Knowledge-based Module Research Articles

The Effectiveness of the Local Knowledge-Based Module (LKBM) to Improve Students’ Scientific Literacy and Thinking Skills

The Effectiveness of the Local Knowledge-Based Module (LKBM) to Improve Students’ Scientific Literacy and Thinking Skills

An automated energy management framework for smart homes

Over the last fifty years, societies across the world have experienced multiple periods of energy insufficiency with the most recent one being the 2022 global energy crisis. In addition, the electric power industry has been experiencing a steady increase in electricity consumption since the second industrial revolution because of the widespread usage of electrical appliances and devices. Newer devices are equipped with sensors and actuators, they can collect a large amount of data that could help in power management. However, current energy management approaches are mostly applied to limited types of devices in specific domains and are difficult to implement in other scenarios. They fail when it comes to their level of autonomy, flexibility, and genericity. To address these shortcomings, we present, in this paper, an automated energy management approach for connected environments based on generating power estimation models, representing a formal description of energy-related knowledge, and using reinforcement learning (RL) techniques to accomplish energy-efficient actions. The architecture of this approach is based on three main components: power estimation models, knowledge base, and intelligence module. Furthermore, we develop algorithms that exploit knowledge from both the power estimator and the ontology, to generate the corresponding RL agent and environment. We also present different reward functions based on user preferences and power consumption. We illustrate our proposal in the smart home domain. An implementation of the approach is developed and two validation experiments are conducted. Both case studies are deployed in the context of smart homes: (a) a living room with a variety of devices and (b) a smart home with a heating system. The obtained results show that our approach performs well given the low convergence period, the high level of user preferences satisfaction, and the significant decrease in energy consumption.

Diagnosis of heart diseases: A fuzzy-logic-based approach.

Cardiovascular diseases (CVD) also known as heart disease are now the leading cause of death in the world. This paper presents research for the design and creation of a fuzzy logic-based expert system for the prognosis and diagnosis of heart disease that is precise, economical, and effective. This system entails a fuzzification module, knowledge base, inference engine, and defuzzification module where seven attributes such as chest pain type, HbA1c (Haemoglobin A1c), HDL (high-density lipoprotein), LDL (low-density lipoprotein), heart rate, age, and blood pressure are considered as input to the system. With the aid of the available literature and extensive consultation with medical experts in this field, an enriched knowledge database has been created with a sufficient number of IF-THEN rules for the diagnosis of heart disease. The inference engine then activates the appropriate IF-THEN rule from the knowledge base and determines the output value using the appropriate defuzzification technique after the fuzzification module fuzzifies each input depending on the appropriate membership function. Moreover, the fusion of web-based technology makes it suitable and cost-effective for the prognosis of heart disease for a patient and then he can take his decision for addressing the problem based on the status of his heart. On the other hand, it can also assist a medical practitioner to reach a more accurate conclusion regarding the treatment of heart disease for a patient. The Mamdani inference method has been used to evaluate the results. The system is tested with the Cleveland dataset and cross-checked with the in-field dataset. Compared with the other existing expert systems, the proposed method performs 98.08% accurately and can make accurate decisions for diagnosing heart diseases.

Validation of Local Knowledge-Based Module (LKBM) as Character Development Modul for Elementary School Students in Kupang, Indonesia

The purpose of the current research was to validate the local knowledge-based module (LKBM) to elementary school student character in Kupang, Indonesia. The participants of the current research were 78 elementary school students in Kupang, Indonesia. This research is quasi-experimental, utilising descriptive analysis and MANOVA to analyse the data. Therefore, the students were divided into a control group (n = 39) and an experiment group (n = 39). Based on the results, the module was valid and reliable to be used as learning material for character education. It was proved through the improvement in the students character development before, during, and after the use of the module. Other than that, MANOVA analysis proved significant in the students character who follow the learning process with the Local Knowledge-Based Module (LKBM) compared to the group of students who use conventional learning. Therefore, it can be concluded that the module can be used in character education learning process.

