Knowledge-based Model Research Articles

Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy.

Numerous studies have shown that immune checkpoint inhibitor (ICI) immunotherapy has great potential as a cancer treatment, leading to significant clinical improvements in numerous cases. However, it benefits a minority of patients, underscoring the importance of discovering reliable biomarkers that can be used to screen for potential beneficiaries and ultimately reduce the risk of overtreatment. Our comprehensive review focuses on the latest advancements in predictive biomarkers for ICI therapy, particularly emphasizing those that enhance the efficacy of programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibitors and cytotoxic T-lymphocyte antigen-4 (CTLA-4) inhibitors immunotherapies. We explore biomarkers derived from various sources, including tumor cells, the tumor immune microenvironment (TIME), body fluids, gut microbes, and metabolites. Among them, tumor cells-derived biomarkers include tumor mutational burden (TMB) biomarker, tumor neoantigen burden (TNB) biomarker, microsatellite instability (MSI) biomarker, PD-L1 expression biomarker, mutated gene biomarkers in pathways, and epigenetic biomarkers. TIME-derived biomarkers include immune landscape of TIME biomarkers, inhibitory checkpoints biomarkers, and immune repertoire biomarkers. We also discuss various techniques used to detect and assess these biomarkers, detailing their respective datasets, strengths, weaknesses, and evaluative metrics. Furthermore, we present a comprehensive review of computer models for predicting the response to ICI therapy. The computer models include knowledge-based mechanistic models and data-based machine learning (ML)models. Among the knowledge-based mechanistic models are pharmacokinetic/pharmacodynamic (PK/PD) models, partial differential equation (PDE) models, signal networks-based models, quantitative systems pharmacology (QSP) models, and agent-based models (ABMs). ML models include linear regression models, logistic regression models, support vector machine (SVM)/random forest/extra trees/k-nearest neighbors (KNN) models, artificial neural network (ANN) and deep learning models. Additionally, there are hybrid models of systems biology and ML. We summarized the details of these models, outlining the datasets they utilize, their evaluation methods/metrics, and their respective strengths and limitations. By summarizing the major advances in the research on predictive biomarkers and computer models for the therapeutic effect and clinical utility of tumor ICI, we aim to assist researchers in choosing appropriate biomarkers or computer models for research exploration and help clinicians conduct precision medicine by selecting the best biomarkers.

Hybrid knowledge-infused collaborative filtering for enhanced movie clustering and recommendation

This article proposes an enhanced knowledge-based collaborative filtering model for movie recommendation services to address the limitations of collaborative filtering in capturing the diverse preferences and specific characteristics of movies. The proposed model integrates external knowledge sources, such as movie plots and reviews, to enrich the recommendation process. By leveraging this additional information, the model can better understand movies’ unique features and attributes, improving recommendation accuracy and relevance. The knowledge-based features are extracted and incorporated into the collaborative filtering framework, enhancing the model’s ability to match user preferences with movie characteristics. Experiments are conducted using the MovieLens dataset to evaluate the proposed model. The MAE and RMSE metrics are employed to assess the quality of recommendations. Comparative analyses are conducted against various baseline approaches, including popularity-based, CF-based, content-based, and hybrid recommendation models. The experimental results demonstrate the effectiveness of the proposed knowledge-based collaborative filtering model. The proposed model consistently outperforms the baselines, providing more accurate and personalized recommendations.

A new prediction method for sodium aluminate solution evaporation integrating process knowledge and data-driven spatial-temporal adaptive model

In alumina production, the evaporation as the key process uses recyclable resources and reduces environmental pollution. In fact, the quality of export product with offline and delayed, results in low precision of process control and high energy consumption. To ensure green and efficient production, in this paper, a new prediction method integrating process knowledge and data-driven spatial-temporal adaptive model is put forward. First, to preprocessed production data for ensuring modeling accuracy, data reconciliation technology is adopted. Then, based on material and heat transfer mechanism, for equipment and industrial process, the mechanism models are established. Furthermore, with time difference and moving window model, an error compensation method is utilized in terms of double locally weighted kernel PLS for estimation error in hypothesis-based mechanism modeling. Finally, the data-driven spatial-temporal adaptive model and the process knowledge-based mechanism model are integrated. To illustrate the model feasibility, an industrial sodium aluminate solution evaporation is used. It demonstrates that, for the developed model, the prediction accuracy can reach more than 90% within the ±2% error range, and effectively estimate the actual product quality and ensure the prediction effect.

