Knowledge-based Decision Research Articles

A knowledge-based decision support system for inferring supportive treatment recommendations for diabetes mellitus.

Diabetes Mellitus (DM) is a significant risk, mostly causing blindness, kidney failure, heart attack, stroke, and lower limb amputation. A Clinical Decision Support System (CDSS) can assist healthcare practitioners in their daily effort and can improve the quality of healthcare provided to DM patients and save time. In this study, a CDSS that can predict DM risk at an early stage has been developed for use by health professionals, general practitioners, hospital clinicians, health educators, and other primary care clinicians. The CDSS infers a set of personalized and suitable supportive treatment suggestions for patients. Demographic data (e.g., age, gender, habits), body measurements (e.g., weight, height, waist circumference), comorbid conditions (e.g., autoimmune disease, heart failure), and laboratory data (e.g., IFG, IGT, OGTT, HbA1c) were collected from patients during clinical examinations and used to deduce a DM risk score and a set of personalized and suitable suggestions for the patients with the ontology reasoning ability of the tool. In this study, OWL ontology language, SWRL rule language, Java programming, Protégé ontology editor, SWRL API and OWL API tools, which are well known Semantic Web and ontology engineering tools, are used to develop the ontology reasoning module that provides to deduce a set of appropriate suggestions for a patient evaluated. After our first-round of tests, the consistency of the tool was obtained as 96.5%. At the end of our second-round of tests, the performance was obtained as 100.0% after some necessary rule changes and ontology revisions were done. While the developed semantic medical rules can predict only Type 1 and Type 2 DM in adults, the rules do not yet make DM risk assessments and deduce suggestions for pediatric patients. The results obtained are promising in demonstrating the applicability, effectiveness, and efficiency of the tool. It can ensure that necessary precautions are taken in advance by raising awareness of society against the DM risk.

Economic valuation of ecosystem services trade-offs of tilapia cage culture farm and native capture fisheries in Lake Maninjau, Indonesia

Research on the effects of cage aquaculture on native fish populations has revealed its negative and positive impacts. However, a separate analysis of its ecological and economic effects creates incomplete information for the decision-makers. Moreover, the inadequacy of time series data has caused challenges to knowledge-based decision-making in establishing new cage aquaculture sites, especially in low-middle income countries. We use the ecosystem services (ES) framework to analyse the synergy and trade-off of cage aquaculture to a native fish species, Gobiopterus sp. in Lake Maninjau, Indonesia, as an effort to provide comprehensive information to support local decision-makers and to fill the information gap. We engaged some modelling techniques such as the Maximum Entropy Model (MaxEnt) validated with field survey data, Bayesian Networks (BN), and Monte Carlo Simulation (MCS) in this research. The results show that cage aquaculture provides habitat services for the species and temporary forgone fish production due to the accelerated sulfur upwelling. The economic value of habitat provision reached 74,500 IDR per year per cage or 1,128 million IDR per year in 2017. Further, the average value of the forgone benefit of fish production in the current cage aquaculture business extends from more than 550 million IDR per year in the dry season to almost 600 million IDR per year in the rainy season. The results indicate that the negative impacts of cage aquaculture on the native fish population outweigh its benefit. The recommendation for management actions includes applying alternative aquaculture techniques and other technological interventions.

Knowledge and inquiry—the missing key for a knowledge-based decision theory

Fassio and Gao (2021) object to a knowledge-based decision theory on the ground that it cannot deal with unsuccessful inquiry. One way for inquiry to fail is not to know what one should know. If one’s inquiry fails in this way, is a subsequent choice in any way wrong when based on one’s limited actual knowledge? This paper discusses two strategies for dealing with this problem. On a first strategy, there is nothing wrong with such a choice (but something went wrong prior to one’s choice). On a second strategy, there is something wrong with one’s choice and a knowledge-based decision theory should require that one’s decisions be based on the knowledge one should have, not merely on the knowledge one actually has.

