• Journal of Information Science Theory and Practice
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• v.4 no.1
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• pp.6-20
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• 2016

Scholarly communication plays a significant role in the development and dissemination of research outputs in library and information science (LIS). This study presents findings from a survey which examines the key attributes that characterize the publishing by Pakistani LIS scholars, i.e. academics and professionals, in national journals. A pilot-tested, electronic questionnaire was used to collect the data from the target population. 104 respondents (or 69.3% of target) provided feedback on areas such as number of articles published, number of citations, and the nature of any collaboration with other authors. The findings of this survey revealed that, among the various designated regions of Pakistan, the Punjab region was the most highly represented. In articles published in national journals, there was a clear preference among all respondents to collaborate with at least one other author. The citation metrics for LIS articles in national journals were relatively low (30.22%), which aligns with Scimago’s Journal and Country Rankings. The uptake of social scholarly networks mirrors international trends. Respondents were asked to score factors which could impact negatively on their ability to undertake research and/or publish the results. The study recommends that concerned stakeholders work together, as appropriate, to address concerns. In addition, it recommends that further research be undertaken to define patterns of Pakistani co-authorship in the social sciences.

• Journal of Information Processing Systems
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• v.6 no.4
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• pp.481-500
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• 2010

The Internet explosion and the increase in crucial web applications such as ebanking and e-commerce, make essential the need for network security tools. One of such tools is an Intrusion detection system which can be classified based on detection approachs as being signature-based or anomaly-based. Even though intrusion detection systems are well defined, their cooperation with each other to detect attacks needs to be addressed. Consequently, a new architecture that allows them to cooperate in detecting attacks is proposed. The architecture uses Software Agents to provide scalability and distributability. It works in two modes: learning and detection. During learning mode, it generates a profile for each individual system using a fuzzy data mining algorithm. During detection mode, each system uses the FuzzyJess to match network traffic against its profile. The architecture was tested against a standard data set produced by MIT's Lincoln Laboratory and the primary results show its efficiency and capability to detect attacks. Finally, two new methods, the memory-window and memoryless-window, were developed for extracting useful parameters from raw packets. The parameters are used as detection metrics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.9
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• pp.3056-3074
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• 2014

Convergecast is probably the most common communication style in wireless sensor networks (WSNs). And linear network coding (LNC) is a promising concept to improve throughput or reliability of convergecast. Most of the existing works have mainly focused on exploiting these benefits without considering its potential adverse effect. In this paper, we argue that LNC may not always benefit convergecast. This viewpoint is discussed within four basic scenarios: LNC-aided and none-LNC convergecast schemes with and without automatic repeat request (ARQ) mechanisms. The most concerned performance metrics, including packet collection rate, energy consumption, energy consumption balance and end-to-end delay, are investigated. Theoretical analyses and simulation results show that the way LNC operates, i.e., conscious overhearing and the prerequisite of successfully decoding, could naturally diminish its advantages in convergecast. And LNC-aided convergecast schemes may even be inferior to none-LNC ones when the wireless link delivery ratio is high enough. The conclusion drawn in this paper casts a new light on how to effectively apply LNC to practical WSNs.

• Mass Spectrometry Letters
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• v.10 no.3
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• pp.93-101
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• 2019

Algorithms and software for predicting tandem mass spectra have been developed in recent years. In this work, we explore how distinct in silico $MS^2$ spectra are predicted for isomers, i.e. compounds having the same formula and similar molecular structures, to differentiate between them. We used the CFM-ID 2.0/3.0 predictor with regard to (a) test compounds, whose experimental mass spectra had been randomly sampled from the MassBank of North America (MoNA) collection, and to (b) the most widespread isomers of test compounds searched in the PubChem database. In the first validation test, in silico mass spectra constitute a reference library, and library searches are performed for test experimental spectra of "unknowns". The searches led to the true positive rate (TPR) of ($46-48{\pm}10$)%. In the second test, in silico and experimental spectra were interchanged and this resulted in a TPR of ($58{\pm}10$)%. There were no significant differences between results obtained with different metrics of spectral similarity and predictor versions. In a comparison of test compounds vs. their isomers, a statistically significant correlation between mass spectral data and structural features was observed. The TPR values obtained should be regarded as reasonable results for predicting tandem mass spectra of related chemical structures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1213-1237
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• 2019

