Knowledge-based System Research Articles

Research on the Development and Innovative Strategy of Knowledge Payment Platform in the Internet Era

The widespread adoption of knowledge-based payment systems and numerous online knowledge-based paid items is made possible by the quick development of knowledge information and networking technology. Paid columns were quickly introduced by platforms like Himalaya FM, Zhihu, and Qingting FM, while pay-for- knowledge businesses like Fenda, iGet, and Qianliao quickly went live online. Users rapidly increased, and it was assumed that the knowledge payment progressive trend had been met. This paper aims to examine the emergence of edge computing into the mobile information system into the presence and inevitability of the knowledge payment platform, evaluate the advantages, challenges, and pathways for knowledge payment platform optimization, and try to provide a conceptual suggestion for aiding in its advancement. During data analysis, the independence of the polynomial characteristics was evaluated using Pearson's chi- squared tests. A brand-new meta-heuristic optimizer dubbed Harris Hawks optimization is inspired by how Harris hawks seek food in the wild. According to the experimental results, 98 of the survey's respondents—or 19.1% of all respondents—were under the age of 18; 201 were between the ages of 18 and 29; 142 were between the ages of 30 and 39; and 73 were over 40, or 14.2% of all respondents. The findings indicate a younger age distribution for the sample, with the concentration being highest among those between the ages of 18 and 29, then 30 to 39. The mobile information system edge-based knowledge payment platform has successfully undergone continuous use behavior analysis.

Strategic concept paper for bioeconomy in Poland: executive summary.

The main objective of the proposed approach is to accelerate the transition of the bioeconomy towards a knowledge-based sustainable system, covering key biobased sectors strongly linked to agriculture in Poland, in line with the European Green Deal. The proposed model of a bioeconomy development strategy, with a special focus on agriculture, is based on two pillars: (1) strengthening traditional, relevant (in terms of economic indicators) sectors of the economy and improving their 'sustainability' by implementing the proposed transformation pathways; and (2) developing economic activities or 'niche or novel sectors' that are prospective accelerators of change in the face of climate challenge, and ensuring their upscaling. This approach forms the basis for policy planning at national and regional level in these European Union (EU) countries, where bioeconomy development strategies have been initiated through dialogue between science, administration and industry stakeholders. The strategic actions we propose are grouped in three areas; (1) Market intervention mainly by introducing sustainability criteria for the national production system; (2) Research, innovation and education that significantly strengthen the relationship between business and science and educational activities in the field of sustainability and climate change, and (3) Governance and policy actions to enforce the relationship between the main sub-sectors of bioeconomy and niche sectors in frame of bio-economy strategy or action plan that will profits by added value of products from bioeconomy sectors and increasing number of newly created jobs.

Mitigating Tacit Knowledge Loss in South African State-Owned Companies: HRM Approach

This study is an interdisciplinary research that sought to explore knowledge loss risks interdependently in relation to the knowledge management (KM) and human resource management (HRM) disciplines in South African state-owned companies (SOCs). This study highlighted the significant role of knowledge-based HRM systems, which will naturally enhance the management capacity of tacit knowledge to mitigate risks and assist SOCs to deliver on a developmental mandate, as well as ensure their survival and the economic growth of developing nations. The paper aims to explore the connection between KM and HRM practices for the effective management of enterprise tacit knowledge loss risks using the SOCs of South Africa as a case. This study is a mixed methods research project, using the exploratory sequential design as a research strategy. Data were collected qualitatively through interviews with 20 purposively nominated human resource managers, and survey data was collected randomly from 25% (145 of the 585 responses) of the workers employed in SOCs. Qualitative data were analysed thematically using Atlas.ti and Statistical data were analysed using SAS and exploratory factor analysis. The relationship between HRM and KM practices in lessening the risks inherent in tacit knowledge loss, mainly from voluntary turnover, is underdeveloped in many South African SOCs. The study established that the role of HRM in supporting KM is limited and underdeveloped in many SOCs, largely because they are lagging in key structures and knowledge-centric cultures, behaviours and HRM practices to drive the effective management of company knowledge. This study assists HRM executives and KM practitioners to develop knowledge-driven HRM practices in order to mitigate risks from the loss of tacit knowledge, whilst ensuring sustained performance in public enterprises. The study and proposed strategies seek to influence the discourse, policies, practices and theories on KM and HRM praxis in companies to mitigate the risks associated with the loss of tacit knowledge.

