• International Journal of Computer Science & Network Security
• /
• v.21 no.6
• /
• pp.286-293
• /
• 2021

Recently, the quick development rate of apps in the Android platform has led to an accelerated increment in creating malware applications by cyber attackers. Numerous Android malware detection tools have utilized conventional signature-based approaches to detect malware apps. However, these conventional strategies can't identify the latest apps on whether applications are malware or not. Many new malware apps are periodically discovered but not all malware Apps can be accurately detected. Hence, there is a need to propose intelligent approaches that are able to detect the newly developed Android malware applications. In this study, Radial Basis Function (RBF) networks are trained using known Android applications and then used to detect the latest and new Android malware applications. Initially, the optimal permission features of Android apps are selected using Information Gain Ratio (IGR). Appropriately, the features selected by IGR are utilized to train the RBF networks in order to detect effectively the new Android malware apps. The empirical results showed that RBF achieved the best detection accuracy (97.20%) among other common machine learning techniques. Furthermore, RBF accomplished the best detection results in most of the other measures.

• International journal of advanced smart convergence
• /
• v.12 no.4
• /
• pp.147-153
• /
• 2023

In recent times, an absence of effective crowd management has led to numerous stampede incidents in crowded places. A crucial component for enhancing on-site crowd management effectiveness is the utilization of crowd counting technology. Current approaches to analyzing congested scenes have evolved beyond simple crowd counting, which outputs the number of people in the targeted image to a density map. This development aligns with the demands of real-life applications, as the same number of people can exhibit vastly different crowd distributions. Therefore, solely counting the number of crowds is no longer sufficient. CSRNet stands out as one representative method within this advanced category of approaches. In this paper, we propose a crowd counting network which is adaptive to the change in the density of people in the scene, addressing the performance degradation issue observed in the existing CSRNet(Congested Scene Recognition Network) when there are changes in density. To overcome the weakness of the CSRNet, we introduce a system that takes input from the image's information and adjusts the output of CSRNet based on the features extracted from the image. This aims to improve the algorithm's adaptability to changes in density, supplementing the shortcomings identified in the original CSRNet.

• Journal of the Korean Society for information Management
• /
• v.25 no.3
• /
• pp.27-39
• /
• 2008

In bibliographic data, the use of personal names to indicate authors makes it difficult to specify a particular author since there are numerous authors whose personal names are the same. Resolving same-name author instances into different individuals is called author resolution, which consists of two steps: calculating author similarities and then clustering same-name author instances into different person groups. Author similarities are computed from similarities of author-related bibliographic features such as coauthors, titles of papers, publication information, using supervised or unsupervised methods. Supervised approaches employ machine learning techniques to automatically learn the author similarity function from author-resolved training samples. So far however, a few machine learning methods have been investigated for author resolution. This paper provides a comparative evaluation of a variety of recent high-performing machine learning techniques on author disambiguation, and compares several methods of processing author disambiguation features such as coauthors and titles of papers.

• International Journal of Computer Science & Network Security
• /
• v.22 no.10
• /
• pp.73-82
• /
• 2022

Early detection continues to be the mainstay of breast cancer control as well as the improvement of its treatment. Even so, the absence of cancer symptoms at the onset has early detection quite challenging. Therefore, various researchers continue to focus on cancer as a topic of health to try and make improvements from the perspectives of diagnosis, prevention, and treatment. This research's chief goal is development of a system with deep learning for classification of the breast cancer as non-malignant and malignant using mammogram images. The following two distinct approaches: the first one with the utilization of patches of the Region of Interest (ROI), and the second one with the utilization of the overall images is used. The proposed system is composed of the following two distinct stages: the pre-processing stage and the Convolution Neural Network (CNN) building stage. Of late, the use of meta-heuristic optimization algorithms has accomplished a lot of progress in resolving these problems. Teaching-Learning Based Optimization algorithm (TIBO) meta-heuristic was originally employed for resolving problems of continuous optimization. This work has offered the proposals of novel methods for training the Residual Network (ResNet) as well as the CNN based on the TLBO and the Genetic Algorithm (GA). The classification of breast cancer can be enhanced with direct application of the hybrid TLBO- GA. For this hybrid algorithm, the TLBO, i.e., a core component, will combine the following three distinct operators of the GA: coding, crossover, and mutation. In the TLBO, there is a representation of the optimization solutions as students. On the other hand, the hybrid TLBO-GA will have further division of the students as follows: the top students, the ordinary students, and the poor students. The experiments demonstrated that the proposed hybrid TLBO-GA is more effective than TLBO and GA.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.3
• /
• pp.570-590
• /
• 2024

Breast cancer ranks among the most prevalent forms of malignancy and foremost cause of death by cancer worldwide. It is not preventable. Early and precise detection is the only remedy for lowering the rate of mortality and improving the probability of survival for victims. In contrast to present procedures, thermography aids in the early diagnosis of cancer and thereby saves lives. But the accuracy experiences detrimental impact by low sensitivity for small and deep tumours and the subjectivity by physicians in interpreting the images. Employing deep learning approaches for cancer detection can enhance the efficacy. This study explored the utilization of thermography in early identification of breast cancer with the use of a publicly released dataset known as the DMR-IR dataset. For this purpose, we employed a novel approach that entails the utilization of a pre-trained MobileNetV2 model and fine tuning it through transfer learning techniques. We created three models using MobileNetV2: one was a baseline transfer learning model with weights trained from ImageNet dataset, the second was a fine-tuned model with an adaptive learning rate, and the third utilized early stopping with callbacks during fine-tuning. The results showed that the proposed methods achieved average accuracy rates of 85.15%, 95.19%, and 98.69%, respectively, with various performance indicators such as precision, sensitivity and specificity also being investigated.

