This page has only limited features, please log in for full access.
Smart services are a concept that provides services to the citizens in an efficient manner. The online shopping and recommender system can play an important role for smart cities in providing relevant item recommendations to the users. One of the famous Recommendation System strategies is known as Collaborative Filtering and provides popular suggestions to the users. The recommendation is generated by identifying a set of similar users from a user-item rating matrix using a similarity measure. The problem with the majority of the recommender systems is whether the generated recommendations are good enough because users usually find recommendations from their circle more appealing. It is important to use only those similar users that have some kind of trust among them. The accuracy of the recommendations also gets affected due to the sparsity of the user-item matrix. To handle these problems, a trust-based technique TrustASVD++ is proposed, which combines a user’s trust data in the Matrix Factorization context. The proposed method combines trust values with user ratings for improved recommendations using Pearson Correlation Coefficient (PCC). PCC is compared with other state-of-the-art similarity measures, and the results obtained show that PCC outperforms all the other relevant measures. To assess the efficiency of the offered strategy, testing on numerous datasets has been carried out including Epinions, FilmTrust, and Ciao. The results illustrate the considerable improvement of the proposed method over numerous contemporary techniques.
Asma Rahim; Mehr Yahya Durrani; Saira Gillani; Zeeshan Ali; Najam Ul Hasan; Mucheol Kim. An efficient recommender system algorithm using trust data. The Journal of Supercomputing 2021, 1 -21.
AMA StyleAsma Rahim, Mehr Yahya Durrani, Saira Gillani, Zeeshan Ali, Najam Ul Hasan, Mucheol Kim. An efficient recommender system algorithm using trust data. The Journal of Supercomputing. 2021; ():1-21.
Chicago/Turabian StyleAsma Rahim; Mehr Yahya Durrani; Saira Gillani; Zeeshan Ali; Najam Ul Hasan; Mucheol Kim. 2021. "An efficient recommender system algorithm using trust data." The Journal of Supercomputing , no. : 1-21.
A typical growth of cells inside tissue is normally known as a nodular entity. Lung nodule segmentation from computed tomography (CT) images becomes crucial for early lung cancer diagnosis. An issue that pertains to the segmentation of lung nodules is homogenous modular variants. The resemblance among nodules as well as among neighboring regions is very challenging to deal with. Here, we propose an end-to-end U-Net-based segmentation framework named DA-Net for efficient lung nodule segmentation. This method extracts rich features by integrating compactly and densely linked rich convolutional blocks merged with Atrous convolutions blocks to broaden the view of filters without dropping loss and coverage data. We first extract the lung’s ROI images from the whole CT scan slices using standard image processing operations and k-means clustering. This reduces the search space of the model to only lungs where the nodules are present instead of the whole CT scan slice. The evaluation of the suggested model was performed through utilizing the LIDC-IDRI dataset. According to the results, we found that DA-Net showed good performance, achieving an 81% Dice score value and 71.6% IOU score.
Muazzam Maqsood; Sadaf Yasmin; Irfan Mehmood; Maryam Bukhari; Mucheol Kim. An Efficient DA-Net Architecture for Lung Nodule Segmentation. Mathematics 2021, 9, 1457 .
AMA StyleMuazzam Maqsood, Sadaf Yasmin, Irfan Mehmood, Maryam Bukhari, Mucheol Kim. An Efficient DA-Net Architecture for Lung Nodule Segmentation. Mathematics. 2021; 9 (13):1457.
Chicago/Turabian StyleMuazzam Maqsood; Sadaf Yasmin; Irfan Mehmood; Maryam Bukhari; Mucheol Kim. 2021. "An Efficient DA-Net Architecture for Lung Nodule Segmentation." Mathematics 9, no. 13: 1457.
Exascale computing, the next-generation computing environment, is expected to be applied to scientific and engineering applications. Accordingly, high-performance computing (HPC) technology is also being developed to improve the performance and high-speed parallelism of many-core processors. Previous researches on improving HPC performance have developed in the form of improving the overall system performance by analyzing the state of the system occurring in the range of the knowledge of expert. However, performance events occurring in a processor in a many-core environment have a large number of indicators, and it is difficult to analyze the correlation between them. In this paper, we propose an application-specific feature selection and clustering approach with HPC system profiling data. The proposed approach performs PCA-based feature selections for efficient performance analysis methods. In addition, the application-specific characteristics from profiling data can be analyzed by unsupervised learning. In our experiments, we evaluated highly parallel supercomputers with NAS parallel benchmark and were able to cluster applications efficiently.
