Subgoal-Based Explanations for Unreliable Intelligent Decision Support Systems

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.

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.

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

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.

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

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.

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

Summary form only given. First, we briefly present the essence of some new computing paradigms that try to attain a synergy, and bridge the gap between the human user and computer systems, notably the so-called: human centric computing, human centered computing, human computing, etc. We present a brief account of modern approaches to real world decision making, emphasize the concept of a decision making process that involves more factors and aspects like: the use of explicit and tacit knowledge, intuition, individual habitual domains, non-trivial rationality, different paradigms, etc. We advocate the need for computer based decision support systems. After a short presentation of their history and classification, we indicate that decision support systems should incorporate some sort of "intelligence", and we first briefly mention some views of intelligence in an individual and collective perspective. We indicate possible advantages of using those new human-centered computing paradigms in the decision making and support context, and advocate soft computing, notably Zadeh's computing with words (and perceptions) as a tools that makes it possible to merge the ideas presented like: human centric computing, decision making processes, intelligent decision support, etc. Finally, we show an example of our own implementation of a data/document driven decision support system in which, first, Zadeh's computing with words and perceptions paradigm is employed via linguistic database summaries, elements of business intelligence and Web intelligence.

Decision support systems DSS can be helpful tools in university's planning and management. Decision support systems can contribute to efficient exchange of information between experts, stakeholders, decision makers and laypeople. However, intelligent Information technology IT governance decision support system for public universities is yet to be thoroughly investigated. This is because IT governance ITG is just evolving as a new paradigm to take care the stakeholders' expectations in leveraging IT with business goals. This study explores issues of IT governance and the suggestion for ITG Intelligent decision making support system IDMSS in a public university in Malaysia. The interviews and documents were analysed to generate themes. The findings show the need for effective IT governance in the university. This will enable optimal usage of IT resources, which are expected to have an impact on the university's performance. The findings is aligned to Control Objectives for Information and related Technology COBIT framework and then several proposals from extant researches on IDMSS are described to form a basis for an intelligent ITG DMSS framework suitable for adoption in the context of public Institutes of Higher learning in Malaysia.

In this article, we present an agile method based on a cycle of meetings that guides the construction of intelligent decision support systems. This method presents the phases of initiation, analysis and planning, negotiation, control and intelligent decision support. A cycle represents a passage through all the phases of the method, where as the execution of a phase means that all the planned meetings were held. Each meeting lasted 15 min, and input and output were composed of artifacts that supported the evolution of each meeting. In the initial phase, a meeting was held with everyone with the cards for the survey of the requirements and the construction of the 3D graph to represent the size. In IT meetings, artifacts, forms and tables were used to define the first packages. In the analysis and planning phases, the objectives by key results form were used. In the negotiation, we use the structural sets form. In the control phase, we have the configuration artifact and its control graph. Finally, in intelligent decision support, we use the essential questions form. The method serves as a guide for building intelligent decision support systems that can help with problems like determining whether or not to sign a contract.•In the initial phase, cards for requirement gathering together with a complexity graph and Board Requirements by Layers and Key Person supported the organization of development packages.•In the control phase, the input structures enabled the creation of a continuous control artifact. Furthermore, the control chart showed what is in scope and is part of ongoing control.•The intelligent decision support phase guaranteed the refinement of requirements, which brought intelligence criteria to the development packages and gave them their unique characteristics.

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.

Community policing (CP) provides benefits for both police/governments and citizens, aiming to improve safety and crime prevention. As part of e-government initiatives, multiple ICT tools for CP have emerged to ease citizen’s engagement with CP. Most of them are, however, tailor-made to specific needs of their direct users in a city or country or provide fragmented functions, focusing, e.g., on citizens’ tools, or police work. This paper presents an end-to-end fully featured privacy respectful crowd-sourced intelligent information management and decision support system (DSS) for next-generation community policing and e-government, resulting from the INSPEC2T project. Further from a rich set of incident reporting, online communities’ interaction and citizen–police communication and collaboration tools, it provides an intelligent data analytics layer to effectively process the bulk of citizens’ data with the aim to facilitate and improve decisions and police work. Extensive feedback received from conducted pilots has demonstrated its added value within stakeholder communities.

• Developed an AIoT-enabled decision support system for precision irrigation management. • Using LSTM-based AI models, achieved 97–98 % accuracy in water stress detection. • Integrated PCA for dimensionality reduction of high-dimensional physiological datasets. • Utilised hyperspectral and point cloud data for robust crop water stress prediction. • Improved irrigation efficiency to promote sustainable agriculture under drought stress. Climate change has increased the severity of droughts, threatening global agricultural productivity. The implementation of information technology for enhancing smart agriculture has proven its great potential for supporting precision agriculture that can provide crops with the ability to defend themselves against environmental threats. Rice, which is a staple food crop in tropical and subtropical regions, is particularly sensitive to water stress during its critical growth stages. This study therefore focused on Tainung No. 67 rice, known for its drought resistance, to develop an intelligent AIoT-based plant watering decision support system. The proposed system aims to optimise water use and enhance agricultural resilience by integrating real-time monitoring, AI-driven analysis, and automated irrigation. Data were collected using hyperspectral imaging, point cloud analysis, and physiological indicators (measured by the LI-600 device), providing a comprehensive time-series dataset for model training. Principal component analysis (PCA) was used to reduce data dimensionality, and an LSTM-based AI framework was used to predict water stress severity. Experimental results showed high accuracy for all datasets, with the AI model achieving 97 % accuracy for point cloud data and 98 % accuracy for hyperspectral imagery. Scenarios with mixed missing data further validated the practicality and robustness of the system. This research highlights the potential to address drought-related challenges in agriculture through the integration of IoT, AI and advanced sensing technologies. The system not only optimises irrigation strategies but also contributes to sustainable farming practices through the preservation of water resources.

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.

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