Nature reserves (zapovedniks) in the Volga catchment: protection, research and management

AbstractIn the Russian Federation, the protection category “Zapovednik” represents a strictly protected area, where the access is restricted and the aim is to keep the area “forever wild”. We provide an overview about the organization of these nature reserves and highlight their contribution to fundamental research. Aquatic ecosystems in zapovedniks provide pristine sites of great importance for long-term monitoring and research, which fosters process understanding regarding interannual variability and development of aquatic coenosis. We present a long term monitoring and research project about abiotic and biotic parameters in the Central Forest State Nature Biosphere Reserve. Examples from the Privolzhskaya Lesostep Nature Reserve include monitoring of invasive fish as well as analyses about the influence of beavers on fish. The Astrakhan Biosphere Nature Reserve in the Volga delta is a hotspot for fish biodiversity; thus, there the state nature reserve security service plays an important role to avoid poaching. Our case studies from the Volga Basin exemplify the structure and function of nature reserves and highlight their contribution to research, protection and management of aquatic ecosystems. Long-term datasets from the zapovedniks can (i) improve process understanding, (ii) ensure protection of aquatic systems and sustainable water management and (iii) support knowledge-based decisions.

From local to global estimations of confidence in perceptual decisions.

Perceptual confidence has been an important topic recently. However, one key limitation in current approaches is that most studies have focused on confidence judgments made for single decisions. In three experiments, we investigate how these local confidence judgments relate and contribute to global confidence judgments, by which observers summarize their performance over a series of perceptual decisions. We report two main results. First, we find that participants exhibit more overconfidence in their local than in their global judgments of performance, an observation mirroring the aggregation effect in knowledge-based decisions. We further show that this effect is specific to confidence judgments and does not reflect a calculation bias. Second, we document a novel effect by which participants' global confidence is larger for sets which are more heterogeneous in terms of difficulty, even when actual performance is controlled for. Surprisingly, we find that this effect of variability also occurs at the level of local confidence judgments, in a manner that fully explains the effect at the global level. Overall, our results indicate that global confidence is based on local confidence, although these two processes can be partially dissociated. We discuss possible theoretical accounts to relate and empirical investigations of how observers develop and use a global sense of perceptual confidence. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A Cyber–Physical Systems-Based Double-Layer Mapping Petri Net Model for Factory Process Flow Control

This study is concerned with the imperfect virtual-reality mapping relationship in cyber–physical systems (CPSs) and the challenge faced in knowledge-based decisions. Regarding those problems, a double-layer mapping Petri net (DMPN) model is proposed. By deploying the programmable automation gateway PAG200, combining the CPS technology with the principle of Petri net and establishing the monitoring Petri net in the cyber space, this model realizes mapping between the physical entity and the digital object. Meanwhile, the knowledge-based decision problem in CPS is defined as a Petri net conflict. In accordance with this, a control network for resolving the conflict is established. Finally, through a practical case, the workflow of DMPN is illustrated and a task allocation algorithm and a quality decision algorithm are proposed to resolve Petri net conflicts. Consequently, it is proven that DMPN is feasible in solving actual production process flow control. At the same time, it also provides a solution for enterprise workflow analysis.

Special Issue on Trans-Disciplinary Approach (TDA) and Decision Making on Building Disaster Resilience

A transdisciplinary approach (TDA) for disaster risk reduction (DRR) is an inclusive and collaborative implementation strategy that brings together stakeholders from various disciplines and sectors. TDA facilitates the collective efforts of diverse stakeholders, encouraging them to work together to co-produce and co-implement solutions that reduce disaster risks. Technical Committee 21, “Transdisciplinary approach for building societal resilience to disasters (TC21),” was established as a technical committee of the Asian Civil Engineering Coordinating Council in 2016. TC21 has been engaged in international collaborative research activities aimed at mitigating water-related disasters by introducing scientific, knowledge-based decision making using the TDA. This special issue features four case studies that analyze DRR efforts through the lens of the TDA. One such case covers Xinshan Village in Taiwan, which sustained devastating impacts from Typhoon Morakot in 2009. Through collaborative efforts involving all levels of government, residents, academia, and specialists, the village successfully prevented there being any casualties. The second study focuses on the Cross-ministerial Strategic Innovation Promotion Program in Japan, a program which is developing disaster risk information systems through collaboration with local municipalities. These systems play crucial disaster management roles on the ground. The third study examines the mechanisms of cost- and responsibility-sharing for disaster management, developed by Japan and the US, and highlights the importance of collaboration and shared accountability in building resilient societies. The fourth study analyzes statement publications at the UN Global Platform for DRR and finds that the number of member states publishing official statements is decreasing. The findings of these articles demonstrate that collaboration, technology, and investment is crucial for DRR and that these areas can be developed through the TDA. By fostering collaboration, embracing technology, and investing in resilient solutions, stakeholders can collectively build more resilient societies.