• Information Systems Review
• /
• v.21 no.1
• /
• pp.39-59
• /
• 2019

E-learning has improved the educational effect by making it possible to learn anytime and anywhere by escaping the traditional infusion education. As the use of e-learning system increases with the increasing popularity of e-learning, it has become important to measure e-learning satisfaction. In this study, we used the mixed research method to identify satisfaction factors of e-learning. The mixed research method is to perform both qualitative research and quantitative research at the same time. As a quantitative research, we collected reviews in Udemy.com by text mining. Then we classified high and low rated lectures and applied topic modeling technique to derive factors from reviews. Also, this study conducted an in-depth 1:1 interview on e-learning learners as a qualitative research. By combining these results, we were able to derive factors of e-learning satisfaction and dissatisfaction. Based on these factors, we suggested ways to improve e-learning satisfaction. In contrast to the fact that survey-based research was mainly conducted in the past, this study collects actual data by text mining. The academic significance of this study is that the results of the topic modeling are combined with the factor based on the information system success model.

• The KIPS Transactions:PartB
• /
• v.10B no.6
• /
• pp.579-586
• /
• 2003

In this paper, we propose a novel approach to employ emotional features to document retrieval systems. Fine emotional features, such as HAPPY, SAD, ANGRY, FEAR, and DISGUST, have been used to represent Korean document. Users are allowed to use these features for retrieving their documents. Next, retrieved documents are learned by classification methods like cohesion factor, naive Bayesian, and, k-nearest neighbor approaches. In order to combine various approaches, voting method has been used. In addition, k-means clustering has been used for our experimentation. The performance of our approach proved to be better in accuracy than other methods, and be better in short texts rather than large documents.

• Journal of Gifted/Talented Education
• /
• v.24 no.5
• /
• pp.851-876
• /
• 2014

As an empirical test of a model of giftedness with learning disabilities (Song & Porath, 2011), this paper investigated domain thoughts of gifted students without learning disabilities and gifted students with learning disabilities (GLD) in reading, writing, and math. Gifted students in each group were interviewed and the data were analyzed for domain thoughts. The results showed that the former group of gifted students exhibited domain thoughts in a more balanced manner, whereas GLD students showed large discrepancies between domain thoughts; they showed weak specific domain thoughts in contrast to strong other domain thoughts. They also showed ambivalent attitudes even in a domain activity; they presented positive and negative thoughts at the same time. With a comprehensive explanation of the differences between the two groups of gifted students through a cognitive mechanism presented in the model of GLD model, this paper provides new approaches for identification and education of gifted students and GLD students.

• The Journal of Korean Philosophical History
• /
• no.39
• /
• pp.53-83
• /
• 2013

This paper aims to examine how issues related to the heaven-human relation (or nature-culture relation) affected Joseon intellectuals in the eighteenth century by considering Huang Yun Seok as a reference. As a significant indicator, the heaven-human relation issue has traditionally been a critical theme in the history of Confucianism. Since Huang Yun Seok accepted Western Learning based on Zhuxi Learning, he is a good example for examining this issue. Huang's ideas didn't depart too much from Confucianism, but he naturally became interested in Western Learning because of the enthusiastic admiration he had of Ancient Learning since he was a child. Principle-Number was consistent with Ancient Learning and Western Learning, and this was somewhat different from the original notion of numerology. It was used for understanding and explaining astronomical phenomenon. In understanding astronomical phenomenon, Huang used both fact-determined and value-centered approaches. Western astronomy allowed him to make an advance in terms of fact-determined approach while the value-centered approach gave him a portentological perspective on astronomical variation such as solar and lunar eclipses. This indicates one of the ways to keep Zhuxi Learning's identity itself amidst an inflow of new learnings.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.8
• /
• pp.3270-3294
• /
• 2020

Some things come easily to humans, one of them is the ability to navigate around. This capability of navigation suffers significantly in case of partial or complete blindness, restricting life activity. Advances in the technological landscape have given way to new solutions aiding navigation for the visually impaired. In this paper, we analyze the existing works and identify the challenges of path selection, context awareness, obstacle detection/identification and integration of visual and nonvisual information associated with real-time assisted mobility. In the process, we explore machine learning approaches for robotic path planning, multi constrained optimal path computation and sensor based wearable assistive devices for the visually impaired. It is observed that the solution to problem is complex and computationally intensive and significant effort is required towards the development of richer and comfortable paths for safe and smooth navigation of visually impaired people. We cannot overlook to explore more effective strategies of acquiring surrounding information towards autonomous mobility.