Human-centric Computing Paradigms, Computing with Words, and 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.

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

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

Under conditions associated with real-time decisions, humans may experience stressors such as information overload, time pressure, emotional intensity, and uncertainty. These pressures increase with distributed teams that must collaborate to make decisions in rapidly changing environments over networks due to factors such as delays or misunderstandings in communication. Intelligent decision support systems attempt to mitigate these issues by integrating capabilities from the human user and artificial intelligence. One of the most promising technologies for real applications is intelligent agents. Agents are generally described as software programs that are autonomous, adaptive, proactive, reactive, communicative, cooperative, mobile, goal- driven and persistent in performing their tasks. Using these characteristics, agents and multi-agent teams can be integrated into decision support systems to perform tasks such as monitoring, alerting, learning, and even making decisions. This talk looks at intelligent decision support for real-time collaboration. We present an application to an autonomous robotic vehicle in which decision making is shared by the human/machine team.

On the basis of their use, the DSS has received positive feedback from the University's decision makers. Making use of Intelligent Decision Support Systems (IDSS) technologies suited to provide decision support in the higher education environments, by generating and presenting relevant information and knowledge which are helpful in taking the decision regarding admission management in higher education colleges or universities. The university decision makers' needs and the DSS components are identified with the help of survey done. In this paper the components of a decision support system (DSS) for developing student admission policies in higher education institute or in the university and the architecture about DSS based on ERP are proposed followed by how intelligent DSS in conjunction with ERP helps to overcome the drawbacks , if ERP is used alone in higher education institutes .

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.

The intelligent production line is a new intelligent production line resource formed by highly integrating modern sensor technology, communication technology, information technology, computer technology and control technology on the large physical real basis of the existing production line. It can finish data acquisition, information processing, dynamic virtual reconfiguration of variable batch production line, embedded algorithm, algorithm optimize the management of the production line manufacturing field, change the dynamic virtual reorganization of the mass production line, and become the choice of the development direction of the next generation of new production lines for mankind. Scheduling is the nerve center of the production line virtual reorganization system. The traditional main auxiliary decision-making tool management system can no longer fully meet the requirements of production line operation monitoring in the new environment, so the construction of an intelligent scheduling system is also one of the important contents of the construction of the production line virtual reorganization. Based on this, this article explores and researches intelligent service scheduling decision-making technology under artificial intelligence technology. The research topic of this article is intelligent scheduling of production lines, so this article first analyzes the current research status of intelligent service scheduling and decision support systems; then conducts research based on Agent decision support systems, and analyzes the framework and characteristics of decision support systems. realizes the intelligent production line scheduling DSS system; then this paper designs the system’s scheduling model with stochastic disturbance, system database and system fault diagnosis function, and realizes an intelligent service scheduling decision support system based on multi-agent technology. Finally, this article tests the data transmission speed and fault diagnosis function of the system. The test results show that the system is relatively fast in data transmission speed and the fault diagnosis function is also very reliable.

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.

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.

I: General Issues.- Modeling Knowledge: Model-based Decision Support and Soft Computations.- Benefits of Decision Support Using Soft Computing.- Evolving Connectionist-based Decision Support Systems.- An Agent-based Soft Computing Society with Application Applications in Financial Investment Planning.- A Rough Sets/Neoral Networks Approach to Knowledge Discovery for the Development of Decision Support Systems.- II: Applications and Implementations.- Decision Support Systems in Healthcare: Emerging Trends and Success Factors.- A View of Public and Private Sectors for Taiwan's BOT Project Financing Using Fuzzy Multi-Criteria Methods.- Relational Structures for the Analysis of Decision Information in an Electronic Market.- A Fuzzy Evaluation Model: A Case for Intermodal Terminals in Europe.- Application of Kemeny's Median for Group Decision Support.- An Internet-based Group Decision and Consensus Reaching Support System.- Limpio: A DSS for the Urban Waste Collection Problem.- A Decision Support System for Air Quality Control Based on Soft Computing Methods.- Multicriteria Genetic Tuning for the Optimization and Control of HVAC Systems.- Intelligent Information Systems for Crime Analysis.- Application of Fuzzy Decision Trees to Reservoir Recognition.- Introducing SACRA: A Decision Support System for the Construction of Cattle Diets.- Prediction of Parthenium Weed Dispersal Using Fuzzy Logic on GIS Spatial Image.

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.

This chapter investigates the potential for block chain and artificial intelligence (AI) to work together to revolutionise agriculture. Block chain’s decentralised ledger and AI's data-driven decision-making provide disruptive answers to the agriculture sector's multiple concerns, such as climate change and market access. Initially, the chapter presents a complete introduction of block chain and AI technologies, highlighting their distinguishing characteristics and agricultural applications. Block chain enables traceability, supply chain management, and decentralised marketplaces, complimenting artificial intelligence's precision farming, crop monitoring, and decision support systems. In terms of their separate responsibilities, block chain helps with traceability, quality assurance, decentralised marketplaces, and climate resilience, whereas AI helps with precision farming, yield prediction, and animal health monitoring. The intersection of block chain and AI opens significant possibilities for data integrity, safe sharing, smart contracts, and transparent supply chains. This integration enables crop management predictive analytics, autonomous farming machinery, and climate-resilient techniques. However, barriers to seamless integration include data quality, connection, acceptance, scalability, energy usage, and regulatory compliance. To overcome these obstacles, stakeholders must work together to provide fair access and knowledge of new technologies. In conclusion, the chapter depicts a positive future in which block chain and AI drive a more robust, transparent, and productive agriculture industry. It emphasises the importance of social activity and technical understanding in order to fully realise the promise of these advances.

• 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.