IDSS: an intelligent decision support system for e-purchasing using CBR and CF

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.

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.

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

Background. The digital transformation of dentistry involves not only the digitization of patient records but also the integration of intelligent Decision Support Systems (DSS), which can enhance diagnostic accuracy, standardize treatment protocols, and optimize clinician – patient interaction. With the rapid development of artificial intelligence (АІ) technologies, DSS are gaining increasing importance in both clinical and educational contexts within dentistry. Purpose – to systematize existing approaches to the classification, design, and implementation of DSS in dentistry, with a focus on their functional capabilities, architectural models, and preconditions for successful clinical deployment. Materials and Methods. The study is based on the analysis of peer-reviewed scientific literature published between 2020 and 2024 from Scopus, PubMed, and IEEE databases, as well as technical documentation of existing DSS solutions. Methods of systems analysis, descriptive classification, comparative evaluation, and synthesis of preconditions for implementation were applied. Two summary tables were developed: one on the advantages and limitations of different DSS types, and another on implementation preconditions. Results. Two primary classification axes of DSS were identified: functional (diagnostic, prognostic, therapeutic planning, administrative, educational, AI-based) and architectural (rule-based, case-based, AI-based, fuzzy logic, hybrid systems). Rule-based systems remain the most widely used due to their transparency and alignment with clinical protocols, while AI-driven systems demonstrate potential but require further validation and explainability. A set of key preconditions for effective implementation was formulated, including structured clinical data, interoperability with electronic health records, appropriate infrastructure, and clinical staff readiness. Conclusions. DSS has demonstrated high potential for improving the quality, safety, and personalization of dental care. Promising directions for future development include hybrid DSS models that combine rule-based and AI approaches, as well as the creation of unified standards for integration and legal compliance.

It is well known that security analysis is a time-consuming and error-prone process. However, it can be improved or enhanced considerably by automated reasoning. Efforts to reduce the inaccuracy and incorrectness of analyses and to enhance the confidence levels of stock selection have led to the development of an intelligent decision support system called TradeExpert, which assists, not replaces, portfolio managers. TradeExpert assumes the role of a hypothetical securities analyst capable of analyzing stocks, calling market turns, and making recommendations. It has a knowledge base of stock trading expertise, and a case base of past episodes and consequences of decisions. By combining knowledge-based problem solving with case-based reasoning and fuzzy inference, TradeExpert demonstrates forms of intelligent behavior not yet observed in traditional decision support systems and expert systems. The novelty of this research lies in its application to analogical reasoning, fuzzy reasoning, and knowledge-based decision making.

Obstructive Sleep Apnea (OSA) is a chronic sleep-related pathology characterized by recurrent episodes of total or partial obstruction of the upper airways during sleep. It entails a high impact on the health and quality of life of patients, affecting more than one thousand million people worldwide, which has resulted in an important public health concern in recent years. The usual diagnosis involves performing a sleep test, cardiorespiratory polygraphy, or polysomnography, which allows characterizing the pathology and assessing its severity. However, this procedure cannot be used on a massive scale in general screening studies of the population because of its execution and implementation costs; therefore, causing an increase in waiting lists which would negatively affect the health of the affected patients. Additionally, the symptoms shown by these patients are often unspecific, as well as appealing to the general population (excessive somnolence, snoring, etc.), causing many potential cases to be referred for a sleep study when in reality are not suffering from OSA. This paper proposes a novel intelligent clinical decision support system to be applied to the diagnosis of OSA that can be used in early outpatient stages, quickly, easily, and safely, when a suspicious OSA patient attends the consultation. Starting from information related to the patient's health profile (anthropometric data, habits, comorbidities, or medications taken), the system is capable of determining different alert levels of suffering from sleep apnea associated with different apnea-hypopnea index (AHI) levels to be studied. To that end, a series of automatic learning algorithms are deployed that, working concurrently, together with a corrective approach based on the use of an Adaptive Neuro-Based Fuzzy Inference System (ANFIS) and a specific heuristic algorithm, allow the calculation of a series of labels associated with the different levels of AHI previously indicated. For the initial software implementation, a data set with 4600 patients from the Álvaro Cunqueiro Hospital in Vigo was used. The results obtained after performing the proof tests determined ROC curves with AUC values in the range 0.8-0.9, and Matthews correlation coefficient values close to 0.6, with high success rates. This points to its potential use as a support tool for the diagnostic process, not only from the point of view of improving the quality of the services provided, but also from the best use of hospital resources and the consequent savings in terms of costs and time.

