• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.39-48
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• 2023

Breast cancer screening makes extensive utilization of mammography. Even so, there has been a lot of debate with regards to this application's starting age as well as screening interval. The deep learning technique of transfer learning is employed for transferring the knowledge learnt from the source tasks to the target tasks. For the resolution of real-world problems, deep neural networks have demonstrated superior performance in comparison with the standard machine learning algorithms. The architecture of the deep neural networks has to be defined by taking into account the problem domain knowledge. Normally, this technique will consume a lot of time as well as computational resources. This work evaluated the efficacy of the deep learning neural network like Visual Geometry Group Network (VGG Net) Residual Network (Res Net), as well as inception network for classifying the mammograms. This work proposed optimization of ResNet with Teaching Learning Based Optimization (TLBO) algorithm's in order to predict breast cancers by means of mammogram images. The proposed TLBO-ResNet, an optimized ResNet with faster convergence ability when compared with other evolutionary methods for mammogram classification.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.29-36
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• 2022

In deep learning classification tasks, most models frequently assume that all labels are available for the training datasets. As such strategies to learn new concepts from unlabeled datasets are scarce. In fingerprint classification tasks, most of the fingerprint datasets are labelled using the subject/individual and fingerprint datasets labelled with finger type classes are scarce. In this paper, authors have developed approaches of classifying fingerprint images using the majorly known fingerprint classes. Our study provides a flexible method to learn new classes of fingerprints. Our classifier model combines both the clustering technique and use of deep learning to cluster and hence label the fingerprint images into appropriate classes. The K means clustering strategy explores the label uncertainty and high-density regions from unlabeled data to be clustered. Using similarity index, five clusters are created. Deep learning is then used to train a model using a publicly known fingerprint dataset with known finger class types. A prediction technique is then employed to predict the classes of the clusters from the trained model. Our proposed model is better and has less computational costs in learning new classes and hence significantly saving on labelling costs of fingerprint images.

• Journal of information and communication convergence engineering
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• v.21 no.4
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• pp.300-305
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• 2023

Audience reactions in response to remote virtual performances must be compressed before being transmitted to the server. The server, which aggregates these data for group insights, requires a distribution code for the transfer. Recently, distributed learning algorithms such as federated learning have gained attention as alternatives that satisfy both the information security and efficiency requirements. In distributed learning, no individual user has access to complete information, and the objective is to achieve a learning effect similar to that achieved with the entire information. It is therefore important to distribute interdependent information among users and subsequently aggregate this information following training. In this paper, we present a new extension technique for minimal code that allows a new minimal code with a different length and Hamming weight to be generated through the product of any vector and a given minimal code. Thus, the proposed technique can generate minimal codes with previously unknown parameters. We also present a scenario wherein these combined methods can be applied.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.186-191
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• 2023

Neural Networks are widely used for huge variety of tasks solution. Machine Learning methods are used also for signal and time series analysis, including electrocardiograms. Contemporary wearable devices, both medical and non-medical type like smart watch, allow to gather the data in real time uninterruptedly. This allows us to transfer these data for analysis or make an analysis on the device, and thus provide preliminary diagnosis, or at least fix some serious deviations. Different methods are being used for this kind of analysis, ranging from medical-oriented using distinctive features of the signal to machine learning and deep learning approaches. Here we will demonstrate a neural network-based approach to this task by building an ensemble of 1D CNN classifiers and a final classifier of selection using logistic regression, random forest or support vector machine, and make the conclusions of the comparison with other approaches.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.2
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• pp.359-363
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• 2019

As commercial drones have been widely used, concerns for collision accidents with people and invading secured properties are emerging. The detection of drone is a challenging problem. The deep learning based object detection techniques for detecting drones have been applied, but limited to the specific cases such as detection of drones from bird and/or background. We have tried not only detection of drones, but classification of different drones with an end-to-end model. YOLOv2 is used as an object detection model. In order to supplement insufficient data by shooting drones, data augmentation from collected images is executed. Also transfer learning from ImageNet for YOLOv2 darknet framework is performed. The experimental results for drone detection with average IoU and recall are compared and analysed.

• Research in Mathematical Education
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• v.26 no.3
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• pp.127-146
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• 2023

Statistical thinking has a broad definition but focuses on the context of regression modelling in the present study. To foster students' statistical thinking within the context, teaching should no longer be seen as transfer of knowledge from teacher to students but as a process of engaging with learning activities in which they develop ownership of knowledge. This study aims at collaborative learning contexts; students were divided into small groups in order to increase opportunities for peer collaboration. Each group of students was asked to do a regression project after class. Through doing the project, they learnt to organize and connect previously accrued piecemeal statistical knowledge in an integrated manner. They could also clarify misunderstandings and solve problems through verbal exchanges among themselves. They gave a clear and lucid account of the model they had built and showed collaborative interactions when presenting their projects in front of class. A survey was conducted to solicit their feedback on how peer collaboration would facilitate learning of statistics. Almost all students found their interaction with their peers productive; they focused on the development of statistical thinking with concerted effort.