Mincheol Shin; Geunchul Park; Chan Yeol Park; Jongmin Lee; Mucheol Kim. Application-specific feature selection and clustering approach with HPC system profiling data. The Journal of Supercomputing 2021, 77, 6817 -6831.
AMA StyleMincheol Shin, Geunchul Park, Chan Yeol Park, Jongmin Lee, Mucheol Kim. Application-specific feature selection and clustering approach with HPC system profiling data. The Journal of Supercomputing. 2021; 77 (7):6817-6831.
Chicago/Turabian StyleMincheol Shin; Geunchul Park; Chan Yeol Park; Jongmin Lee; Mucheol Kim. 2021. "Application-specific feature selection and clustering approach with HPC system profiling data." The Journal of Supercomputing 77, no. 7: 6817-6831.
Road traffic accidents are a ‘global tragedy’ that generates unpredictable chunks of data having heterogeneity. To avoid this heterogeneous tragedy, we need to fraternize and categorize the datasets. This can be done with the help of clustering and association rule mining techniques. As the trend of accidents is increasing throughout the year, agglomerative hierarchical clustering approach is proposed for time series big data for trend analysis. This clustering approach segments the time sequence data into different clusters after normalizing the discrete time sequence data. Agglomerative hierarchical clustering takes the objects with similar properties and groups them together to form the group of clusters. The paradigmatic time sequence (PTS) data for each cluster with the help of dynamic time warping are identified that calculate the closest time sequence. The PTS analyzes various zone details and forms a cluster to report the data. This approach is more useful and optimal than the traditional statistical techniques.
Subbulakshmi Pasupathi; Vimal Shanmuganathan; Kaliappan Madasamy; Harold Robinson Yesudhas; Mucheol Kim. Trend analysis using agglomerative hierarchical clustering approach for time series big data. The Journal of Supercomputing 2021, 77, 6505 -6524.
AMA StyleSubbulakshmi Pasupathi, Vimal Shanmuganathan, Kaliappan Madasamy, Harold Robinson Yesudhas, Mucheol Kim. Trend analysis using agglomerative hierarchical clustering approach for time series big data. The Journal of Supercomputing. 2021; 77 (7):6505-6524.
Chicago/Turabian StyleSubbulakshmi Pasupathi; Vimal Shanmuganathan; Kaliappan Madasamy; Harold Robinson Yesudhas; Mucheol Kim. 2021. "Trend analysis using agglomerative hierarchical clustering approach for time series big data." The Journal of Supercomputing 77, no. 7: 6505-6524.
As the interest in health increased, people are more interested in mental health as well as physical health. Predominantly, due to the development of IT technology and digital contents, production of wellness contents through fusion with digital contents is increasing. Although many types of research that pursue wellness through the satisfaction of the visual sense are increasing, they were dealing with the western painting that emphasizes color and saturation. On the other hand, oriental painting is different from western painting in color and composition, and the expression is also very subjective. In addition, due to the material characteristics and composition of oriental painting, it is often used for mental health treatments such as mental health and self-growth. In this paper, we analyze characteristics of materials and composition of the oriental painting and propose the feature extraction method suitable for them. We also suggest the oriental painting recommendation approach that can provide users with customized digital contents to support wellness. In the experiment, feature extraction results are compared and the appropriateness of the recommendation results is evaluated. The results of the proposed approach are expected to be utilized as a personalized digital contents recommendation service for mental health management of people in the future.
Mucheol Kim; Dongwann Kang; Namyeon Lee. Feature Extraction From Oriental Painting for Wellness Contents Recommendation Services. IEEE Access 2019, 7, 59263 -59270.
AMA StyleMucheol Kim, Dongwann Kang, Namyeon Lee. Feature Extraction From Oriental Painting for Wellness Contents Recommendation Services. IEEE Access. 2019; 7 ():59263-59270.
Chicago/Turabian StyleMucheol Kim; Dongwann Kang; Namyeon Lee. 2019. "Feature Extraction From Oriental Painting for Wellness Contents Recommendation Services." IEEE Access 7, no. : 59263-59270.
