Development of a Web-Based Intelligent Spatial Decision Support System (WEBISDSS)

Web-based spatial decision support system for optimum route to forest fires: A case of Viphya plantations

Spatial Decision Support Systems (SDSS), which support spatial analysis and decision making, are currently receiving much attention. Research on SDSS originated from two distinct sources, namely, the GIS community and the DSS community. The synergy between these two research groups has lead to the adoption of state of the art technical solutions and the development of sophisticated SDSS that satisfy the needs of geographers and top-level decision makers. Recently, the Web has added a new dimension to SDSS and Web-based SDSS (WebSDSS) are being developed in a number of application domains. This article provides an overview of the emergence of SDSS, its architecture and applications, and discusses some of the enabling technologies and research challenges for future SDSS development and deployment.

SmartScape™: A web-based decision support system for assessing the tradeoffs among multiple ecosystem services under crop-change scenarios

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 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

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.

Geospatial web services within a scientific workflow: Predicting marine mammal habitats in a dynamic environment

In this research, a web-based spatial decision support system (SDSS) was developed to demonstrate how the Internet can be used to deliver low-cost, user-friendly and interactive land use decision support to a wide audience. Although the resulting Cape Land Use Expert System (CLUES) was specifically developed to perform land suitability analysis in the Western Cape, the technology can also be applied to other regions and modified for other applications. CLUES consists of five components: a land unit database (LUD), knowledge base, inference engine, web map service (WMS) and graphical user interface (GUI). The LUD consists of polygons (land units) and attributes (land properties), while the knowledge base stores each user's land use requirement rules. These rules are used by the inference engine to rate the suitability of each land unit in the LUD. The result is then mapped by the WMS and presented to the users as suitability maps. Users can direct the entire analysis through a user-friendly GUI. The development and demonstration of CLUES exposed several advantages and limitations of current web technology and has demonstrated that the Internet offers great opportunities for the deployment of land use suitability information to a wide audience.

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 for crop production planning is essentially driven by location-based or more precisely by space-oriented information. Therefore, farmers and regional experts in the field mostly rely on new spatial-data-oriented decision making tools. IVIP is a prototype for a Web-based Spatial Decision Support System (WSDSS) demonstrating the benefits of location-based decision making using digitalized geographic information about ground allocation and soil quality. We present how the library of potential models for the IVIP WSDSS has been realized by extending the Scientific Workflow Management System Kepler that assists the collaboration of agricultural experts and computer scientists during model development. We first describe the requirements of our WSDSS, and then give a short introduction to the Kepler platform and explain in detail which extensions have been realized: cascading client-server architecture, spatial operations support, and WSDL interface. Finally, we illustrate how the biomass yield model has been modeled in our system.

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.

Intelligent methods in technology of web-based e-government help citizens in many areas of theirs life. The construction and subsequent use of such systems is in the status of low-level accumulation of experiential knowledge which is contrary to the requesttopic time and many documents that establish the strategy of development in this area, like EU i2010 strategy. Model for Web-based multi-attribute decision support with the appropriate algorithm is the subject of discussing of this paper.

Extracting effective features from dynamic networks underpins the development of network-based artificial intelligence (AI) methods and decision support systems. Despite existing methods for constructing network features, a notable gap exists in addressing the propagation influence of direct and indirect neighbors. Given the sparse and interacting nature of neighbor information as well as the requirements of interpretability, modeling neighbor influence presents an essential yet challenging research problem. To tackle this challenge, we propose a novel Time-vAriant Graph Contrastive Learning method (TAGOL). TAGOL seeks to improve both the effectiveness and interpretability of constructing features related to the propagation influence by explicitly modeling sparse and interacting neighbor information in time-variant networks. We perform a comprehensive evaluation of the proposed method through two case studies: credit risk prediction and financial distress prediction. Experimental results demonstrate the efficacy of TAGOL and shed light on the varied influences of the joint propagation of interacting neighbor information on financial risk prediction. The proposed TAGOL and experimental findings offer generalizable methodological and theoretical insights, which can contribute to a broader spectrum of network-related research endeavors, such as short video recommendation systems and transit flow prediction.

Multiple Criteria Decision Analysis/Making (MCDA/MCDM) is constantly increasing its presence in various scientific fields but also in new fields of its application. This expansion became possible mainly due to the integration and implementation of its methodologies in the Decision Support Systems (DSS). This enabled their successful applications in many new fields resulting in their further expansion as Multi-Criteria Decision Support Systems (MCDSS). Their combination with Artificial Intelligence (intelligent agents, recommender systems, expert systems) in general and especially in recent years with the field of Machine Learning and Data Mining has led to the development of Intelligent Multi-Criteria Decision Support Systems (IMCDSS) with enormous application in analytics. At the same time, MCDA was used to solve various problems and to develop new methodologies in the field of Machine Learning. The aim of this speech is to present on the one hand the theoretical issues and methodologies of the cooperation of the Multi-Criteria DSS with the Artificial Intelligence and on the other hand to present a series of such IMCDSS and their applications in various fields such as marketing, health, defense, agricultural, etc.

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.