Expert knowledge-based modelling approach for mapping beekeeping suitability area

It is becoming increasingly accepted that beekeeping is declining due to the damaging effect of global changes such as climate and land-use change that directly and indirectly impact Apis Melliferas. Despite numerous investigations, a comprehensive study that incorporates both global and local knowledge has yet to be conducted. For a long time, researchers have suggested that expert knowledge should be taken into account when creating decision support tools for managing activities related to natural resources, such as beekeeping. Unlike previous studies, this research seeks to tackle these questions while also introducing the concept of ecosystem service in modelling, offering a fresh perspective on sustainable land use. To achieve this goal, we combined several methods, including using literature knowledge, beekeeper knowledge, and multi-source geospatial data. These data are employed in a hierarchical fuzzy inference system in a unified way. The proposed approach was applied in the Québec region and the suggested technique appears to be both reliable and effective. The validation step revealed that the landscape variable, particularly the area used for agriculture or grassland, had the greatest impact on changes in hive weight throughout the season. In addition, we demonstrated that meteorological factors such as rainfall and relative humidity are strongly correlated to beekeeping. We showed that access to data and knowledge can be a critical factor in decision-making in the beekeeping industry, and thus we suggest that wild-bees conservationists, decision-makers, farmers, beekeepers, and other stakeholders adopt a collaborative approach.

An Accounting Classification System Using Constituency Analysis and Semantic Web Technologies

ABSTRACT To aid accountants in making professional judgments and decisions regarding the accounting methods for transactions, we propose a classification system by integrating computational linguistics with semantic web technologies. We use constituency parsing to convert the classification rules in accounting standards into a machine-processable data structure: Resource Description Framework (RDF) triples. When an accounting classification question is input, the system converts it into an RDF triple, compares it with the established triples of different accounting methods, and subsequently identifies the most appropriate accounting method. We showcased and evaluated our proposed model using IFRS 9 and IAS 28. Our study provides both scholarly and practical applications by (1) incorporating computational linguistics and semantic web technologies to create an interpretable, process-traceable, and explainable classification system aligned with regulatory requirements; and (2) proving that the knowledge-based model can be established without substantial training data, enhancing its accessibility and utility for accounting professionals. Data Availability: Data are available from the authors upon request.

Data Mining from Knowledge Cases of COVID-19

A lot of articles were produced during the pandemic of COVID-19 and continue to be produced. The article proposes a system for diagnosis of COVID-19 disease. Also nowadays, the presentation of knowledge and the research for the reasoning algorithms are progressively improving in the domain of Artificial Intelligence. Besides these, distributed reasoning as a part of data mining has become a solution for the increasing everyday data amount. As a result, the paper proposes a case-based non-monotonic reasoner for uncertain and vague COVID-19 information that is appropriate for work with Big Data. Also, a COVID-19 knowledge base model is proposed. The reasoner implements rules for the distribution of the information that gives the possibility to work with Big data. The proposed reasoning algorithm is applied for COVID-19. It shows the implementation of the reasoner into the data mining system and the returned results from the system are evaluated. The results show that the system returns relatively high results concerning the other system for recommendation.

