• Journal of Internet Computing and Services
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• v.12 no.2
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• pp.63-73
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• 2011

In general, conventional search engines provide the same search results for the same queries of users, and however such techniques do not consider users' characteristics. To overcome this problem, we need a new way of personalized search which returns customized search results according to users' preference. In this paper, we propose a concept network profile-based personalized web search system in which the concept network is developed for accumulating users' characteristics. The concept network-based user profile is used to expand initial search queries to achieve personalized search. The concept network is a network structure of concepts where each concept is generated whenever each query is submitted, and it can be defined as a set of keywords extracted from the selected documents. Furthermore, we have improved the concept networks by augmenting intent keywords of each concept with a set of classification tags, called folksonomy, assigned to each document. For an additional personalized search technique, we propose a new re-ranking method that analayzes the degree of overlapped search results.

• Journal of Internet Computing and Services
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• v.15 no.4
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• pp.9-20
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• 2014

Making good predictions about the outcome of one's actions would seem to be essential in the context of social interaction and decision-making. This paper proposes a computational model for learning articulated motion patterns for action recognition, which mimics biological-inspired visual perception processing of human brain. Developed model of cortical architecture for the unsupervised learning of motion sequence, builds upon neurophysiological knowledge about the cortical sites such as IT, MT, STS and specific neuronal representation which contribute to articulated motion perception. Experiments show how the model automatically selects significant motion patterns as well as meaningful static snapshot categories from continuous video input. Such key poses correspond to articulated postures which are utilized in probing the trained network to impose implied motion perception from static views. We also present how sequence selective representations are learned in STS by fusing snapshot and motion input and how learned feedback connections enable making predictions about future input sequence. Network simulations demonstrate the computational capacity of the proposed model for motion recognition.

• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.65-72
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• 2019

Data augmentation is a method that increases the amount of data through various algorithms based on a small amount of sample data. When machine learning and deep learning techniques are used to solve real-world problems, there is often a lack of data sets. The lack of data is at greater risk of underfitting and overfitting, in addition to the poor reflection of the characteristics of the set of data when learning a model. Thus, in this paper, through the layer-wise data augmenting method at each layer of deep neural network, the proposed method produces augmented data that is substantially meaningful and shows that the method presented by the paper through experimentation is effective in the learning of the model by measuring whether the method presented by the paper improves classification accuracy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.730-737
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• 2018

With the increasing number of health consumers aiming for a happy quality of life, the O2O medical marketing market is activated by choosing reliable health care facilities and receiving high quality medical services based on the medical information distributed on web's blog. Because unstructured text data used on the Internet, mobile, and social networks directly or indirectly reflects authors' interests, preferences, and expectations in addition to their expertise, it is difficult to guarantee credibility of medical information. In this study, we propose a blog reading system that provides users with a higher quality medical information service by classifying medical information blogs (medical blog, ad blog) using bigdata and MLP processing. We collect and analyze many domestic medical information blogs on the Internet based on the proposed big data and machine learning technology, and develop a personalized health information recommendation system for each disease. It is expected that the user will be able to maintain his / her health condition by continuously checking his / her health problems and taking the most appropriate measures.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.112-120
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• 2021

Recently, the number of consumers using digital online distribution platforms is increasing. This caused the rapid growth of the e-commerce market and increased delivery volume in urban areas. The logistics system, designed ar006Fund the city center to handle the delivery volume, operates a delivery system from the outskirts of the city to the urban area using cargo trucks. This maintains an ecosystem of high-cost and inefficient structures that increase social costs such as road traffic congestion and environmental problems. To solve this problem, research is being conducted worldwide to establish a high-efficiency urban joint logistics system using urban railway facilities and underground space infrastructure existing in existing cities. The joint logistics system begins with linking unmanned delivery automation services that link terminal delivery such as cargo classification and stacking, infrastructure construction that performs cargo transfer function by separating from passengers such as using cargo platform. To this end, it is necessary to apply the device to the vertical and horizontal transportation machine supporting the vertical transfer in the flat space of the joint logistics terminal, which is the base technology for transporting cargo using the transfer robot to the destination designated as a freight-only urban railway vehicle. Therefore, this paper aims to derive holistic viewpoints needs for design requirements for vertical and vertical transportation machines and freight transportation standard containers, which are underground railway logistics transport devices to be constructed by urban logistics ecosystem changes.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.91-102
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• 2022

With the evolution of wireless sensor network (WSN) technology, the routing policy has foremost importance in the Internet of Things (IoT). A systematic routing policy is one of the primary mechanics to make certain the precise and robust transmission of wireless sensor networks in an energy-efficient manner. In an IoT environment, WSN is utilized for controlling services concerning data like, data gathering, sensing and transmission. With the advantages of IoT potentialities, the traditional routing in a WSN are augmented with decision-making in an energy efficient manner to concur finer optimization. In this paper, we study how to combine IoT-based deep learning classifier with routing called, Kriging Regressive Deep Belief Neural Learning (KR-DBNL) to propose an efficient data packet routing to cope with scalability issues and therefore ensure robust data packet transmission. The KR-DBNL method includes four layers, namely input layer, two hidden layers and one output layer for performing data transmission between source and destination sensor node. Initially, the KR-DBNL method acquires the patient data from different location. Followed by which, the input layer transmits sensor nodes to first hidden layer where analysis of energy consumption, bandwidth consumption and light intensity are made using kriging regression function to perform classification. According to classified results, sensor nodes are classified into higher performance and lower performance sensor nodes. The higher performance sensor nodes are then transmitted to second hidden layer. Here high performance sensor nodes neighbouring sensor with higher signal strength and frequency are selected and sent to the output layer where the actual data packet transmission is performed. Experimental evaluation is carried out on factors such as energy consumption, packet delivery ratio, packet loss rate and end-to-end delay with respect to number of patient data packets and sensor nodes.

• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.15-26
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• 2022

Various subjects are carrying out cyberattacks using a variety of tactics and techniques. Additionally, cyberattacks for political and economic purposes are also being carried out by groups which is sponsored by its nation. To deal with cyberattacks, researchers used to classify the malware family and the subjects of the attack based on malware signature. Unfortunately, attackers can easily masquerade as other group. Also, as the attack varies with subject, techniques, and purpose, it is more effective for defenders to identify the attacker's purpose and goal to respond appropriately. The essential goal of cyberattacks is to threaten the information security of the target assets. Information security is achieved by preserving the confidentiality, integrity, and availability of the assets. In this paper, we relabel the attacker's goal based on MITRE ATT&CK® in the point of CIA triad as well as classifying cyber security reports to verify the labeling method. Experimental results show that the model classified the proposed CIA label with at most 80% probability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.1101-1121
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• 2024

Thorough understanding of user demands and formulation of product development strategies are crucial in product design, and can effectively stimulate consumer behavior. Scientific categorization and classification of demands contribute to accurate design development, design efficiency, and success rates. In recent years, e-commerce has become important consumption platforms for smart wearable products. However, there are few studies on product design and development among those related to promoting platform product services and sales. Meanwhile, design strategies focusing on real consumer needs are scarce among smart wearable product design studies. Therefore, an empirical consumer demand analysis method is proposed and design development strategies are formulated based on a categorized interpretation of demands. Using representative smart bracelets from wearable smart products as a case, this paper classifies consumer demands with three methods: big data semantic analysis, KANO model analysis, and satisfaction analysis. The results reveal that analysis methods proposed herein can effectively classify consumer demands and confirm that differences in consumer demand categories have varying impacts on consumer behavior. On this basis, corresponding design strategies are proposed based on four categories of consumer demands, aiming to make product design the leading factor and promote consumer behavior on e-commerce platforms. This research further enriches demand research on smart wearable products on e-commerce platforms, and optimizes products from a design perspective, thereby promoting consumption. In future research, different data analysis methods will be tried to compare and analyze changes in consumer demands and influencing factors, thus improving research on impact factors of product design in e-commerce.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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• pp.24-38
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• 2018

Since a social networking service (SNS) isconsidered as an effective means to communicate and interact with customers, companies are trying to utilize SNS effectively. There is a lack of theory relating to the attributes of SNS. This study aims to investigate the attributes of SNS to classify SNS. Based on the social network theory, and previous studies on internet, blog, homepage, communication attributes, this study proposes the seven attributes to classify SNS: interaction, communication, entertainment, information, sharing, intimacy and connection. A pre-test, a pilot test and a main test are conducted. In the main test, 239 SNS users are participated. Through a factor analysis this study verifies the seven attributes of SNS. An analysis of variance with multiple comparisons of $Scheff{\acute{e}}$ method identifies that three attributes, interaction, communication and connection, are found to play significant roles to differentiate SNS. Looking at the overall mean values of the SNS by attribute, interaction, sharing, entertainment, intimacy and communication were relatively high in Facebook. Facebook showed higher values in attributes of interaction, sharing, entertainment, intimacy and communication. Twitter shows the relatively high scores for information and connection. Regarding interaction, Facebook shows higher scores than Twitter and Cyworld. For connection, Cyworld showed a significantly lower score than Twitter and Facebook. Cyworld was separated from the others in the light of communication. Cyworld is relatively weak in communication as it is limited to the message exchanges. The results will help in identifying major attributes for each SNS and classifying SNS.

• Information Systems Review
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• v.13 no.3
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• pp.47-63
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• 2011

The rapid spread of the Smartphone usage among the public has brought great changes to the overall society. Aiming to gain their competitiveness with better Smartphone service quality, manufacturers are endeavoring to keep the pace with the popularization of mobile internet and social changes. Researches on the Smartphone service quality are actively undergoing in the academic circles as well. A great many of studies ranging from the past mobile services to the recent Smartphone services have thus far focused on proposing the systematic arrangement and the typology in terms of service quality, which in turn have provided the theoretical foundation and broaden the scope of comprehension. Besides technical aspects of the mobile and Smartphone services, the earlier studies in the behavioral domain, however, only took into considerations the positive aspect of users' satisfaction with the quality of services via new media devices like Smartphone. The rationale behind this mainly comes from the assumption that as the opposite definition of satisfaction is dissatisfaction, the services are not adopted if dissatisfied. However, it is not always true to conclude that service users are satisfied when the service is functionally fulfilled and dissatisfied otherwise. That is because there exist some cases that quality attributes provide satisfaction when achieved fully, but do not cause dissatisfaction when not fulfilled. And there also exist other cases that quality attributes are taken for granted when fulfilled but result in dissatisfaction when not fulfilled. To account this multi-dimensional feature of service quality attributes in relation with user satisfaction, this study took advantage of Kano model following the identification of a set of the Smartphone service quality attributes by investigating the previous studies. Categorizing of the service quality elements reflecting the customers' needs would perhaps help manage Smartphone service quality, enabling business managers to identify which quality attributes more emphasis to put on and what strategy to establish for the future.