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KSII Transactions on Internet and Information Systems (TIIS) update

  • : 한국인터넷정보학회
  • : 공학분야  >  기타(공학)
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수록범위 : 1권1호(2007)~14권9호(2020) |수록논문 수 : 2,733
KSII Transactions on Internet and Information Systems (TIIS)
14권9호(2020년 09월) 수록논문
최근 권호 논문
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KCI등재 SCI SCOPUS

1A Parallel Deep Convolutional Neural Network for Alzheimer's disease classification on PET/CT brain images

저자 : Husnu Baris Baydargil , Jangsik Park , Do-young Kang , Hyun Kang , Kook Cho

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 14권 9호 발행 연도 : 2020 페이지 : pp. 3583-3597 (15 pages)

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In this paper, a parallel deep learning model using a convolutional neural network and a dilated convolutional neural network is proposed to classify Alzheimer's disease with high accuracy in PET/CT images. The developed model consists of two pipelines, a conventional CNN pipeline, and a dilated convolution pipeline. An input image is sent through both pipelines, and at the end of both pipelines, extracted features are concatenated and used for classifying Alzheimer's disease. Complimentary abilities of both networks provide better overall accuracy than single conventional CNNs in the dataset. Moreover, instead of performing binary classification, the proposed model performs three-class classification being Alzheimer's disease, mild cognitive impairment, and normal control. Using the data received from Dong-a University, the model performs classification detecting Alzheimer's disease with an accuracy of up to 95.51%.

KCI등재 SCI SCOPUS

2Traffic Flow Prediction Model Based on Spatio-Temporal Dilated Graph Convolution

저자 : Xiufang Sun Jianbo Li , Zhiqiang Lv , Chuanhao Dong

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 14권 9호 발행 연도 : 2020 페이지 : pp. 3598-3614 (17 pages)

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With the increase of motor vehicles and tourism demand, some traffic problems gradually appear, such as traffic congestion, safety accidents and insufficient allocation of traffic resources. Facing these challenges, a model of Spatio-Temporal Dilated Convolutional Network (STDGCN) is proposed for assistance of extracting highly nonlinear and complex characteristics to accurately predict the future traffic flow. In particular, we model the traffic as undirected graphs, on which graph convolutions are built to extract spatial feature informations. Furthermore, a dilated convolution is deployed into graph convolution for capturing multi-scale contextual messages. The proposed STDGCN integrates the dilated convolution into the graph convolution, which realizes the extraction of the spatial and temporal characteristics of traffic flow data, as well as features of road occupancy. To observe the performance of the proposed model, we compare with it with four rivals. We also employ four indicators for evaluation. The experimental results show STDGCN's effectiveness. The prediction accuracy is improved by 17% in comparison with the traditional prediction methods on various real-world traffic datasets.

KCI등재 SCI SCOPUS

3A Heterogeneous IoT Node Authentication Scheme Based on Hybrid Blockchain and Trust Value

저자 : Shiqiang Zhang , Yang Cao , Zhenhu Ning , Fei Xue , Dongzhi Cao , Yongli Yang

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 14권 9호 발행 연도 : 2020 페이지 : pp. 3615-3638 (24 pages)

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Node identity authentication is an essential means to ensure the security of the Internet of Things. Existing blockchain-based IoT node authentication schemes have many problems. A heterogeneous IoT node authentication scheme based on an improved hybrid blockchain is proposed. Firstly, the hybrid blockchain model is designed to make the blockchain and IoT environment more compatible. Then the proxy node selection mechanism is intended to establish a bridge between the ordinary IoT node and the blockchain, building by calculating the trust value between nodes. Finally, based on the improved hybrid blockchain, the node authentication scheme of the model and proxy node selection mechanism establishes a secure connection for communication between nodes. Safety and performance analysis shows proper safety and performance.