Pilots’ Knowledge-Based Decision-Making Process in Flight Operations in Civil Aviation

Sivil havacılık sektörü özellikle II. Dünya Savaşı sonrasında yaşanan teknolojik gelişmeler ve yapılan yatırımlar sayesinde büyük bir gelişim ve dönüşüm süreci içerisine girmiştir. Küreselleşmeye bağlı olarak gelişen pazar payı ve artan rekabet koşulları nedeniyle geçmişe göre daha ekonomik, daha hızlı, güvenli, tercih edilen bir ulaşım seçeneği haline gelmiştir. Pilotlar uçuş faaliyetlerinin temel aktörüdür ve uçuş operasyonlarının merkezinde yer almaktadır. Çalışmanın amacı, sivil havayolu ulaşımında görevli pilotların bilgiye dayalı kararları nasıl aldıklarının anlaşılmasıdır. Pilotların uçuşun planlanmasından tamamlanmasına kadar geçen tüm aşamalarda ihtiyaç duydukları bilgilere bilişim teknolojileri ve otomasyona dayalı kokpit sistemleri aracılığıyla gerçek zamanlı olarak ulaşabilmesi önemlidir. Bu çalışmada literatür taraması yapılmış, pilotların uçuş operasyonlarında bilgiyi edinme ve kullanımlarına yönelik konular açıklanmıştır.

The Application of Knowledge-Based Clinical Decision Support Systems to Enhance Adherence to Evidence-Based Medicine in Chronic Disease.

Among the technology-based solutions, clinical decision support systems (CDSSs) have the ability to keep up with clinicians with the latest evidence in a smart way. Hence, the main objective of our study was to investigate the applicability and characteristics of CDSSs regarding chronic disease. The Web of Science, Scopus, OVID, and PubMed databases were searched using keywords from January 2000 to February 2023. The review was completed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. Then, an analysis was done to determine the characteristics and applicability of CDSSs. The quality of the appraisal was assessed using the Mixed Methods Appraisal Tool checklist (MMAT). A systematic database search yielded 206 citations. Eventually, 38 articles from sixteen countries met the inclusion criteria and were accepted for final analysis. The main approaches of all studies can be classified into adherence to evidence-based medicine (84.2%), early and accurate diagnosis (81.6%), identifying high-risk patients (50%), preventing medical errors (47.4%), providing up-to-date information to healthcare providers (36.8%), providing patient care remotely (21.1%), and standardizing care (71.1%). The most common features among the knowledge-based CDSSs included providing guidance and advice for physicians (92.11%), generating patient-specific recommendations (84.21%), integrating into electronic medical records (60.53%), and using alerts or reminders (60.53%). Among thirteen different methods to translate the knowledge of evidence into machine-interpretable knowledge, 34.21% of studies utilized the rule-based logic technique while 26.32% of studies used rule-based decision tree modeling. For CDSS development and translating knowledge, diverse methods and techniques were applied. Therefore, the development of a standard framework for the development of knowledge-based decision support systems should be considered by informaticians.