Integrated Decision Support Framework of Optimal Scaffolding System for Construction Projects

Selecting the appropriate temporary facilities is important for reducing cost and improving the productivity and safety of craft professionals in construction projects. However, the manual planning process for scaffolding systems is typically prone to inefficiencies. This paper aims to develop a knowledge-based framework for a scaffolding decision support system for industry. An integrated two-phase system was established, including a technical evaluation module and a knowledge-based module. First, the system identifies feasible scaffolding alternatives from the database through a rule-based algorithm. Second, a knowledge-based module was designed to assess the alternative performance. The framework effectively generated the ranking of scaffolding alternatives, and the top three influential factors were identified, including the site accessibility, protection to workers and health risk. Thus, an application study of an industrial steel project was proffered to validate the effectiveness of the framework. The proposed framework may help decision-making regarding the implementation of temporary facility planning in industry practices. It has wider applicability because it simultaneously considers site conditions, productivity, safety, and financial benefits, and is designed and implemented through a computerized path. The paper contributes to the industry by developing an integrated decision support system for temporary facilities. Additionally, the practical contribution of this research is the provision of an optimized scaffolding planning method that could be utilized as a guide when implementing the decision support system.

Expert System Hama dan Penyakit Tanaman Bawang Merah dengan Certainty Factor

This study aims to produce an expert system using the Certainty Factor (CF) Method for Diagnosing Pests and Diseases on Shallot Plants. The background of this research is to obtain information about pests and diseases. Expert System or expert system of Shallot Pests and Diseases with Certainty Factor is an expert system developed to identify pests and diseases in shallot plants. The development method used in the development of this system is the waterfall method. In the early stages of development, a needs analysis is carried out with the aim of understanding the requirements of the expert system, including the types of pests and diseases that must be identified, the associated symptoms, and the required expert knowledge. Furthermore, system design is carried out to design the system architecture and rule structure as well as the Certainty Factor weights that will be used in the diagnosis. In applying the certainty factor method, it gets a value of 46% for armyworm pests. After the design is complete, the implementation phase begins. At this stage, the expert system is designed and coded based on the designs that have been made before. The required modules, such as the inference module and the knowledge base module, are developed according to predetermined specifications. After the implementation is complete, the testing phase is carried out. using the black-box testing technique, with a score of 4.0 from the expert and 3.7 from the implementation of the beta user graph with a valid category. This test aims to ensure that the system provides an accurate diagnosis according to the symptoms observed in shallot plants.

Engineering user-centered explanations to query answers in ontology-driven socio-technical systems

The role of explanations in intelligent systems has in the last few years entered the spotlight as AI-based solutions appear in an ever-growing set of applications. Though data-driven (or machine learning) techniques are often used as examples of how opaque (also called black box) approaches can lead to problems such as bias and general lack of explainability and interpretability, in reality these features are difficult to tame in general, even for approaches that are based on tools typically considered to be more amenable, like knowledge-based formalisms. In this paper, we continue a line of research and development towards building tools that facilitate the implementation of explainable and interpretable hybrid intelligent socio-technical systems, focusing on features that users can leverage to build explanations to their queries. In particular, we present the implementation of a recently-proposed application framework (and make available its source code) for developing such systems, and explore user-centered mechanisms for building explanations based both on the kinds of explanations required (such as counterfactual, contextual, etc.) and the inputs used for building them (coming from various sources, such as the knowledge base and lower-level data-driven modules). In order to validate our approach, we develop two use cases, one as a running example for detecting hate speech in social platforms and the other as an extension that also contemplates cyberbullying scenarios.