Knowledge and ICT based networks: towards a taxonomy

Abstract Purpose The article proposes a taxonomy of knowledge-based and information and communication technology (ICT) networks based on a systematic literature review. The specific objectives focus on (1) presenting knowledge-based and ICT network models, indicating their theoretical context (trends); (2) identifying proposals for knowledge-based network models in the literature (based on SLR); (3) developing a taxonomy of knowledge-based network models; and (4) indicating the directions of their development. Design/methodology/approach The systematic literature review (SLR) methodology was used, which allows not only for a formalized and objectified synthesis of the existing scientific achievements or for the evaluation of previous research. In addition, the “snowball” method was used, and its application was supported by the use of the ResearchRabbit tool. In addition, the taxonomy method (typological analysis, typological approach) was used. Findings The results of SLR were presented: a list of selected types of knowledge-based and ICT networks along with a discussion of the results, and the author’s proposals for a taxonomy of knowledge-based and ICT networks were presented along with a synthetic description of the leading models. The directions of their development were also indicated. Practical implications (*if any, optional). The practical usefulness of the proposal contained in the study is related to the possibility of using the proposed concept to evaluate various types of network models, which will enable the formulation of diagnostic findings and conducting comparative and dynamic research in the field of analysis and evaluation of knowledge-based and ICT-based network models. Originality/value The added value of the article is related to the development of a proprietary taxonomy of knowledge- and ICT-based networks and, consequently, to the organization and systematization of models of these networks. This taxonomy is a valuable tool for organizing and categorizing information systematically. When it comes to knowledge and ICT-based networks, a taxonomy can help classify and understand the different types and components involved in starting point division into network models that were created as a result of the virtualization process and models that were created as a result of exchange processes and the flow of intangible assets. The added value is also related to the practical usefulness of the developed taxonomy.

Neurophysiological and psychophysical references for trends in supervised VQA multimodal deep learning: An interdisciplinary meta-analysis

Leading trends in multimodal deep learning for visual-question answering include Multimodal joint-embedding model, multimodal attention-based model, and multimodal external knowledge-based model. Several mechanisms and strategies are used in these models, including representation fusion methods, co-attention mechanisms, and knowledge base retrieval mechanisms. While a variety of works have comprehensively reviewed these strategies, a key gap in research is that there is no interdisciplinary analysis that connects these mechanisms with discoveries on human. As discussions of Neuro-AI continues to thrive, it is important to consider synergies among human level investigations and ANNs, specifically for using AI to reproduce higher order cognitive functions such as multisensory integration. Thus, Present meta-analysis aimed at the reviewing and connecting neurophysiological and psychophysical references to trends in VQA multimodal deep learning, focusing on 1) Providing back-up explanations for why several strategies in VQA MMDL leads to performances that are closer to human level and 2) Using VQA MMDL as an example to demonstrate how interdisciplinary perspective may foster the development of human level AI. The result of the meta-analysis builds connections between several sub-fields: Joint embedding mechanisms and SC neurons, multimodal attention mechanism and the retro-cue effect, and external knowledge base and engram mechanisms.

Knowledge-based circular economics model for sustainable competitiveness: framework development and analysis

AbstractGiven the numerous changes in global consumption and production models that have occurred in recent years, promoting the development of a circular economy and a knowledge economy would appear to be an appropriate shift for sustainable development and competitiveness, yet no previous studies have integrated their adoption for sustainable competitiveness from the business aspects. This study aims to integrate knowledge-based circular economics adoption for sustainable competitiveness. The research conducts a comprehensive literature review and a critical analysis of secondary data from a real-world case. The main results are a framework development of knowledge-based circular economics and a critical analysis of a modified model toward sustainable competitiveness, that addresses the above gap and derives the originality and novelty of the paper. Future research is proposed in which statistical software, such as SPSS and SmartPLS-SEM, would be applied to validate hypotheses.

Enhancing dosimetric practices through knowledge-based predictive models: a case study on VMAT prostate irradiation.