Mucheol Kim; Ka Lok Man; Nurmamat Helil. Advanced Internet of Things and Big Data Technology for Smart Human-Care Services. Journal of Sensors 2019, 2019, 1 -3.
AMA StyleMucheol Kim, Ka Lok Man, Nurmamat Helil. Advanced Internet of Things and Big Data Technology for Smart Human-Care Services. Journal of Sensors. 2019; 2019 ():1-3.
Chicago/Turabian StyleMucheol Kim; Ka Lok Man; Nurmamat Helil. 2019. "Advanced Internet of Things and Big Data Technology for Smart Human-Care Services." Journal of Sensors 2019, no. : 1-3.
With the recent advances of information and communication technology, people communicate with each other through online communities or social networking services, such as PatientsLikeMe and Facebook. One of the key challenges in aspects of providing sustainable situation-aware services is how to utilize peoples’ experiences shared as reusable social-intelligence. If domain-specific collective intelligence is well constructed, the knowledge usages can be extended to situation-awareness-based personal situation understanding, and sustainable recommendation services with user intent. In this paper, we introduce a sustainable situation-awareness supporting framework based on text-mining techniques and a domain-specific knowledge model, the so-called Service Quality Model for Hospitals (SQM-H). Different from obtaining sustainable contexts from heterogeneous sensors surrounding users, it aggregates SQM-H based service-specific knowledge from online health communities. Our framework includes a set of components: data aggregation, text-mining, service quality analysis, and open Application Programming Interface (APIs) for recommendation services. Those components have been designed to deal with users’ immediate request, providing service quality related information reflected in collective intelligence and analyzed information based on that along with the SQM-H. As a proof of concept, we implemented a prototype system which interacts with users through smartphone user interface. Our framework supports qualitative and quantitative information based on SQM-H and statistical analyses for the given user queries. Through the implementation and user tests, we confirmed an increased knowledge support for decision-making and an easy mashup with provided Open APIs. We believe that the suggested situation-awareness supporting framework can be applied to numerous sustainable applications related to healthcare and wellness domain areas if domain-specific knowledge models are redesigned.
Yuchul Jung; Cinyoung Hur; Mucheol Kim. Sustainable Situation-Aware Recommendation Services with Collective Intelligence. Sustainability 2018, 10, 1632 .
AMA StyleYuchul Jung, Cinyoung Hur, Mucheol Kim. Sustainable Situation-Aware Recommendation Services with Collective Intelligence. Sustainability. 2018; 10 (5):1632.
Chicago/Turabian StyleYuchul Jung; Cinyoung Hur; Mucheol Kim. 2018. "Sustainable Situation-Aware Recommendation Services with Collective Intelligence." Sustainability 10, no. 5: 1632.
Mucheol Kim; B.B. Gupta; Seunmin Rho. Crowdsourcing based scientific issue tracking with topic analysis. Applied Soft Computing 2018, 66, 506 -511.
AMA StyleMucheol Kim, B.B. Gupta, Seunmin Rho. Crowdsourcing based scientific issue tracking with topic analysis. Applied Soft Computing. 2018; 66 ():506-511.
Chicago/Turabian StyleMucheol Kim; B.B. Gupta; Seunmin Rho. 2018. "Crowdsourcing based scientific issue tracking with topic analysis." Applied Soft Computing 66, no. : 506-511.
Multimedia content comprises the graphics, audio & video clips, animation and text to present learning materials in a style, which improves learner expectation in eLearning paradigm. Electronic learning gained the popularity due to its immense coverage of students and subjects all over the world. The aim of this study is enhancements using agent-based framework through multimedia data in eLearning paradigm. Analysis of multimedia contents and eLearning data are helpful for the course designers, teachers, and administrators of eLearning environments to hunt for undetected patterns and underlying data in learning processes. This research improves the learning curves for the students. It also needs to improve the overall processes in eLearning paradigm. Information and Communication Technologies supported education, and virtual classrooms environments are mandatory. In eLearning data is evolving day by day that includes the semi-structured data, unstructured data, and structured data which is also collectively marked as multimedia big data. Multimedia data has the potential to mining for the analytics and learning. The learning outcomes for the students are very important to find the facts that what impacts the input data on the student. There are 1108 students posted questions in online Learning Management System (LMS) and instructors reply these queries. Sensor data is also gathered by the mobile GPS to find the student location. The system has analyzed the relevance of the replied answers. The student satisfaction is achieved by providing the multimedia-based student-teacher interaction. This can lead to synchronous communication and multimedia content conversation in eLearning paradigm. Machine learning techniques are applied to that data to discover the patterns and behavioral trends. It can also be used in the eLearning environments for the teacher to assist and enhance the pedagogical skills and for student’s learning curve enhancements.