Vehicular Ad-hoc Network (VANET) facilities envision future Intelligent Transporting Systems (ITSs) by providing inter-vehicle communication for metrics such as road surveillance, traffic information, and road condition. In recent years, vehicle manufacturers, researchers and academicians have devoted significant attention to vehicular communication technology because of its highly dynamic connectivity and self-organized, decentralized networking characteristics. However, due to VANET's high mobility, dynamic network topology and low communication coverage, dissemination of large data packets (e.g. multimedia content) is challenging. Clustering enhances network performance by maintaining communication link stability, sharing network resources and efficiently using bandwidth among nodes. This paper proposes a mobility-based, multi-hop clustering algorithm, (MBCA) for multimedia content broadcasting over an IEEE 802.11p-LTE-enabled hybrid VANET architecture. The OMNeT++ network simulator and a SUMO traffic generator are used to simulate a network scenario. The simulation results indicate that the proposed clustering algorithm over a hybrid VANET architecture improves the overall network stability and performance, resulting in an overall 20% increased cluster head duration, 20% increased cluster member duration, lower cluster overhead, 15% improved data packet delivery ratio and lower network delay from the referenced schemes [46], [47] and [50] during multimedia content dissemination over VANET.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.499-510
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• 2022

This study examines the reporting factors of crime against business in Korea and proposes a corresponding predictive model using machine learning. While many previous studies focused on the individual factors of theft victims, there is a lack of evidence on the reporting factors of crime against a business that serves the public good as opposed to those that protect private property. Therefore, we proposed a crime prevention model for the willingness factor of theft reporting in businesses. This study used data collected through the 2015 Commercial Crime Damage Survey conducted by the Korea Institute for Criminal Policy. It analyzed data from 834 businesses that had experienced theft during a 2016 crime investigation. The data showed a problem with unbalanced classes. To solve this problem, we jointly applied the Synthetic Minority Over Sampling Technique and the Tomek link techniques to the training data. Two prediction models were implemented. One was a statistical model using logistic regression and elastic net. The other involved a support vector machine model, tree-based machine learning models (e.g., random forest, extreme gradient boosting), and a stacking model. As a result, the features of theft price, invasion, and remedy, which are known to have significant effects on reporting theft offences, can be predicted as determinants of such offences in companies. Finally, we verified and compared the proposed predictive models using several popular metrics. Based on our evaluation of the importance of the features used in each model, we suggest a more accurate criterion for predicting var.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.1-8
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• 2021

With the rapid growth of intelligent devices and communication technologies, 5G network environment has become more heterogeneous and complex in terms of service management and orchestration. 5G architecture requires supportive technologies to handle the existing challenges for improving the Quality of Service (QoS) and the Quality of Experience (QoE) performances. Among many challenges, traffic steering is one of the key elements which requires critically developing an optimal solution for smart guidance, control, and reliable system. Mobile edge computing (MEC), software-defined networking (SDN), network functions virtualization (NFV), and deep learning (DL) play essential roles to complementary develop a flexible computation and extensible flow rules management in this potential aspect. In this proposed system, an accurate flow recommendation, a centralized control, and a reliable distributed connectivity based on the inspection of packet condition are provided. With the system deployment, the packet is classified separately and recommended to request from the optimal destination with matched preferences and conditions. To evaluate the proposed scheme outperformance, a network simulator software was used to conduct and capture the end-to-end QoS performance metrics. SDN flow rules installation was experimented to illustrate the post control function corresponding to DL-based output. The intelligent steering for network communication traffic is cooperatively configured in SDN controller and NFV-orchestrator to lead a variety of beneficial factors for improving massive real-time Internet of Things (IoT) performance.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.98-106
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• 2022