Acquisition of dosimetric knowledge by radiation therapy planners is a protracted and complex process. This study delves into the impact of empirical predictive models based on the knowledge-based planning (KBP) methodology, aimed at detecting suboptimal results and homogenizing and improving existing practices for prostate cancer. Moreover, the dosimetric effect of implementing these models into routine clinical practice was also assessed. Based on the KBP method, we analyzed 25 prostate treatment plans performed using VMAT by expert operators, aiming to correlate dose indicators with patient geometry. The , and of the peritoneal cavity and the and of the rectum and bladder were linked to geometric characteristics such as the distance from the planning target volume (PTV) to the organs at risk (OAR), the volume of the OAR, or the overlap between the PTV and the OAR. In the second phase, the KBP was used in routine clinical practice in a prospective cohort of 25 patients and compared with the 41 patient plans calculated before implementing the tool. Using linear regression, we identified strong geometric predictive factors for the peritoneal cavity, rectum, and bladder (> 0.8), with an average prescribed dose of 97.8%, covering 95% of the target volume. The use of the model led to a significant dose reduction for all evaluated OARs. This trend was most notable for . Significant reductions were also obtained in average doses to the rectum and bladder, and respectively. Based on this model, we reduced the number of plans with OAR constraints above the clinical recommendations from 19% to 8%. The KBP methodology established a robust and personalized predictive model for dose estimation to organs at risk in prostate cancer. Implementing the model resulted in improved sparing of these organs. Notably, it yields a solid foundation for harmonizing dosimetric practices, alerting us to suboptimal results, and improving our knowledge.

Application of Cognitive Information Systems in Medical Image Semantic Analysis

Cognitive information systems create a new class of intelligent systems focused on semantic data analysis tasks. Such systems are based on cognitive resonance processes, which use a knowledge-based perception model, to analyze and semantically classify visual data. Such systems can therefore be used for image analysis and classification, including semantic analysis of medical images, aimed at supporting diagnostic processes and determining the severity of lesions visualized by diagnostic imaging methods. This paper will describe various types of cognitive information systems designed for lesion recognition in selected abdominal and coronary structures, as well as skeletal parts of the human body, made visible by the application of various modalities in medical diagnostic imaging procedures. In this paper, a new generation of cognitive systems will also be described, and when compared to existing systems, will have the ability to perform extended cognitive resonance processes. Inference based on extended resonance inference allows the system to acquire additional knowledge, as well as expand the knowledge base used for semantic analysis. This paper will also propose the implementation of new efficient formal grammars, which increase the efficiency of lesion recognition in selected medical images to over 90%.

Social Impact Assessment in Urban Security Management Projects: A Case Study from Pakistan

Public safety and security management projects are devised to reduce crime, fear and calamities by prevailing law and order to reduce the harm in society. In a certain context, social impact assessment is a novel way to reveal the extent of effectiveness for these projects. This case study presents an Expert System based methodology regarding social impact assessment for two urban security management projects in Pakistan. An Expert System, DoctuS is employed as a tool to build a rule-based model tool using social impact attributes (as variables) from literature and expert knowledge (Author’s own insight as being involved in projects). Case-based reasoning (CBR) method is employed for the measurement of the impact of urban security management KPIs (Key Performance Indicators) in the two case projects. The empirical findings in this case study approaches the impact assessment by quantitative analysis of crime rates in JMP statistical package and Qualitative Expert knowledge-based model in DoctuS. Thereby, the study evaluates the pre and post ante project situations in order to assess the project’s effectiveness and impact on improved urban safety and security. In the first step, the projects are briefly described, followed by the crime rate analysis under the project jurisdictions and rules between attributes are defined. The second step defines the qualitative rule-based model along with the two cases described, and validated in DoctuS tool. Some attributes and rules have changed, and a new concise model is presented. The Expert System compares the project cases and evaluates the social impact assessment based on the defined KPIs. The Expert system validation presents a novel model for social impact assessment of subject urban security management projects. We propose this methodology and model as useful in investigating social impact assessment of projects specifically dealing with urban safety and security management. However, the generalizability of the findings of this case-study based articles need more sophisticated tests as recommendation for the extension to this study.
 