Muhammad Munwar Iqbal; Yasir Saleem; Kashif Naseer; Mucheol Kim. Multimedia based student-teacher smart interaction framework using multi-agents in eLearning. Multimedia Tools and Applications 2017, 77, 5003 -5026.
AMA StyleMuhammad Munwar Iqbal, Yasir Saleem, Kashif Naseer, Mucheol Kim. Multimedia based student-teacher smart interaction framework using multi-agents in eLearning. Multimedia Tools and Applications. 2017; 77 (4):5003-5026.
Chicago/Turabian StyleMuhammad Munwar Iqbal; Yasir Saleem; Kashif Naseer; Mucheol Kim. 2017. "Multimedia based student-teacher smart interaction framework using multi-agents in eLearning." Multimedia Tools and Applications 77, no. 4: 5003-5026.
In this paper, we propose a music management framework to manage the distribution of large volume music contents at home and abroad. In this paper, we define the music contents as an expression model that can be distributed internationally, and distribute the sound sources, analyze all the transaction information and related tasks of the distributed music, process them into various types of data. By defining standardized music expression model specific to music contents and managing music by using big data technology using proposed model, it is possible to automate all transaction information and related tasks online in the music market to provide statistical, analysis, and visualization information and proposed a music management framework to provide the sound source.
Ahyoung Kim; Junwoo Lee; Mucheol Kim. A Study on Music Distribution Framework Using Music Representation Model. 2017 International Conference on Platform Technology and Service (PlatCon) 2017, 1 -3.
AMA StyleAhyoung Kim, Junwoo Lee, Mucheol Kim. A Study on Music Distribution Framework Using Music Representation Model. 2017 International Conference on Platform Technology and Service (PlatCon). 2017; ():1-3.
Chicago/Turabian StyleAhyoung Kim; Junwoo Lee; Mucheol Kim. 2017. "A Study on Music Distribution Framework Using Music Representation Model." 2017 International Conference on Platform Technology and Service (PlatCon) , no. : 1-3.
Multimedia is an essential and integral part of electronic learning (e-learning). In this study, teaching performance and student learning experience are measured using real-time multimedia processing tools and techniques for the e-learning paradigm. Visual attention and visual engagement analysis are performed using two developed algorithms. Video lectures are recorded and delivered to students in e-learning pedagogical setup, which are examined for the visual attention and visual engagement of the student and teacher, respectively. Proposed methodology integrates the assessment on both student and teacher ends. Multimedia processing of video lectures for teaching performance produces scoring dataset. The same methodology on student end for visual attention is used to investigate student experience. These types of datasets then reduced to time-based datasets from the image-based dataset. Correlation and association of both datasets provide the opportunity to relate both student experience and teaching performance as well as to move forward to create content that is more useful. Computational performance of the developed algorithms is compared using different video lectures with their processed frames per second, which is analyzed as per their corresponding bins. Mean, max, and median of the processed frames of all the processed videos are also compared.
Muhammad Farhan; Muhammad Aslam; Sohail Jabbar; Shehzad Khalid; Mucheol Kim. Real-time imaging-based assessment model for improving teaching performance and student experience in e-learning. Journal of Real-Time Image Processing 2017, 13, 491 -504.
AMA StyleMuhammad Farhan, Muhammad Aslam, Sohail Jabbar, Shehzad Khalid, Mucheol Kim. Real-time imaging-based assessment model for improving teaching performance and student experience in e-learning. Journal of Real-Time Image Processing. 2017; 13 (3):491-504.
Chicago/Turabian StyleMuhammad Farhan; Muhammad Aslam; Sohail Jabbar; Shehzad Khalid; Mucheol Kim. 2017. "Real-time imaging-based assessment model for improving teaching performance and student experience in e-learning." Journal of Real-Time Image Processing 13, no. 3: 491-504.
