• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.420-426
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• 2022

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.171-179
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• 2022

This paper introduces a monitoring and tracking system for corona19 confirmed patients based on the collected data by installing a device that can manage the access list at the entrance to each building on the campus. The existing QR-based electronic access list can't measure the temperature of the person entering the building and it is inconvenient that members have to scan their QR codes with a smartphone. In addition, when the state manages information about confirmed patients and contacts on campus, it is not easy for members to quickly share and track information. These could lead to cases where a person is in close contact with an infected person developing another patient. Therefore, this paper introduces a device using face recognition library and a temperature sensor installed at the entrance of each building on the campus, enabling the administrator to monitor the access status and quickly track members of each building in real-time.

• Journal of Biosystems Engineering
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• v.21 no.3
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• pp.325-335
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• 1996

Visual features are crucial for monitoring the growth state, indexing the drying performance, and grading the quality of oak mushrooms. A computer vision system with neural net information processing technique was utilized to quantize quality factors of a dried oak mushrooms distributed over the cap and gill sides. In this paper, visual feature extraction algorithm were integrated with the neural net processing to deal with various fuzzy patterns of mushroom shapes and to compensate the fault sensitiveness of the crisp criteria and heuristic rules derived from the image processing results. The proposed algorithm improved the segmentation of the skin features of each side, the identification of cap and gill surfaces, the identification of stipe states and removal of the stipe, etc. And the visual characteristics of dried oak mushrooms were analyzed and primary visual features essential to tile quality evaluation were extracted and quantized. In this study, black and white gray images were captured and used for the algorithm development.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.115-125
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• 2023

This paper relates to the development of CCTV camera posture calibration algorithm that can simultaneously collect traffic information such as traffic volume and speed in the state of view of the CCTV camera set for traffic monitoring. The developed autonomous posture calibration algorithm uses vehicle recognition and tracking techniques to identify the road, and automatically determines the angle of view for the operator's traffic surveillance and traffic information collection. To verify the performance of the proposed algorithm, a CCTV installed on site was used, and the results of the angle of view automatically calculated by the autonomous posture calibration algorithm for the angle of view set for traffic surveillance and traffic information collection were compared.

• Convergence Security Journal
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• v.22 no.4
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• pp.65-73
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• 2022

Since cyber operations are performed in a virtual cyber battlefield, the measurement indicators that can evaluate and visualize the current state of the cyber environment in a consistent form are required for the commander to effectively support the decision-making of cyber operations. In this paper, we propose a method to define various evaluation indicators that can be collected on the cyber battlefield, normalized them, and evaluate the cyber status in a consistent form. The proposed cyber battlefield status element consists of cyber asset-related indicators, target network-related indicators, and cyber threat-related indicators. Each indicator has 6 sub-indicators and can be used by assigning weights according to the commander's interests. The overall status of the cyber battlefield can be easily recognized because the measured indicators are visualized in time series on a single screen. Therefore, the proposed method can be used for the situational awareness required to effectively conduct cyber warfare.

• Physical Therapy Korea
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• v.30 no.4
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• pp.306-313
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• 2023

Background: As social problems due to the acceleration of the aging era and the increase in the elderly population are becoming serious, virtual reality (VR)-based healthcare is emerging as an approach for preventing and managing health issues. Objects: This study used validity and reliability analyses to examine the clinical efficacy that is, the clinical value and usability of a novel VR cognitive evaluation system index that we developed. Methods: We developed a VR cognitive evaluation system based on motion recognition analysis evaluation for individuals aged 65 to 85. After conducting the Korean version of the Mini-Mental State Exam (K-MMSE) cognitive evaluation, the evaluation score was verified through correlation analysis in the VR cognitive evaluation system. To verify the construct validity of the two groups, the Global Deterioration Scale (GDS) grades were categorized into a normal cognitive group (GDS grade 1) and a cognitive impairment group (GDS grades 2 and 3). The data were measured twice to determine the reliability between the two measurements and assess the stability and clinical value of the evaluation system. Results: Our evaluation system had a high correlation of 0.85 with the widely used K-MMSE cognitive evaluation. The system had strong criterion-related validity at the 95% confidence interval. Compared to the average score of GDS grade 1 in the VR cognitive evaluation system, the average score of GDS grades 2 and 3 in the VR cognitive evaluation system was statistically significantly lower while also having strong construct validity at the 95% confidence interval. To measure the reliability of the VR cognitive evaluation system, tests-retests were conducted using the intraclass correlation coefficient (3,1), which equaled 0.923 and was statistically significant. Conclusion: The VR cognitive evaluation system we developed is a valid and reliable clinical tool to distinguish between normal cognitive status and mild cognitive impairment.

