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KSII Transactions on Internet and Information Systems (TIIS) update

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수록정보
수록범위 : 1권1호(2007)~16권5호(2022) |수록논문 수 : 3,123
KSII Transactions on Internet and Information Systems (TIIS)
16권5호(2022년 05월) 수록논문
최근 권호 논문
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KCI등재 SCOPUS

1A Strategy of Assessing Climate Factors' Influence for Agriculture Output

저자 : Chin-hung Kuan , Yungho Leu , Chien-pang Lee

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 5호 발행 연도 : 2022 페이지 : pp. 1414-1430 (17 pages)

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Due to the Internet of Things popularity, many agricultural data are collected by sensors automatically. The abundance of agricultural data makes precise prediction of rice yield possible. Because the climate factors have an essential effect on the rice yield, we considered the climate factors in the prediction model. Accordingly, this paper proposes a machine learning model for rice yield prediction in Taiwan, including the genetic algorithm and support vector regression model. The dataset of this study includes the meteorological data from the Central Weather Bureau and rice yield of Taiwan from 2003 to 2019. The experimental results show the performance of the proposed model is nearly 30% better than MARS, RF, ANN, and SVR models. The most important climate factors affecting the rice yield are the total sunshine hours, the number of rainfall days, and the temperature. The proposed model also offers three advantages: (a) the proposed model can be used in different geographical regions with high prediction accuracies; (b) the proposed model has a high explanatory ability because it could select the important climate factors which affect rice yield; (c) the proposed model is more suitable for predicting rice yield because it provides higher reliability and stability for predicting. The proposed model can assist the government in making sustainable agricultural policies.

KCI등재 SCOPUS

2A ResNet based multiscale feature extraction for classifying multi-variate medical time series

저자 : Junke Zhu , Le Sun , Yilin Wang , Sudha Subramani , Dandan Peng , Shangwe Charmant Nicolas

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 5호 발행 연도 : 2022 페이지 : pp. 1431-1445 (15 pages)

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We construct a deep neural network model named ECGResNet. This model can diagnosis diseases based on 12-lead ECG data of eight common cardiovascular diseases with a high accuracy. We chose the 16 Blocks of ResNet50 as the main body of the model and added the Squeeze-and-Excitation module to learn the data information between channels adaptively. We modified the first convolutional layer of ResNet50 which has a convolutional kernel of 7 to a superposition of convolutional kernels of 8 and 16 as our feature extraction method. This way allows the model to focus on the overall trend of the ECG signal while also noticing subtle changes. The model further improves the accuracy of cardiovascular and cerebrovascular disease classification by using a fully connected layer that integrates factors such as gender and age. The ECGResNet model adds Dropout layers to both the residual block and SE module of ResNet50, further avoiding the phenomenon of model overfitting. The model was eventually trained using a five-fold cross-validation and Flooding training method, with an accuracy of 95% on the test set and an F1-score of 0.841.We design a new deep neural network, innovate a multi-scale feature extraction method, and apply the SE module to extract features of ECG data.

KCI등재 SCOPUS

3SaaS application mashup based on High Speed Message Processing

저자 : Zhiguo Chen , Myoungjin Kim , Yun Cui

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 5호 발행 연도 : 2022 페이지 : pp. 1446-1465 (20 pages)

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Diversified SaaS applications allow users more choices to use, according to their own preferences. However, the diversification of SaaS applications also makes it impossible for users to choose the best one. Furthermore, users can't take advantage of the functionality between SaaS applications. In this paper, we propose a platform that provides an SaaS mashup service, by extracting interoperable service functions from SaaS-based applications that independent vendors deploy and supporting a customized service recommendation function through log data binding in the cloud environment. The proposed SaaS mashup service platform consists of a SaaS aggregation framework and a log data binding framework. Each framework was concreted by using Apache Kafka and rule matrix-based recommendation techniques. We present the theoretical basis of implementing the high-performance message-processing function using Kafka. The SaaS mashup service platform, which provides a new type of mashup service by linking SaaS functions based on the above technology described, allows users to combine the required service functions freely and access the results of a rich service-utilization experience, using the SaaS mashup function. The platform developed through SaaS mashup service technology research will enable various flexible SaaS services, expected to contribute to the development of the smart-contents industry and the open market.