Ahyoung Kim; Junwoo Lee; Mucheol Kim. User Interface Design Platform based on Usage Log Analysis. The Journal of Society for e-Business Studies 2016, 21, 151 -159.
AMA StyleAhyoung Kim, Junwoo Lee, Mucheol Kim. User Interface Design Platform based on Usage Log Analysis. The Journal of Society for e-Business Studies. 2016; 21 (4):151-159.
Chicago/Turabian StyleAhyoung Kim; Junwoo Lee; Mucheol Kim. 2016. "User Interface Design Platform based on Usage Log Analysis." The Journal of Society for e-Business Studies 21, no. 4: 151-159.
In this article, we propose the dynamic resources management model in a cloud computing environment. For monitoring the certain resource, we should utilize not only a cloud management module but also a network management module. However, it is difficult to check the duration time and to observe the digested information about the resources. To investigate these problems in a cloud computing environment, we designed and deployed the cloud service infrastructure based on open-source software, namely, CloudStack. The proposed model regularly stores the usage data for computing resources based on Hadoop and HBase. In addition, our model analyzes the raw data for virtual machines and makes an effective recommendation regarding the consumption of computing resources.
Ahyoung Kim; Junwoo Lee; Mucheol Kim. Resource management model based on cloud computing environment. International Journal of Distributed Sensor Networks 2016, 12, 1 .
AMA StyleAhyoung Kim, Junwoo Lee, Mucheol Kim. Resource management model based on cloud computing environment. International Journal of Distributed Sensor Networks. 2016; 12 (11):1.
Chicago/Turabian StyleAhyoung Kim; Junwoo Lee; Mucheol Kim. 2016. "Resource management model based on cloud computing environment." International Journal of Distributed Sensor Networks 12, no. 11: 1.
The analysis of leukocyte images has drawn interest from fields of both medicine and computer vision for quite some time where different techniques have been applied to automate the process of manual analysis and classification of such images. Manual analysis of blood samples to identify leukocytes is time-consuming and susceptible to error due to the different morphological features of the cells. In this article, the nature-inspired plant growth simulation algorithm has been applied to optimize the image processing technique of object localization of medical images of leukocytes. This paper presents a random bionic algorithm for the automated detection of white blood cells embedded in cluttered smear and stained images of blood samples that uses a fitness function that matches the resemblances of the generated candidate solution to an actual leukocyte. The set of candidate solutions evolves via successive iterations as the proposed algorithm proceeds, guaranteeing their fit with the actual leukocytes outlined in the edge map of the image. The higher precision and sensitivity of the proposed scheme from the existing methods is validated with the experimental results of blood cell images. The proposed method reduces the feasible sets of growth points in each iteration, thereby reducing the required run time of load flow, objective function evaluation, thus reaching the goal state in minimum time and within the desired constraints.
Deblina Bhattacharjee; Anand Paul; Jeong Hong Kim; Mucheol Kim. An object localization optimization technique in medical images using plant growth simulation algorithm. SpringerPlus 2016, 5, 1 -20.
AMA StyleDeblina Bhattacharjee, Anand Paul, Jeong Hong Kim, Mucheol Kim. An object localization optimization technique in medical images using plant growth simulation algorithm. SpringerPlus. 2016; 5 (1):1-20.
Chicago/Turabian StyleDeblina Bhattacharjee; Anand Paul; Jeong Hong Kim; Mucheol Kim. 2016. "An object localization optimization technique in medical images using plant growth simulation algorithm." SpringerPlus 5, no. 1: 1-20.
With the development of web, the amount of information from science and technology is generated and managed in web environments. Then, many researchers are interested in the extracting and analyzing scientific issues from various science data. The proposed approach analyzed the issue keywords from metadata in research projects. Furthermore, we extracted the related science data, such as paper and patent, from science document database. The proposed approach performed social network analysis between typical science data. It generated the clusters which represent the scientific topics with time divisions. Moreover, we could deduce the relationship between science data and social data, such as newsletter and blog data.
Mucheol Kim. Scientific trend analysis and curation with Korean R&D information. The Journal of Supercomputing 2016, 72, 3663 -3673.
AMA StyleMucheol Kim. Scientific trend analysis and curation with Korean R&D information. The Journal of Supercomputing. 2016; 72 (9):3663-3673.
Chicago/Turabian StyleMucheol Kim. 2016. "Scientific trend analysis and curation with Korean R&D information." The Journal of Supercomputing 72, no. 9: 3663-3673.