 Received: 31 May 2023 / Accepted: 19 November 2023 / Published: 5 January 2024

Knowledge-based modeling of simulation behavior for Bayesian optimization

Numerical simulations consist of many components that affect the simulation accuracy and the required computational resources. However, finding an optimal combination of components and their parameters under constraints can be a difficult, time-consuming and often manual process. Classical adaptivity does not fully solve the problem, as it comes with significant implementation cost and is difficult to expand to multi-dimensional parameter spaces. Also, many existing data-based optimization approaches treat the optimization problem as a black-box, thus requiring a large amount of data. We present a constrained, model-based Bayesian optimization approach that avoids black-box models by leveraging existing knowledge about the simulation components and properties of the simulation behavior. The main focus of this paper is on the stochastic modeling ansatz for simulation error and run time as optimization objective and constraint, respectively. To account for data covering multiple orders of magnitude, our approach operates on a logarithmic scale. The models use a priori knowledge of the simulation components such as convergence orders and run time estimates. Together with suitable priors for the model parameters, the model is able to make accurate predictions of the simulation behavior. Reliably modeling the simulation behavior yields a fast optimization procedure because it enables the optimizer to quickly indicate promising parameter values. We test our approach experimentally using the multi-scale muscle simulation framework OpenDiHu and show that we successfully optimize the time step widths in a time splitting approach in terms of minimizing the overall error under run time constraints.

Flexible Sensing Enabled Nondestructive Detection on Viability/Quality of Live Edible Oyster.

Environmental and physiological fluctuations in the live oyster cold chain can result in reduced survival and quality. In this study, a flexible wireless sensor network (F-WSN) monitoring system combined with knowledge engineering was designed and developed to monitor environmental information and physiological fluctuations in the live oyster cold chain. Based on the Hazard Analysis and Critical Control Point (HACCP) plan to identify the critical control points (CCPs) in the live oyster cold chain, the F-WSN was utilized to conduct tracking and collection experiments in real scenarios from Yantai, Shandong Province, to Beijing. The knowledge model for shelf-life and quality prediction based on environmental information and physiological fluctuations was established, and the prediction accuracies of TVB-N, TVC, and pH were 96%, 85%, and 97%, respectively, and the prediction accuracy of viability was 96%. Relevant managers, workers, and experts were invited to participate in the efficiency and applicability assessment of the established system. The results indicated that combining F-WSN monitoring with knowledge-based HACCP modeling is an effective approach to improving the transparency of cold chain management, reducing quality and safety risks in the oyster industry, and promoting the sharing and reuse of HACCP knowledge in the oyster cold chain.

Mivar-based route planning simulation model for obstacle-aware autonomous agricultural machinery

Autonomous robot navigation is increasingly becoming an important task that requires solutions. This paper explores the practical application of logical artificial intelligence to address the problem of route planning, using the example of a computer game. Within the scope of this work, a knowledge base model was created, and a new version of the mivar reasoner was developed for integration with Unity. This reasoner allows the activation of logical rules with linear complexity. As a result, a system was developed that processes user input for position and moves an autonomous agent in a virtual environment according to the rules. This work confirms the feasibility of using mivar technologies to improve control systems of autonomous robots in the area of agriculture. The study also emphasizes the adaptability of mivar networks in dynamic environments, demonstrating their ability to effectively process changes in real-time. This research shows enhanced decision-making capabilities and reliable navigation strategies for autonomous agents, setting a precedent for future developments in autonomous digital solutions for agriculture. The results obtained open new prospects for the advancement of technologies in the field of autonomous navigation.

Electoral Recommender System for Indonesian Regional People's Representative Councils (DPRD) Using Knowledge-based and collaborative filtering approach.

General Election is the most prominent political activity in the election of people representatives in democratic countries. In the Information era, the veracity and volume of available information opportunities to inform the decision-making of election candidates. This research outlines the development of a Business Analytics Electoral Recommender System. The system aims to provide recommendations for political candidates based on various factors such as demographics, political preferences, and socio-economic status. We also propose an election candidate's recommender system using a hybrid of collaborative filtering (CF) and a knowledge-based (KB) model. The KB approach used a web crawler to scrape and process relevant information on various internet sources and social media to nominate the representative candidates based on the voter's preferences. We used a hybrid approach of The Cross Industry Standard Process for Data Mining (CRISP-DM) to the web scraping process of unstructured and semi-structured static and dynamic information of representative candidates. The recommendation uses the CF approach based on criteria for informed electoral decision-making. The results from user acceptance test showed the score are 86% for ease of use and 78.5% for the perceived usefulness of the proposed system's key features. The average percentage of behavioral intentions, and attitudes towards the actual use and utilization of the system, was 88.5%, which concludes that the respondents were generally satisfied with the proposed system.