Discrimination and monitoring of rice cultural types using dense time series of Sentinel-1 SAR data

Details about the spatial extent of rice cultivation in different seasons are essential for ensuring global food security as well as addressing environmental issues and climate change. This study aims to introduce a novel approach to discriminate between different rice cultural types and subtypes and identify their optimum stages to represent crop growth profiles using Synthetic Aperture Radar (SAR) data. Dense time series Sentinel-1 SAR backscatter data were analyzed at 12 days intervals throughout the growth period of summer and kharif rice. Statistical analysis using ANOVA at 5% level of significance was conducted to identify the critical stages of discrimination for all cultural types and subtypes. Knowledge-based decision tree algorithm was employed to classify all major types of rice grown during summer and kharif season. A comprehensive analysis revealed significant variations in backscatter profiles among different rice types. The most critical period for effectively distinguishing between various rice cultural types and subtypes was found to be from transplanting to tillering stage. Decision tree algorithm was able to discriminate between rice cultural types and subtypes with high overall accuracy and Kappa coefficient, viz., 94.74% and 0.94 for summer rice and 91.80% and 0.90 for kharif rice, respectively. The findings of this study revealed that C-band co and cross-polarized Sentinel-1 data have the potential to detect differences between different cultural types and subtypes of rice and, therefore, will be helpful in crop growth monitoring and agro-ecological change detection in the cultivation pattern.

A knowledge-based decision support system for incident traffic congestion management

A Knowledge-based Decision Support System (KB-DSS) based on a multi-stage Fuzzy Logic Controller (MS-FLC) is developed for traffic congestion management on expressways. The MS-FLC receives real-time traffic and incident data to analyse and anticipate the traffic conditions, to recommend alternative control measures in the form of natural languages for the human operator to select control decisions, and to calculate control settings to manage traffic congestion. In a case study, the KB-DSS is evaluated on a simulated network in comparison to ALINEA\Q, a popular ramp control method, across various traffic and incident situations. The results showed that: (i) the KB-DSS provides a systematic procedure in deriving control actions and a good capability to deliver linguistic expressions; (ii) the KB-DSS outperforms ALINEA\Q with respect to global objectives across many scenarios, attains significant improvements of mainline travel conditions and substantial reductions of ramp queues. These advantages make the KB-DSS a robust tool for traffic control for incident congestion management on expressways.

Closing Data Gaps and Paving the Way for Pan-European Fire Safety Efforts: Part II—Terminology of Fire Statistical Variables

A well-defined terminology of fire-related variables is important for correct analyses and supporting knowledge-based decisions regarding the evaluation of building fires at the European level. After developing an overview of current practices for fire statistics in Part I, the terminology used and the data collected by the EU Member States and eight other countries regarding fire incidents, property damage and human losses were mapped to increase awareness of their practice and support a comprehensive assessment of several fire statistical datasets. A questionnaire was distributed to relevant authorities responsible for the collection, elaboration/analysis, and fire statistical data publications to define and select the essential variables for an appropriate fire assessment and fire incident description. Based on the results of the questionnaire able to identify the essential fire statistical variables and a detailed analysis of current definitions adopted in the fire statistics of the EU Member States and other countries, a common terminology is proposed to collect the necessary data in the EU Member States and obtain meaningful datasets based on standardised terms and definitions. The results will generate essential outputs to move towards harmonised fire statistics at the EU level and contribute to an appropriate analysis able to improve fire prevention and fire mitigation in building fires.

Conditional Knowledge Extraction Using Contextual Information Enhancement

Conditional phrases provide fine-grained domain knowledge in various industries, including medicine, manufacturing, and others. Most existing knowledge extraction research focuses on mining triplets with entities and relations and treats that triplet knowledge as plain facts without considering the conditional modality of such facts. We argue that such approaches are insufficient in building knowledge-based decision support systems in vertical domains, where specific and professional instructions on what facts apply under given circumstances are indispensable. To address this issue, this paper proposes a condition-aware knowledge extraction method using contextual information. In particular, this paper first fine-tunes the pre-training model to leverage a local context enhancement to capture the positional context of conditional phrases; then, a sentence-level context enhancement is used to integrate sentence semantics; finally, the correspondences between conditional phrases and relation triplets are extracted using syntactic attention. Experimental results on public and proprietary datasets show that our model can successfully retrieve conditional phrases with relevant triplets while improving the accuracy of the matching task by 2.68%, compared to the baseline.