Performance prediction and adaptation for database management system workload using Case-Based Reasoning approach

Under the background of intelligent transformation of coal mines, an intelligent decision support system based on case-based reasoning (CBR) has become crucial for improving production control. This paper constructs such a system and innovatively proposes an adaptive weight dynamic case retrieval algorithm (AWDCR). The algorithm leverages real-time monitoring of multi-source production data, dynamically adjusting case attribute weights based on data change characteristics and decision influence through a hybrid AHP-entropy weight mechanism. Using MATLAB simulation with 100,000+ actual production records across 100 scenarios (normal, equipment failure, environmental anomaly), results show AWDCR reduces average retrieval time by 20% and improves decision accuracy from 80% to 90% compared to traditional CBR. enhancing retrieval accuracy by 20%. The system effectively enhances production efficiency and safety, laying a foundation for intelligent coal mining.

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.

Accurately identifying potentially failing companies is beneficial to stakeholders of the companies. This paper uses a case-based reasoning (CBR) approach to predict business failure. CBR is a problem-solving paradigm that uses past experiences to solve new problems. Nearest neighbor (NN) is a common CBR algorithm for retrieving similar cases, but its similarity function is sensitive to irrelevant attributes. To ensure the effective retrieval of similar cases, statistical evaluations are used for automatically assigning relative importance of the attributes for the NN retrieval. The results of this study indicate that this approach is an effective and competitive alternative to predict business failure in a comprehensible manner.

It is generally accepted that adaptive design methods and flexible constructs facilitate the design and implementation of decision support systems. Flexible constructs in the form of decision support system generators have been proposed for the timely construction of specific decision support systems. Effective communication, however, underlies the success of applying adaptive design methods and creating and modifying decision support system generators. Accordingly, we propose to investigate communication as a mode of action that allows the participants to structure and to exert control over the design environment. A descriptive model is presented that describes how different modes of communicative action are used to enhance the prospects of success when building decision support systems.

Many studies on land use change (LUC), using different approaches and models, have yielded good results. Applications of these methods have revealed both advantages and limitations. However, LUC is a complex problem due to influences of many factors, and variations in policy and natural conditions. Hence, the characteristics and regional suitability of different methods require further research, and comparison of typical approaches is required. Since the late 1980s, CA has been used to simulate urban growth, urban sprawl and land use evolution successfully. Nowadays it is very popular in resolving the LUC estimating problem. Case-based reasoning (CBR), as an artificial intelligence technology, has also been employed to study LUC by some researchers since the 2000s. More and more researchers used the CBR method in the study of LUC. The CA approach is a mathematical system constructed from many typical simple components, which together are capable of simulating complex behavior, while CBR is a problem-oriented analysis method to solve geographic problems, particularly when the driving mechanisms of geographic processes are not yet understood fully. These two methods were completely different in the LUC research. Thus, in this paper, based on the enhanced CBR model, which is proposed in our previous research (Du et al. 2009), a comparison between the CBR and CA approaches to assessing LUC is presented. LUC in Dongguan coastal region, China is investigated. Applications of the improved CBR and the cellular automata (CA) to the study area, produce results demonstrating a similarity estimation accuracy of 89% from the improved CBR, and 70.7% accuracy from the CA. From the results, we can see that the accuracies of the CA and CBR approaches are both >70%. Although CA method has the distinct advantage in predicting the urban type, CBR method has the obvious tendency in predicting non-urban type. Considering the entire analytical process, the preprocessing workload in CBR is less than that of the CA approach. As such, it could be concluded that the CBR approach is more flexible and practically useful than the CA approach for estimating land use change.

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.