KCI등재 SCI SCOPUS

4Survivability Analysis of MANET Routing Protocols under DOS Attacks

저자 : Sohail Abbas , Muhammad Haqdad , Muhammad Zahid Khan , Haseeb Ur Rehman , Ajab Khan , Atta Ur Rehman Khan

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 14권 9호 발행 연도 : 2020 페이지 : pp. 3639-3662 (24 pages)

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The network capability to accomplish its functions in a timely fashion under failures and attacks is known as survivability. Ad hoc routing protocols have been studied and extended to various domains, such as Intelligent Transport Systems (ITSs), Unmanned Aerial Vehicles (UAVs), underwater acoustic networks, and Internet of Things (IoT) focusing on different aspects, such as security, QoS, energy. The existing solutions proposed in this domain incur substantial overhead and eventually become burden on the network, especially when there are fewer attacks or no attack at all. There is a need that the effectiveness of these routing protocols be analyzed in the presence of Denial of Service (DoS) attacks without any intrusion detection or prevention system. This will enable us to establish and identify the inherently stable routing protocols that are capable to survive longer in the presence of these attacks. This work presents a DoS attack case study to perform theoretical analysis of survivability on node and network level in the presence of DoS attacks. We evaluate the performance of reactive and proactive routing protocols and analyse their survivability. For experimentation, we use NS-2 simulator without detection or prevention capabilities. Results show that proactive protocols perform better in terms of throughput, overhead and packet drop.

KCI등재 SCI SCOPUS

5Deep Learning Based Security Model for Cloud based Task Scheduling

저자 : K. Devi , D. Paulraj , B. Muthusenthil

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 14권 9호 발행 연도 : 2020 페이지 : pp. 3663-3679 (17 pages)

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Scheduling plays a dynamic role in cloud computing in generating as well as in efficient distribution of the resources of each task. The principle goal of scheduling is to limit resource starvation and to guarantee fairness among the parties using the resources. The demand for resources fluctuates dynamically hence the prearranging of resources is a challenging task. Many task-scheduling approaches have been used in the cloud-computing environment. Security in cloud computing environment is one of the core issue in distributed computing. We have designed a deep learning-based security model for scheduling tasks in cloud computing and it has been implemented using CloudSim 3.0 simulator written in Java and verification of the results from different perspectives, such as response time with and without security factors, makespan, cost, CPU utilization, I/O utilization, Memory utilization, and execution time is compared with Round Robin (RR) and Waited Round Robin (WRR) algorithms

KCI등재 SCI SCOPUS

6E-customized Product: User-centered Co-design Experiences

저자 : Pei Li , Zi Yang Liu

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 14권 9호 발행 연도 : 2020 페이지 : pp. 3680-3692 (13 pages)

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The purpose of this study is to orient users' touchpoints in co-design experience, to identify their need via visualized experience map, to recommend valid design information in online e-customization services. A user-centered co-design experience map (UCEM) is adopted to analyze the relation between users' desire and time spent, so as to evaluate the online co-design experiences. Based on evolutionary algorithm and fuzzy theory, data of this study is collected from 30 participants. The data was analyzed by descriptive analysis in SPSS, and frequency query and word cloud in NVivo. Employing design category and evaluating users' time spent, the findings are that (a) vamp color matching is consistent with interview data; (b) supported by qualitative feedback, the virtual experience map played an important role in the co-design process and the visualized interaction process; and (c) participants prefer to get more information and professional help on color matching and exterior design. Based on the findings in design category, future work should be focused on developing a better understanding of design resource recommendations and multi-stakeholder communication.

KCI등재 SCI SCOPUS

7Land Registration: Use-case of e-Governance using Blockchain Technology

저자 : Karthika Veeramani , Suresh Jaganathan

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 14권 9호 발행 연도 : 2020 페이지 : pp. 3693-3711 (19 pages)

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e-Governance is a medium to offer various services to citizens through a web portal, that exists in many countries nowadays. The existing e-Governance technology is a vast, centrally managed database and a set of applications that connect to it via web interfaces. Despite the modernisation of services, it remains with the lack of transparency. Thus, the existing infrastructure of e-Governance paves the way for corrupt practises by the bureaucrats. e-Governance needs a powerful underlying technology which doesn't provide any way to allow tampering of the record and which in turn eliminates corruption. In this paper, we took land registration as a use-case for building e-Governance by keeping Blockchain as an underlying technology, to put off the corrupt practices and to bring transparency. Once transactions in land registration added to the Blockchain, it is immutable as it is cryptographically secured. Besides, the blockchain technology is secured as the ledger is distributed over the network. If a hacker wants to modify the ledger, he needs to hack every node in the blockchain network. Hyperledger Fabric, a permissioned Blockchain adopted for implementation and Hyperledger Caliper for performance analysis with these evaluation metrics such as throughput, latency and execution time.