Ending the suitability quantification dilemma: intelligent decision support system for modular integrated construction in a high-density metropolis

PurposeModular integrated construction (MiC) is a modern construction method innovating and reinventing the traditional site-based construction method. As it integrates advanced manufacturing principles and requires offsite production of volumetric building components, several factors and conditions must converge to make the MiC method suitable and efficient for building projects in each context. This paper aims to present a knowledge-based decision support system (KB-DSS) for assessing a project’s suitability for the MiC method.Design/methodology/approachThe KB-DSS uses 21 significant suitability decision-making factors identified through literature review, consultation of experts and questionnaire surveys. It has a knowledge base, a DSS and a user interface. The knowledge base comprises IF-THEN production rules to compute the MiC suitability score with the efficient use of the powerful reasoning and explanation capabilities of DSS.FindingsThe tool receives the inputs of a decision-maker, computes the MiC suitability score for a given project and generates recommendations based on the score. Three real-world projects in Hong Kong are used to demonstrate the applicability of the tool for solving the MiC suitability assessment problem.Originality/valueThis study established the complex and competing significant conditions and factors determining the suitability of the MiC method for construction projects. It developed a unique tool combining the capabilities of expert systems and decision support system to address the complex problem of assessing the suitability of the MiC method for construction projects in a high-density metropolis.

COGNITIVE MODELING OF INTELLECTUAL SYSTEMS OF ANALYSIS OF THE FINANCIAL CONDITION OF THE ENTITY

The article identifies basic approaches to modelling information systems of the entity, based on the retrospective processes on the issues of cognitive approach to modelling complex information systems. In the article described approaches of usage of the cognitive modelling apparatus to analyse the financial condition and the efficiency of the entity. Information intellectual decision-making systems are found in applied multi-agent systems, geoinformation systems, economic systems, and systems in which decisions are to be made based on knowledge. One of the elements used to remove information from the knowledge base is cognitive maps and they show the dynamic of the properties of various situations. A cognitive map that reflects a specific situation can be considered as a kind of graphic interpretation of a mathematical model, which clearly reflects the situation and allows for the formalization of the problem to present a complex system as a set of interdependent concepts. Cognitive maps are used to solve problems that are primarily related to the analysis of the existing state of the object and decision-making. Cognitive maps make it possible to establish cause and effect ratios and to form a knowledge base for decision-making. The financial condition of the entity requires modelling of intellectual systems, which is recommended to be based on the combination of methods of system analysis and cognitive modelling. This approach allows you to reliably evaluate the financial condition of the entity, because the basis of the information intellectual system for the decision-making is a mathematical model created on the basis of classical system analysis and cognitive methods. The use of artificial intelligence elements in intellectual management and analytical systems for the analysis of the financial condition of an entity is a modern powerful tool for any country's economic business processes.

Machine Learning Techniques to Enhance the Mental Age of Down Syndrome Individuals: A Detailed Review

Down syndrome individuals are known as intellectually disabled people. Their intellectual ability is classified into four categories known as mild, moderate, severe, and profound. These individuals have significant limitations in learning and adapting skills. Psychologists evaluate mental capability of such individuals using conventional intellectual quotient method instead of using any technology. The research matrix shows most of research has been carried out on analyzing neuroimaging, antenatal screening, and hearing impairment of individuals. But there is still an obvious gap of evaluating mental age using artificial intelligence. We have proposed an artificial neural network model, which supervises how software is used to obtain dataset using Knowledge Base Decision Support System. In a survey (N = 120) individuals examined by psychiatrist, medical expert, and a teacher to assess the presence of Down’s syndrome by analyzing their physical and facial appearances, and communication skills. Only (N = 62) individuals declared as Down syndrome. Selected individuals invited to perform mental ability assessment using Interactive Mental Learning Software. The results of mental age of Down syndrome with a raise in IQ from severe to moderate (20% to 35%), moderate to mild (35% to 75%) severity were carried out with the help of assessing the interactive series of software opinion polls based on comparison, logic, and basic mathematical operations using initial IQ (iIQ), and enhanced IQ (eIQ) variables input and output parameters.