Integrated carbon footprint with cutting parameters for production scheduling

A large part of the global carbon footprint comes from manufacturing. Apart from material distribution during product assembly, rational optimization of cutting parameters and production scheduling during processing are the main ways to realize low-carbon manufacturing. The existing researches mainly focus on cutting parameters or production scheduling when reducing manufacturing carbon emissions, but seldom considers the coupling relationship between the two. This limits the reduction of emissions to some extent. On this basis, a collaborative low-carbon optimization system suitable for the actual use of enterprises was developed. The optimization of production scheduling is a discrete problem, while cutting parameters are continuous and processing time brought by different cutting parameters will cause different effects on the scheduling scheme. Therefore, a two-stage optimization strategy is adopted to achieve collaborative optimization of cutting parameters and production scheduling. To achieve multi-objective optimization of the model and speed up convergence, the Crowding Niche mechanism and elite retention strategy are introduced into the selection operator to improve the genetic algorithm based on Pareto Optimality. In addition, different from the traditional that need to manually determine the machine tool and cutting tool according to the demand, a knowledge base module is integrated into the system. The knowledge base module is qualified for the knowledge acquisition and instance matching of the model in order to meet the processing capacity and constraints and enhance the ability of intelligent decision-making during production. Finally, the interactive application interface of the collaborative low-carbon optimization system has been implemented and the effectiveness of the system is verified by taking the manufacturing case of electromechanical enterprises as an example.

Knowledge Base component of Intelligent ALMM System based on the ontology approach

This paper presents the implementation of a knowledge base supporting an intelligent system to solve problems of optimization especially problems of discrete production processes optimization called Intelligent Algebraic-Logical Meta-Model (ALMM) Solver. Using a unified description of selected optimization problems, an ontological knowledge base was designed, which allows for selective selection of Intelligent ALMM Solver components necessary to solve and model problems. Using the definitions of the properties of optimization problems, scalable components describing exemplary optimization jobs were selected. Ontology for this area was developed, with particular emphasis on the requirements of the ALMM Solver. Using the possibility of interactive communication with the ALMM ontology in the form of SQL queries in the experimental part of the work, exemplary queries for the designed Knowledge Base (KB) module were presented, and the response generated by the system is a scenario of intelligent selection of a set of components modeling and solving a given problem. Such an innovative approach allows for dynamic construction of algorithms solving problems of discrete optimization. The use of knowledge about the properties of the considered processes and ALMM technology universalizes the proposed KB system making it an intelligent and efficient tool for solving discrete optimization jobs. The key advantage of the proposed ontological approach is the ability to flexibly expand it and extend its use to other classes of problems which have already been described in the ALMM technology.

A Multi-Mode Education Program to Enhance Asthma Care by Pharmacists

Objective. To provide optimal asthma care, community pharmacists must have advanced, contemporary knowledge, and the skills to translate that knowledge into practice. The development and evaluation of an innovative multi-mode education program to enhance pharmacists' clinical knowledge and practical skills is described.Methods. The online education modules were collaboratively developed alongside asthma and pharmacy organizations. The education program was comprised of five evidence-based education modules delivered online and a skills review conducted either in-person with real-time feedback (urban pharmacists) or via video upload and scheduled video-conference feedback (regional and remote pharmacists). A mixed methods approach was used to evaluate the feedback obtained from pharmacists to assess the content, efficacy, and applicability of the education.Results. Ninety-seven pharmacists opted into the program and successfully completed all education requirements. A larger proportion of pharmacists did not pass trial protocol-based education modules on their first attempts compared to the number that passed the asthma and medication knowledge-based modules. Prior to skills review, the proportion of pharmacists demonstrating device technique competency was suboptimal. Pharmacists rated the education modules highly in both quantitative and qualitative evaluations and reported that the program adequately prepared them to better deliver care to asthma patients.Conclusion. We developed, implemented, and evaluated a novel multi-mode asthma education program for community pharmacists that supports knowledge and practical skill development in this crucial area of patient care. The education program was well received by pharmacists. This form of education could be used more broadly in international collaborative trials.

Development and feasibility testing of a training programme for community pharmacists to deliver a culturally responsive medication review intervention

