• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권10호
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• pp.4787-4807
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• 2016

Although the dense interconnection datacenter networks (DCNs) (e.g. FatTree) provide multiple paths and high bisection bandwidth for each server pair, the single-path TCP (SPT) and ECMP which are widely used currently neither achieve high bandwidth utilization nor have good load balancing. Due to only one available transmission path, SPT cannot make full use of all available bandwidth, while ECMP's random hashing results in many collisions. In this paper, we present OFPT, an OpenFlow based Parallel Transport framework, which integrates precise routing and scheduling for better load balancing and higher network throughput. By adopting OpenFlow based centralized control mechanism, OFPT computes the optimal path and bandwidth provision for each flow according to the global network view. To guarantee high throughput, OFPT dynamically schedules flows with Seamless Flow Migration Mechanism (SFMM), which can avoid packet loss in flow rerouting. Finally, we test OFPT on Mininet and implement it in a real testbed. The experimental results show that the average network throughput in OFPT is up to 97.5% of bisection bandwidth, which is higher than ECMP by 36%. Besides, OFPT decreases the average flow completion time (AFCT) and achieves better scalability.

• International Journal of Computer Science & Network Security
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• 제22권7호
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• pp.165-170
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• 2022

Lying is nuisance to all, and all liars knows it is nuisance but still keep on lying. Sometime people are in confusion how to escape from or how to detect the liar when they lie. In this research we are aiming to establish a dynamic platform to identify liar by using video analysis especially by calculating the ratio of changes in their appearance when they lie. The platform will be developed using a machine learning algorithm along with the dynamic classifier to classify the liar. For the experimental analysis the dataset to be processed in two dimensions (people lying and people tell truth). Both parameter of facial appearance will be stored for future identification. Similarly, there will be standard parameter to be built for true speaker and liar. We hope this standard parameter will be able to diagnosed a liar without a pre-captured data.

• Journal of information and communication convergence engineering
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• 제22권3호
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• pp.221-230
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• 2024

The aim of this study is to investigate the automatic recognition and analysis of Jeju marine-life images using artificial intelligence (AI) technology. The dataset of marine-life images was prepared using tools such as Python, TensorFlow, and Google Colab (Google Colaboratory). We also developed models by training deep learning AI in image recognition to automatically recognize the species found in these images and extract their associated information, such as taxonomy, characteristics, and distribution. This study is innovative in that it uses deep learning technology combined with imagerecognition technology for marine biodiversity research. In addition, these results will lead to the development of the marine-life industry in Jeju by supporting marine environment monitoring and marine resource conservation. Furthermore, this study is anticipated to contribute to academic advancement, specifically in the study of marine species diversity.

• 산업공학
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• 제17권4호
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• pp.407-413
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• 2004

Recently, many enterprises are adopting EAI (Enterprise Application Integration) technologies for integrating heterogeneous enterprise information systems. Among EAI levels, data-level integration is relatively straightforward and most popular. However, most commercial solutions provide complex functionalities and are expensive for implementing the data integration tasks at the small & medium enterprises. Also, they have their own proprietary architectures and have a restricted interoperability. Proposed in this paper is the development of a data integration middleware for facilitating data exchanges between the heterogeneous information systems. The main feature of this middleware is a explicit mapping of meta data about the relationships between source and target data. Based on this explicit mapping, users who do not have expertise in information technology at the small & medium enterprises can easily execute data exchange tasks among various information systems.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권3호
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• pp.253-258
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• 2005

In this study, we introduce a new category of fuzzy inference systems based on information granulation to carry out the model identification of complex and nonlinear systems. Informal speaking, information granules are viewed as linked collections of objects (data, in particular) drawn together by the criteria of proximity, similarity, or functionality. To identify the structure of fuzzy rules we use genetic algorithms (GAs). Granulation of information with the aid of Hard C-Means (HCM) clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms and the least square method (LSM). The proposed model is contrasted with the performance of the conventional fuzzy models in the literature.

• Journal of Information Processing Systems
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• 제4권4호
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• pp.145-152
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• 2008

The specification of the Home Evolved NodeB (Home-eNB), which is a small base station designed for use in residential or small business environment, is currently ongoing in 3GPP LTE (Long Term Evolution) systems. One of the key requirements for its feasibility in the LTE system is the mobility management in the deployment of the numerous Home-eNBs and other 3GPP network. In this paper, we overview the characteristic of Home-eNB and also describe the mobility management issues and the related approaches in 3GPP LTE based Home-eNB systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권4호
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• pp.1538-1552
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• 2021

Machine learning models are vulnerable to adversarial examples generated by adding a deliberately designed perturbation to a benign sample. Particularly, for automatic speech recognition (ASR) system, a benign audio which sounds normal could be decoded as a harmful command due to potential adversarial attacks. In this paper, we focus on the countermeasures against audio adversarial examples. By analyzing the characteristics of ASR systems, we find that frame offsets with silence clip appended at the beginning of an audio can degenerate adversarial perturbations to normal noise. For various scenarios, we exploit frame offsets by different strategies such as defending, detecting and hybrid strategy. Compared with the previous methods, our proposed method can defense audio adversarial example in a simpler, more generic and efficient way. Evaluated on three state-of-the-arts adversarial attacks against different ASR systems respectively, the experimental results demonstrate that the proposed method can effectively improve the robustness of ASR systems.

• International Journal of Computer Science & Network Security
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• 제22권11호
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• pp.319-323
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• 2022

Handwritten text recognition is one of the active research areas nowadays. The progress in this field differs in every language. For example, the progress in Arabic handwritten text recognition is still insignificant and needs more attentions and efforts. One of the most important fields in this is Arabic handwritten manuscript text recognition which focuses in extracting text from historical manuscripts. For eons, ancients used manuscripts to write everything. Nowadays, there are millions of manuscripts all around the world. There are two main challenges in dealing with these manuscripts. The first one is that they are at the risk of damage since they are written in primitive materials, the second challenge is due to the difference in writing styles, hence most people are unable to read these manuscripts easily. Therefore, we discuss in this study different papers that are related to this important research field.

• International Journal of Computer Science & Network Security
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• 제22권12호
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• pp.132-138
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• 2022

Due to the impacts of the current pandemic COVID-19 and the continuation of studying online. There is an urgent need for an effective and efficient education platform to help with the continuity of studying online. Therefore, the question bank system (QB) is introduced. The QB system is designed as a website to create a single platform used by faculty members in universities to generate questions and store them in a bank of questions. In addition to allowing them to add two types of questions, to help the lecturer create exams and present the results of the students to them. For the implementation, two languages were combined which are PHP and Python to generate questions by using Artificial Intelligence (AI). These questions are stored in a single database, and then these questions could be viewed and included in exams smoothly and without complexity. This paper aims to help the faculty members to reduce time and efforts by using the Question Bank System by using AI and Natural Language Processing (NLP) to extract and generate questions from given text. In addition to the tools used to create this function such as NLTK and TextBlob.

• 품질경영학회지
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• 제28권2호
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• pp.89-102
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• 2000

Many organizations have developed their own traditional quality information systems. But, they think of it as one of the functional information systems not as a company-wide decision support information systems. A study on traditional quality information systems(QIS) has been conducted and a new conceptual framework of quality information system is proposed in this paper. In order to support enterprise wide total quality management aggressively, a new conceptual framework, named quality management support information system(QMSIS) is developed and proposed. This framework is based upon Malcolm Baldrige National Quality Award(MBNQA) model integrates management information system approach and traditional quality information system concept. In this model, organizational performance and process performance can be monitored to support managers , decision making about organizational quality management activities.