Rule-based Knowledge Research Articles

Data-Driven Prediction of Students’ Online Learning Needs and Optimization of Knowledge Library Management

Thanks to the advancement of information technology, online learning has become a crucial tool of modern education, and the management of modern education is facing the challenges of how to effectively predict students’ learning needs and how to optimize the management of the knowledge library to support these needs. However, existing data-driven prediction approaches are flawed in handling complex learning environments and timely adapting to changes, so this study attempts to solve these questions by exploring the correlation between learning needs, the extraction of knowledge linkages, and the optimization of knowledge libraries based on rule updates. In our work, a new method was proposed for extracting learning needs and knowledge linkages to more accurately identify and predict students’ learning needs, and a rule-based knowledge library management optimization method was introduced to allow the knowledge library to more flexibly adapt to students’ learning needs and the changes in educational resources. In this way, this study offers a comprehensive and flexible solution for education management via the combination of these two aspects, which not only increases student satisfaction and improves teaching quality but also reduces resource waste and gives students a more personalized and efficient learning experience. Furthermore, the methods and findings of this study could also be used as references for data-driven decision-making in other fields.

An intelligent telemonitoring application for coronavirus patients: reCOVeryaID.

The COVID-19 emergency underscored the importance of resolving crucial issues of territorial health monitoring, such as overloaded phone lines, doctors exposed to infection, chronically ill patients unable to access hospitals, etc. In fact, it often happened that people would call doctors/hospitals just out of anxiety, not realizing that they were clogging up communications, thus causing problems for those who needed them most; such people, often elderly, have often felt lonely and abandoned by the health care system because of poor telemedicine. In addition, doctors were unable to follow up on the most serious cases or make sure that others did not worsen. Thus, uring the first pandemic wave we had the idea to design a system that could help people alleviate their fears and be constantly monitored by doctors both in hospitals and at home; consequently, we developed reCOVeryaID, a telemonitoring application for coronavirus patients. It is an autonomous application supported by a knowledge base that can react promptly and inform medical doctors if dangerous trends in the patient's short- and long-term vital signs are detected. In this paper, we also validate the knowledge-base rules in real-world settings by testing them on data from real patients infected with COVID-19.

Context-Aware Expert for Software Architecture Recovery (CAESAR): An automated approach for recovering software architectures

Software architecture recovery approaches help to reconstruct the architecture of complex software systems. However, the manual effort and expertise required to use such techniques are high, as the user demands detailed knowledge about software architecture recovery process and preparing additional software artifacts to use as input for such approaches. Such practices lack consideration for the context standards of the recovered software. Additionally, proper module detection rules that are aware of the modeled system’s context should be studied to enhance the cluster quality of the detected modules. We introduce a novel approach to software architecture recovery by implementing an automated knowledge-based rules solution that considers the context of the developed software while identifying and clustering that system’s software modules. Such solution is aided by a novel code distance model that is utilized to enhance the cohesiveness of the recovered software modules, improving the recovered software architecture quality, and enhancing readability through providing visualizations at different abstraction levels for the recovered architecture. The proposed method has been evaluated on two software systems with varying sizes and from different contexts and involved experienced participants. The results demonstrate significant improvements in cohesion and coupling of the produced clusters compared to alternative architecture recovery approaches.

A cooperative coevolutionary algorithm with problem-specific knowledge for energy-efficient scheduling in seru system

