• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.209-214
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• 2024

This is an extended research paper focusing on the applications of Machine Learing and Artificial Intelligence in virtual learning environment. The world is moving at a fast pace having the application of Machine Learning (ML) and Artificial Intelligence (AI) in all the major disciplines and the educational sector is also not untouched by its impact especially in an online learning environment. This paper attempts to elaborate on the benefits of ML and AI in E-Learning (EL) in general and explain how King Khalid University (KKU) EL Deanship is making the best of ML and AI in its practices. Also, researchers have focused on the future of ML and AI in any academic program. This research is descriptive in nature; results are based on qualitative analysis done through tools and techniques of EL applied in KKU as an example but the same modus operandi can be implemented by any institution in its EL platform. KKU is using Learning Management Services (LMS) for providing online learning practices and Blackboard (BB) for sharing online learning resources, therefore these tools are considered by the researchers for explaining the results of ML and AI.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.16-21
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• 2014

In recent, the maritime traffic has increased with an increase in international trading volumes and the growing popularity of marine leisure activities. As increasing of maritime traffic, marine accidents happened continually and there are possibilities of accidents at sea. According to the analysis of marine accidents, most accidents occurred by human error of seafarers. To reduce the accidents by human error, the various assistance system for assist seafarers have been proposed. It is required to real-time data management method for applying to real-time system, but most proposed assistance system used off-line data for analysis. In this paper, we aim to build a navigation supporting system for providing safety information to deck officer with data of AIS(Automatic Identification System) and ARPA Radar(Automatic Radar Plotting Aids Radar), and proposed a management algorithm for real-time ship information with blackboard system and verified the validity.

• Journal of Korean Society for Quality Management
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• v.32 no.2
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• pp.168-190
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• 2004

In these days of world-wide business environment, the characteristics of quality management are variety, specialty, decentralization, totality, etc. Thus nowadays quality management is demanded to incorporate these new concepts. We propose a web-bused distributed expert system for this purpose. The system consists of four expert systems for design of experiment, acceptance inspection, statistical process control and reliability management corresponding to design quality, incoming-material quality, manufacturing quality and usability quality, respectively, throughout the total product life cycle. Each distributed expert system at the horizontal level in the hierarchy carries out its own quality jobs independently. At the lower level in the hierarchy there is an expert system for measurement analysis to provide reliable data, and at the upper level, an expert system for total quality management to coordinate, integrate and make final decisions. A prototype has been developed and its application is presented.

• Journal of Ship and Ocean Technology
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• v.4 no.4
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• pp.1-18
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• 2000

The paper describes a formalisation of a Design for Safety methodology in an integrated envi-ronment, outlines early developments of a software tool, and presents the results of an appli-cation of the methodology to a case study. The approach adopted attempts link safety per-formance prediction through the utilisation of appropriate technical tools, safety assessment deriving from risk-based methodologies and disparate design activities and issues. Black-board systems have been utilised as the platform in the development of the integrated design environment, allowing safety assessment to become an integral part of the design process. Finally, the case study addresses the application of the developed methodology to three dif-ferent arrangements of a conventional passenger Ro-Ro vessel, with the aim to demonstrate the validity of the process and methodology adopted. The findings are presented and dis-cussed, and recommendations given for the way forward.

• Journal of Mechanical Science and Technology
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• v.15 no.8
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• pp.1108-1118
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• 2001

The purpose of this study is to establish a CAD (Computer Aided Design) system for research and development(R&D) of a new product. In the R&D process of a new product, the design objects are frequently redesigned based on the experimental results obtained with prototypes. The CAD/CAE systems (which is based on computer simulation of physical phenomena) are effective in reducing the number of useless prototypes of a new product. These kinds of conventional CAD/CAE systems do not provide a function to reflect the experimental results to the redesign process, however. This paper proposes a methodology to establish the CAD system, which possesses the engineering model of a designed object in the model database, and refines the model on the basis of experimental results of prototype. The blackboard inference model has been applied to infer model refinement and redesign counterplan by using insufficient knowledge of R&D process of new products.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.2
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• pp.157-185
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• 1998

One of the essential issues in model systems is how to represent and manipulate mathematical modeling knowledge. As the bases of integrated modeling environments, current modeling frameworks have limitations: lack of facility to coordinate different users perpectives and lack of mechanism to reuse modeling knowledge. In this paper, multi-facetted modeling approach is proposed as a basis for the development of integrated modeling environment which provides facilities for (1) independent management of modeling knowledge from individual models； (2) object-oriented conceptual blackboard concept； (3) multi-facetted modeling； and (4) declarative representation of mathematical knowledge. The proposed multi-facetted approach is illustrated using multicommodity transportation models.