A Fused Deep Fuzzy Neural Network Controller and Its Application to Pneumatic Flexible Joint

With multilayered deep neural networks (DNNs) distinguished from the traditional single-layer neural networks (SNNs) by the depth, deep learning is a powerful paradigm possessing the capability of feature learning from big data. However, as a completely deterministic model, deep learning does not perform well in dealing with uncertain information and must be pretrained before being applied, which hinders the broad application of deep learning in real-time control. In this article, we introduce the knowledge-based fuzzy logic system (FLS) into deep learning and propose a fused deep fuzzy neural network (FDFNN) controller. The FDFNN controller derives the control signal from both deep fuzzy neural networks and the knowledge-based FLS. At the beginning of control, the FDFNN controller performs like a traditional fuzzy logic controller (FLC), which eliminates the pretraining procedure of deep learning. With another FLS providing supervised signal, the network weights of DFNN's hidden layers can be updated during the control process, which endows the FDFNN controller with online learning capability. A Lyapunov-based convergence analysis is conducted to guarantee the stability of the learning procedure. Both numerical simulations and real-world experiments validate that the proposed method effectively improves the real-time tracking control performance of a pneumatic flexible joint system.

Optimization deep learning with rough set approach model classification Otitis

<span>Otitis is a disease that occurs in the middle ear in the form of inflammation. This research aims to develop an analysis model for the classification of Otitis disease based on knowledge patterns based on symptoms and type of disease. The analysis methods used include the performance of the certainty factor (CF), rough set (RS), artificial neural network (ANN), and decision tree (DT) methods. CF and RS performance can be used to generate classification rule patterns. These rule patterns become new knowledge in the classification analysis process using the concept of deep learning (DL). DL analysis with ANN and DT performance can work optimally in exploring and discovering hidden knowledge. Based on the results of performance testing, the combination of CF and RS in preprocessing can present a classification pattern of 106 rules. The output of DL analysis results is proven to produce precise and accurate classification results with an accuracy of 89%. Based on these results, the analytical model developed was proven to be effective in classifying Otitis disease. Not only that, this research is also able to contribute to updating the knowledge-based system in the classification process.</span>

Human activity mining in multi-occupancy contexts based on nearby interaction under a fuzzy approach

Multioccupation encompasses real-life environments in which people interact in the same common space. Recognizing activities in this context for each inhabitant has been challenging and complex. This work presents a fuzzy knowledge-based system for mining human activities in multi-occupancy contexts based on nearby interaction based on the Ultra-wideband. First, interest zone spatial location is modelled using a straightforward fuzzy logic approach, enabling discriminating short-term event interactions. Second, linguistic protoforms use fuzzy rules to describe long-term events for mining human activities in a multi-occupancy context. A data set with multimodal sensors has been collected and labelled to exhibit the application of the approach. The results show an encouraging performance (0.9 precision) in the discrimination of multiple occupations.

Diagnostic clinical decision support based on deep learning and knowledge-based systems for psoriasis: From diagnosis to treatment options

Psoriasis is an acute immuno-dermatological disease, affecting people of all ages, which significantly decreases quality of life. While the standard approach to identification and diagnosis of psoriasis is based on dermatologist decisions, various Deep Learning (DL) methods have been utilized to create Computer-Aided Diagnosis (CAD) systems to detect and classify psoriasis cases. In response to the knowledge gap of an existing practical and functional DL-based solution to psoriasis diagnosis, this study proposed an ensemble Convolutional Neural Network (CNN) model using Residual Network 50 Version 2 (ResNet50V2), ResNet101V2, and ResNet152V2 networks to create a CAD system for detecting and classifying psoriatic images. This ensemble model determines whether an input image is psoriatic using a binary classification procedure in the initial stage and classifies the psoriatic images into seven variants utilizing a multi-class classification. Furthermore, a treatment suggestion system was embedded within the diagnostic algorithm to suggest the best treatment options for psoriasis variants using a Multi-Criteria Decision Making (MCDM) method with the aim of reducing the disease symptoms in patients. A web-based Decision and Diagnostic Support System (D&DSS) is constructed to determine whether an input image is psoriatic, classify the psoriatic images into different variants, and accordingly recommend the best treatment options based on the observed disease symptoms in a patient. Nevertheless, the functionality and reliability of the proposed D&DSS are validated with high accuracy rates in both diagnostic and identification stages of the approach, which ratifies the practicality of this proposition.

