development projects.

A practical decision support system aims at providing decision-makers with intelligent service on the basis of analyzing the current conditions. Such a system must therefore combine techniques for memorizing, learning and processing information with methods of approximate reasoning. In the paper, we put forward a four-layered fuzzy neural network model for an intelligent decision support system (IDSS). The contribution of the paper is the design of a hierarchy neural network and three kinds of fuzzy information process neurons. The novel model is able to realize fuzzy mapping, fuzzy reasoning and decision making according to the initial information relative to the problem. The emphasis is placed on describing the construction, characteristics and reasoning mechanism of the four-layered fuzzy neural network. Furthermore, we analyze the feasibility and rationality of the model as an IDSS in theory.

Procurement holds significant business value, as most organisations spend at least one-third of their overall budget on purchasing goods and services. In this paper, we describe the design and implementation of an Intelligent Decision Support System (IDSS) for electronic purchasing. The theory of purchasing decision traditionally assumes that the offered quantity and quality are fixed prior to source selection. Decision Support Systems (DSS) are the need of the hour to assure results at a faster rate that best match the buyers' preferences and give valid recommendations. A two-dimensional approach is proposed: first, the Case-Based Reasoning (CBR) approach is used, which is a novel paradigm that solves a new problem by remembering a previous similar situation and reusing the information on and knowledge of that situation to bring out similar cases at a faster rate, depending on the predetermined similarity criteria. Second, the slope one predictors for online rating-based Collaborative Filtering (CF) and item-to-item CF are combined to produce accurate recommendations on the basis of the resources rating given by the users to help each other find better content. Our approach is database-driven, which provides buyers with more flexibility in the specification of their purchasing request and allows for an efficient information exchange among the participants. Finally, the purchaser can improve his profit.

Microcomputer-based decision support systems (DSSs) offer many advantages over mainframe DSSs, the greatest of which is the cost7sol; benefit ratio. The use of microcomputers, however, causes numerous management control problems. Because of the limitations of microcomputers, IS managers must pay special attention to the design and implementation of microcomputer-based DSSs. With the emergence of AI technology, most managers should benefit from the integration of AI with DSSs. This article addresses issues relevant to the design of intelligent microcomputer-based DSSs from an applications standpoint. The article also presents conceptual frameworks and strategies for implementing such a system

This paper presents a method to integrate computational models in emergency intelligent decision support system (IDSS) for more scientific and effective loss prevention and disaster manipulation. Various computational models that support disaster evolution forecast, impact analysis on infrastructure and human as well as rescue actions are integrated in this IDSS. Moreover, the pertinency between original disaster and secondary disasters are modeled using disaster chain theory. To achieve effective emergency response, not only should we react to the original disaster in a comprehensive way, but also prepare for and control various secondary disasters according to its disaster chain. With the integration of computational models involved in emergency management, the IDSS is capable of providing sequential analysis of emergency situations and presenting suggestions on preparing against the next possible disaster in advance.

To survey person centered survival rate in population based screening program by an intelligent clinical decision support system. Colorectal cancer is the most common malignancy and major cause of morbidity and mortality throughout the world. Colorectal cancer is the sixth leading cause of cancer death in Iran. In this survey, we used cosine similarity as data mining technique and intelligent system for estimating survival of at risk groups in the screening plan. In the first step, we determined minimum data set (MDS). MDS was approved by experts and reviewing literatures. In the second step, MDS were coded by python language and matched with cosine similarityformula. Finally, survival rate by percent was illustrated in the user interface of national intelligent system. The national intelligent system was designed in PyCharm environment. Main data elements of intelligent system consist demographic information, age, referral type, risk group, recommendation and survival rate. Minimum data set related to survival comprise of clinical status, past medical history and socio-demographic information. Information of the covered population as a comprehensive database was connected to intelligent system and survival rate estimated for each patient. Mean range of survival of HNPCC patients and FAP patients were respectively 77.7% and 75.1%. Also, the mean range of the survival rate and other calculations have changed with the entry of new patients in the CRC registry by real-time. National intelligent system monitors the entire of risk group and reports survival rates by electronic guidelines and data mining technique and also operates according to the clinical process. This web base software has a critical role in the estimation survival rate in order to health care planning.