• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.526-538
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• 2021

Machine and deep learning-based models are emerging techniques that are being used to address prediction problems in biomedical data analysis. DNA sequence prediction is a critical problem that has attracted a great deal of attention in the biomedical domain. Machine and deep learning-based models have been shown to provide more accurate results when compared to conventional regression-based models. The prediction of the gene sequence that leads to cancerous diseases, such as prostate cancer, is crucial. Identifying the most important features in a gene sequence is a challenging task. Extracting the components of the gene sequence that can provide an insight into the types of mutation in the gene is of great importance as it will lead to effective drug design and the promotion of the new concept of personalised medicine. In this work, we extracted the exons in the prostate gene sequences that were used in the experiment. We built a Deep Neural Network (DNN) and Bi-directional Long-Short Term Memory (Bi-LSTM) model using a k-mer encoding for the DNA sequence and one-hot encoding for the class label. The models were evaluated using different classification metrics. Our experimental results show that DNN model prediction offers a training accuracy of 99 percent and validation accuracy of 96 percent. The bi-LSTM model also has a training accuracy of 95 percent and validation accuracy of 91 percent.

• The KIPS Transactions:PartB
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• v.13B no.6 s.109
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• pp.615-624
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• 2006

The objective of this paper is to implement parallel multi-layer ANN(Artificial Neural Network) simulator based on the mobile agent system which is executed in parallel in the virtual parallel distributed computing environment. The Multi-Layer Neural Network is classified by training session, training data layer, node, md weight in the parallelization-level. In this study, We have developed and evaluated the simulator with which it is feasible to parallel the ANN in the training session and training data parallelization because these have relatively few network traffic. In this results, we have verified that the performance of parallelization is high about 3.3 times in the training session and training data. The great significance of this paper is that the performance of ANN's execution on virtual parallel computer is similar to that of ANN's execution on existing super-computer. Therefore, we think that the virtual parallel computer can be considerably helpful in developing the neural network because it decreases the training time which needs extra-time.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.116-124
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• 2022

Information and communication technologies used in the social sphere are born due to the development of computer technologies. The main task of the distance learning process in higher education institutions is not to provide information, but to teach how to obtain and use it. The purpose of the article: to identify innovative approaches in the training of specialists in higher education institutions in the context of distance learning. Various innovative approaches to organizing the work of students of higher educational institutions in the context of distance learning are considered. Based on the conducted research, it is concluded that each of the approaches described by us outlines the study of the phenomenon of professional training of a specialist in the condition of distance learning. All the described approaches significantly contribute to the improvement of professional training of specialists, encourage students to self-improvement, professional development and enrich their professional competence in modern conditions. The emergence and spread of innovative technologies means not only a change in the activity itself and its inherent means and mechanisms of its implementation, but also a significant restructuring of goals, value orientations, specific knowledge, skills and abilities. Therefore, the current stage of the development of civilization, scientific and technological progress requires the emergence of such specialists who would have broad humanitarian thinking, would have good psychological training, would be able to build professional activities according to laws that take into account the relationship between economic productivity and creativity, as well as the desire of the individual for constant renewal, self-realization. Only such qualities will help you master the specifics of innovative technologies well. We see the prospects in the study of innovative approaches to training specialists in higher education institutions in the condition of distance learning in foreign countries.

• Journal of Korea Multimedia Society
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• v.14 no.12
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• pp.1517-1527
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• 2011

This paper presents a motion capture based rehabilitation training system for a lower-limb paretic patient. The system evaluates the rehabilitation status of the patient by using the bend posture of the knees and the weight balance of the body. The posture of both legs is captured with a single camera using the planar mirror. The weight distribution is obtained by the Wii Balance Board. Self-occlusion problem in the tracking of the legs is resolved by using k-nearest neighbor based clustering with body symmetry and local-linearity of the posture data. To do this, we present data normalization and its symmetric property in the normalized vector space.