Models and algorithms for the formation of an event tree used in analysing the quality of system software for knowledge-based intelligent decision support systems

A formal statement of the problem of improving the quality of software for intelligent systems under unfavourable circumstances is made. Its belonging to the class of problems of the calculus of variations on a conditional extremum is established. A ranked approach based on heuristic algorithms to solve this scientific problem is proposed and substantiated.

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.

Machine learning and knowledge engineering for cognitive memory assessment of age groups by anomalies in a serious game

In psychology, clinical instruments are often used for assessing the cognitive domains and detecting mental alterations. Nevertheless, after the screening stage, the recommendation of activities for training or new assessments could take a long time. Other instruments such digital serious games are commonly used to train cognitive domains, unlike classical instruments, can collect extra data on players' behavior without altering a person's state. In the other hand, decision-making tools are based mainly on two techniques, machine learning for revealing hidden data patterns, and knowledge engineering for building knowledge-based systems. However, the synergy among these both techniques is not completely leveraged, otherwise, when it is, this intelligent data analysis allows the creation of hypotheses and validate contradictions. In this context, this paper presents a tool for supporting the cognitive memory assessment of people based on machine learning and knowledge engineering (M&K-CogMem). The source of data is a matching game for memory training, and after that, this tool determines the memory status of players among their respective age groups, it based on the scores and times in the game. In case of detecting a cognitive memory alteration, it recommends activities for memory improvement through its inference engine. Testing its adoption in industry, an empirical evaluation carried out by psychologists through a technology acceptance model showed positive indicators.

Can ChatGPT provide appropriate meal plans for NCD patients?

ObjectivesDietary habits significantly affect health conditions and are closely related to the onset and progression of non-communicable diseases (NCDs). Consequently, a well-balanced diet plays an important role in lessening the effects of various disorders, including NCDs. Several artificial intelligence recommendation systems have been developed to propose healthy and nutritious diets. Most of these systems use expert knowledge and guidelines to provide tailored diets and encourage healthier eating habits. However, new advances in large language models such as ChatGPT, with their ability to produce human-like responses, have led individuals to search for advice in several tasks, including diet recommendations. This study aimed to determine the ability of ChatGPT models to generate appropriate personalized meal plans for patients with obesity, cardiovascular diseases, and type 2 diabetes. MethodsUsing a state-of-the-art knowledge-based recommendation system as a reference, we assessed the meal plans generated by two large language models in terms of energy intake, nutrient accuracy, and meal variability. ResultsExperimental results with different user profiles revealed the potential of ChatGPT models to provide personalized nutritional advice. ConclusionAdditional supervision and guidance by nutrition experts or knowledge-based systems are required to ensure meal appropriateness for users with NCDs.

Healthcare information management and operational cost performance: empirical evidence.

Healthcare knowledge management systems can mitigate hospitals' operational inefficiency. As a healthcare information technology, the electronic health record (EHR) receives much attention from medical institutions due to its considerable impact on operational cost performance. This paper focuses on EHR systems to address operational inefficiency by which patients pay more for health care services, and many U.S. hospitals are filing for bankruptcy. From the theoretical perspective of the practice-based view, this paper introduces a path to implement EHR systems for improving cost performance. The empirical investigation is archival data of 200 hospitals collected from the U.S. healthcare agencies. Findings contribute to prior work by hypothesizing moderating and mediating roles in EHR systems implementation. This paper introduces absorptive capacity and monitoring mechanisms as enablers of implementing EHR systems. The results showed that hospital monitoring strengthens the relationship between absorptive capacity and electronic health record systems implementation, which results in better operational cost performance. Theoretically, this study supports the long-term potential benefits of EHR adoption, and its findings are consistent with optimizing efficiency through data standardization and interoperability. From a practical perspective, this study supports hospitals' investments in evolving healthcare information technology systems through the development of a knowledge-based system employing EHR, particularly when hospitals are merging or need a financial strategic plan to control expenses.

Semantic configuration model with natural transformations

In the present work, efforts have been made to create a configuration-based approach to knowledge extraction. The notion of granularity is developed, which allows fine-tuning the expressive possibilities of the semantic network. As known, the central issues for knowledge-based systems are what’s-in-a-node and what’s-in-a-link. As shown, the answer can be obtained from the functor-as-object representation. Then the nodes are functors, and the main links are natural transformations. Such a model is applicable to represent morphing, and the object is considered as a process, which is in a harmony with current ideas on computing. It is possible to represent information channels that carry out the transformations of processes. The possibility of generating displaced concepts and the generation of families of their morphs is shown, the evolvent of stages of knowledge and properties of the process serve as parameters.