A condition of the retina known as hypertensive retinopathy (HR) is connected to high blood pressure. The severity and persistence of hypertension are directly correlated with the incidence of HR. To avoid blindness, it is essential to recognize and assess HR as soon as possible. Few computer-aided systems are currently available that can diagnose HR issues. On the other hand, those systems focused on gathering characteristics from a variety of retinopathy-related HR lesions and categorizing them using conventional machine-learning algorithms. Consequently, for limited applications, significant and complicated image processing methods are necessary. As seen in recent similar systems, the preciseness of classification is likewise lacking. To address these issues, a new CAD HR-diagnosis system employing the advanced Deep Dense CNN Learning (DD-CNN) technology is being developed to early identify HR. The HR-diagnosis system utilized a convolutional neural network that was previously trained as a feature extractor. The statistical investigation of more than 1400 retinography images is undertaken to assess the accuracy of the implemented system using several performance metrics such as specificity (SP), sensitivity (SE), area under the receiver operating curve (AUC), and accuracy (ACC). On average, we achieved a SE of 97%, ACC of 98%, SP of 99%, and AUC of 0.98. These results indicate that the proposed DD-CNN classifier is used to diagnose hypertensive retinopathy.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.152-152
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• 2023

하천의 건강성을 평가하기 위해 일반적으로 수생태계 건강성 지표(TDI, BMI, FAI, HRI, RVI)가 사용되고 있다. 이 지표는 5가지 등급으로 구분하여 매우 좋음(A), 좋음(B), 보통(C), 나쁨(D), 매우나쁨(E)으로 구분된다. 하지만, 하천의 건강성 관점에서 수질, 토지이용, 지리적 특성, 경관지수와의 상관성을 바탕으로 어떤 영향을 미치는지에 대한 연구가 필요하다. 본 연구에서는 하천의 수생태계 건강성에 영향을 미치는 환경적 인자들과의 관계성을 분석하여 수생태계 건강성이 '좋음'에 해당되는 하천으로 분류하고자 한다. 이를 통해 환경적 인자들의 임계값을 산출하여 하천 관리에 대한 구체적인 우선순위 설정 방안을 제안하고자 한다. 낙동강대권역을 대상으로 수질, 토지이용, 지리적 특성, 경관지수의 여러 변수 중 수생태계 건강성과의 관계에서 대표성을 나타낼 수 있는 환경적 인자를 선정하기 위하여 정준상관분석(CCA)을 수행하였다. 또한 모델 기반의 클러스터 분석을 활용하여 소권역별로 수생태계 건강성이 '좋음'에 해당할 확률을 파악하고, 여기에 해당하는 소권역에 대하여 각각의 환경적 인자에 대한 임계값을 정량적으로 평가하였다. 본 연구에서는 하천의 환경 인자들과의 관계를 분석하여 수생태계 건강성을 평가하고 하천 관리에 대한 구체적인 우선순위를 파악하는 방법을 제안한다. 주성분 분석 및 모델 기반 클러스터 분석을 사용하여 각 소권역에 대한 환경 인자의 임계값을 평가하고, 정책 결정자들이 하천의 건강성을 유지하고 개선할 수 있는 정보를 제공할 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.459-461
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• 2008

오늘날 전자정부는 초고속 정보통신망과 네트워크, 인터넷 기반기술을 이용한 정부간, 정부와 시민, 기업간 다양한 분야에 원활한 행정서비스를 제공하고 있다. 지금까지 일반 패키지 소프트웨어나 임베디드 소프트웨어 등에 관한 품질평가기술 개발 연구는 다양하게 진행해 왔지만 전자정부 소프트웨어에 대한 연구는 활발하지 못한 실정이다. 본 연구에서는 ISO/IEC 12119와 ISO/IEC 9126 및 ISO/IEC 14598을 기반으로 전자정부 소프트웨어의 품질요구사항을 체계화하고 전자정부 소프트웨어 품질평가를 위한 메트릭을 시험모듈 형식으로 구축하고 이를 적용하기 위한 시험표를 구축하였다.