scholarly journals Similarity Analysis of Small- and Medium-Sized Watersheds Based on Clustering Ensemble Model

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 69 ◽  
Author(s):  
Qun Zhao ◽  
Yuelong Zhu ◽  
Dingsheng Wan ◽  
Yufeng Yu ◽  
Yuqing Lu
Keyword(s):  
Random Sampling ◽  
Spectral Clustering ◽  
Automated Analysis ◽  
Similarity Analysis ◽  
Data Sets ◽  
Ensemble Model ◽  
Analysis Method ◽  
Clustering Ensemble ◽  
Consensus Function ◽  
Manual Work

Similarity analysis of small- and medium-sized watersheds mainly depends on manual work, and there is no complete automated analysis method. In order to solve this problem, we propose a similarity analysis method based on clustering ensemble model. First, the iterative clustering ensemble construction algorithm with weighted random sampling (WRS-CCE) is proposed to get great clustering collectives. Then, we combine spectral clustering with the fuzzy C-means method to design a consensus function for small- and medium-sized watershed data sets. Finally, the similarity analysis of small- and medium-sized watersheds is carried out according to the clustering results. Experiments show that the proposed clustering ensemble model can effectively find more potential similar watersheds and can output the similarity of these watersheds.

scholarly journals Spectral clustering ensemble model based on distance decision

2018 ◽  
Vol 54 (13) ◽  
pp. 823-825 ◽  
Author(s):  
Bowen Fei ◽  
Yunfei Qiu ◽  
Daqian Liu
Keyword(s):  
Spectral Clustering ◽  
Ensemble Model ◽  
Clustering Ensemble ◽  
Model Based

scholarly journals Nomograms predicting the overall survival and cancer-specific survival of patients with stage IIIC1 cervical cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yifan Feng ◽  
Ye Wang ◽  
Yangqin Xie ◽  
Shuwei Wu ◽  
Yuyang Li ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Overall Survival ◽  
Random Sampling ◽  
Tumour Size ◽  
Training Group ◽  
Data Sets ◽  
Prognostic Variables ◽  
Cancer Specific Survival ◽  
External Data ◽  
Seer Data

Abstract Background To explore the factors that affect the prognosis of overall survival (OS) and cancer-specific survival (CSS) of patients with stage IIIC1 cervical cancer and establish nomogram models to predict this prognosis. Methods Data from patients in the Surveil-lance, Epidemiology, and End Results (SEER) programme meeting the inclusion criteria were classified into a training group, and validation data were obtained from the First Affiliated Hospital of Anhui Medical University from 2010 to 2019. The incidence, Kaplan-Meier curves, OS and CSS of patients with stage IIIC1 cervical cancer in the training group were evaluated. Nomograms were established according to the results of univariate and multivariate Cox regression models. Harrell’s C-index, calibration plots, receiver operating characteristic (ROC) curves and decision-curve analysis (DCA) were calculated to validate the prediction models. Results The incidence of pelvic lymph node metastasis, a high-risk factor for the prognosis of cervical cancer, decreased slightly over time. Eight independent prognostic variables were identified for OS, including age, race, marriage status, histology, extension range, tumour size, radiotherapy and surgery, but only seven were identified for CSS, with marriage status excluded. Nomograms of OS and CSS were established based on the results. The C-indexes for the nomograms of OS and CSS were 0.687 and 0.692, respectively, using random sampling of SEER data sets and 0.701 and 0.735, respectively, using random sampling of external data sets. The AUCs for the nomogram of OS were 0.708 and 0.705 for the SEER data sets and 0.750 and 0.750 for the external data sets, respectively. In addition, AUCs of 0.707 and 0.709 were obtained for the nomogram of CSS when validated using SEER data sets, and 0.788 and 0.785 when validated using external data sets. Calibration plots for the nomograms were almost identical to the actual observations. The DCA also indicated the value of the two models. Conclusions Eight independent prognostic variables were identified for OS. The same factors predicted CSS, with the exception of the marriage status. Both OS and CSS nomograms had good predictive and clinical application value after validation. Notably, tumour size had the largest contribution to the OS and CSS nomograms.

Facial Nerve EMG: Low-Tech Monitoring with a Stopwatch

2021 ◽  
Author(s):  
Julian Prell ◽  
Christian Scheller ◽  
Sebastian Simmermacher ◽  
Christian Strauss ◽  
Stefan Rampp
Keyword(s):  
Facial Nerve ◽  
Real Time ◽  
Random Order ◽  
Automated Analysis ◽  
Data Sets ◽  
Time Analysis ◽  
Frequency Pattern ◽  
Real Time Analysis ◽  
Offline Analysis ◽  
Automated Algorithms