A Rule-Based Expert Advisory System for Restaurants Using Machine Learning and Knowledge-Based Systems Techniques

A healthy diet and daily physical activity are a cornerstone in preventing serious diseases and conditions such as heart disease, diabetes, high blood pressure, and hypertension. They also play an important role in the healthy growth and cognitive development for young and old people. Thus, this paper presents a new restaurant advisory system (RAS) using artificial intelligence (AI) techniques such as machine learning, decision tree, and rule-based methods. The proposed system makes a smart decision based on the user's input information to generate a list of appropriate meals that fit his/her health condition. For accuracy and efficiency measurement procedure in the decision-making process, a dataset from 1100 participants suffering from several diseases such as allergy, age, and body has been created and validated. The performance of the RAS was tested using Visual Basic.net Framework and prolog language. The RAS achieves an accuracy of 100% by testing 30 different live cases.

Application of fuzzy analytic hierarchy process – multi-layer fuzzy inference system in product design evaluation

To enhance profitability of production cycle, any manufacturer needs effective product design and evaluation procedures. This study proposed a novel approach combining fuzzy analytic hierarchy process (FAHP) and multi-layer fuzzy inference system (MFIS). It is based on consumer online comments to improve product design. This method possesses several advantages over traditional design evaluation methods. It can quickly acquire consumer preferences, effectively handle multi-criteria decision problems and integrate uncertain and fuzzy information. The Fuzzy Analytic Hierarchy Process–Multi-layer Fuzzy Inference System (FAHP-MFIS) involves the following steps: screening of factors, hierarchical modeling, quantification of qualitative factors, and conversion of these factors into quantitative values. It is a knowledge-based system that uses logical rules. The quantity and levels of input variables directly correlates with the quantity of logical rules. However, with multi-factor and multi-level inference, the establishment of a rule base becomes impractical due to the overwhelming number of rules. To address this issue, the Taguchi orthogonal table is applied to reduce the number of logical rules. Taking a household oxygen generator for medical devices as an example, the proposed model is applied in real-time. In the first stage, web crawlers are used to collect user reviews of the household oxygen generators on large e-commerce platforms. Latent Dirichlet Allocation (LDA) models are used to screen for principal and sub-factors in the second stage. Then, sub-factors of the FAHP screening are used as inputs, and the principal factors are used as outputs. In the third stage, priority indicators are established based on principal factors such as Appearance, Basic Function, and Advanced Function. Established evaluation models are then used to rank the selected designs. The results show that the higher the priority index value of the product design scheme, the better the scheme, and vice versa. This study holds significant reference value in aiding enterprises to enhance the efficiency of their manufacturing cycle and determining the direction of product design and innovation with improved pace and accuracy. Moreover, it can be applied to other fields such as supply chain management, risk assessment, and investment decisions.

Artificial Intelligence Methods for the Construction and Management of Buildings.

Artificial intelligence covers a variety of methods and disciplines including vision, perception, speech and dialogue, decision making and planning, problem solving, robotics and other applications in which self-learning is possible. The aim of this work was to study the possibilities of using AI algorithms at various stages of construction to ensure the safety of the process. The objects of this research were scientific publications about the use of artificial intelligence in construction and ways to optimize this process. To search for information, Scopus and Web of Science databases were used for the period from the early 1990s (the appearance of the first publication on the topic) until the end of 2022. Generalization was the main method. It has been established that artificial intelligence is a set of technologies and methods used to complement traditional human qualities, such as intelligence as well as analytical and other abilities. The use of 3D modeling for the design of buildings, machine learning for the conceptualization of design in 3D, computer vision, planning for the effective use of construction equipment, artificial intelligence and artificial superintelligence have been studied. It is proven that automatic programming for natural language processing, knowledge-based systems, robots, building maintenance, adaptive strategies, adaptive programming, genetic algorithms and the use of unmanned aircraft systems allow an evaluation of the use of artificial intelligence in construction. The prospects of using AI in construction are shown.