With the increasingly prominent issues about the global environment, green manufacturing has become a research hotspot. To cope with the turbulent market environment, seru production system (SPS) as an innovative manufacturing mode has attracted much attention, yet energy consumption is still rarely considered in SPS. This paper addresses an energy-efficient scheduling problem in seru system (EESPSS) with the minimization of energy consumption and makespan simultaneously. It contains three coupled subproblems, i.e., worker-seru assignment, batch-seru assignment, and worker-task assignment. To tackle the problem effectively, we build a mathematical model and design a cooperative coevolutionary algorithm (CCA) with three phases: hybrid initialization, multi-population cooperation exploration with feedback, and knowledge-guided greedy search. Firstly, a knowledge-based rule is designed to produce high-quality initial solutions. Secondly, multi-population cooperation is proposed to achieve subproblem co-optimization. Specifically, two populations are constructed for adjusting worker-seru assignment and batch-seru assignment and an elite population is applied for information sharing by individual migration. Moreover, to enhance algorithm search capability, a feedback strategy is designed for operator selection and population size adjustment. Thirdly, to exploit the nondominated solutions effectively, several properties are derived as the problem-specific knowledge to design a greedy search for the batch-seru assignment and worker-task assignment. Numerical tests and statistical comparisons are carried out, which demonstrate the effectiveness of the specific designs of the CCA and its superiority to the existing algorithms in solving the EESPSS.

Autonomous control model for emergency operation of small modular reactor

Advancements in computer performance and Artificial Intelligence, especially Deep Neural Networks and reinforcement learning, have caught the attention of many researchers. Specifically, the potential for achieving autonomy (a higher cognitive concept than legacy automation systems) has garnered significant interest. Thus, many autonomous systems like self-driving cars, are in the research/ development phase. Despite its numerous advantages, the adoption of autonomous control for Nuclear Power Plants is deficient mainly due to stringent safety concerns. Especially, autonomous control for Small Modular Reactors is a desirous capability to accrue its full potential. This study proposes an AI-based model for autonomous control of IP-200 NPP, a Small Modular Reactor (SMR) under development. The model adopts a modular architecture for performing different functions of monitoring/ diagnosis, strategy formulation and assessment. Despite following knowledge-based rules, the network decisions are optimized through supervised learning and fine-tuned through reinforcement learning. The results demonstrate that the proposed model can handle plant emergencies while ensuring plant safety at optimal performance.

Widening the applicability of most-open-valve (MOV) strategy for aeration control at full scale WWTPs by combining fuzzy-logic control and knowledge-based rules

An adaptation of a pressure-based most-open-valve control strategy to be applied in aeration control systems of any full scale Wastewater Treatment Plant (WWTP) is described in this paper. This control strategy minimises aeration energy consumption by leading the aeration system to work under the minimum pressure level that is possible given the air distribution system design characteristics and the effluent criteria. Several fuzzy-logic-based control loops are used in the different levels of a supervisory control architecture where knowledge-based rules are also applied. Results obtained in the implementation of this control strategy in three WWTPs with different configurations showed the capability of fuzzy algorithms to maintain dissolved oxygen concentration and pressure always close to setpoint values. Average electrical energy savings of 25 %, 22 % and 16 %, where achieved, respectively, in each WWTP, quantified as kWh per kg of removed COD.

Usages of AI by Entrepreneur in Business to Boost their Revenue

Artificial intelligence (AI) refers to what information about the language structure being transmitted to the machine It should affect in a more intuitive and briskly result, grounded on a literacy algorithm that repeats patterns in new data. Artificial intelligence (AI) enables machines to prize, integrate, exchange, and dissect large miscellaneous datasets to answer complex problems in a timely manner. Artificial Intelligence (AI) is a multidisciplinary field whose thing is to automate conditioning that presently bear mortal intelligence. Recent successes in AI include motorized medical differentia and systems that automatically customize tackle to particular stoner conditions. The major problem areas addressed in AI can be epitomized as Perception, Manipulation, logic, Communication, and Learning. Good results are attained in imitating the cognitive process whose several layers of densely connected natural subsystems are steady to numerous input metamorphoses. AI system with a medium for fastening its attention and controlling its searching processes. Generally, AI systems serve grounded on a Knowledge Base of data and rules that characterize the system's sphere of proficiency. The rudiments of a Knowledge Base correspond of singly valid (or at least presumptive) gobbets of information.