Abstract Objective The quantity of A-trains, a high-frequency pattern of free-running facial nerve electromyography, is correlated with the risk for postoperative high-grade facial nerve paresis. This correlation has been confirmed by automated analysis with dedicated algorithms and by visual offline analysis but not by audiovisual real-time analysis. Methods An investigator was presented with 29 complete data sets measured during actual surgeries in real time and without breaks in a random order. Data were presented either strictly via loudspeaker (audio) or simultaneously by loudspeaker and computer screen (audiovisual). Visible and/or audible A-train activity was then quantified by the investigator with the computerized equivalent of a stopwatch. The same data were also analyzed with quantification of A-trains by automated algorithms. Results Automated (auto) traintime (TT), known to be a small, yet highly representative fraction of overall A-train activity, ranged from 0.01 to 10.86 s (median: 0.58 s). In contrast, audio-TT ranged from 0 to 1,357.44 s (median: 29.69 s), and audiovisual-TT ranged from 0 to 786.57 s (median: 46.19 s). All three modalities were correlated to each other in a highly significant way. Likewise, all three modalities correlated significantly with the extent of postoperative facial paresis. As a rule of thumb, patients with visible/audible A-train activity < 1 minute presented with a more favorable clinical outcome than patients with > 1 minute of A-train activity. Conclusion Detection and even quantification of A-trains is technically possible not only with intraoperative automated real-time calculation or postoperative visual offline analysis, but also with very basic monitoring equipment and real-time good quality audiovisual analysis. However, the investigator found audiovisual real-time-analysis to be very demanding; thus tools for automated quantification can be very helpful in this respect.

scholarly journals Spectral Clustering Ensemble Applied to SAR Image Segmentation

2008 ◽  
Vol 46 (7) ◽  
pp. 2126-2136 ◽  
Author(s):  
Xiangrong Zhang ◽  
Licheng Jiao ◽  
Fang Liu ◽  
Liefeng Bo ◽  
Maoguo Gong
Keyword(s):  
Image Segmentation ◽  
Spectral Clustering ◽  
Sar Image ◽  
Clustering Ensemble ◽  
Sar Image Segmentation

Automated Analysis of SEM X-Ray Spectral Images: A Powerful New Microanalysis Tool

2003 ◽  
Vol 9 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Paul G. Kotula ◽  
Michael R. Keenan ◽  
Joseph R. Michael
Keyword(s):  
Image Data ◽  
Large Data ◽  
Automated Analysis ◽  
Phase Identification ◽  
Data Sets ◽  
Multivariate Statistical ◽  
X Ray ◽  
Analysis Technique ◽  
Statistical Analysis Technique ◽  
Multi Phase

Spectral imaging in the scanning electron microscope (SEM) equipped with an energy-dispersive X-ray (EDX) analyzer has the potential to be a powerful tool for chemical phase identification, but the large data sets have, in the past, proved too large to efficiently analyze. In the present work, we describe the application of a new automated, unbiased, multivariate statistical analysis technique to very large X-ray spectral image data sets. The method, based in part on principal components analysis, returns physically accurate (all positive) component spectra and images in a few minutes on a standard personal computer. The efficacy of the technique for microanalysis is illustrated by the analysis of complex multi-phase materials, particulates, a diffusion couple, and a single-pixel-detection problem.

scholarly journals The Effect Of Milenial Generation Characteristics Of Job Satisfaction Relations And Employee Engagement

2020 ◽  
Vol 1 (4) ◽  
pp. 239-248
Author(s):  
Alfatih Sikki Manggabarani ◽  
Faisal Marzuki ◽  
Mahendro
Keyword(s):  
Employee Engagement ◽  
Random Sampling ◽  
Employee Satisfaction ◽  
Sampling Methods ◽  
Simple Random Sampling ◽  
Partial Least Square ◽  
Least Square ◽  
Analysis Method ◽  
Partial Least Square Analysis ◽  
Analysis Technique

This research is a quantitative study that aims to determine the Millennial Generation Characteristics of Employee Engagement. The population in this study The study was conducted by taking samples of Millennials who are actively working at Micro Finance companies with a total of 150 respondents. The sample size was taken as many as 150 respondents, with probability sampling methods especially simple random sampling. Data collection was carried out through questionnaires. The analysis technique used is the PLS (Partial Least Square) analysis method. The results of this study indicate that the value of R- Square (R2) Employee Engagement is 0.786 and Employee Satisfaction is 0.647 thus indicating that the contribution of Grit, Worklife Balance, and Jon Resources variables to Employee Engagement and Employee Satisfaction are 0.786 or 78.6% and 0.647 or 64.7%. And the rest is influenced by other factors not examined.

scholarly journals Input data preprocessing method for exchange rate forecasting via neural network

2014 ◽  
Vol 11 (4) ◽  
pp. 597-608
Author(s):  
Dragan Antic ◽  
Miroslav Milovanovic ◽  
Stanisa Peric ◽  
Sasa Nikolic ◽  
Marko Milojkovic
Keyword(s):  
Neural Network ◽  
Input Data ◽  
Principal Component ◽  
Economic Systems ◽  
Data Sets ◽  
Foreign Exchange Rates ◽  
Analysis Method ◽  
Feed Forward Neural Network ◽  
Parameters Selection ◽  
Neural Network Input