KCI등재 SCOPUS

4MalDC: Malicious Software Detection and Classification using Machine Learning

저자 : Jaewoong Moon , Subin Kim , Park Jangyong , Jieun Lee , Kyungshin Kim , Jaeseung Song

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 5호 발행 연도 : 2022 페이지 : pp. 1466-1488 (23 pages)

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Recently, the importance and necessity of artificial intelligence (AI), especially machine learning, has been emphasized. In fact, studies are actively underway to solve complex and challenging problems through the use of AI systems, such as intelligent CCTVs, intelligent AI security systems, and AI surgical robots. Information security that involves analysis and response to security vulnerabilities of software is no exception to this and is recognized as one of the fields wherein significant results are expected when AI is applied. This is because the frequency of malware incidents is gradually increasing, and the available security technologies are limited with regard to the use of software security experts or source code analysis tools. We conducted a study on MalDC, a technique that converts malware into images using machine learning, MalDC showed good performance and was able to analyze and classify different types of malware. MalDC applies a preprocessing step to minimize the noise generated in the image conversion process and employs an image augmentation technique to reinforce the insufficient dataset, thus improving the accuracy of the malware classification. To verify the feasibility of our method, we tested the malware classification technique used by MalDC on a dataset provided by Microsoft and malware data collected by the Korea Internet & Security Agency (KISA). Consequently, an accuracy of 97% was achieved.

KCI등재 SCOPUS

5Keywords and Spatial Based Indexing for Searching the Things on Web

저자 : Muhammad R. Faheem , Tayyaba Anees , Muzammil Hussain

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 5호 발행 연도 : 2022 페이지 : pp. 1489-1515 (27 pages)

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The number of interconnected real-world devices such as sensors, actuators, and physical devices has increased with the advancement of technology. Due to this advancement, users face difficulties searching for the location of these devices, and the central issue is the findability of Things. In the WoT environment, keyword-based and geospatial searching approaches are used to locate these devices anywhere and on the web interface. A few static methods of indexing and ranking are discussed in the literature, but they are not suitable for finding devices dynamically. The authors have proposed a mechanism for dynamic and efficient searching of the devices in this paper. Indexing and ranking approaches can improve dynamic searching in different ways. The present paper has focused on indexing for improving dynamic searching and has indexed the Things Description in Solr. This paper presents the Things Description according to the model of W3C JSON-LD along with the open-access APIs. Search efficiency can be analyzed with query response timings, and the accuracy of response timings is critical for search results. Therefore, in this paper, the authors have evaluated their approach by analyzing the search query response timings and the accuracy of their search results. This study utilized different indexing approaches such as key-words-based, spatial, and hybrid. Results indicate that response time and accuracy are better with the hybrid approach than with keyword-based and spatial indexing approaches.

KCI등재 SCOPUS

6A Cyber-Physical Information System for Smart Buildings with Collaborative Information Fusion

저자 : Qing Liu , Lanlan Li

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 5호 발행 연도 : 2022 페이지 : pp. 1516-1539 (24 pages)

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This article shows a set of physical information fusion IoT systems that we designed for smart buildings. Its essence is a computer system that combines physical quantities in buildings with quantitative analysis and control. In the part of the Internet of Things, its mechanism is controlled by a monitoring system based on sensor networks and computer-based algorithms. Based on the design idea of the agent, we have realized human-machine interaction (HMI) and machine-machine interaction (MMI). Among them, HMI is realized through human-machine interaction, while MMI is realized through embedded computing, sensors, controllers, and execution. Device and wireless communication network. This article mainly focuses on the function of wireless sensor networks and MMI in environmental monitoring. This function plays a fundamental role in building security, environmental control, HVAC, and other smart building control systems. The article not only discusses various network applications and their implementation based on agent design but also demonstrates our collaborative information fusion strategy. This strategy can provide a stable incentive method for the system through collaborative information fusion when the sensor system is unstable in the physical measurements, thereby preventing system jitter and unstable response caused by uncertain disturbances and environmental factors. This article also gives the results of the system test. The results show that through the CPS interaction of HMI and MMI, the intelligent building IoT system can achieve comprehensive monitoring, thereby providing support and expansion for advanced automation management.