Paving the way for automated factory planning – applying rule-based expert systems to capacity planning

Today, numerous different software systems aid factory planners in their tasks. Nevertheless, due to their lacking interoperability and project-specific approaches, generic support for automated decision-making is still missing. Investigating the state of the art, we conclude that knowledge-based information modeling is needed for decision-making support. However, as the identified approaches of previous works propose no general automation concepts. Therefore, we define the guiding research question as how to model processual domain knowledge for automating factory planning processes. In this paper, we propose a planning assistance on rule-based expert systems. The planning assistance is composed of an ontology-based information model, a planning model consisting of individual planning functions, and a domain-specific inference engine. We implement the planning assistance with Semantic Web technologies and validate the solution using an application example from capacity planning. Thereby, we demonstrate the applicability of rule-based expert systems for automated factory planning. Finally, implications for future research are drawn for exploring further application areas and developing anticipated hybrid solution concepts.

Innovation and Informatization of Civics Teaching Mode in Colleges and Universities under Integrated Transfer Learning Strategies

Abstract This study addresses the problem of predicting student performance by proposing a heterogeneous transfer learning method based on FMBCC-FT, which aims to accurately predict students’ Civics knowledge mastery. The study achieves effective knowledge transfer by defining university Civics teaching as the source domain and considering the university’s demand for students’ Civics dynamics as the target domain. After collecting and standardizing students’ Civics education data, this study constructed a knowledge base model of Civics teaching based on the source domain data and migrated it to the target domain. To compensate for the bias in the migration, the study designed a three-stage iterative optimization migration weight learning method, which significantly improved the quality of Civics teaching by updating the parameter weights of the source domain model. The process was verified through the experimental control group pre and post-tests. The results showed that the total score of critical thinking ability pre and post-tests was −3.39. The students’ innovative consciousness score in the experimental group was 3.96, which was significantly higher than that of the control group, which was 0.18. In addition, the scores of the quality dimensions of the students in the control group and the experimental group ranged from 3.22 to 3.40 and 3.43 to 3.68, respectively. This empirical result effectively confirms that the Civics teaching model proposed in this paper can effectively improve students’ critical thinking ability, innovation consciousness and overall quality, which is of great guiding significance to the current practice of Civics teaching in colleges and universities.

Taxonomy-Informed Neural Networks for Smart Manufacturing

A neural network (NN) is known to be an efficient and learnable tool supporting decision-making processes particularly in Industry 4.0. The majority of NNs are data-driven and, therefore, depend on training data quantity and quality. The current trend in enhancing data-driven models with knowledge-based models promises to enable effective NNs with less data. So-called physics-informed NNs use additional knowledge from computational science to improve NN training. Quite much of the knowledge is available as logical constraints from domain ontologies, and NNs may benefit from using it. In this paper, we study the concept of Taxonomy-Informed NN (TINN), which combines data-driven training of NNs with ontological knowledge. We study different patterns of NN training with additional knowledge on class-subclass hierarchies and instance-class relationships with potential for federated learning. Our experiments show that additional knowledge, which influences TINNs’ training process through the loss function at backpropagation, improves the quality of trained models.

Model generation and comparative analysis of legal knowledge bases

The article deals with the possibilities of computerized comparative legal analysis. Legal do­cuments are formalized using frame logic (F-logic) and are stored in knowledge bases. Their dif­ference is defined as the difference between conclusions made from possible cases. This article presents an elaborated rule of knowledge base model generation designed for F-logic expressions.