• Journal of Fisheries and Marine Sciences Education
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• v.27 no.4
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• pp.1160-1171
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• 2015

The purpose of this study is to design and develop a computer based learning and test system, which supports not only testing learner's ability but also learning contents with giving feedback and hint. In order to design and develop a computer based learning and test system, Visual Basic dot Net software is used. The system works in three stages: sequential problem solving stage, randomized problem solving stage, and the challenge stage of pass/fail. The results of this study are as follows: (a) We propose the context of design for the computer based learning and test system. (b) We design and develop items display function with sequential and random algorithm in this system. (c) We design and develop pass/fail function by applying SPRT(Sequential Probability Ratio Testing) algorithm in the computer based learning and test system.

• Journal of Platform Technology
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• v.6 no.4
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• pp.34-40
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• 2018

Currently, artificial neural networks perform well for a single task, but NN have the problem of forgetting previous learning by learning other kinds of tasks. This is called catastrophic forgetting. To use of artificial neural networks in general purpose this should be solved. There are many efforts to overcome catastrophic forgetting. However, even though there was a lot of effort, it did not completely overcome the catastrophic forgetting. In this paper, we propose sequential iterative learning using core concepts used in elastic weight consolidation (EWC). The experiment was performed to reproduce catastrophic forgetting phenomenon using EMNIST data set which extended MNIST, which is widely used for artificial neural network learning, and overcome it through sequential iterative learning.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.116-122
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• 2023

Sentiment categorization technique be commonly isolated interested in threes significant classifications name Machine Learning Procedure (ML), Lexicon Based Method (LB) also finally, the Hybrid Method. In Machine Learning Methods (ML) utilizes phonetic highlights with apply notable ML algorithm. In this paper, in classification and identification be complete base under in optimizations technique called sequential minimal optimization with Random Forest algorithm (SMORF) for expanding the exhibition and proficiency of sentiment classification framework. The three existing classification algorithms are compared with proposed SMORF algorithm. Imitation result within experiential structure is Precisions (P), recalls (R), F-measures (F) and accuracy metric. The proposed sequential minimal optimization with Random Forest (SMORF) provides the great accuracy.

• Industrial Engineering and Management Systems
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• v.14 no.3
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• pp.318-324
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• 2015

This paper proposes a dynamic short-term load forecasting method that utilizes a new sequential learning algorithm based on Relevance Vector Machine (RVM). The method performs general optimization of weights and hyperparameters using the current relevance vectors and newly arriving data. By doing so, the proposed algorithm is trained with the most recent data. Consequently, it extends the RVM algorithm to real-time and nonstationary learning processes. The results of application of the proposed algorithm to prediction of electrical loads indicate that its accuracy is comparable to that of existing nonparametric learning algorithms. Further, the proposed model reduces computational complexity.

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.43-49
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• 2018

In this paper, we propose a displacement measurement method based on deep learning using image data obtained from tensile tests of a material specimen. We focus on the fact that the sequential images during the tension are generated and the displacement of the specimen is represented in the image data. So, we designed sample generation model which makes sequential images of specimen. The behavior of generated images are similar to the real specimen images under tensile force. Using generated images, we trained and validated our model. In the deep neural network, sequential images are assigned to a multi-channel input to train the network. The multi-channel images are composed of sequential images obtained along the time domain. As a result, the neural network learns the temporal information as the images express the correlation with each other along the time domain. In order to verify the proposed method, we conducted experiments by comparing the deformation measuring performance of the neural network changing the displacement range of images.

• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.477-484
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• 2004

The technique of sequential pattern mining means generating a set of inter-transaction patterns residing in time-dependent data. This paper proposes a new method for generating sequential patterns with the use of Hellinger measure. While the current methods are generating single dimensional sequential patterns within a single attribute, the proposed method is able to detect multi-dimensional patterns among different attributes. A number of heuristics, based on the characteristics of Hellinger measure, are proposed to reduce the computational complexity of the sequential pattern systems. Some experimental results are presented.

• Journal of Internet Computing and Services
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• v.11 no.3
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• pp.25-32
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• 2010

It takes quite amount of time to study a game because there are many game characters and different stages are exist for games. This paper used reinforcement learning algorithm for characters to learn, and so they can move intelligently. On learning early, the learning speed becomes slow. Improved sequential prediction method was used to improve the speed of learning. To compare a normal learning to an improved one, a game was created. As a result, improved character‘s ability was improved 30% on learning speed.

• Journal of Korea Multimedia Society
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• v.13 no.12
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• pp.1820-1831
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• 2010

When interacting with unknown environments, an autonomous agent needs to decide which action or action order can result in a good state and determine the transition probability based on the current state and the action taken. The traditional multiple sequential learning model requires predefined probability of the states' transition. This paper proposes a multiple sequential learning and prediction system with definition of autonomous states to enhance the automatic performance of existing AI algorithms. In sequence learning process, the sensed states are classified into several group by a set of proposed motivation filters to reduce the learning computation. In prediction process, the learning agent makes a decision based on the estimation of each state's cost to get a high payoff from the given environment. The proposed learning and prediction algorithms heightens the automatic planning of the autonomous agent for interacting with the dynamic unknown environment. This model was tested in a virtual library.

• Research in Mathematical Education
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• v.25 no.1
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• pp.1-20
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• 2022

Collaborative learning has been highlighted as an effective method of teachers' professional development in various studies. To disclose teachers' discourse threads in the process of collaborative learning for developing their knowledge, this paper adopted two methods including "content analysis" and "time-sequential analysis" of learning analytics. Such analyses were implemented for mining teachers' updated knowledge and the discourse threads in the discussion during collaborative learning. The materials for analysis involved two aspects: one was from the video-taped lesson observation reports written by teachers before and after discussing, and the other was from their discourses during the discussion process. The results proved that teachers' knowledge for teaching the centroid of a triangle was updated in the collaborative learning period, and also revealed the discourse threads of teachers' collaboration contained "requesting information or opinions", "building on ideas", and "providing evidence or reasoning", with the emphasis on "challenging ideas or re-focusing talk"

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.44-55
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• 2024

High-utility itemset mining (HUIM) is a dominant technology that enables enterprises to make real-time decisions, including supply chain management, customer segmentation, and business analytics. However, classical support value-driven Apriori solutions are confined and unable to meet real-time enterprise demands, especially for large amounts of input data. This study introduces a groundbreaking model for top-N high utility itemset mining in real-time enterprise applications. Unlike traditional Apriori-based solutions, the proposed convolutional sequential embedding metrics-driven cosine-similarity-based multilayer perception learning model leverages global and contextual features, including semantic attributes, for enhanced top-N recommendations over sequential transactions. The MATLAB-based simulations of the model on diverse datasets, demonstrated an impressive precision (0.5632), mean absolute error (MAE) (0.7610), hit rate (HR)@K (0.5720), and normalized discounted cumulative gain (NDCG)@K (0.4268). The average MAE across different datasets and latent dimensions was 0.608. Additionally, the model achieved remarkable cumulative accuracy and precision of 97.94% and 97.04% in performance, respectively, surpassing existing state-of-the-art models. This affirms the robustness and effectiveness of the proposed model in real-time enterprise scenarios.