Development of a polymodel resource management complex for intelligent decision support systems

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.

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.

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.

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.

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.

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.

In the coming years the quantity of information will grow exponentially, creating real labyrinths where tourists and tourist professionals will have to solve complex problems quickly. In the most far-sighted visions intelligent systems will have an important role [4, 18]. They will be imperatively used as research tools in tourism, but also as integrators of existing technologies and performers of such tasks that until now have only been executed by people. The article illustrates some achievements in the application of intelligent systems in tourism obtained with the project “Intelligent decision support systems in management of complex systems”. The aim of the project is to research and create the methodology of developing the second generation of intelligent systems [9, 10]. Next, second generation of intelligent systems, according to F. Hayes-Roth, should overcome the limitations of today’s intelligent systems, introducing some new characteristics: “context reusable building blocks, reusable knowledge, high value component functions, composite architecture for multi-task systems, architecture for semantic interoperability, and a small number of domains, which should be attacked persistently [2]”.

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.

This paper describes the structure and function of the intelligent decision support system (IDSS) on the process of nickel matte smelter. The knowledge and model base system based on fuzzy-decision rules are specially suggested. The IDSS possesses the self-learning and adaptive properties, and has been used for managing and analyzing the production information, optimizing the composition of the charge mixture, and deciding the optimal operational conditions. Electric energy consumption has been reduced remarkably and the yield of nickel has been increased.

Intelligent decision support for effectively evaluating and selecting ships under uncertainty in marine transportation

The rapid development of Artificial Intelligence technology has led to the victory of AlphaGo, making people see the hope of intelligent military command decision support system. However, battlefield is quite complex, while chessboard is significantly abstracted. It is impossible to copy its method directly, as machine learning from observations directly to orders, or from orders directly to results is too difficult. This paper attempts to decompose the problem of intelligent command decision support system into more specific problems, and analyze which type of AI methods suit which problems, through the local technology breakthrough to achieve the overall level of intelligence to enhance, with a certain guiding significance to development of intelligent command decision support system.

Unconventional emergency shows typical unconventionality. Its occurrence, development, evolution mechanism are very complicated, and it may touch off serious derivative disasters. So the emergency management pattern of unconventional emergency has to change from “Predict-Response” to “Scenarios-Response”. This paper summarized and analyzed the typical features of unconventional emergency in three aspects and the current situation and existing problems in emergency management research and practice. Then a “Scenarios-Response” intelligent decision support system (IDSS) framework of unconventional emergency management was established based on Scenario Analysis (SA) and Case-Based Reasoning (CBR). The method of SA provided a detailed and rigorous scenario description for the implementation of unconventional emergency case-based reasoning system; the technology of CBR provided a fast and effective emergency plan supporting for the scenario analysis of unconventional emergency. The IDSS of unconventional emergency management based on SA and CBR could predict accurately the scenarios of unconventional emergency, and provide support and reference for achieving rapid and timely decisions-making and emergency rescue, and it has great significance of establishing and promoting the National Emergency Management System.

Many students and academics face difficulties in English academic writing, particularly non-native speakers of English. This paper examines the use of Artificial Intelligence (AI) in optimizing English academic writing in intelligent decision supporting systems (IDSS). Various literatures were studied to find out how AI can help improve English academic writing. The findings reveal that AI can improve grammar, spelling, sentence construction and make suggestions for clarity and cohesion through tools like Grammarly. Furthermore, integrating AI into IDSS also promotes more efficient and data driven decisions. However, there are still barriers like complex data analysis, ensuring accuracy and reliability of AI-generated content, and addressing ethical issues including bias and transparency. This simple paper highlights the importance of developing more sophisticated and user-friendly AI technologies and the need for further research to explore the long-term impact of using AI in English academic writing for Indonesia context. The results are beneficial to learners, educators and learning institutions as they employ AI tools with the purpose to improve the English academic writing quality and support better decision making.

Today’s supply chains (SC) are immersed in extremely dynamic environments, and supply chain management (SCM) has to deal with a multitude of risks. The domain of supply chain risk management (SCRM) has emerged, providing approaches on how to cope with risks in SC. However, due to increased complexity, volatility, and uncertainty, the number of risks in global SC has increased significantly. Harnessing the power of predictive analytics (PA), implemented in intelligent decision support systems (IDSS), offers huge potential in SCRM. However, research at the intersection of the domains of SCRM, PA, and IDSS is still in its infancy, and several research gaps have yet to be addressed. The paper elaborates on these research gaps by means of a systematic literature review. The results include a set of seven research questions and proposed research directions for future studies. Future research is presented with a plethora of starting points, which originate from the business perspective (i.e., the SCRM domain), the data-driven (i.e., the PA domain) as well as an IT-system perspective (i.e., the IDSS domain).KeywordsSupply chain managementPredictive analyticsSupply chain risk managementDecision support