• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.73-88
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• 2023

Brain tumors can also be an abnormal collection or accumulation of cells in the brain that can be life-threatening due to their ability to invade and metastasize to nearby tissues. Accurate diagnosis is critical to the success of treatment planning, and resonant imaging is the primary diagnostic imaging method used to diagnose brain tumors and their extent. Deep learning methods for computer vision applications have shown significant improvements in recent years, primarily due to the undeniable fact that there is a large amount of data on the market to teach models. Therefore, improvements within the model architecture perform better approximations in the monitored configuration. Tumor classification using these deep learning techniques has made great strides by providing reliable, annotated open data sets. Reduce computational effort and learn specific spatial and temporal relationships. This white paper describes transfer models such as the MobileNet model, VGG19 model, InceptionResNetV2 model, Inception model, and DenseNet201 model. The model uses three different optimizers, Adam, SGD, and RMSprop. Finally, the pre-trained MobileNet with RMSprop optimizer is the best model in this paper, with 0.995 accuracies, 0.99 sensitivity, and 1.00 specificity, while at the same time having the lowest computational cost.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.2
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• pp.11-25
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• 2023

With the increasing advancement of VLSI technology, health care system is also developing to serve the humanity with better care. Therefore, biomedical implantable devices are one of the amazing important invention of scientist to collect data from the body cell for the diagnosis of diseases without any pain. This Biomedical implantable transceiver circuit has several important issues. Oscillator is one of them. For the design flexibility and complete transistor-based architecture ring oscillator is favorite to the oscillator circuit designer. This paper represents the design and analysis of the a 9-stage CMOS ring oscillator using cadence virtuoso tool in 180nm technology. It is also designed to generate the carrier signal of 403.5MHz frequency. Ring oscillator comprises of odd number of stages with a feedback circuit forming a closed loop. This circuit was designed with 9-stages of delay inverter and simulated for various parameters such as delay, phase noise or jitter and power consumption. The average power consumption for this oscillator is 9.32㎼ and average phase noise is only -86 dBc/Hz with the source voltage of 0.8827V.

• Genomics & Informatics
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• v.21 no.3
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• pp.28.1-28.13
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• 2023

Mild cognitive impairment (MCI) is a clinical syndrome characterized by the onset and evolution of cognitive impairments, often considered a transitional stage to Alzheimer's disease (AD). The genetic traits of MCI patients who experience a rapid progression to AD can enhance early diagnosis capabilities and facilitate drug discovery for AD. While a genome-wide association study (GWAS) is a standard tool for identifying single nucleotide polymorphisms (SNPs) related to a disease, it fails to detect SNPs with small effect sizes due to stringent control for multiple testing. Additionally, the method does not consider the group structures of SNPs, such as genes or linkage disequilibrium blocks, which can provide valuable insights into the genetic architecture. To address the limitations, we propose a Bayesian bi-level variable selection method that detects SNPs associated with time of conversion from MCI to AD. Our approach integrates group inclusion indicators into an accelerated failure time model to identify important SNP groups. Additionally, we employ data augmentation techniques to impute censored time values using a predictive posterior. We adapt Dirichlet-Laplace shrinkage priors to incorporate the group structure for SNP-level variable selection. In the simulation study, our method outperformed other competing methods regarding variable selection. The analysis of Alzheimer's Disease Neuroimaging Initiative (ADNI) data revealed several genes directly or indirectly related to AD, whereas a classical GWAS did not identify any significant SNPs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.881-902
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• 2024

Fast decision support systems and accurate diagnosis have become significant in the rapidly growing healthcare sector. As the number of disparate medical IoT devices connected to the human body rises, fast and interrelated healthcare data retrieval gets harder and harder. One of the most important requirements for the Healthcare Internet of Things (HIoT) is semantic interoperability. The state-of-the-art HIoT systems have problems with bandwidth and latency. An extension of cloud computing called fog computing not only solves the latency problem but also provides other benefits including resource mobility and on-demand scalability. The recommended approach helps to lower latency and network bandwidth consumption in a system that provides semantic interoperability in healthcare organizations. To evaluate the system's language processing performance, we simulated it in three different contexts. 1. Polysemy resolution system 2. System for hyponymy-hypernymy resolution with polysemy 3. System for resolving polysemy, hypernymy, hyponymy, meronymy, and holonymy. In comparison to the other two systems, the third system has lower latency and network usage. The proposed framework can reduce the computation overhead of heterogeneous healthcare data. The simulation results show that fog computing can reduce delay, network usage, and energy consumption.

