• Journal of Broadcast Engineering
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• v.16 no.5
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• pp.797-800
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• 2011

The objective of MPEG Media Transport (MMT), which is on going standard, is to develop efficient delivery of media over packet based networks in an adaptive, progressive, download/streaming fashion over various IP based networks, including terrestrial, satellite and cable broadcast networks. In this paper we introduce utilization of signal strength information based on Cross Layer Design(CLD) to efficient multimedia delivery over wireless network in which in practice the wireless conditions can vary significantly. Many recent studies have shown that a significant improvement in wireless video throughput can be achieved by utilizing signal strength information on CLD [1][2]. Despite of its usefulness, however, it was difficult to employ signal strength information in rate adaptation applications due to different representation of signal strength information for each underlying wireless network. To that end, we proposed syntax and semantics of signal strength information in such a way that the information can be interpreted in the unified way. The proposed signal strength information was proposed for the MMT standardization.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.155-164
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• 2023

Machine learning with its high precision algorithms, Precision agriculture (PA) is a new emerging concept nowadays. Many researchers have worked on the quality and quantity of PA by using sensors, networking, machine learning (ML) techniques, and big data. However, there has been no attempt to work on trends of artificial intelligence (AI) techniques, dataset and crop type on precision agriculture using internet of things (IoT). This research aims to systematically analyze the domains of AI techniques and datasets that have been used in IoT based prediction in the area of PA. A systematic literature review is performed on AI based techniques and datasets for crop management, weather, irrigation, plant, soil and pest prediction. We took the papers on precision agriculture published in the last six years (2013-2019). We considered 42 primary studies related to the research objectives. After critical analysis of the studies, we found that crop management; soil and temperature areas of PA have been commonly used with the help of IoT devices and AI techniques. Moreover, different artificial intelligence techniques like ANN, CNN, SVM, Decision Tree, RF, etc. have been utilized in different fields of Precision agriculture. Image processing with supervised and unsupervised learning practice for prediction and monitoring the PA are also used. In addition, most of the studies are forfaiting sensory dataset to measure different properties of soil, weather, irrigation and crop. To this end, at the end, we provide future directions for researchers and guidelines for practitioners based on the findings of this review.

• Asian review of World Histories
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• v.4 no.1
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• pp.3-34
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• 2016

This introduction is both a statement of a research problem and an account of the first research results for its solution. As more historical databases come online and overlap in coverage, we need to discuss the two main issues that prevent 'big' results from emerging so far. Firstly, historical data are seen by computer science people as unstructured, that is, historical records cannot be easily decomposed into unambiguous fields, like in population (birth and death records) and taxation data. Secondly, machine-learning tools developed for structured data cannot be applied as they are for historical research. We propose a complex network, narrative-driven approach to mining historical databases. In such a time-integrated network obtained by overlaying records from historical databases, the nodes are actors, while thelinks are actions. In the case study that we present (the world as seen from Venice, 1205-1533), the actors are governments, while the actions are limited to war, trade, and treaty to keep the case study tractable. We then identify key periods, key events, and hence key actors, key locations through a time-resolved examination of the actions. This tool allows historians to deal with historical data issues (e.g., source provenance identification, event validation, trade-conflict-diplomacy relationships, etc.). On a higher level, this automatic extraction of key narratives from a historical database allows historians to formulate hypotheses on the courses of history, and also allow them to test these hypotheses in other actions or in additional data sets. Our vision is that this narrative-driven analysis of historical data can lead to the development of multiple scale agent-based models, which can be simulated on a computer to generate ensembles of counterfactual histories that would deepen our understanding of how our actual history developed the way it did. The generation of such narratives, automatically and in a scalable way, will revolutionize the practice of history as a discipline, because historical knowledge, that is the treasure of human experiences (i.e. the heritage of the world), will become what might be inherited by machine learning algorithms and used in smart cities to highlight and explain present ties and illustrate potential future scenarios and visionarios.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.185-196
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• 2022

The Internet of Things (IoT) has become more and more widespread in recent years, thus attackers are placing greater emphasis on IoT environments. The IoT connects a large number of smart devices via wired and wireless networks that incorporate sensors or actuators in order to produce and share meaningful information. Attackers employed IoT devices as bots to assault the target server; however, because of their resource limitations, these devices are easily infected with IoT malware. The Distributed Denial of Service (DDoS) is one of the many security problems that might arise in an IoT context. DDOS attempt involves flooding a target server with irrelevant requests in an effort to disrupt it fully or partially. This worst practice blocks the legitimate user requests from being processed. We explored an intelligent intrusion detection system (IIDS) using a particular sort of machine learning, such as Artificial Neural Networks, (ANN) in order to handle and mitigate this type of cyber-attacks. In this research paper Feed-Forward Neural Network (FNN) is tested for detecting the DDOS attacks using a modified version of the KDD Cup 99 dataset. The aim of this paper is to determine the performance of the most effective and efficient Back-propagation algorithms among several algorithms and check the potential capability of ANN- based network model as a classifier to counteract the cyber-attacks in IoT environments. We have found that except Gradient Descent with Momentum Algorithm, the success rate obtained by the other three optimized and effective Back- Propagation algorithms is above 99.00%. The experimental findings showed that the accuracy rate of the proposed method using ANN is satisfactory.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.516-524
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• 2022

