• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.18-25
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• 2014

A implanted sensor's error probability is more likely to external sensor's error probability by biological characteristic in WBAN. In this paper, we present method that external sensor transmits frame instead of doing implanted sensor's retransmission for improving lifetime of implanted sensors in WBAN. The proposed method, LI(Lifetime Increment) protocol is to add external sensor's id in transmission data frame of a implanted sensor. When the retransmission is required, external sensor that have to registered id in data frame retransmits frame instead of implanted sensors' retransmission. The comparison result shows that the proposed protocol reduces power consumption and improves life time.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.7
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• pp.3191-3196
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• 2011

Smart home system are being installed in the most new construction of building for the convenience of living life. As smart home systems are becoming more common and their diffusion rates are faster, hacker's attack for the smart home system will be increased. In this paper, Risk level of smart home's to do respond to intrusion that occurred from the wired network and wireless network intrusion cases and attacks can occur in a virtual situation created scenarios to build a database. This is based on the smart home users vulnerable to security to know finding illegal intrusion traffic in real-time and attack prevent was designed the intrusion detection algorithm.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.191-204
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• 2021

The software development life cycle (SDLC) is a procedure used to develop a software system that meets both the customer's needs and real-world requirements. The first phase of the SDLC involves creating a conceptual model that represents the involved domain in reality. In requirements engineering, building such a model is considered a bridge to the design and construction phases. However, this type of model can also serve as a basic model for identifying business processes and how these processes are interconnected to achieve the final result. This paper focuses on process modeling in organizations, per se, beyond its application in the SDLC when an organization needs further documentation to meet its growth needs and address regular changes over time. The resultant process documentation is created alongside the daily operations of the business process. The model provides visualization and documentation of processes to assist in defining work patterns, avoiding redundancy, or even designing new processes. In this paper, a proposed diagrammatic representation models each process using one diagram comprising five actions and two types of relations to build three levels of depiction. These levels consist of a static description, events, and the behavior of the modeled process. The viability of a thinging machine is demonstrated by re-modeling some examples from the literature.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.83-88
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• 2022

The main purpose of the study is to determine the key aspects of the public-private partnership system in the context of the economic development of the state. At first glance, the mutually exclusive interests of the state and business do not contribute to the development of common and agreed development goals. At the same time, ignoring the versatility of interests and the aggravation of the discussion regarding the two sides under consideration, the study of the theoretical foundations of the interaction between the public and private sectors of the economy allows us to draw the following conclusion: world economic theory from classical political economy to new institutionalism has a clear structured position on the ancient historical depth of existence of the form of partnerships Based on the results of the study, the main elements of the public-private partnership system in the context of the economic development of the state were identified.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.77-86
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• 2022

The destructive impact of contagious diseases outbreaks on all life facets necessitates developing effective solutions to control these diseases outbreaks. This research proposes an end-to-end, data-driven platform which consists of multiple modules that are working in harmony to achieve a concrete goal: early detection of contagious diseases outbreaks (i.e., epidemic diseases detection). Achieving that goal enables decision makers and people in power to act promptly, resulting in robust prevention management of contagious diseases. It must be clear that the goal of this proposed platform is not to predict or forecast the spread of contagious diseases, rather, its goal is to promptly detect contagious diseases outbreaks as they happen. The front end of the proposed platform is a web-based dashboard that visualizes diseases outbreaks in real-time on a real map. These outbreaks are detected via another component of the platform which utilizes data mining techniques and algorithms on gathered datasets. Those gathered datasets are managed by yet another component. Specifically, a mobile application will be the main source of data to the platform. Being a vital component of the platform, the datasets are managed by a DBMS that is specifically tailored for this platform. Preliminary results are presented to showcase the performance of a prototype of the proposed platform.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1233-1241
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• 2022

Tumors of the brain are the deadliest, with a life expectancy of only a few years for those with the most advanced forms. Diagnosing a brain tumor is critical to developing a treatment plan to help patients with the disease live longer. A misdiagnosis of brain tumors will lead to incorrect medical treatment, decreasing a patient's chance of survival. Radiologists classify brain tumors via biopsy, which takes a long time. As a result, the doctor will need an automatic classification system to identify brain tumors. Image classification is one application of the deep learning method in computer vision. One of the deep learning's most powerful algorithms is the convolutional neural network (CNN). This paper will introduce a novel deep learning structure and image gradient to classify brain tumors. Meningioma, glioma, and pituitary tumors are the three most popular forms of brain cancer represented in the Figshare dataset, which contains 3,064 T1-weighted brain images from 233 patients. According to the numerical results, our method is more accurate than other approaches.

• Journal of Korean Society of Rural Planning
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• v.28 no.4
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• pp.105-117
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• 2022

This study tested SVM(support vector machine), RF(random forest), and ANN(artificial neural network) machine-learning models that can predict net irrigation water requirements in paddy fields. For the Jeonju and Jeongeup meteorological stations, the net irrigation water requirement was calculated using K-HAS from 1981 to 2021 and set as the label. For each algorithm, twelve models were constructed based on cumulative precipitation, precipitation, crop evapotranspiration, and month. Compared to the CE model, the R² of the CEP model was higher, and MAE, RMSE, and MSE were lower. Comprehensively considering learning performance and learning time, it is judged that the RF algorithm has the best usability and predictive power of five-days is better than three-days. The results of this study are expected to provide the scientific information necessary for the decision-making of on-site water managers is expected to be possible through the connection with weather forecast data. In the future, if the actual amount of irrigation and supply are measured, it is necessary to develop a learning model that reflects this.

• 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.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.79-84
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• 2023

Social studies show that the number of separated families have lately increased due to different reasons. Despite the causes for family rift, many problems are resulted which affected the children physically and psychologically. This effect may cause them fail in their life especially at school. This paper focuses on the negative reaction of the parents' separation with other factors from the computer science prospective. Since the artificial intelligent field is the most common widespread in computer science, a predictive model is built to predict if a specific child whose parents separated, may complete the school successfully or fail to continue his education. This will be done using Decision Tree that have proved their effectiveness on the predication applications. As an experiment, a sample of individuals is randomly chosen and applied on our prediction model. As a result, this model shows that the separation may cause the child success at school if other factors are satisfied; the intelligent of the guardian, the relation between the parents after the separation, his age at the separation time, etc.

• 전자공학회논문지 IE
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• v.47 no.3
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• pp.41-46
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• 2010

WSN, or Wireless Sensor Network, consists of a multitude of inexpensive micro-sensors. Because the batteries in sensor nodes can not be replaced once they are deployed, the life of a WSN is absolutely determined by the batteries. So, energy efficiency of a network is a critical factor for long-life operation. LEACH protocol which divides WSN into two groups is a typical routing protocol based on the clustering scheme for the efficient use of limited energy. It is composed of round units which are separated into set-up and steady state. In this paper we propose a power saving scheme to minimize set-up phase itself and to involve a data comparison algorithm. We evaluate the performance of the proposed scheme in comparison with original LEACH protocol. Simulation results validate our scheme has better performance in terms of the number of alive nodes as time evolves and average energy dissipated.