• Korean Journal of Construction Engineering and Management
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• v.19 no.1
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• pp.21-31
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• 2018

The rapid increase of the elderly living alone is a critical issue in worldwide as it leads to a rapid increase of a social support costs (e.g., medical expenses) for the elderly. In early stages of dementia, the activities of daily living (ADL) including self-care tasks can be affected by abnormal patterns or behaviors and used as an evidence for the early diagnosis. However, extracting activities using non-intrusive approach is still quite challenging and the existing methods are not fully visualized to understand the behavior pattern or routine. To address these issues, this research suggests a model to extract the activities from coarse-grained data (spatio-temporal data log) and visualize the behavioral context information. Our approach shows the process of extracting and visualizing the subject's spaceactivity map presenting the context of each activity (time, room, duration, sequence, frequency). This research contributes to show a possibility of detecting subject's activities and behavioral patterns using coarse-grained data (limited to spatio-temporal information) with little infringement of personal privacy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.399-401
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• 2022

A definitive diagnosis to classify the breast malignancy status may be achieved by microscopic analysis using surgical open biopsy. However, this procedure requires experts in the specializing of histopathological image analysis directing to time-consuming and high cost. To overcome these issues, deep learning is considered practically efficient to categorize breast cancer into benign and malignant from histopathological images in order to assist pathologists. This study presents a pre-trained convolutional neural network model architecture with a 100% fine-tuning scheme and Adagrad optimizer to classify the breast cancer histopathological images into benign and malignant using a 40× magnification BreaKHis dataset. The pre-trained architecture was constructed using the InceptionResNetV2 model to generate a modified InceptionResNetV2 by substituting the last layer with dense and dropout layers. The results by demonstrating training loss of 0.25%, training accuracy of 99.96%, validation loss of 3.10%, validation accuracy of 99.41%, test loss of 8.46%, and test accuracy of 98.75% indicated that the modified InceptionResNetV2 model is reliable to predict the breast malignancy type from histopathological images. Future works are necessary to focus on k-fold cross-validation, optimizer, model, hyperparameter optimization, and classification on 100×, 200×, and 400× magnification.

• Journal of the Korean Institute of Landscape Architecture
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• v.37 no.1
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• pp.78-86
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• 2009

Recently, there seems to besome problems in the urban visual landscape as a result of continuous economic growth and industrial development. At the same time, the public has begun to be aware of the importance of visual resources, and the necessity for visual landscape conservation and improvement. Therefore, the development of evaluative indicators for systematic visual landscape planning and design is urgent. The purpose ofthis study is to discover evaluative models and indicators for the diagnosis of urban visual landscapes. This study included the selection of 18 physical indicators(statistical data) by literature reviews, adoption of field and questionnaire surveys at 12 autonomous districts in Seoul and surrounding major mountain valleys and river streams(i.e. Mt. Nam and Han-River). The content of the questionnaire is scenic beauty. Moreover, the linear regression analysis between the scenic beauty mean scores and the physical indicator scores figure out the scenic beauty prediction model. As this study suggests, the most important indicators in urban visual landscapes are 'Greens', 'Park' and 'the number of apartment buildings(higher than 20 stories).' Based on the results, greens and parks should be priority elements to considerin urban landscape planning and design. Moreover, since the number of apartment buildings that are higher than 20 stories has a negative correlation with the scenic beauty score, it can be used as basic data for landscape planning. For the scenic beauty prediction models and evaluative indicators suggest a direction of urban management, each indicator becomes basic data for visual landscape planning and design. In following studies, if physical indicators and case studies are added, the scenic beauty prediction models and evaluative indicators could be more synthetic and systematic. Moreover, the development of physical indicators in three dimensions(3D)(i.e. results from visual district analysis, view surface analysis) could be expected to obtain more general and varied results.