The construction industry is considered the most hazardous industry globally. Therefore, safety education is crucial for raising the safety awareness of construction workers working at construction sites and creating a safe working environment. However, the current safety education method and tools cannot provide trainees with realistic and practical experiences that might help better safety awareness in practice. A metaverse, a real-time network of 3D virtual worlds focused on social connection, was created for more interactive communication, collaboration, and coordination between users. Several previous studies have noted that the metaverse has excellent potential for improved safety education performance, but its required functions and practical applications have not been thoroughly researched. In order to fill the research gap, this paper reviewed the potential benefits of a metaverse based on the current research and suggested its application for safety education purposes. This paper scrutinized the metaverse's key functions, particularly its information and knowledge sharing function and reality capture function. Then, the authors created a metaverse prototype based on the two key functions described above. The main contribution of this paper is reviewing the potential benefits of a metaverse for safety education. A realistic and feasible metaverse platform should be developed in future studies, and its impact on safety education should be quantitatively verified.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.6
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• pp.1091-1101
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• 2015

Cache mechanism, which has been a research subject for a significant period of time in computer science, has become realized in the form of web caching in network practice. Web caching has various advantages, such as saving of network resources and response time reduction, depends its performance on cache replacement policy, therefore, analysis and consideration of the environment in which a web cache operates is essential for designing better replacement policies. Thus, in the current web environment where is rapidly changing relative to the past, a new cache replacement policy is necessary to reflect those changes. In this paper we stipulate some characteristics of the web at present, propose a new cache replacement policy, and evaluate it.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.294-300
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• 2022

The article highlights the European experience of innovative educational technologies of training management specialists. Based on existing strategies, relevant in the European educational space, the introduction of regulatory elements to maintain a balance between the traditional and innovative format of the educational process, which is typical for the Ukrainian education system is proposed. The article aims to single out educational and technological innovations into a separate cluster of managerial training at different levels in the context of the principles of the modern synergetic sociocultural paradigm. The main objectives of the work are to develop settings to ensure the effective functioning of innovative educational technologies. Among the synergetic principles of educational technologies, providing the formation of necessary competencies of future managers, are: self-organization, interdisciplinarity, nonlinearity, individuality, and technologization. The methods used in the scientific study can be attributed to the group of scientific synergetic methodology. So, the training of specialists in management, implemented in the European practice assumes the use of new educational strategies. These technologies provide both the necessary skills of different levels (hard-soft-digital skills) and the observance of value components (solidarity, ethics, inclusiveness, openness).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.773-777
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• 2007

Very various infusion by development of systems that is based on network is spread. Therefore, Evaluation Tool has been an active research area to reduce the risk from intrusion. On this thesis, during threat assesment, we have planned possible an equal-weight applied assesment and considering the characteristics of the organization an assesment which security factor's weight is variably applied to, and respective organizations to examine its security by itself in order to support the easy findings of the vulnerabilities on the management point of view, and to show the advices to practice.

• Journal of KIBIM
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• v.7 no.1
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• pp.28-35
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• 2017

Cities have complex system composed diverse activities. The activities in cities have complex relationship that creates diverse urban phenomena. Big Data is emerging technology in order to understand such complex network. This research aims to understand such relations by analysing the diverse city indexes. 28 indexes were collected in 25 of districts in Seoul city and analysed to find a weighted correlation. By defining the correlation values of certain years, it tries to predict the missed index values, "happiness" of each districts in other years. The result presents that the overall prediction accuracy 70.25%. However, for further discussion, the result is considered that this methods may not enough to use in practice, since the data has inconstant accuracy by different learning years.

• Journal of Information Technology Applications and Management
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• v.21 no.3
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• pp.19-33
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• 2014

Crowdsourcing is a popular tool for firms to harness external knowledge and resources. One variation of crowdsourcing entails the use of corporate channels in social network services (SNS) such as Twitter to hold public idea competitions. This study examined the role of feedback interaction between participants of idea competitions. More specifically, the study examined the impact of incentives to provide feedback on other participants' ideas. We found that idea competitions where explicit incentives were introduced to elicit crowdsourced feedback in the form of qualitative comments resulted in improved idea generation performance-with more ideas generated overall, and more ideas generated through participant collaborations, through increased comment-posting activities. Based on the findings, implications for theory and practice are discussed.