The aim of this paper is to present a method for neural network input parameters selection and preprocessing. The purpose of this network is to forecast foreign exchange rates using artificial intelligence. Two data sets are formed for two different economic systems. Each system is represented by six categories with 70 economic parameters which are used in the analysis. Reduction of these parameters within each category was performed by using the principal component analysis method. Component interdependencies are established and relations between them are formed. Newly formed relations were used to create input vectors of a neural network. The multilayer feed forward neural network is formed and trained using batch training. Finally, simulation results are presented and it is concluded that input data preparation method is an effective way for preprocessing neural network data.

scholarly journals Analisis Pengaruh Pengeluaran Pemerintah Terhadap Pertumbuhan Ekonomi dan Indeks Pembangunan Manusia di Indonesia

2016 ◽  
Vol 1 (1) ◽  
pp. 39-50
Author(s):  
Ginanjar Aji Nugroho
Keyword(s):  
Economic Growth ◽  
Human Development ◽  
Human Development Index ◽  
Random Sampling ◽  
Simple Random Sampling ◽  
Government Expenditures ◽  
Analysis Method ◽  
Statistical Average ◽  
Development Index ◽  
The Mean

The puposes of this study are (1) to analyze the effects of government expenditures on education, health and infrastructure sectors toward economic growth and human development index in Indonesia, (2) to investigate the impacts of economic growth on human dvelopment index, (3) and to examine the effects of government expenditures on education, health and infrastructure sectors toward human development index both directly and through the economic growth. The study used samples from twenty provinces, which were selected using simple random sampling, divided into two groups; the first group comprised higher HDI provinces; the second group consisted of lower HDI provinces. To examine the model, the study applied path analysis method. The mean test was also applied to determine whether there were statistical average differences between the two groups. The results of this study show different responds between the higher HDI provinces and lower HDI provinces. The higher HDI provinces show that government expenditures on health and infrastructure have positive and significant impacts on human development index through economic growth indirectly; on the other hand, the lower HDI provinces show that only expenditure on education gives positive and significant impacts on human development index. Meanwhile, the economic growth shows positive and significant impacts on human development index in both higher HDI and lower HDI provinces.  Abstrak Penelitian ini bertujuan untuk: (1).Mengetahui pengaruh pengeluaran pemerintah pada sektor pendidikan, kesehatan dan infrastruktur terhadap pertumbuhan ekonomi dan indeks pembangunan manusia (IPM) di Indonesia, (2).Mengetahui pengaruh pertumbuhan ekonomi terhadap IPM, dan (3).Mengetahui pengaruh pengeluaran pemerintah pada sektor pendidikan, kesehatan dan infrastruktur terhadap IPM, baik secara langsung maupun melalui pertumbuhan ekonomi. Penelitian ini menggunakan sampel dua puluh provinsi yang dipilih dengan teknik simple random sampling yang kemudian dibagi kedalam dua kelompok, yaitu kelompok daerah dengan angka IPM tinggi dan kelompok daerah dengan angka IPM rendah. Metode yang digunakan dalam penelitian ini adalah analisis jalur. Sebagai pendukung, juga dilakukan uji beda rata-rata untuk mengetahui ada tidaknya perbedaan rata-rata secara statistik terhadap dua kelompok tersebut. Hasil estimasi menunjukkan bahwa terdapat perbedaan respon diantara dua kelompok daerah tersebut. Pada kelompok daerah dengan angka IPM tinggi, terlihat bahwa pengeluaran kesehatan dan infrastruktur mempunyai pengaruh positif dan signifikan terhadap IPM melalui pertumbuhan ekonomi, sedangkan pada kelompok daerah dengan angka IPM rendah terlihat bahwa hanya pengeluaran pendidikan yang mempunyai pengaruh positif dan signifikan terhadap angka IPM. Adapun pertumbuhan ekonomi, terlihat menunjukkan pengaruh yang positif dan signifikan terhadap IPM. Hal ini terjadi pada kedua kelompok daerah, baik kelompok daerah dengan IPM tinggi maupun IPM rendah.

scholarly journals PENGARUH PEMANFAATAN KOPERASI TERHADAP TINGKAT KESEJAHTERAAN WARGA PADA KOPERASI SIMPAN PINJAM “KARYA AGUNG” BOJONEGORO

2019 ◽  
Vol 11 (2) ◽  
pp. 133-136
Author(s):  
Rifky Ardhana Kisno Saputra
Keyword(s):  
Data Collection ◽  
Correlation Analysis ◽  
Random Sampling ◽  
Sampling Technique ◽  
Analysis Method ◽  
Analysis Techniques ◽  
Savings And Loan ◽  
Business Entities

Cooperative business entities have the main goal not to seek profit but to serve members of the cooperative to be more prosperous based on the principle of family. The savings and loan cooperative "Karya agung" collects funds from its members who then redistribute the funds to its members. This study aims to provide an overview of the effect of cooperative use on the level of welfare of citizens. The taking technique used in this research is random sampling technique. The method of data collection used is to use questionnaires, interviews and documentation. The analysis method uses correlation analysis techniques with the product moment. Calculation of the value of  which states that there is an influence between the use of savings and loan cooperatives to the level of welfare of citizens.  

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