Ontology-Based Knowledge Acquisition Method for Natural Language Texts

The main task of knowledge acquisition (also named knowledge extraction) from natural language texts is to extract knowledge from natural language texts into fragment of knowledge base of intelligent system. Through the induction of the related literature about knowledge acquisition at a home country and abroad, this paper analyses the strengths and weaknesses of the classical approach. After emphatically researching the rulebased knowledge extraction technology and the method of building ontology of linguistics, this article proposes a solution to the implementation of knowledge acquisition based on the OSTIS technology. The main feature of this solution is to construct a unified semantic model that is able to utilize ontologies of linguistics (mainly, syntactic and semantic aspect) and integrate various problem-solving models (e. g., rule-based models, neural network models) for solving knowledge extraction process from natural language texts.

Intelligent Fuzzy System to Predict the Wisconsin Breast Cancer Dataset

Decision Support Systems (DSSs) are solutions that serve decision-makers in their decision-making process. For the development of these intelligent systems, two primary components are needed: the knowledge database and the knowledge rule base. The objective of this research work was to implement and validate diverse clinical decision support systems supported by Mamdani-type fuzzy set theory using clustering and dynamic tables. The outcomes were evaluated with other works obtained from the literature to validate the suggested fuzzy systems for categorizing the Wisconsin breast cancer dataset. The fuzzy Inference Systems worked with different input features, according to the studies obtained from the literature. The outcomes confirm that most performance' metrics in several cases were greater than the achieved results from the literature for the output variable for the different Fuzzy Inference Systems-FIS, demonstrating superior precision.

Cases Integration System for Fault Diagnosis of CNC Machine Tools Based on Knowledge Graph

Due to the time-consuming and labor-consuming maintenance required by appealing to service engineers during the diagnosis of CNC machine tool faults, the CNC machine tool fault diagnosis system has become the economical practical choice of intelligent workshop now, which can help CNC machine tool operators quickly locate and eliminate faults. Knowledge graph technology plays a great role in the use of structured data, and has also achieved a certain degree of research progress and good applications in other professional fields. This article focuses on how to extract semi-structured and unstructured fault knowledge from the abundant CNC machine tool fault cases, fault repair manuals and on-site maintenance logs on the market. A complete system is used to integrate this fault diagnosis knowledge with different formats and content but interrelated, so as to realize the structured application of fault diagnosis knowledge of CNC machine tools. This paper first fully analyzes the content and structural characteristics of these fault cases, and optimizes the design of a set of domain ontology for machine tool fault diagnosis, which contains 8 types of ontoly and 7 types of relation. Using the rule-based knowledge extraction method combined with the BiLSTM-CRF information extraction algorithm to extract effective information from this knowledge. Finally, the structured knowledge is stored through the Neo4J graph database for visualization. Under the application of a good ontology, the accuracy and effectiveness of the composite extraction algorithm have been proved to be greatly improved compared with the single information extraction algorithm in the experimental verification process.

Assessing water resources systems’ dynamic resilience under hazardous events via a genetic fuzzy rule-based system

AbstractIn this paper, the use of a novel genetic fuzzy rule-based system (FRBS) is proposed for assessing the resilience of a water resources system to hazards. The proposed software framework generates a set of highly interpretable rules that transparently represent the causal relationships of hazardous events, their timings, and intensities that can lead to the system's failure. This is achieved automatically through an evolutionary learning procedure that is applied to the data acquired from system dynamics (SD) and hazard simulations. The proposed framework for generating an explainable predictive model of water resources system resilience is applied to the Pirot water resources system in the Republic of Serbia. The results indicate that our approach extracted high-level knowledge from the large datasets derived from multi-model simulations. The rule-based knowledge structure facilitates its common-sense interpretation. The presented approach is suitable for identifying scenario components that lead to increased system vulnerability, which are very hard to detect from massive raw data. The fuzzy model also proves to be a satisfying fuzzy classifier, exhibiting precisions of 0.97 and 0.96 in the prediction of low resilience and high rapidity, respectively.