• Journal of Power Electronics
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• v.11 no.6
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• pp.922-930
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• 2011

In this paper, two radial basis function neural networks (RBFNNs) are used to dynamically identify harmonics content in converter waveforms based on the p-q (real power-imaginary power) theory. The converter waveforms are analyzed and the types of harmonic content are identified over a wide operating range. Constant power and sinusoidal current compensation strategies are investigated in this paper. The RBFNN filtering training algorithm is based on a systematic and computationally efficient training method called the hybrid learning method. In this new methodology, the RBFNN is combined with the p-q theory to extract the harmonics content in converter waveforms. The small size and the robustness of the resulting network models reflect the effectiveness of the algorithm. The analysis is verified using MATLAB simulations.

• Journal of the Korea Society of Computer and Information
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• v.27 no.4
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• pp.153-160
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• 2022

In this paper, we propose the relationship between self-determination, home training satisfaction, and reuse of police-related students who participate in home training and train their physical strength. The subjects of this study were students enrolled in the Department of Police Administration and Police Martial Arts at D, S, and K universities in Busan and Gyeongnam area. Among them, 349 students who had participated in home training were surveyed. As a result of verifying the relationship between self-determination, exercise satisfaction, and reuse factors using the collected data, SPSSWIN VER 25+, AMOS 20.0 program, First, autonomy, competence, and relationship, which are sub-factors of self-determination of home training participants, had a positive effect on home training satisfaction. Second, exercise satisfaction of home training participants had a positive effect on home training reuse. Based on the research results,It is essential to identify and manage what home training participants demand. It is believed that this can be a positive process for the development of home training.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.15 no.10 s.89
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• pp.996-1004
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• 2004

1-Dimensional Cluster-Based Sequence Equalizer(1-D CBSE) lessens computational load, compared with the classic maximum likelihood sequence estimation(MLSE) equalizers, and has the superiority in the nonlinear channels. In this paper, we proposed an algorithm of searching for optimal training sequence that estimates the cluster centers instead of time-varying multipath fading channel estimation. The proposed equalizer not only resolved the problems in 1-D CBSE but also improved the bandwidth efficiency using the shorten length of taming sequence to improve bandwidth efficiency. In experiments, the superiority of the new method is demonstrated by comparing conventional 1-D CBSE and related analysis.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.149-156
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• 2022

The study aimed to identify the effectiveness of the training program based on the social story strategy for developing self-determination skills among students with autism spectrum disorder. The population of the study consisted of all students with autism spectrum disorder in the Desired Hope Association for People with Disabilities in Makkah Al-Mukarramah and the sample of the study consisted of (6) students. The study used the quasi-experimental approach with one group. To achieve the objectives of the study, the ARCS scale for self-determination skills was used, Hassan Al-Masry (2018). The results of the study revealed, through comparing the students' performances in their pre and post-tests regarding to the ARKS scale and through their answers on the scale, the effectiveness of the training program based on the social story for developing the skills of self-determination among students with autism spectrum disorder. The results also showed that there were statistically significant differences after applying the program when significance level was (0.001). The result came in favor of the post-test.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2018.11a
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• pp.31-31
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• 2018

본 연구팀은 2017년부터 CBT (Computer Based Training)기반 연안여객선 비상대응 훈련 프로그램 개발을 진행하고 있으며, 이로인해 기존 여객선 교육 중 이론 파트를 프로그램으로 대체할 수 있다. CBT 프로그램으로 확보된 교육시간을 활용하여 기존의 체험형 훈련에서 탈피할 수 있으며, 표준화된 실습교육으로 다양한 교육기관에서 활용할 수 있도록 하고자 한다.

• Journal of the military operations research society of Korea
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• v.25 no.1
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• pp.199-214
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• 1999

The Constructive Battle Simulation Model is very important to the recent military training for the substitution of the field training. However, real battlefield systems operate under real-time conditions, they are inherently distributed, concurrent and dynamic. In order to reflect these properties by the computer-based simulation systems which represent real world processes, we have been developing constructive simulation model for several years. Conventionally, scheduling and resource allocation activities which have timing constraints, we elaborated on these issues and developed the simulation system on commercially available hardware and operating system with lock-free resource allocation scheme and rate monotonic scheduling.