• Journal of the Korean Institute of Landscape Architecture
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• v.44 no.6
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• pp.73-83
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• 2016

This study modified and applied the ecological network(Indicator 2) from the City Biodiversity Index(CBI) to be tailored to Korea. It is calculated by utilizing a biotope map and a land cover map. The ecological network of Gyeryong-Si was 13,713,703(33.8%) with the biotope map and 17,686,966(37.9%) with the land cover map. The result of the biotope map was lower than the land cover map. The ecological network of Goyang-Si was 4,961,922(4.9%) with the biotope map and 4,383,207(3.7%) with the land cover map. The result of the land cover map was lower than the biotope map. As a main result of the research, an error was discovered in which, when calculating the ecological network, the types of the military unit facilities were distinguished into a special area on the biotope map and into an urbanization promotion area and a forest area on the land cover map. In the case of a middle-classified, land cover map, the land use in the surroundings of the forest area was not subdivided. An error in the development area expressed as a forest green was discovered. When selecting the natural elements, too, regarding the types of artificially-created rivers, artificial ponds, and artificial grasslands, etc. on a biotope map, the exclusions were necessary. Regarding the natural, bare ground on a land cover map, there was a need to calculate by including the natural elements. It was judged that, in the future, the ecological network in the unit of the entire nation can be analyzed roughly by utilizing a land cover map. It was judged that, in a city having a biotope map, the calculation of the ecological network utilizing a map of the present situation of the urban ecology will be a more accurate diagnosis of the present situation.

• KIISE Transactions on Computing Practices
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• v.22 no.10
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• pp.521-531
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• 2016

In this paper, since most business process of D-agency is being performed through some information systems, including Onnara System is a government standard operating management system, computerized accumulated in the system documentation based on, even if there is no independent job analysis system, in a judgment that can be can be tissue diagnosis, it presented a job analysis plan that leverages the existing information system. Most material is passed online in business processing between departments and between colleagues, it is returned. In situations where most information systems for such business processing is built developed, grasp the work procedures and information systems D-agency data accumulated to derive the necessary elements for job analysis quantified, and verified the validity of the element in the regression statistics.In addition, classification system (BRM, Business Reference Model) of the existing functionality that is available only Onnara System, and to establish a job analysis architecture to be able to function diagnostic departments to leverage common also in other information systems, related implement illustrating additional features of the information system, to derive a department duties value calculation formula with it, and present various job analysis plan that can actually be utilized to diagnose and derived elements department appropriate personnel.

• Land and Housing Review
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• v.2 no.3
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• pp.249-258
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• 2011

In order to achieve an efficient urban regeneration of the nation, it is required to determine the extent of decline nation-wide and the declined areas for each district and also to evaluate the potentials of the concerned areas. For this task to be accomplished, a construction of a comprehensive diagnostic system based on spatial information considering diversity and complexity is required. In this study, a total information system architecture for urban regeneration is designed as part of the construction of such a diagnostic system. In order to develop the system, a city and district level unit decline diagnostic indicators has been constructed and a decline diagnostic system has been developed. Also, a scheme to promote the advancement of the system is proposed. The DB construction is based on the city and district level nation-wide and metadata for the concerned level is constructed as well. The system is based on the Open API and designed to be flexible for extension. Also, an RIA-based intuitive UI has been implemented. Main features of the system consist of the management of the indicators, diagnostic analysis (city and district level decline diagnosis), related information, etc. As for methods for the advancement, an information model in consideration of the spation relations of the urban regeneration DB has been designed and application methods of semantic webs. Also, for improvement methods for district unit analytical model, district level analysis models, GIS based spatial analysis platforms and linked utiliation of KOPSS analysis modules are suggested. A use of a total information system for urban regeneration is anticipated to facilitate concerned policy making through the identification of the status of city declines to identify and the understanding of the demands for regeneration.