• Smart Media Journal
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• v.13 no.7
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• pp.36-44
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• 2024

Identifying plant species and diseases is crucial for maintaining biodiversity and achieving optimal crop yields, making it a topic of significant practical importance. Recent studies have extended plant disease recognition from traditional closed-set scenarios to open-set environments, where the goal is to reject samples that do not belong to known categories. However, in open-world tasks, it is essential not only to define unknown samples as "unknown" but also to classify them further. This task assumes that images and labels of known categories are available and that samples of unknown categories can be accessed. The model classifies unknown samples by learning the prior knowledge of known categories. To the best of our knowledge, there is no existing research on this topic in plant-related recognition tasks. To address this gap, this paper utilizes knowledge distillation to model the category space relationships between known and unknown categories. Specifically, we identify similarities between different species or diseases. By leveraging a fine-tuned model on known categories, we generate pseudo-labels for unknown categories. Additionally, we enhance the baseline method's performance by using a larger pre-trained model, dino-v2. We evaluate the effectiveness of our method on the large plant specimen dataset Herbarium 19 and the disease dataset Plant Village. Notably, our method outperforms the baseline by 1% to 20% in terms of accuracy for novel category classification. We believe this study will contribute to the community.

• Journal of KIISE:Software and Applications
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• v.29 no.3
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• pp.167-179
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• 2002

In this paper, we propose a new acoustic model for characterizing segmental features and an algorithm based upon a general framework of hidden Markov models (HMMs) in order to compensate the weakness of HMM assumptions. The segmental features are represented as a trajectory of observed vector sequences by a polynomial regression function because the single frame feature cannot represent the temporal dynamics of speech signals effectively. To apply the segmental features to pattern classification, we adopted segmental HMM(SHMM) which is known as the effective method to represent the trend of speech signals. SHMM separates observation probability of the given state into extra- and intra-segmental variations that show the long-term and short-term variabilities, respectively. To consider the segmental characteristics in acoustic model, we present segmental-feature HMM(SFHMM) by modifying the SHMM. The SFHMM therefore represents the external- and internal-variation as the observation probability of the trajectory in a given state and trajectory estimation error for the given segment, respectively. We conducted several experiments on the TIMIT database to establish the effectiveness of the proposed method and the characteristics of the segmental features. From the experimental results, we conclude that the proposed method is valuable, if its number of parameters is greater than that of conventional HMM, in the flexible and informative feature representation and the performance improvement.

• Journal of Digital Convergence
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• v.10 no.11
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• pp.619-626
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• 2012

The purpose of this study is to compare and analyze the utilization state of smart devices and the recognition level of educational application development of the general high school and the specialized high school. Specialized high school students preferred the utilization of smart devices more and daily spent on the devices more time than general high school students. As for the learning field, language for the general high school and the certificate of qualification for the specialized high school were shown high. The merit of smart device utilization is the use of spare time and its infrastructure was most required. The most expected content is a video lecture for the general high school and cooperative learning for the specialized high school and the most satisfied point was mobility. The specialized high school students feel more necessity about the application development for culinary education and had a plan to utilize it more and more preferred practice videos. As for the food development areas, the general high school students hoped simple food and the specialized high school students did cooking technician food and they both hoped the application to be uploaded in portal sites and the department homepage. The application development for culinary education is required to focus simulation learning including practice videos and cooking recipes and add an evaluation function to check the academic achievement levels. It is required to provide the subject goals of each course and concrete information on solving problems. Contents including video, music, texts need to be attached to improve learning immersion. There should be the beginning and development of a lesson and the flow of arrangement and communication between main bodies of learning should be improved by utilization of SNS cooperative learning services.

• Korean Journal of Child Education & Care
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• v.17 no.3
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• pp.111-151
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• 2017

The purpose of this study is to investigate the perception and actual condition of early childhood teachers about the activities of cooperative art in early childhood education institutions. The subjects of the study were 280 teachers of early childhood education institutions in Y city. The research method was a questionnaire survey on the perception and actual condition of the cooperative art activities. For the research problems, frequency, percentage, and average were calculated by using descriptive statistics, and t - test, F - test, and ${\chi}^2$ - test were conducted to examine the differences according to background variables of early childhood teachers. In conclusion, this study showed that the need for cooperative art activities of early childhood education institutions was highly perceived, and there were significant differences according to the background variables of teachers in educational goals, contents and method. There were significant differences according to the teachers' background variables in the actual cooperative art activities in terms of frequency, method, time, evaluation method, teacher training and support. Based on the results of this study, various supporting methods such as activities plan and method, teacher education and training appropriate for the cooperative art activity program and the presentation of young children are sought so that more desirable and efficient cooperative art activities can be operated in the field of early childhood education suggesting that it should be done.