Development of a Computational Model for Cassava Food Processing Using Coloured Petri Net

A food system is composed of a complex network of activities and processes for production, distribution, transportation and consumption, which interact with each other, thus leading to changeable behaviour. Most existing empirical studies on cassava processing have focused on the technical efficiency analysis of the cassava crop processing techniques among processors indicating that the modelling of the events and operations involved in the processing of the cassava crop is highly limited. In this context, different strategies have been used to solve difficult environmental and agro-informatic systems model-based problems such as system dynamics, agent based, rule-based knowledge and mathematical modeling. However, the structural comprehension and behavioral investigation of this modeling are constrained. In this regard, formal computational modeling is a method that enables modeling and simulation of the dynamical characteristics of these food systems to be examined. In this study, the system specification is designed using Unified Modelling language (UML) to show the structural process and system design modelled and simulated using Coloured Petri Net (CPN), a formal method for analyzing the behavioural properties of complex system because of its efficient analysis. For the purpose of observing and analyzing the behaviour of the cassava food process, a series of simulation runs was proposed.

Remaining useful life prediction of lubrication oil by integrating multi-source knowledge and multi-indicator data

Insufficient data restrains the accuracy of the remaining useful life (RUL) prediction for lubricating oil. For this problem, multi-indicator modeling can be an effective solution. However, the unknown coupling effects of the indicators still impose difficulties. To address this issue, a coupling model integrating both knowledge and data is proposed. Originally, multi-source knowledge is embedded to guide both the state characterization and the threshold setting for improving the accuracy of the multi-indicator oil RUL prediction. In particular, the multi-indicator evidences and the knowledge-based rules, are adopted to handle overlapping information and inconsistent decision-making by considering interactions between indicators. A three-layer hierarchical model integrating evidence and rules is firstly established to connect the indicator, attribute, and oil state. Furthermore, the rule-base containing multi-failure modes is constructed to extract the random thresholds from the training samples. Finally, an exponential Wiener stochastic process embedding the oil degradation mechanism is used to describe data fluctuation. After the training stage, the probability density function of RUL is estimated. Finally, the proposed method is validated with four sets of test data from a real-world hydraulic pump bench test.

Research on Industry Data Analytics on Processing Procedure of Named 3-4-8-2 Components Combination for the Application Identification in New Chain Convenience Store

With the rapid economic boom of Asian countries, the president of Country-A has made great efforts to reform in recent years. The prospect of economic development is promising, and business opportunities are emerging gradually, depicting a prosperous scene; accordingly, people’s livelihood consumption also has changed significantly. The original main point of consumption for urban and rural people was the old and traditional grocery store with poor sanitation, but due to the economic improvement, the quality of consumption has also improved, and convenience stores are gradually replacing grocery store. However, convenience store management involves performance, logistic, competition, and personnel costs. Both whether the store can create a net profit and evaluate and select a new store will be important keys that significantly influence business performance. Therefore, this study attempts to use the industry data analysis method for highlighting a concept of processing an experience procedure of named 3-4-8-2 components combination in two stages. First, in the data preprocessing stage, this research considers 22 condition attributes and two types of decision factors, that include net profit and new store selection, and use both techniques of attribute selection and data discretization through the analysis and prediction of data mining tools. Next, in the experiment execution stage, three well-known classifiers (Bayes net, logistic regression, and J48 decision tree) with past good performance and four models (without preprocessing, with attribute selection, with data discretization, and with attribute selection and data discretization) are used for eight different experiments through two data verification methods (percentage split and cross-validation). Conclusively, three key results are identified from empirical analysis: (1) It is found that the prediction accuracy of the J48 decision tree classifier is relatively high and stable among the three classifiers in this study; at the same time, the J48 decision tree can yield comprehensible knowledge-based rules to instruct interested parties. (2) The results of this study show that the important attributes for the net profit decision attribute include the store type, POS number, and cashier number, while the important attributes for the new store selection include the store type and cashier number. (3) There is a difference in the selection of important attributes. Furthermore, four key valuable contributions are addressed from the empirical results, including academic contributions, enterprise contributions, application contributions, and management contributions. It is expected that the direction of store layout expansion can be found and identified through this study, but there are still many risks hidden behind the considerable business opportunities that need to be carefully managed.