A Lean Knowledge-Based Decision Support System for Cost Estimation in the Foundry Industry

A Lean Knowledge-Based Decision Support System for Cost Estimation in the Foundry Industry

Combining the External Medical Knowledge Graph Embedding to Improve the Performance of Syndrome Differentiation Model.

The electronic medical records (EMRs) of traditional Chinese medicine (TCM) include a wealth of TCM knowledge and syndrome diagnosis information, which is crucial for improving the quality of TCM auxiliary decision-making. In practical diagnosis, one disease corresponds to one syndrome, posing considerable hurdles for the informatization of TCM. The purpose of this work was to create an end-to-end TCM diagnostic model, and the knowledge graph (KG) created in this article is used to improve the model's information and realize auxiliary decision-making for TCM disorders. We approached auxiliary decision-making for syndrome differentiation in this article as a multilabel classification task and presented a knowledge-based decision support model for syndrome differentiation (KDSD). Specifically, we created a KG based on TCM features (TCMKG), supplementing the textual representation of medical data with embedded information. Finally, we proposed fusing medical text with KG entity representation (F-MT-KER) to get prediction results using a linear output layer. After obtaining the vector representation of the medical record text using the BERT model, the vector representation of various KG embedded models can provide additional hidden information to a certain extent. Experimental results show that our method improves by 1% (P@1) on the syndrome differentiation auxiliary decision task compared to the baseline model BERT. The usage of EMRs can aid TCM development more efficiently. With the help of entity level representation, character level representation, and model fusion, the multilabel classification method based on the pretraining model and KG can better simulate the TCM syndrome differentiation of the complex cases.

Gewissen im 21. Jahrhundert – Potenziale eines veralteten Begriffs

Conscience is an old-fashioned sounding term. Responsibility is mostly used today instead of conscience. Responsibility should be taken on, preferably on the basis of evidence-based rational guidelines. The center of current notions of responsibility forms the concept of an enlightened self, aware of its feelings and needs, which makes conscious, knowledge-based decisions and considers the consequences. Significantly, conscience does not refer to such an autonomous self, but implies a second instance in the ego. Conscience refers to an inner objection. The objecting voice results in the special freedom of a decision of conscience on the one hand, and in the incorporation of social norms and culture-bound orders, on the other. It thus becomes clear that against the current dominance of autonomy and responsibility, the older model of conscience cannot simply be revived. Both models have their ambivalences, as demonstrated by the excessive demand on self-realization in postmodernity. Philosophy and the history of science cannot answer how much conscience we need. This short survey traces the evolvement of the term, explores the paths the idea of an inner voice has taken and points to the effects that have been released in the process.

Integrated framework of knowledge-based decision support system for user-centered residential design

A user-oriented residential environment is considered essential to providing occupants with a comfortable living experience while meeting their demands. However, design assessment in the early stage of residential design is a complex problem due to multifactor coupling, the diversity in user needs, and the incompleteness of design knowledge. This study proposes an integrated framework of knowledge-based decision support system (KBDSS) to mitigate such problems. In this framework, the user segmentation is performed in accordance with people’s particular requirements priority and, thus, enables design decisions that create better satisfaction among a wider audience. Meanwhile, the quality function deployment approach, decision support system, and fuzzy set theory are used to translate user requirements into quantifiable design specifications, thereby making the design a multicriteria decision-making problem. To illustrate and validate the proposed framework, a case study of a KBDSS for a multiunit residential building kitchen design is demonstrated to explore a user-centered residential design with such decision support. This proposed framework provides naive design practitioners with specific knowledge about decision problems and assists them in consistently making user-centered design decisions. The research results represent a further step toward a systematic methodology of user-centered built environment design.