• 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.

• Journal of Korean Elementary Science Education
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• v.36 no.3
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• pp.286-301
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• 2017

Nowadays, the public as well as science educators pays much attention to the fourth industrial revolution and wonders what will happen to the societies in the future. Thus, this study aimed at predicting the education environment which will be brought from the fourth industrial revolution, and suggesting the solutions or tasks to be investigated in science education. Through the literature review, this study categorized the major changes of future society into a wild fluctuation of job market, the shift from possession-based economy to sharing economy, post-urbanized and distributed system, and the crisis of dehumanization. According to the four major changes, this study predicted the future environment that will occur to the educational system. First, the students should the competences necessary for the future and the school curriculum will be changed in terms of width and depth. Second, sharing economy may bring about the open platform similar to MOOC (Massive Open Online Course) or TED. Third, the manifestation of artificial intelligence in education will enable the individual and paced learning, and thanks to the change, the concept of distributed cognition will be more focused in education research. Fourth, the collaborative learning and character education should be more stressed to resist the dehumanization. This study suggests relevant tasks and issues that should be tackled for the successful change in primary and secondary schools.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.3
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• pp.264-269
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• 2006

This paper presents the strategy for searching a hidden object in an unknown area for using by multiple distributed autonomous robotic systems (DARS). To search the target in Markovian space, DARS should recognize th ε ir surrounding at where they are located and generate some rules to act upon by themselves. First of all, DARS obtain 6-distances from itself to environment by infrared sensor which are hexagonally allocated around itself. Second, it calculates 6-areas with those distances then take an action, i.e., turn and move toward where the widest space will be guaranteed. After the action is taken, the value of Q will be updated by relative formula at the state. We set up an experimental environment with five small mobile robots, obstacles, and a target object, and tried to research for a target object while navigating in a un known hallway where some obstacles were placed. In the end of this paper, we present the results of three algorithms - a random search, an area-based action making process to determine the next action of the robot and hexagon-based Q-learning to enhance the area-based action making process.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.5
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• pp.85-91
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• 1989

In this paper, we propose a neural net system for speech recognition, which is composed of two neural networks. Firstly the self-supervised BP(Back Propagation) network generates the distributed concept corresponding to the activity pattern in the hidden units. And then the self-organizing neural network forms a concept map which directly displays the similarity relations between concepts. By doing the above, the difficulty in learning the conventional BP network is solved and the weak side of BP falling into a pattern matcher is gone, while the strong point of generating the various internal representations is used. And we have obtained the concept map which is more orderly than the Kohonen's SOFM. The proposed neural net system needs not any special preprocessing and has a self-learning ability.

• Journal of Communications and Networks
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• v.18 no.3
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• pp.411-419
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• 2016

This article investigates the problem of distributed channel selection in opportunistic spectrum access networks from a perspective of interference minimization. The traditional physical (PHY)-layer interference model is for information theoretic analysis. When practical multiple access mechanisms are considered, the recently developed binary medium access control (MAC)-layer interference model in the previous work is more useful, in which the experienced interference of a user is defined as the number of competing users. However, the binary model is not accurate in mathematics analysis with poor achievable performance. Therefore, we propose a real-valued one called stochastic MAC-layer interference model, where the utility of a player is defined as a function of the aggregate weight of the stochastic interference of competing neighbors. Then, the distributed channel selection problem in the stochastic MAC-layer interference model is formulated as a weighted stochastic MAC-layer interference minimization game and we proved that the game is an exact potential game which exists one pure strategy Nash equilibrium point at least. By using the proposed stochastic learning-automata based uncoupled algorithm with heterogeneous learning parameter (SLA-H), we can achieve suboptimal convergence averagely and this result can be verified in the simulation. Moreover, the simulated results also prove that the proposed stochastic model can achieve higher throughput performance and faster convergence behavior than the binary one.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3120-3137
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• 2021