Detecting outliers in rule-based knowledge bases using Self-Organizing Map and Local Outlier Factor algorithms

Our research deals with intelligent decision support systems based on rule-based knowledge bases. Decision support systems use rules ”If a condition, then a decision” as a form of knowledge representation. In the process of inference, which mirrors the process of human reasoning, we look for rules that confirm the facts and thus generate new knowledge. Such rule-based knowledge bases can (and often do) contain outlier rules. Our goal is to find such unusual rules. Thanks to this, we can influence the completeness of the knowledge base by finding unusual rules and asking domain experts to supplement knowledge in a rare area. To enhance the effectiveness of decision support systems, we conducted separate investigations into two distinct methods. The first method involved the utilisation of the Local Outlier Factor (LOF) algorithm in detecting rule outliers, while the second method employed the Self-Organizing Maps (SOM) algorithm for the same purpose. Our experiments not only confirmed the effectiveness of both the LOF and SOM algorithms but also involved comparing the results obtained from both methods. The discovery of outlier rules can aid knowledge engineers and domain experts in knowledge exploration and enhance the completeness of the knowledge base, which is crucial for decision support systems.

Edge Detection Method Driven by Knowledge-Based Neighborhood Rules

Edge detection is a fundamental process, and therefore there are still demands to improve its efficiency and computational complexity. This study proposes a knowledge-based edge detection method to meet this requirement by introducing a set of knowledge-based rules. The methodology to derive the rules is based on the observed continuity properties and the neighborhood characteristics of the edge pixels, which are expressed as simple arithmetical operations to improve computational complexity. The results show that the method has an advantage over the gradient-based methods in terms of performance and computational load. It is appropriately four times faster than Canny method and shows superior performance compared to the gradient-based methods in general. Furthermore, the proposed method provides robustness to effectively identify edges at the corners. Due to its light computational requirement and inherent parallelization properties, the method would be also suitable for hardware implementation on field-programmable gate arrays (FPGA).

Monitoring Large Scale Production Processes Using a Rule-Based Visualization Recommendation System

Data visualization plays an important role in the analysis of data and the identification of insights and characteristics within the dataset. Current visualization tools suffer from limitations with respect to the number of dimensions that can be displayed simultaneously. This often results in the inability to highlight hidden patterns or trends, affecting the analysts’ decision-making capabilities. In this scenario, it becomes imperative to propose systems that recommend useful insights to guide users to understand and explore data more efficiently. Addressing this problem, we propose a rule-based visualization recommendation(VizRec) system which caters for the research goal of providing an intelligent assistant that can guide users directly to relevant insights in the data. Our proposed VizRec system automates aspects of visualization design and recommends visualizations incorporating both the data characteristics and the diverse tasks representing users’ goals and intents using a knowledge-based rule engine. To ensure the correctness of knowledge-based rules and the dynamic nature of the rule engine, a formalization theory is proposed. We implemented our proposed model into a working tool for the exploration of complex production data generated from the manufacturing processes for the German excellence cluster “Internet of Production”. Our tool was able to generate recommendations capable of visualizing data insights regardless of the domain it is used in. The existing systems face several shortcomings ranging from the number of dimensions they can handle to the limited number of supported visualizations. The recommended visualizations derived from our proposed system were able to mitigate such challenges by adopting a generic rule-based approach incorporating visualizations handling multidimensional data. We evaluated our system in multiple fields, including the engineering domain, where production systems datasets are used to achieve user-intended tasks and gain valuable insights.

A Reverse Modification Method for Binary Code and Data.