An Expert Knowledge-Based System to Evaluate the Efficiency of Dry Construction Methods

The issues that the construction sector currently faces with regard to productivity and efficiency are well acknowledged. In the construction industry, there is plenty of space for efficiency to improve, with an increasing number of new tools and methods coming out. One of the solutions to increase efficiency is the application of modern methods of construction. The modern methods of construction, especially dry construction techniques, are developing so that there is a larger volume of high-quality production with a shorter time for procurement. Not only in the construction of skeletons but also in the finishing works, it is a huge advantage if there are implemented techniques that eliminate traditional wet construction works and thus shorten the construction time. On the other hand, however, the question of efficiency in relation to their costs is raised. Based on theoretical and empirical research, the aim of this study is to demonstrate the potential of modern dry construction systems and solutions for finishing works, especially in relation to the construction time and construction cost. For this purpose, an expert knowledge system, named the complex COMBINATOR, was developed. Through a set of simulations with the help of the COMBINATOR, the effects of different combinations of dry construction systems and techniques (DCSTs) and traditional wet construction systems and techniques (WCSTs) on the time and cost of finishing construction works were measured. Based on the results of simulations carried out through the complex COMBINATOR with an inference engine that enabled these simulations, the potential of dry construction techniques for the implementation of finishing works in the construction of residential buildings was demonstrated. Without simulating the effects of the individual technological models for finishing construction works in relation to two of the most important parameters of construction projects, namely time and cost, it would not be possible to obtain the resulting parameters for different combinations of DCSTs and WCSTs from the study presented. Therein lies the huge importance of the presented knowledge system for deciding on the benefits of DCSTs.

AN EXPERT SYSTEM FOR DIAGNOSING INFANTS’ DISEASES USING CLIPS.

This paper proposes a knowledge-based system to help parents diagnose some of their children's diseases at an early stage and to give them insight into whether they should see a doctor. It covers the most common diseases encountered by pediatric specialists, such as intestinal obstruction, dehydration, anemia, etc. An overview of these diseases is provided, the etiology of the disease is outlined, and treatment suggestions for the most common diseases are given when possible. CLIPS was used to develop the proposed expert system.

Development of a knowledge-based healthcare-associated infections surveillance system in China

BackgroundIn the modern era of antibiotics, healthcare-associated infections (HAIs) have emerged as a prominent and concerning health threat worldwide. Implementing an electronic surveillance system for healthcare-associated infections offers the potential to not only alleviate the manual workload of clinical physicians in surveillance and reporting but also enhance patient safety and the overall quality of medical care. Despite the widespread adoption of healthcare-associated infections surveillance systems in numerous hospitals across China, several challenges persist. These encompass incomplete coverage of all infection types in the surveillance, lack of clarity in the alerting results provided by the system, and discrepancies in sensitivity and specificity that fall short of practical expectations.MethodsWe design and develop a knowledge-based healthcare-associated infections surveillance system (KBHAIS) with the primary goal of supporting clinicians in their surveillance of HAIs. The system operates by automatically extracting infection factors from both structured and unstructured electronic health data. Each patient visit is represented as a tuple list, which is then processed by the rule engine within KBHAIS. As a result, the system generates comprehensive warning results, encompassing infection site, infection diagnoses, infection time, and infection probability. These knowledge rules utilized by the rule engine are derived from infection-related clinical guidelines and the collective expertise of domain experts.ResultsWe develop and evaluate our KBHAIS on a dataset of 106,769 samples collected from 84,839 patients at Gansu Provincial Hospital in China. The experimental results reveal that the system achieves a sensitivity rate surpassing 0.83, offering compelling evidence of its effectiveness and reliability.ConclusionsOur healthcare-associated infections surveillance system demonstrates its effectiveness in promptly alerting patients to healthcare-associated infections. Consequently, our system holds the potential to considerably diminish the occurrence of delayed and missed reporting of such infections, thereby bolstering patient safety and elevating the overall quality of healthcare delivery.

Harnessing Knowledge-Based Concepts for Streamlined Mind Mapping in Academic Study

The main goal of this research project is to build and implement a knowledge-based system that will produce more accurate and significant mind maps from textual input. This study suggests the creation of a knowledge-based system devoted to mind map construction, as opposed to current text mining algorithms and machine learning techniques, which might not be able to properly capture the semantic subtleties of input material. The suggested system aims to reduce the constraints of conventional techniques by incorporating domain-specific information and rules into the analytical process. The accuracy and relevance of the produced mind maps are projected to improve with the addition of this knowledge. This project's main objective is to build a knowledge-based framework that overcomes the limitations of the existing text mining and machine learning approaches, resulting in more sophisticated and theoretically rich mind maps from textual data.