In real pattern recognition applications, the complete and accurate information of a given set is not always easy to get. Such incomplete knowledge may lead to imperfect expressions of the set using many pattern recognition methods. Rough sets theory is designed to approximately describe an imprecise set by a pair of lower and upper approximations which are weighted by different parameters. As the distributive character varies from one set to another, it is undesirable to employ a constant weighted parameter for controlling the importance of the lower and upper approximations on describing various given sets. This paper presents an improved rough-fuzzy c-means clustering algorithm in which a parameter selection strategy is designed to adaptively adjust the weighted parameter depending on the distributive character of each cluster instead of manually choosing a constant parameter. Such an online-decision method enables the formed prototype to get close to the desirable location. Experimental results on synthetic datasets, real-life datasets, and image segmentation problems confirm the effectiveness of the proposed adaptive parameter selection strategy. With the introduction of adaptive parameter selection strategy, the improved rough sets-based clustering algorithm outperforms its counterparts in certain cases.
Jiao Shi; Yu Lei; Jiaji Wu; Anand Paul; Mucheol Kim; Gwanggil Jeon. Uncertain clustering algorithms based on rough and fuzzy sets for real-time image segmentation. Journal of Real-Time Image Processing 2016, 13, 645 -663.
AMA StyleJiao Shi, Yu Lei, Jiaji Wu, Anand Paul, Mucheol Kim, Gwanggil Jeon. Uncertain clustering algorithms based on rough and fuzzy sets for real-time image segmentation. Journal of Real-Time Image Processing. 2016; 13 (3):645-663.
Chicago/Turabian StyleJiao Shi; Yu Lei; Jiaji Wu; Anand Paul; Mucheol Kim; Gwanggil Jeon. 2016. "Uncertain clustering algorithms based on rough and fuzzy sets for real-time image segmentation." Journal of Real-Time Image Processing 13, no. 3: 645-663.
Recently, various types of log data have been collected and used due to the explosive increase of mobile devices. In mobile environment with high portability and mobility, in addition, the user context information is an important factor for recommendation process. This study attempted to analyze usability log data collected from the mobile device through an application analysis platform. We suggested a context-aware recommendation model to recommend mobile applications or contents by recognizing users’ context data. The usability data of applications consist of activities which were active during the use of a mobile device. The features of these activities are related with time, location and device information. A model proposed in this study has a flexible structure which can be selectively used depending on user circumstances and performs a usability patterns of the applications based on the collaborative filtering method.
Ahyoung Kim; Junwoo Lee; Mucheol Kim. Context-Aware Recommendation Model based on Mobile Application Analysis Platform. Multimedia Tools and Applications 2015, 75, 14783 -14794.
AMA StyleAhyoung Kim, Junwoo Lee, Mucheol Kim. Context-Aware Recommendation Model based on Mobile Application Analysis Platform. Multimedia Tools and Applications. 2015; 75 (22):14783-14794.
Chicago/Turabian StyleAhyoung Kim; Junwoo Lee; Mucheol Kim. 2015. "Context-Aware Recommendation Model based on Mobile Application Analysis Platform." Multimedia Tools and Applications 75, no. 22: 14783-14794.
In big data, data originates from many distributed and different sources in the shape of audio, video, text and sound on the bases of real time; which makes it massive and complex for traditional systems to handle. For this, data representation is required in the form of semantically-enriched for better utilization but keeping it simplified is essential. Such a representation is possible by using Resource Description Framework (RDF) introduced by World Wide Web Consortium (W3C). Bringing and transforming data from different sources in different formats into the RDF form having rapid ratio of increase is still an issue. This requires improvements to cover transition of information among all applications with induction of simplicity to reduce complexities of prominently storing data. With the improvements induced in the shape of big data representation for transformation of data to form into Extensible Markup Language (XML) and then into RDF triple as linked in real time. It is highly needed to make transformation more data friendly. We have worked on this study on developing a process which translates data in a way without any type of information loss. This requires to manage data and metadata in such a way so they may not improve complexity and keep the strong linkage among them. Metadata is being kept generalized to keep it more useful than being dedicated to specific types of data source. Which includes a model explaining its functionality and corresponding algorithms focusing how it gets implemented. A case study is used to show transformation of relational database textual data into RDF, and at end results are being discussed.