This paper reveals the hidden dangers of reverse data modifications on distributed software with network synchronization, during the era of 5G, which may occur in more important domains, such as telemedicine and automatic driving. We used pseudo-codes to formally elaborate the distributed software architectures and design patterns. It is necessary to deal with three challenges for the modification of binary code and data in the distributed software architectures: (1) the base virtual addresses of software objects are changed frequently for safety; (2) prior knowledge of the reverse is not considered; (3) system memory values of some target objects are changed with extreme speed. For this purpose, a novel reverse modification method for binary code and data is proposed. According to the knowledge-based rules, our method can manipulate physical data, sight data, animation data, etc., while the game synchronization mechanism cannot detect the changes. The implementation details of our method are presented using high-level programming languages (C++) and low-level programming languages (assembly), based on multiple snippets, so that readers can understand both the overall distributed software developments and the corresponding reverse processes. In particular, two network games are used for the demonstrations in this paper. The demonstration results show that our proposed methodology is efficient (as proved by formulas and practices) to manipulate the codes and data of distributed software using a synchronization mechanism.

Who Was Wrong? An Object Detection Based Responsibility Assessment System for Crossroad Vehicle Collisions

Car crashes, known also as vehicle collisions, are recurrent events that occur every day. As long as vehicles exist, vehicle collisions will, unfortunately, continue to occur. When a car crash occurs, it is important to investigate and determine the actors’ responsibilities. The police in charge of that task, as well as claims adjusters, usually process manually by going to the crash spot, collecting data on the field, drafting the crash, and assessing responsibilities based on road rules. This later task of assessing responsibilities usually takes time and requires enough knowledge of road rules. With the aim to support the police and claims adjusters and simplify the process of responsibility determination, we built a system that can automatically assess actors’ responsibilities within a crossroad crash. The system is mainly based on image detection and uses a rule-based knowledge system to assess responsibilities within driving recorders’ videos. It uses the crash video recorded by one of the involved vehicles’ driving recorders as the input data source and outputs the evaluation of each actor’s responsibility within the crash. The rule-based knowledge system was implemented to make the reasoning about responsibility assessment clear and allow users to easily understand the reasons for the results. The system consists of three modules: (a) a crash time detection module, (b) a traffic sign detection module, and (c) a responsibility assessment module. To detect a crash within a video, we discovered that the simple application of existing vehicle detection models would result in wrong detections with many false positives. To solve the issue, we made our proposed model take into account only the collided vehicle, its shape, and its position within the video. Moreover, with the biggest challenge being finding data to train the system’s detection models, we built our own dataset from scratch with more than 1500 images of head-on car crashes within the context of crossroad accidents taken by the driving recorder of one of the vehicles involved in the crash. The experiment’s results show how the system performs in (1) detecting the crash time, (2) detecting traffic signs, and (3) assessing each party’s responsibility. The system performs well when light conditions and the visibility of collided objects are good and traffic lights’ view distances are close.

Ballast Water Dynamic Allocation Optimization for Revolving Floating Cranes Based on a Hybrid Algorithm of Fuzzy-Particle Swarm Optimization with Domain Knowledge

Ballast systems and ballast water dynamic allocation between ballast tanks are very important for ensuring the offshore operation efficiency and safety of the revolving floating crane (RFC). Its modeling and solving have multiple difficulties such as modeling complexity, solving complexity and engineering practicability. Early studies showed that domain knowledge is of great significance for the optimization of the design quality and innovation of such complex engineering issues. By analyzing the coupled operation process characteristics among the floating crane, ship hull and ballast system, a ballast water allocation optimization model based on dynamic programming strategy is established. The domain knowledge of ship ballasting is extracted, and a domain knowledge base of expert experience rules for the ballast water allocation is established. A Fuzzy-Particle Swarm Optimization (FPSO) algorithm is given to obtain the optimal allocation scheme, which uses fuzzy logic inference to process domain knowledge and improve the solving quality. Three different cases are given to illustrate the validity of the proposed model and algorithm by comparing it with other algorithms. The analysis results show that the established optimization method can effectively improve the operation efficiency and reduce the calculation time and the number of ballast tanks involved in allocation, which makes the optimal scheme more suitable for engineering applications.