KCI등재 SCI SCOPUS

8Video smoke detection with block DNCNN and visual change image

저자 : Tong Liu , Jianghua Cheng , Zhimin Yuan , Honghu Hua , Kangcheng Zhao

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 14권 9호 발행 연도 : 2020 페이지 : pp. 3712-3729 (18 pages)

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Smoke detection is helpful for early fire detection. With its large coverage area and low cost, vision-based smoke detection technology is the main research direction of outdoor smoke detection. We propose a two-stage smoke detection method combined with block Deep Normalization and Convolutional Neural Network (DNCNN) and visual change image. In the first stage, each suspected smoke region is detected from each frame of the images by using block DNCNN. According to the physical characteristics of smoke diffusion, a concept of visual change image is put forward in this paper, which is constructed by the video motion change state of the suspected smoke regions, and can describe the physical diffusion characteristics of smoke in the time and space domains. In the second stage, the Support Vector Machine (SVM) classifier is used to classify the Histogram of Oriented Gradients (HOG) features of visual change images of the suspected smoke regions, in this way to reduce the false alarm caused by the smoke-like objects such as cloud and fog. Simulation experiments are carried out on two public datasets of smoke. Results show that the accuracy and recall rate of smoke detection are high, and the false alarm rate is much lower than that of other comparison methods.

KCI등재 SCI SCOPUS

9Deep Learning-based Evolutionary Recommendation Model for Heterogeneous Big Data Integration

저자 : Hyun Yoo , Kyungyong Chung

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 14권 9호 발행 연도 : 2020 페이지 : pp. 3730-3744 (15 pages)

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This study proposes a deep learning-based evolutionary recommendation model for heterogeneous big data integration, for which collaborative filtering and a neural-network algorithm are employed. The proposed model is used to apply an individual's importance or sensory level to formulate a recommendation using the decision-making feedback. The evolutionary recommendation model is based on the Deep Neural Network (DNN), which is useful for analyzing and evaluating the feedback data among various neural-network algorithms, and the DNN is combined with collaborative filtering. The designed model is used to extract health information from data collected by the Korea National Health and Nutrition Examination Survey, and the collaborative filtering-based recommendation model was compared with the deep learning-based evolutionary recommendation model to evaluate its performance. The RMSE is used to evaluate the performance of the proposed model. According to the comparative analysis, the accuracy of the deep learning-based evolutionary recommendation model is superior to that of the collaborative filtering-based recommendation model.

KCI등재 SCI SCOPUS

10Low-Rank Representation-Based Image Super-Resolution Reconstruction with Edge-Preserving

저자 : Rui Gao , Deqiang Cheng , Jie Yao , Liangliang Chen

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 14권 9호 발행 연도 : 2020 페이지 : pp. 3745-3761 (17 pages)

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Low-rank representation methods already achieve many applications in the image reconstruction. However, for high-gradient image patches with rich texture details and strong edge information, it is difficult to find sufficient similar patches. Existing low-rank representation methods usually destroy image critical details and fail to preserve edge structure. In order to promote the performance, a new representation-based image super-resolution reconstruction method is proposed, which combines gradient domain guided image filter with the structure-constrained low-rank representation so as to enhance image details as well as reveal the intrinsic structure of an input image. Firstly, we extract the gradient domain guided filter of each atom in high resolution dictionary in order to acquire high-frequency prior information. Secondly, this prior information is taken as a structure constraint and introduced into the low-rank representation framework to develop a new model so as to maintain the edges of reconstructed image. Thirdly, the approximate optimal solution of the model is solved through alternating direction method of multipliers. After that, experiments are performed and results show that the proposed algorithm has higher performances than conventional state-of-the-art algorithms in both quantitative and qualitative aspects.

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