• Journal of Intelligence and Information Systems
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• v.1 no.1
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• pp.61-72
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• 1995

Dynamic and explicit ordering of strategies is a key process in modeling knowledge-level problem-solving behavior. This paper addressed the important problem of howl to make the scheduler more knowledge-intensive in a way that facilitates the acquisition, integration, and maintenance of the scheduler control knowledge. The solution a, pp.oach described in this paper involved formulating the scheduler task as a heuristic classification problem, and then implementing it as a classification expert system. By doing this, the wide spectrum of known methods of acquiring, refining, and maintaining the knowledge of a classification expert system are a, pp.icable to the scheduler control knowledge. One important innovation of this research is that of recursive heuristic classification : this paper demonstrates that it is possible to formulate and solve a key subcomponent of heuristic classification as heuristic classification problem. Another key innovation is the creation of a method of dynamic heuristic classification : the classification alternatives that are selected among are dynamically generated in real-time and then evidence is gathered for and aginst these alternatives. In contrast, the normal model of heuristic classification is that of structured selection between a set of preenumerated fixed alternatives.

• Korean Journal of Cognitive Science
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• v.2 no.2
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• pp.309-339
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• 1990

The most common way that people communicate is by speaking or writing natural languages.But if people use computers in the modern technology,they should learn artificial programming languages.If computers could understand what people mean when people speak or type natural languages,people would use the computers more easily and naturally.but there is a problem.The language which people use has vagueness.For example,the convential computer system cant's handle the subjective feeling like 'tall' or 'young'.So peole must specify the exact threshold like 'more'than 25 ages'.We have developed the knowledge-based natural language question answering system which can handle sentences having fuzzy concepts by using blackboard model.Our goal of this research is to develop a portable question answering system as interface for database systems or understanding systems.

• Journal of the Korean Society of Safety
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• v.19 no.1
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• pp.23-30
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• 2004

Modern production systems are very complex by request of automation, and failure modes that occur in thisautomatic system are very various and complex. The efficient fault diagnosis for these complex systems is essential for productivity loss prevention and cost saving. Traditional fault diagnostic system which perforns sequential fault diagnosis can cause catastrophic failure during diagnosis when fault propagation is very fast. This paper describes the Real-time Intelligent Multiple Fault Diagnosis System (RIMFDS). RIMFDS assesses current machine condition by using sensor signals. This system deals with multiple fault diagnosis, comprising of two main parts. One is a personal computer for remote signal generation and transmission and the other is a host system for multiple fault diagnosis. The signal generator generates various faulty signals and image information and sends them to the host. The host has various modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault diagnosis and graphic representation of the results. RIMFDS diagnoses multiple faults with fast fault propagation and complex physical phenomenon. The new system based on multiprocessing diagnoses by using Hierarchical Artificial Neural Network (HANN).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.9
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• pp.2564-2588
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• 2024

Due to COVID19 pandemic, most of educational institutions and schools changed the traditional way of teaching to online teaching and learning using well-known Learning Management Systems (LMS) such as Moodle, Canvas, Blackboard, etc. Accordingly, LMS started to generate a large data related to students' characteristics and achievements and other course-related information. This makes it difficult to teachers to monitor students' behaviour and performance. Therefore, a need to support teachers with a tool alerting student who might be in risk based on their recorded activities and achievements in adopted LMS in the school. This paper focuses on the benefits of using recorded data in LMS platforms, specifically Moodle, to predict students' performance by analysing their behavioural data and engagement activities using data mining techniques. As part of the overall process, this study encountered the task of extracting and selecting relevant data features for predicting performance, along with designing the framework and choosing appropriate machine learning techniques. The collected data underwent pre-processing operations to remove random partitions, empty values, duplicates, and code the data. Different machine learning techniques, including k-NN, TREE, Ensembled Tree, SVM, and MLPNNs were applied to the processed data. The results showed that the MLPNNs technique outperformed other classification techniques, achieving a classification accuracy of 93%, while SVM and k-NN achieved 90% and 87% respectively. This indicates the possibility for future research to investigate incorporating other neural network methods for categorizing students using data from LMS.