• Title/Summary/Keyword: heterogeneous data learning

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A Study on the Influences of Organizational Culture and Learning on BPC Success (조직문화와 조직학습이 BPC성공에 미치는 영향에 관한 연구)

  • Kang, Hee Joo
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.7 no.2
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    • pp.127-145
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    • 2011
  • According to Klempa(1995), the fit between environmental factors of a company may have the impact on its BPC success. Under this background, this paper empirically examines the effects of the fit between the organizational culture and the organizational learning the success of BPC. Organizational cultures, organizational learning, as well as knowledge sharing are the dominant causes that have impact on the innovation characters of organization. Whether an organization has safety-oriented homogeneous culture or it has the change-oriented heterogeneous culture may have impact on its implementation of BPC. Also the implementation of BPC may be affected by whether an organizational learning type has adaptive-oriented learning is single loop or it has the innovative-oriented learning is double loop. Thus, we analyzed the effect of the fit between the organization's culture and organizational learning type on BPC success by using the survey data collected from the companies that have adopted BPC. The findings presented in this paper show that the organization having heterogeneous culture practicing double loop learning the improvement project resulted in the excellent BPC success. These findings provide the perceptive guidelines for managers of organizations contemplating the implementation of BPC project.

Performance Improvement of Fuzzy C-Means Clustering Algorithm by Optimized Early Stopping for Inhomogeneous Datasets

  • Chae-Rim Han;Sun-Jin Lee;Il-Gu Lee
    • Journal of information and communication convergence engineering
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    • v.21 no.3
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    • pp.198-207
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    • 2023
  • Responding to changes in artificial intelligence models and the data environment is crucial for increasing data-learning accuracy and inference stability of industrial applications. A learning model that is overfitted to specific training data leads to poor learning performance and a deterioration in flexibility. Therefore, an early stopping technique is used to stop learning at an appropriate time. However, this technique does not consider the homogeneity and independence of the data collected by heterogeneous nodes in a differential network environment, thus resulting in low learning accuracy and degradation of system performance. In this study, the generalization performance of neural networks is maximized, whereas the effect of the homogeneity of datasets is minimized by achieving an accuracy of 99.7%. This corresponds to a decrease in delay time by a factor of 2.33 and improvement in performance by a factor of 2.5 compared with the conventional method.

User Association and Power Allocation Scheme Using Deep Learning Algorithmin Non-Orthogonal Multiple Access Based Heterogeneous Networks (비직교 다중 접속 기반 이종 네트워크에서 딥러닝 알고리즘을 이용한 사용자 및 전력 할당 기법)

  • Kim, Donghyeon;Lee, In-Ho
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.26 no.3
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    • pp.430-435
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    • 2022
  • In this paper, we consider the non-orthogonal multiple access (NOMA) technique in the heterogeneous network (HetNET) consisting of a single macro base station (BS) and multiple small BSs, where the perfect successive interference cancellation is assumed for the NOMA signals. In this paper, we propose a deep learning-based user association and power allocation scheme to maximize the data rate in the NOMA-based HetNET. In particular, the proposed scheme includes the deep neural network (DNN)-based user association process for load balancing and the DNN-based power allocation process for data-rate maximization. Through the simulation assuming path loss and Rayleigh fading channels between BSs and users, the performance of the proposed scheme is evaluated, and it is compared with the conventional maximum signal-to-interference-plus-noise ratio (Max-SINR) scheme. Through the performance comparison, we show that the proposed scheme provides better sum rate performance than the conventional Max-SINR scheme.

Asymmetric data storage management scheme to ensure the safety of big data in multi-cloud environments based on deep learning (딥러닝 기반의 다중 클라우드 환경에서 빅 데이터의 안전성을 보장하기 위한 비대칭 데이터 저장 관리 기법)

  • Jeong, Yoon-Su
    • Journal of Digital Convergence
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    • v.19 no.3
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    • pp.211-216
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    • 2021
  • Information from various heterogeneous devices is steadily increasing in distributed cloud environments. This is because high-speed network speeds and high-capacity multimedia data are being used. However, research is still underway on how to minimize information errors in big data sent and received by heterogeneous devices. In this paper, we propose a deep learning-based asymmetric storage management technique for minimizing bandwidth and data errors in networks generated by information sent and received in cloud environments. The proposed technique applies deep learning techniques to optimize the load balance after asymmetric hash of the big data information generated by each device. The proposed technique is characterized by allowing errors in big data collected from each device, while also ensuring the connectivity of big data by grouping big data into groups of clusters of dogs. In particular, the proposed technique minimizes information errors when storing and managing big data asymmetrically because it used a loss function that extracted similar values between big data as seeds.