BackgroundCultural differences between health professionals and Indigenous peoples contribute to health inequalities, and effective cross-cultural communication and person-centred healthcare are critical remedial elements. Community pharmacists can play a significant role by reducing medication-related problems through medication reviews, yet barriers to access include cultural and linguistic challenges. The Indigenous Medication Review Service (IMeRSe) aimed to address these barriers via a culturally responsive intervention. The aim of this paper is to present the cross-cultural training framework developed as a component of this intervention and the feasibility evaluation of the first stage of the training framework.MethodsA training framework was developed, emphasising pharmacists’ skills and confidence in effective cross-cultural communication and relationship-building with Indigenous Australians (Please note that the use of the term ‘Indigenous’ in this manuscript includes all Aboriginal and Torres Strait Islander people and acknowledges their rich traditions and heterogenous cultures) across three stages: (1) online and workshop-based, covering Indigenous history and health, cross-cultural communication and a holistic, strengths-based approach to intervention delivery; (2) orientation to local Aboriginal Health Services, community and cultural protocols; and (3) ongoing mentoring. The feasibility evaluation of the first stage included the following: self-reported levels of cultural capability, cultural confidence and skills, motivators and barriers to working with Indigenous Australians, assessed pre- and post-training. Participants completed self-administered questionnaires including a 22-item validated Cultural Capability Measurement Tool. Paired t tests assessed change in mean scores of Likert scale data.ResultsStage 1 development resulted in an 8.5-h standardised cross-cultural training programme tested with 39 pharmacists working across urban and rural/remote Australia. Thirty-six pharmacists completed the feasibility evaluation (75.7% female, all non-Indigenous, 75.7% never attended prior cross-cultural training). Participants reported overall acceptability with training; the majority perceived it added value to their practice. Improved cultural capability post-training was reflected in increased scores for 21/22 items, nine reaching statistical significance. There were significant improvements for all 26 confidence and skills statements, and selected motivational and barrier statements, particularly participants role in improving Indigenous health outcomes and cross-cultural communication.ConclusionsThis study provides preliminary evidence that the training programme was feasible to deliver and prepared pharmacists to deliver a culturally responsive medication review intervention. The online knowledge-based modules and face-to-face workshops provide a standardised framework for larger-scale implementation of the intervention training.Trial registrationAustralia and New Zealand Clinical Trials Registry ACTRN12618000188235.Prospectively registered 22 January 2018.

Design of Psychological Consultation System Based on Weighted Fuzzy Hybrid Algorithm

In order to overcome the problems of low security and high message delay in traditional counseling systems, this article designs and proposes a new counseling system based on the weighted hybrid algorithm. According to the principle of system design, the system architecture diagram and system role are designed. In the hardware part of the system, the user management module, online consultation module, and knowledge base module are designed to realize the psychological consultation function of the system. In the software part, the weighted hybrid algorithm is used to divide the psychological information state of the consultants effectively. Combined with the hardware and software of the system, the overall design of the system is completed. The experimental results show that, compared with the traditional consulting system, the designed system can effectively ensure the information security of consultants and reduce the message delay of the system, which has higher practical application value.

Knowledge, attitude and practice on usage and toxicity of local anaesthetic agents (LA) amongst health care professionals in obstetrics and gynaecology

A sound knowledge of LA and their dosage is essential for effective anaesthesia and patient safety as LA toxicity could be potentially life-threatening and clinicians undertaking LA procedures must be competent in early recognition of toxicity and instigating remedial measures timely. This is a prospective national-level survey using self-administered questionnaires among health care professionals in Obstetrics and Gynaecology primarily based in Worcestershire Acute Hospital NHS Trust and further extended to other regions of the United Kingdom. The survey was focussed on evaluating the knowledge, attitude and practice (KAP) of the common local anaesthetic agents, their dosage, early recognition and management of LA systemic toxicity(LAST). We understood that all groups of health care professionals did not demonstrate adequate knowledge of safe and effective use of LA agents. We thereby propose an accredited knowledge-based learning module, laminated safety information cards for LA use and ensuring availability of Lipid-emulsion therapy in clinical areas. IMPACT STATEMENT What is already known on this subject? Health care professionals using LA should have sufficient knowledge of LA including the required and maximum dose allowance, pharmacokinetic properties, possible complications and management of systemic toxicity. The lack this can trigger potentially fatal and life-threatening complications if misused. Therefore, the key objectives of the use of LA in clinical settings are to optimise pain management while ensuring safe practice. What do the results of this study add? We understood that all groups of health care professionals did not demonstrate adequate knowledge of the safe and effective use of LA agents and appropriate management of its toxicity. This highlights the urgent need for extending awareness among the professionals on the safe administration of LA, early identification and prompt management of LAST. This will improve not only the safety but also the overall patient experience and quality of care. What are the implications of these findings for clinical practice and/or further research? We thereby propose that an accredited knowledge-based module on safe administration and safety concerns of LA must be made compulsory for all those administering LA. Laminated safety information cards on LA with explicit algorithms/guidelines should be made available in all clinical areas to avoid any delays in the management of toxicity. Moreover, all health-care institutions must ensure the availability of Lipid Emulsion Therapy.

