• Title/Summary/Keyword: Correlation Network

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On Interesting Correlation between Meteorological Parameters and COVID-19 Pandemic in Saudi Arabia

  • Haq, Mohd Anul;Ahmed, Ahsan
    • International Journal of Computer Science & Network Security
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    • v.22 no.4
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    • pp.159-168
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    • 2022
  • The recent outbreak of COVID-19 pandemic cases around the globe has affected Saudi Arabia with around 15, 00,000 confirmed cases within the initial 4 months of transmission. The present investigation analyzed the relationship between daily COVID-19 confirmed cases and meteorological parameters in seventeen cities of KSA. We used secondary published data from the Ministry of Health, KSA daily dataset of COVID-19 confirmed case counts. The meteorological parameters used in the present investigation are temperature, humidity, dew point, and wind speed. Pearson correlation and Spearman rank correlation tests were utilized for data analysis. The incubation period of COVID-19 varies from 1 day to 14 days as per available information. Therefore, an attempt has been made to analyze the effects of meteorological factors with bins of 1, 3, 7, and 14 days. The results suggested that the highest number of correlations (15 cities) was observed for temperature (maximum, minimum, and average) and humidity (12 cities) (minimum and average). The dew point showed relationships for 7 cities and wind showed moderate correlations only for 2 cities. The study results might be useful for authorities and stakeholders in taking specific measures to combat the Covid-19 pandemic.

Comparison of machine learning algorithms to evaluate strength of concrete with marble powder

  • Sharma, Nitisha;Upadhya, Ankita;Thakur, Mohindra S.;Sihag, Parveen
    • Advances in materials Research
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    • v.11 no.1
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    • pp.75-90
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    • 2022
  • In this paper, functionality of soft computing algorithms such as Group method of data handling (GMDH), Random forest (RF), Random tree (RT), Linear regression (LR), M5P, and artificial neural network (ANN) have been looked out to predict the compressive strength of concrete mixed with marble powder. Assessment of result suggests that, the overall performance of ANN based model gives preferable results over the different applied algorithms for the estimate of compressive strength of concrete. The results of coefficient of correlation were maximum in ANN model (0.9139) accompanied through RT with coefficient of correlation (CC) value 0.8241 and minimum root mean square error (RMSE) value of ANN (4.5611) followed by RT with RMSE (5.4246). Similarly, other evaluating parameters like, Willmott's index and Nash-sutcliffe coefficient value of ANN was 0.9458 and 0.7502 followed by RT model (0.8763 and 0.6628). The end result showed that, for both subsets i.e., training and testing subset, ANN has the potential to estimate the compressive strength of concrete. Also, the results of sensitivity suggest that the water-cement ratio has a massive impact in estimating the compressive strength of concrete with marble powder with ANN based model in evaluation with the different parameters for this data set.

Development of Solar Power Output Prediction Method using Big Data Processing Technic (태양광 발전량 예측을 위한 빅데이터 처리 방법 개발)

  • Jung, Jae Cheon;Song, Chi Sung
    • Journal of the Korean Society of Systems Engineering
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    • v.16 no.1
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    • pp.58-67
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    • 2020
  • A big data processing method to predict solar power generation using systems engineering approach is developed in this work. For developing analytical method, linear model (LM), support vector machine (SVN), and artificial neural network (ANN) technique are chosen. As evaluation indices, the cross-correlation and the mean square root of prediction error (RMSEP) are used. From multi-variable comparison test, it was found that ANN methodology provides the highest correlation and the lowest RMSEP.

Correlation Propagation Neural Networks for processing On-line Interpolation of Multi-dimention Information (임의의 다차원 정보의 온라인 전송을 위한 상관기법전파신경망)

  • Kim, Jong-Man;Kim, Won-Sop
    • Proceedings of the KIEE Conference
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    • 2007.11c
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    • pp.83-87
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    • 2007
  • Correlation Propagation Neural Networks is proposed for On-line interpolation. The proposed neural network technique is the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. Information propagates among neighbor nodes laterally and inter-node interpolation is achieved. Through several simulation experiments, real time reconstruction of the nonlinear image information is processed. 1-D CPNN hardware has been implemented with general purpose analog ICs to test the interpolation capability of the proposed neural networks. Experiments with static and dynamic signals have been done upon the CPNN hardware.

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CLASSIFICATION OF BRAIN EVOKED POTENTIAL USING CORRELATION COEFFICIENTS AND NEURAL NETWORK (상관계수와 뉴럴 네트워크를 이용한 뇌 유발 전위의 분류)

  • Chee, Young-Joon;Park, Kwang-Suk
    • Proceedings of the KOSOMBE Conference
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    • v.1995 no.11
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    • pp.189-192
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    • 1995
  • In Visually Evoked Potentials(VEP) or Auditory Evoked Potentials(AEP), the components by the stimulation and the components which are irrelevant to the stimulation(noise or nonstationary spontaneous EEG) are mixed together. So one should average hundreds of EP waves to extract the components by the stimulation only. In this study, we have classified EP's, which are the responses of the different stimulations and different states of subjects. To classify the EP waves, the cross-correlation coefficients and neural network method(error back propagation) are used and compared.