Small-sized IoT wireless sensing devices can be deployed with small aircraft such as drones, and the deployment of mobile IoT devices can be relocated to suit data collection with efficient relocation algorithms. However, the terrain may not be able to predict its shape. Mobile IoT devices suitable for these terrains are hopping devices that can move with jumps. So far, most hopping sensor relocation studies have made the unrealistic assumption that all hopping devices know the overall state of the entire network and each device's current state. Recent work has proposed the most realistic distributed network environment-based relocation algorithms that do not require sharing all information simultaneously. However, since the shortest path-based algorithm performs communication and movement requests with terminals, it is not suitable for an area where the distribution of obstacles is uneven. The proposed scheme applies a simple Monte Carlo method based on relay nodes selection random variables that reflect the obstacle distribution's characteristics to choose the best relay node as reinforcement learning, not specific relay nodes. Using the relay node selection random variable could significantly reduce the generation of additional messages that occur to select the shortest path. This paper's additional contribution is that the world's first distributed environment-based relocation protocol is proposed reflecting real-world physical devices' characteristics through the OMNeT++ simulator. We also reconstruct the three days-long disaster environment, and performance evaluation has been performed by applying the proposed protocol to the simulated real-world environment.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.135-146
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• 2023

An effective educational program warrants the inclusion of an innovative construction which enhances the higher education efficacy in such a way that accelerates the achievement of desired results and reduces the risk of failures. Educational Decision Support System (EDSS) has currently been a hot topic in educational systems, facilitating the pupil result monitoring and evaluation to be performed during their development. Insufficient information systems encounter trouble and hurdles in making the sufficient advantage from EDSS owing to the deficit of accuracy, incorrect analysis study of the characteristic, and inadequate database. DMTs (Data Mining Techniques) provide helpful tools in finding the models or forms of data and are extremely useful in the decision-making process. Several researchers have participated in the research involving distributed data mining with multi-agent technology. The rapid growth of network technology and IT use has led to the widespread use of distributed databases. This article explains the available data mining technology and the distributed data mining system framework. Distributed Data Mining approach is utilized for this work so that a classifier capable of predicting the success of students in the economic domain can be constructed. This research also discusses the Intelligent Knowledge Base Distributed Data Mining framework to assess the performance of the students through a mid-term exam and final-term exam employing Multi-agent system-based educational mining techniques. Using single and ensemble-based classifiers, this study intends to investigate the factors that influence student performance in higher education and construct a classification model that can predict academic achievement. We also discussed the importance of multi-agent systems and comparative machine learning approaches in EDSS development.

• The Journal of the Korea Contents Association
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• v.10 no.6
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• pp.429-439
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• 2010

This study was proceeded to find out the effect of learning orientation on the organizational cynicism by introducing organization-based self esteem as a moderating variable. For the experimental study 400 questionnaires were distributed to employees working in deluxe hotel in Seoul and 236 responses were used for the analysis. With SPSS statistical program reliability test, confirmatory factor analysis, and regression analysis were performed. The study found that the learning orientation had a significant effect on organizational cynicism and organizational-based self-esteem had a moderating effect between learning orientation and organizational cynicism.

• Journal of Distribution Science
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• v.15 no.1
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• pp.43-49
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• 2017

Purpose - This study is to integrate organizational factors into UCD process. For this research purpose, we investigated the organizational factors which influence people behaviors in the context of user-centred design practice(UCP). And this study presents organizational culture, organizational learning and change management as the organizational factors. Especially, this study is to investigate how change management influences the relationship between the organizational culture/learning and UCD performance. Research design, data, and methodology - Using the survey methodology with a questionnaire, this study distributed the questionnaire to the experienced 112 practitioners of user-centred design practice in 52 Korean small and medium companies. The organizations differed in range and size from medium-scale, which is under 100 of employees, and to small-scale, which is from 100 to 500. Results - Organizational culture and organizational learning have positive effects on user-centred design practice performance as expected. And change management strengthens the positive relationship between organizational learning and user-centred design practice performance but has no effect on the relationship between organizational culture and user-centred design practice performance. Conclusions - This is the first empirical study of investigating and demonstrating some key organizational factors' relationships and UCD performance of an organization, which will support to institutionalize UCD within an organization, providing theoretical foundations.

• Journal of The Korean Association of Information Education
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• v.12 no.4
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• pp.385-393
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• 2008

The purpose of this research is to suggest the direction of using blog in e-PBL. In this research we used blog for supporting individual self-regulated learning activity. We distributed space for individual learning and collaborative learning by using blog and community. 39 students participated during the 16 weeks, which includes 8 weeks of traditional e-PBL and 8 weeks of Blog based e-PBL. We conducted questionnaires and interviews, and analyzed learners' reflection notes. Data show learners feel comfortable with using blog in their independent learning activity. And blog activated learners' reflective activity and motivated their self-regulated learning.