Ontology-Based Recommender System for a Learning Sequence in Programming Languages

The same learning process in educational systems could be boring and time consuming for some learners. This problem arises from the lack of personalized learning sequence for learners with different knowledge level. Recommender systems play an important role in assisting the learners to find suitable learning materials and personalized learning sequence. Use of ontology for knowledge representation in knowledge-based recommender systems would facilitate sharing, reuse and common terminology. Since programming concepts have logical relationships among together so, traditional education systems are more stressful and very time-consuming. This paper aims to propose an ontology based recommender system to present a Personalized Learning Sequence in Programming (PLSP) domain which is depended to learner's knowledge level. A recommender module and, the knowledge base module are integrated together in the proposed framework. The recommender module as the main module in the framework, has three stages which is working based on semantic rules and ontology representation. Evaluation of the system was carried out by comparing the non-recommender system (web-based search) using 32 ICT respondents. Results demonstrate that the participants who used the proposed system spent 1119 seconds to find the suitable learning path in comparison to those who used a non-recommender system (3480 seconds) in the same learning material. It means that learners who follow learning path with PLSP, are more suitable for them. Furthermore, the average mean value of usability test is 4.47, (5 maximum scale) which indicates that the system proved to be useful, was easy to use, and satisfied the users.

Database, Registries and Monitoring of Congenital and Genetic Malformation.(Dept.E)

With technical and industrial advances which have become a significant part of today's economic growth, remarkable advances have been made in the development and use of technical capabilities that enhance the endeavor of the medical profession. The developed countries have adopted the essential ''corner stone'' of database and registries development for monitoring birth defects. The use of National and Regional Registries has become ''a must'' to have a database that is necessary for rehabilitation, control and prevention programs. In Saudi Arabia, towards this objective, a study to establish a database and national registry to monitor birth defects and determine frequency, type, distribution and magnitude of the problem has been initiated in coordination with the Department of Human Genetics and the Computer department at the National Research Center aiming at developing an Arab Registry for Congenital and Genetic Malformation. The Prince Center for Disability Research (PSCDR)as well as the outpatient clinic for the human genetics department have developed a comprehensive research plan to study various aspects of handicaps and to assess the present care delivery status and consequently devise the most appropriate and applicable plan of care. Specific forms have been designed or collection of relevant information. The completeness and accuracy of reporting is ascertained by close supervision and adequate training of the team (composed of doctors, nurses and social workers) and a system by which uniformity of diagnosis can be curtained. It is expected that the data derived from this study will establish a regional registries of the handicapped, incorporating congenital and genetic anomalies in Arabs and will provide the basis for further research in prevention and care programs combating the handicap problem. Towards this goal, this paper uses a pediatric knowledge base acquired from medical geneticists and proposes a system containing mainly three modules a knowledge base module, a knowledge processing module, and an interface module. This endeavor concentrates mainly on the genetic symptoms knowledge base representation that allows the knowledge processing module to perform diagnosis advise according to the abnormal features and assist in detecting new syndromes. The system can be extended and gene or the medical problem domains.

A Method for Constructing Automatic Rolling Bearing Fault Identification Model Based on Refined Composite Multi-Scale Dispersion Entropy

In this paper, one of most widely utilized rolling bearings in rotating machinery is selected as the research object. Automatic rolling bearing fault identification model including support vector machine (SVM) training module, fault classification knowledge base module, and fault automatic identification module is proposed. A generalized method for automatic identification of rolling bearing faults based on refined composite multi-scale dispersion entropy (RCMDE) is developed. First, in order to solve the problem of setting the value range of the decomposition level K based on empirical knowledge for variational modal decomposition (VMD), a maximum kurtosis value method is proposed to determine the preset value range of K in whale optimization algorithm. Then, an improved VMD method is used to adaptively decompose the signal into a series of intrinsic mode function components. Next, the correlation coefficient method is employed to screen effective feature components of bearings in different health states for reconstruction. Through theoretical analysis, the calculated RCMDE value of reconstructed signal is screened and input as a feature value into the optimized SVM classifier for fault pattern recognition. The input of rolling bearing vibration data without preprocessing and the output of the fault identification which don't rely on empirical knowledge of external experts is realized. Experimental and engineering case data of rolling bearings under different equipment and operating environments are tested and validated. The results indicate that the model proposed in this paper shows good fault identification, demonstrates good generalization performance, and has beneficial industrial application prospect.