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.303-308
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• 2022

Simulating the heat transfer in a composite material is an important topic in material science. Difficulties arise from the fact that adjacent materials cannot match perfectly, resulting in discontinuity in the temperature variables. Although there have been several numerical methods for solving the heat-transfer problem in imperfect contact conditions, the methods known so far are complicated to implement, and the computational times are non-negligible. In this study, we developed a ResNet-type deep neural network for simulating a heat transfer model in a composite material. To train the neural network, we generated datasets by numerically solving the heat-transfer equations with Kapitza thermal resistance conditions. Because datasets involve various configurations of composite materials, our neural networks are robust to the shapes of material-material interfaces. Our algorithm can predict the thermal behavior in real time once the networks are trained. The performance of the proposed neural networks is documented, where the root mean square error (RMSE) and mean absolute error (MAE) are below 2.47E-6, and 7.00E-4, respectively.

• Journal of Internet Computing and Services
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• v.9 no.6
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• pp.127-139
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• 2008

The value of up-to-date Mobile PC such as UMPC (Ultra Mobile Personal Computer) is recognized greatly in learning environment that busywork such as characteristic of transfer easy and real time communication possibility etc. and conversation with a colleague student, free sending of studying data and public ownership etc. is required. Wish to recognize whether is acting relevant role in u - unfold learning that inflect UMPC in integration research model, and UMPC is u searching for relevant element at studying activity unfolding process u - integration Inquiry-Based Learning that present in Korean education & research information service (KERIS) at fifth-year student science time In primary school in this research. This research result could take charge role of UMPCs' studying-activity though there is persistent feedback with teacher among studying-activity although UMPC's role is utilized on constituent that can be related with studying-activity in learning process.

• Korean Journal of Radiology
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• v.24 no.6
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• pp.541-552
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• 2023

Objective: Detection of pneumoperitoneum using abdominal radiography, particularly in the supine position, is often challenging. This study aimed to develop and externally validate a deep learning model for the detection of pneumoperitoneum using supine and erect abdominal radiography. Materials and Methods: A model that can utilize "pneumoperitoneum" and "non-pneumoperitoneum" classes was developed through knowledge distillation. To train the proposed model with limited training data and weak labels, it was trained using a recently proposed semi-supervised learning method called distillation for self-supervised and self-train learning (DISTL), which leverages the Vision Transformer. The proposed model was first pre-trained with chest radiographs to utilize common knowledge between modalities, fine-tuned, and self-trained on labeled and unlabeled abdominal radiographs. The proposed model was trained using data from supine and erect abdominal radiographs. In total, 191212 chest radiographs (CheXpert data) were used for pre-training, and 5518 labeled and 16671 unlabeled abdominal radiographs were used for fine-tuning and self-supervised learning, respectively. The proposed model was internally validated on 389 abdominal radiographs and externally validated on 475 and 798 abdominal radiographs from the two institutions. We evaluated the performance in diagnosing pneumoperitoneum using the area under the receiver operating characteristic curve (AUC) and compared it with that of radiologists. Results: In the internal validation, the proposed model had an AUC, sensitivity, and specificity of 0.881, 85.4%, and 73.3% and 0.968, 91.1, and 95.0 for supine and erect positions, respectively. In the external validation at the two institutions, the AUCs were 0.835 and 0.852 for the supine position and 0.909 and 0.944 for the erect position. In the reader study, the readers' performances improved with the assistance of the proposed model. Conclusion: The proposed model trained with the DISTL method can accurately detect pneumoperitoneum on abdominal radiography in both the supine and erect positions.

• Journal of The Korean Association For Science Education
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• v.37 no.6
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• pp.1063-1073
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• 2017

This study analyzed the difficulties elementary school students have in role-playing analogy activities for concept learning of heat transfer. Eleven fifth graders were given an orientation class on role-playing analogy activity, a class for concept learning of heat transfer as presented in a textbook, and then they performed the role-playing analogy activity with concepts of heat transfer. After the classes, researchers investigated the difficulties students encountered through questionnaires and interviews. The following are results of the research: Difficulties that students faced in role-playing analogy activity can be classified into five types. First, students encountered difficulties in expressing the analogy. The students who had difficulties in expressing the analogy focused on expressions outside the science concepts or lacked understanding of the science concepts. They also had difficulties in expressing the analogy because they lacked the expressiveness of analogy or abilities in mapping errors. They had difficulties in expressing the planned role-playing analogy in a narrow space. Second, students also experienced difficulties in performing activities due to lack of understanding on activities or lack of experience. Third, students experienced difficulties in selecting roles because they preferred the specific role or unwanted the specific roles. Fourth, the members of group experienced difficulties in group activities because they did not concentrate on activities, failed to perform roles, or showed attitudes of an onlooker. Fifth, they experienced difficulties in communications due to unilateral communication, conflicts of opinions, and lack of opinions.