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An Analysis of Shifting Cultivation Areas in Luang Prabang Province, Lao PDR, Using Satellite Imagery and Geographic Information Systems (위성영상과 지리정보시스템을 이용한 라오스 루앙프라방 지역의 화전지역 분석)

  • 조명희
    • Korean Journal of Remote Sensing
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    • v.10 no.1
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    • pp.43-53
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    • 1994
  • By Using MOS-1 satellite image(taken on 24 April 1990, after slash and burn), Shifting cultivation areas were estimated for the sub-basin area. In tropical region to analyse the correlation between shifting cultivation rate and bifurcation rate network which was calculated from topographic map, PC Arc - Info and IDRISI GIS software were used. As the distribution rate of shifting cultivation increases, the bifurcation rate is high. From the correlation analysis between the shifting cultivation and drainage network, it was found that shifting cultivation leads to land degradation and head erosion at the stream valley. To prevent such problems, it is mecessary that shifting cultivation areas should be converted to permanent paddy fields.

Cross-Project Pooling of Defects for Handling Class Imbalance

  • Catherine, J.M.;Djodilatchoumy, S
    • International Journal of Computer Science & Network Security
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    • v.22 no.10
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    • pp.11-16
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    • 2022
  • Applying predictive analytics to predict software defects has improved the overall quality and decreased maintenance costs. Many supervised and unsupervised learning algorithms have been used for defect prediction on publicly available datasets. Most of these datasets suffer from an imbalance in the output classes. We study the impact of class imbalance in the defect datasets on the efficiency of the defect prediction model and propose a CPP method for handling imbalances in the dataset. The performance of the methods is evaluated using measures like Matthew's Correlation Coefficient (MCC), Recall, and Accuracy measures. The proposed sampling technique shows significant improvement in the efficiency of the classifier in predicting defects.

Quantization and Calibration of Color Information From Machine Vision System for Beef Color Grading (소고기 육색 등급 자동 판정을 위한 기계시각 시스템의 칼라 보정 및 정량화)

  • Kim, Jung-Hee;Choi, Sun;Han, Na-Young;Ko, Myung-Jin;Cho, Sung-Ho;Hwang, Heon
    • Journal of Biosystems Engineering
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    • v.32 no.3
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    • pp.160-165
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    • 2007
  • This study was conducted to evaluate beef using a color machine vision system. The machine vision system has an advantage to measure larger area than a colorimeter and also could measure other quality factors like distribution of fats. However, the machine vision measurement is affected by system components. To measure the beef color with the machine vision system, the effect of color balancing control was tested and calibration model was developed. Neural network for color calibration which learned reference color patches showed a high correlation with colorimeter in L*a*b* coordinates and had an adaptability at various measurement environments. The trained network showed a very high correlation with the colorimeter when measuring beef color.

The Design of an Extended Complex Event Model for the Event Correlation Based Network Management Systems (이벤트 상관 기반의 네트워크 관리 시스템을 위한 복합 이벤트 모델의 설계)

  • Lee, Ki-Seong;Lee, Chang-Ha;Lee, Chan-Gun
    • Journal of KIISE:Information Networking
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    • v.37 no.1
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    • pp.8-15
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    • 2010
  • In this study, we present an extended complex event model by considering both of the complex event and the aspect-oriented programming. We propose an advanced scheme for the event specification suited for the event correlation based network management systems by merging these two models. Specifically, we extend the model to support hierarchical event structures and let the model recognize point-cuts of aspect-oriented programming as events. We provide the event operators designed to specify the events on instances and handle temporal relations of the instances. Lastly, we compare the proposed model with other event models and present the benefits of it.

Heterogeneous interaction network of yeast prions and remodeling factors detected in live cells

  • Pack, Chan-Gi;Inoue, Yuji;Higurashi, Takashi;Kawai-Noma, Shigeko;Hayashi, Daigo;Craig, Elizabeth;Taguchi, Hideki
    • BMB Reports
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    • v.50 no.9
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    • pp.478-483
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    • 2017
  • Budding yeast has dozens of prions, which are mutually dependent on each other for the de novo prion formation. In addition to the interactions among prions, transmissions of prions are strictly dependent on two chaperone systems: the Hsp104 and the Hsp70/Hsp40 (J-protein) systems, both of which cooperatively remodel the prion aggregates to ensure the multiplication of prion entities. Since it has been postulated that prions and the remodeling factors constitute complex networks in cells, a quantitative approach to describe the interactions in live cells would be required. Here, the researchers applied dual-color fluorescence cross-correlation spectroscopy to investigate the molecular network of interaction in single live cells. The findings demonstrate that yeast prions and remodeling factors constitute a network through heterogeneous protein-protein interactions.