KCI등재 SCOPUS

7EvaluationOf LoRaWAN In A Highly Dense Environment With Design Of Common Automated Metering Platform (CAMP) Based On LoRaWAN Protocol

저자 : Timothy D Paul , Vimalathithan Rathinasabapathy

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 5호 발행 연도 : 2022 페이지 : pp. 1540-1560 (21 pages)

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Latest technological innovation in the development of compact lower power radios has led to the explosion of Internet of Things. With Wi-Fi, Zigbee and other physical layer protocols offering short coverage area there was a need for a RF protocol that had a larger coverage area with low power consumption. LoRa offers Long Range with lower power consumption. LoRa offers point to point and point to multipoint connections. with Single hop communication in place the need for routing protocols are eliminated. LoRa Wide Area Network stack can accommodate thousands of nodes under a single LoRa gateway with a single hop communication between the end nodes and LoRaWAN gateway. This paper takes an experimental approach to analyze the basic physical layer parameters of LoRa and the practical coverage offered by a LoRaWAN under highly dense urban conditions with variable topography. The insights gained from the practical deployment of the LoRaWAN network, and the subsequent performance analysis is used to design a novel public utility monitoring platform. The second half of the papers is designing a robust platform to integrate both existing wired sensor water meters, current and future generation wireless water meters. The Common Automated Metering Platform is designed to integrate both wired sensors and wireless (LoRaWAN and Wi-Fi) supported water meters. This integrated platform reduces the number of nodes under each LoRaWAN gateway and thus improves the scalability of the network. This architecture is currently designed to accommodate one utility application but can be modified to integrate multi-utility applications.

KCI등재 SCOPUS

8Classification Trends Taxonomy of Model-based Testing for Software Product Line: A Systematic Literature Review

저자 : Rabatul Aduni Sulaiman , Dayang Norhayati Abang Jawawi , Shahliza Abdul Halim

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 5호 발행 연도 : 2022 페이지 : pp. 1561-1583 (23 pages)

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Context: Testing is one of the techniques that can assure the quality of software including the domain of Software Product Line (SPL). Various techniques have been deliberated to enhance the quality of SPL including Model-based Testing (MBT).
Objective: The objective of this study is to analyze and classify trends of MBT in SPL covering the solutions, issues and evaluation aspects by using taxonomy form.
Method: A Systematic Literature Review (SLR) was conducted involving 63 primary studies from different sources. The selected studies were categorized based on their common characteristics.
Results: Several findings can guide future research on MBT for SPL. The important finding is that the multiple measurements are still open to improving current metrics to evaluate test cases in MBT for SPL. The multiple types of measurement required a trade-off between maximization and minimization results to ensure the testing method which could satisfy multiple test criteria for example cost and effectiveness at the same time.

KCI등재 SCOPUS

9Research of 3D image processing of VR technology in medicine based on DNN

저자 : Gong Zhaozhe , Li Xiaodong , Shi Xiaoying , Liu Geng , Chen Bin

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 5호 발행 연도 : 2022 페이지 : pp. 1584-1596 (13 pages)

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According to a survey published in an authoritative journal in January 2020, the globalincidence rate of mental illness is 8.3% for men and 10.6% for women, which indicates thatmental illness has become a globally recognized obstacle. Therefore, specific psychotherapy including mental illness will become an important research topic. It is very effective forpatients with special mental diseases, such as mental illness, to reduce their mental reaction byexposure therapy; the system uses the virtual reality system of medical images processed by learningalgorithm to reproduce the effect of virtual reality exposure method of the high scene of transparent ladder. Compared with the old invasive exposure scene, the results show that theimprovement of both conditions has obvious effect, and the effect of human treatment underthe two conditions is not good. There are obvious differences, which show that virtual reality model will gradually replace the on-the-spot feeling. Finally, with more and more researchers have put forward a variety of other virtual reality image processing models, the research of image processing has gradually become more and more interested.

KCI등재 SCOPUS

10Deep Local Multi-level Feature Aggregation Based High-speed Train Image Matching

저자 : Jun Li , Xiang Li , Yifei Wei , Xiaojun Wang

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 5호 발행 연도 : 2022 페이지 : pp. 1597-1610 (14 pages)

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At present, the main method of high-speed train chassis detection is using computer vision technology to extract keypoints from two related chassis images firstly, then matching these keypoints to find the pixel-level correspondence between these two images, finally, detection and other steps are performed. The quality and accuracy of image matching are very important for subsequent defect detection. Current traditional matching methods are difficult to meet the actual requirements for the generalization of complex scenes such as weather, illumination, and seasonal changes. Therefore, it is of great significance to study the high-speed train image matching method based on deep learning. This paper establishes a high-speed train chassis image matching dataset, including random perspective changes and optical distortion, to simulate the changes in the actual working environment of the high-speed rail system as much as possible. This work designs a convolutional neural network to intensively extract keypoints, so as to alleviate the problems of current methods. With multi-level features, on the one hand, the network restores low-level details, thereby improving the localization accuracy of keypoints, on the other hand, the network can generate robust keypoint descriptors. Detailed experiments show the huge improvement of the proposed network over traditional methods.

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