Application of intelligent technologies and neural network modeling methods in the development of a hybrid learning environment

We consider the problems of applying mathematical modeling and intelligent technologies in the development of methods and tools to support educational processes. As a part of creating a hybrid intelligent learning environment, we offer the construction of differential mathematical models with delayed feedback. We characterize a generalized model of the educational process. This model contains a knowledge base module and an interaction module based on artificial intelligence. We study a number of qualitative properties for the dynamic «director–teacher–student» task transfer model with delayed feedback and intelligent components. The described approach to analyzing model properties allows us to consider uncertainties and control actions in hybrid learning environments.

Developing A Thinking Strategy of Understanding Numeracy Among Students of SDH

The article describes the process of developing as well as evaluating the effectiveness of module in identifying levels of numeracy understanding among students in School for Hospitalized Children (SDH). The development process involves the preparation of materials, expert review and content evaluation of the module. During the development process, the module is reviewed by four experts from numerous field of study. Some suggestions has been identified and improvements has been made based on the suggestions provided. The refined module was assessed for accuracy by 20 evaluators, consisting of four experts from the same field of study and 16 SDH teachers. The results showed that the development of modules that were based on needs analysis, theory, literature review and curriculum review plays vital roles in producing a comprehensive module. All the information collected has led to the establishment of more knowledge-based modules. In general it was found that; needs analysis is necessary in order to produce modules that can be used by teachers, the process of developing modules should take into consideration on the combination of several theories in order to produce a comprehensive knowledge-based module, literature review also provides important information in producing child-centered learning activities, curriculum review is crucial in creating teaching and learning activities that meet the curriculum objectives finally, the results of the expert review from various fields of study have provided a wealth of information in theory and practice. Module assessments performed by both experts and users are also essential in producing modules that are well-researched and user-friendly.

Central threat register – a complex system for risk analysis and decision support in railway transport

Risk management is an important element in the operations of each organisation. In order to provide active support of risk analysis and decision making processes, a system was developed, called Central Threat Register. The goal of the system is to collect information about events and threats in the area of railway transport and to support the system users in risk analysis and decision making. The presented system complies with European and Polish requirements for railway transport. It consists of the following basic modules: knowledge base module, data exchange and anonymisation module, threat and risk assessment module, and decision support module. In addition, this study features two cases of the system's application: an analysis pertaining to preventing railway traction breaks due to icing and analysis regarding the prevention of train collisions.

Design framework of adaptive intelligent tutoring systems

The aim of this study is to examine adaptation elements and Intelligent Tutoring System (ITS) elements used in Adaptive Intelligent Tutoring Systems (AITSs), using meta-synthesis methods to analyze the results of previous research. Toward this end, articles appearing in the Web of Science, Google Scholar, Eric and Science Direct databases in 2000 and later were identified with the keyphrase “adaptive intelligent tutoring system.” Application of exclusion and inclusion procedures to the articles accessed in the search resulted in the selection of 32 articles, which were analyzed using meta-synthesis methods and then evaluated in the light of prespecified themes and elements used in AITSs were determined. According to results, the systems were designed for a wide range of fields such as Information Technologies, Mathematics, Science, Medicine, and Foreign Language Education. In these systems, content adaptation was generally used, based mostly on such criteria as feedback, student level, student learning and cognitive styles, and student performance. And besides 4 basic ITS modules (knowledge, student, teaching and user interface), some different modules such as guide module, strategy module, personal learning module, knowledge base module, communication module, system administrator module and messaging module were used. Finally, some suggestions were given for such studies in the future.