A Study on the Cloud Detection Technique of Heterogeneous Sensors Using Modified DeepLabV3+ (DeepLabV3+를 이용한 이종 센서의 구름탐지 기법 연구)

  • Kim, Mi-Jeong;Ko, Yun-Ho
    • Korean Journal of Remote Sensing
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    • v.38 no.5_1
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    • pp.511-521
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    • 2022
  • Cloud detection and removal from satellite images is an essential process for topographic observation and analysis. Threshold-based cloud detection techniques show stable performance because they detect using the physical characteristics of clouds, but they have the disadvantage of requiring all channels' images and long computational time. Cloud detection techniques using deep learning, which have been studied recently, show short computational time and excellent performance even using only four or less channel (RGB, NIR) images. In this paper, we confirm the performance dependence of the deep learning network according to the heterogeneous learning dataset with different resolutions. The DeepLabV3+ network was improved so that channel features of cloud detection were extracted and learned with two published heterogeneous datasets and mixed data respectively. As a result of the experiment, clouds' Jaccard index was low in a network that learned with different kind of images from test images. However, clouds' Jaccard index was high in a network learned with mixed data that added some of the same kind of test data. Clouds are not structured in a shape, so reflecting channel features in learning is more effective in cloud detection than spatial features. It is necessary to learn channel features of each satellite sensors for cloud detection. Therefore, cloud detection of heterogeneous sensors with different resolutions is very dependent on the learning dataset.

A Semi-Markov Decision Process (SMDP) for Active State Control of A Heterogeneous Network

  • Yang, Janghoon
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.7
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    • pp.3171-3191
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    • 2016
  • Due to growing demand on wireless data traffic, a large number of different types of base stations (BSs) have been installed. However, space-time dependent wireless data traffic densities can result in a significant number of idle BSs, which implies the waste of power resources. To deal with this problem, we propose an active state control algorithm based on semi-Markov decision process (SMDP) for a heterogeneous network. A MDP in discrete time domain is formulated from continuous domain with some approximation. Suboptimal on-line learning algorithm with a random policy is proposed to solve the problem. We explicitly include coverage constraint so that active cells can provide the same signal to noise ratio (SNR) coverage with a targeted outage rate. Simulation results verify that the proposed algorithm properly controls the active state depending on traffic densities without increasing the number of handovers excessively while providing average user perceived rate (UPR) in a more power efficient way than a conventional algorithm.

Detecting outliers in segmented genomes of flu virus using an alignment-free approach

  • Daoud, Mosaab
    • Genomics & Informatics
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    • v.18 no.1
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    • pp.2.1-2.11
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    • 2020
  • In this paper, we propose a new approach to detecting outliers in a set of segmented genomes of the flu virus, a data set with a heterogeneous set of sequences. The approach has the following computational phases: feature extraction, which is a mapping into feature space, alignment-free distance measure to measure the distance between any two segmented genomes, and a mapping into distance space to analyze a quantum of distance values. The approach is implemented using supervised and unsupervised learning modes. The experiments show robustness in detecting outliers of the segmented genome of the flu virus.

Extended Knowledge Graph using Relation Modeling between Heterogeneous Data for Personalized Recommender Systems (이종 데이터 간 관계 모델링을 통한 개인화 추천 시스템의 지식 그래프 확장 기법)

  • SeungJoo Lee;Seokho Ahn;Euijong Lee;Young-Duk Seo
    • Smart Media Journal
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    • v.12 no.4
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    • pp.27-40
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    • 2023
  • Many researchers have investigated ways to enhance recommender systems by integrating heterogeneous data to address the data sparsity problem. However, only a few studies have successfully integrated heterogeneous data using knowledge graph. Additionally, most of the knowledge graphs built in these studies only incorporate explicit relationships between entities and lack additional information. Therefore, we propose a method for expanding knowledge graphs by using deep learning to model latent relationships between heterogeneous data from multiple knowledge bases. Our extended knowledge graph enhances the quality of entity features and ultimately increases the accuracy of predicted user preferences. Experiments using real music data demonstrate that the expanded knowledge graph leads to an increase in recommendation accuracy when compared to the original knowledge graph.