Kaleem Razzaq Malik; Tauqir Ahmad; Muhammad Farhan; Muhammad Aslam; Sohail Jabbar; Shehzad Khalid; Mucheol Kim. Big-data: transformation from heterogeneous data to semantically-enriched simplified data. Multimedia Tools and Applications 2015, 75, 12727 -12747.
AMA StyleKaleem Razzaq Malik, Tauqir Ahmad, Muhammad Farhan, Muhammad Aslam, Sohail Jabbar, Shehzad Khalid, Mucheol Kim. Big-data: transformation from heterogeneous data to semantically-enriched simplified data. Multimedia Tools and Applications. 2015; 75 (20):12727-12747.
Chicago/Turabian StyleKaleem Razzaq Malik; Tauqir Ahmad; Muhammad Farhan; Muhammad Aslam; Sohail Jabbar; Shehzad Khalid; Mucheol Kim. 2015. "Big-data: transformation from heterogeneous data to semantically-enriched simplified data." Multimedia Tools and Applications 75, no. 20: 12727-12747.
A new wavelet threshold denoising function and an improved threshold are proposed. It not only retains the advantages of hard and soft denoising functions but also overcomes the disadvantages of the continuity problem of hard denoising function and the constant deviation of the soft denoising function in the new method. In the case of the improved threshold conditions, the new threshold function has a better performance in outstanding image details. It can adapt to different images by joining an adjusting factor. Simulation results show that the new threshold function has a better ability of performing image details and a higher peak signal to noise ratio (PSNR).
Xiaoyu Wang; Xiaoxu Ou; Bo-Wei Chen; Mucheol Kim. Image Denoising Based on Improved Wavelet Threshold Function for Wireless Camera Networks and Transmissions. International Journal of Distributed Sensor Networks 2015, 11, 1 .
AMA StyleXiaoyu Wang, Xiaoxu Ou, Bo-Wei Chen, Mucheol Kim. Image Denoising Based on Improved Wavelet Threshold Function for Wireless Camera Networks and Transmissions. International Journal of Distributed Sensor Networks. 2015; 11 (9):1.
Chicago/Turabian StyleXiaoyu Wang; Xiaoxu Ou; Bo-Wei Chen; Mucheol Kim. 2015. "Image Denoising Based on Improved Wavelet Threshold Function for Wireless Camera Networks and Transmissions." International Journal of Distributed Sensor Networks 11, no. 9: 1.
This study highlights the importance of the physical layer and its impact on network performance in Mobile Ad Hoc Networks (MANETs). This was demonstrated by simulating various MANET scenarios using Network Simulator-2 (NS-2) with enhanced capability by adding propagation loss models (e.g., modified Two-Ray Ground model, ITU Line of Sight and Nonline of Sight (ITU-LoS and NLoS) model into street canyons and combined path loss and shadowing model (C-Shadowing)). The simulation results were then compared with the original Two-Ray Ground (TRG) model already available into NS-2. The scenario primarily simulated was that of a mobile environment using Random Way Point (RWP) mobility model with a variable number of obstacles in the simulation field (such as buildings, etc., causing variable attenuation) in order to analyze the extent of communication losses in various propagation loss models. Performance of the Ad Hoc On-demand Distance Vector (AODV) routing protocol was also analyzed in an ad hoc environment with 20 nodes.
Kashif Amjad; Muhammad Ali; Sohail Jabbar; Majid Hussain; Seungmin Rho; Mucheol Kim. Impact of Dynamic Path Loss Models in an Urban Obstacle Aware Ad Hoc Network Environment. Journal of Sensors 2015, 2015, 1 -8.
AMA StyleKashif Amjad, Muhammad Ali, Sohail Jabbar, Majid Hussain, Seungmin Rho, Mucheol Kim. Impact of Dynamic Path Loss Models in an Urban Obstacle Aware Ad Hoc Network Environment. Journal of Sensors. 2015; 2015 (7):1-8.
Chicago/Turabian StyleKashif Amjad; Muhammad Ali; Sohail Jabbar; Majid Hussain; Seungmin Rho; Mucheol Kim. 2015. "Impact of Dynamic Path Loss Models in an Urban Obstacle Aware Ad Hoc Network Environment." Journal of Sensors 2015, no. 7: 1-8.