Improving the Accuracy of Document Classification by Learning Heterogeneity (이질성 학습을 통한 문서 분류의 정확성 향상 기법)

  • Wong, William Xiu Shun;Hyun, Yoonjin;Kim, Namgyu
    • Journal of Intelligence and Information Systems
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    • v.24 no.3
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    • pp.21-44
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    • 2018
  • In recent years, the rapid development of internet technology and the popularization of smart devices have resulted in massive amounts of text data. Those text data were produced and distributed through various media platforms such as World Wide Web, Internet news feeds, microblog, and social media. However, this enormous amount of easily obtained information is lack of organization. Therefore, this problem has raised the interest of many researchers in order to manage this huge amount of information. Further, this problem also required professionals that are capable of classifying relevant information and hence text classification is introduced. Text classification is a challenging task in modern data analysis, which it needs to assign a text document into one or more predefined categories or classes. In text classification field, there are different kinds of techniques available such as K-Nearest Neighbor, Naïve Bayes Algorithm, Support Vector Machine, Decision Tree, and Artificial Neural Network. However, while dealing with huge amount of text data, model performance and accuracy becomes a challenge. According to the type of words used in the corpus and type of features created for classification, the performance of a text classification model can be varied. Most of the attempts are been made based on proposing a new algorithm or modifying an existing algorithm. This kind of research can be said already reached their certain limitations for further improvements. In this study, aside from proposing a new algorithm or modifying the algorithm, we focus on searching a way to modify the use of data. It is widely known that classifier performance is influenced by the quality of training data upon which this classifier is built. The real world datasets in most of the time contain noise, or in other words noisy data, these can actually affect the decision made by the classifiers built from these data. In this study, we consider that the data from different domains, which is heterogeneous data might have the characteristics of noise which can be utilized in the classification process. In order to build the classifier, machine learning algorithm is performed based on the assumption that the characteristics of training data and target data are the same or very similar to each other. However, in the case of unstructured data such as text, the features are determined according to the vocabularies included in the document. If the viewpoints of the learning data and target data are different, the features may be appearing different between these two data. In this study, we attempt to improve the classification accuracy by strengthening the robustness of the document classifier through artificially injecting the noise into the process of constructing the document classifier. With data coming from various kind of sources, these data are likely formatted differently. These cause difficulties for traditional machine learning algorithms because they are not developed to recognize different type of data representation at one time and to put them together in same generalization. Therefore, in order to utilize heterogeneous data in the learning process of document classifier, we apply semi-supervised learning in our study. However, unlabeled data might have the possibility to degrade the performance of the document classifier. Therefore, we further proposed a method called Rule Selection-Based Ensemble Semi-Supervised Learning Algorithm (RSESLA) to select only the documents that contributing to the accuracy improvement of the classifier. RSESLA creates multiple views by manipulating the features using different types of classification models and different types of heterogeneous data. The most confident classification rules will be selected and applied for the final decision making. In this paper, three different types of real-world data sources were used, which are news, twitter and blogs.

Enhance Health Risks Prediction Mechanism in the Cloud Using RT-TKRIBC Technique

  • Konduru, Venkateswara Raju;Bharamgoudra, Manjula R
    • Journal of information and communication convergence engineering
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    • v.19 no.3
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    • pp.166-174
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    • 2021
  • A large volume of patient data is generated from various devices used in healthcare applications. With increase in the volume of data generated in the healthcare industry, more wellness monitoring is required. A cloud-enabled analysis of healthcare data that predicts patient risk factors is required. Machine learning techniques have been developed to address these medical care problems. A novel technique called the radix-trie-based Tanimoto kernel regressive infomax boost classification (RT-TKRIBC) technique is introduced to analyze the heterogeneous health data in the cloud to predict the health risks and send alerts. The infomax boost ensemble technique improves the prediction accuracy by finding the maximum mutual information, thereby minimizing the mean square error. The performance evaluation of the proposed RT-TKRIBC technique is realized through extensive simulations in the cloud environment, which provides better prediction accuracy and less prediction time than those provided by the state-of-the-art methods.