Prediction of cavitation damage on spillway using K-nearest neighbor modeling

2014 ◽  
Vol 71 (3) ◽  
pp. 347-352 ◽  
Author(s):  
E. Fadaei Kermani ◽  
G. A. Barani ◽  
M. Ghaeini-Hessaroeyeh
Keyword(s):  
Present Model ◽  
Nearest Neighbor ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
K Nearest Neighbor ◽  
Flow Rates ◽  
Cavitation Damage ◽  
Using Data ◽  
The Stability ◽  
Observed Damage

Cavitation is a common and destructive process on spillways that threatens the stability of the structure and causes damage. In this study, based on the nearest neighbor model, a method has been presented to predict cavitation damage on spillways. The model was tested using data from the Shahid Abbaspour dam spillway in Iran. The level of spillway cavitation damage was predicted for eight different flow rates, using the nearest neighbor model. Moreover, based on the cavitation index, five damage levels from no damage to major damage have been determined. Results showed that the present model predicted damage locations and levels close to observed damage during past floods. Finally, the efficiency and precision of the model was quantified by statistical coefficients. Appropriate values of the correlation coefficient, root mean square error, mean absolute error and coefficient of residual mass show the present model is suitable and efficient.

scholarly journals A Hierarchical Framework for Facial Age Estimation

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yuyu Liang ◽  
Xianmei Wang ◽  
Li Zhang ◽  
Zhiliang Wang
Keyword(s):  
Age Estimation ◽  
Nearest Neighbor ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Age Group ◽  
K Nearest Neighbor ◽  
Complex Issue ◽  
Ordinal Information ◽  
Hierarchical Framework ◽  
Estimation System

Age estimation is a complex issue of multiclassification or regression. To address the problems of uneven distribution of age database and ignorance of ordinal information, this paper shows a hierarchic age estimation system, comprising age group and specific age estimation. In our system, two novel classifiers, sequence k-nearest neighbor (SKNN) and ranking-KNN, are introduced to predict age group and value, respectively. Notably, ranking-KNN utilizes the ordinal information between samples in estimation process rather than regards samples as separate individuals. Tested on FG-NET database, our system achieves 4.97 evaluated by MAE (mean absolute error) for age estimation.

Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

2015 ◽  
Vol 1 (4) ◽  
pp. 270
Author(s):  
Muhammad Syukri Mustafa ◽  
I. Wayan Simpen
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Sample Data ◽  
New Students ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

Optimized Collaborative Filtering Recommendation Based on User’s Social Relationships

2014 ◽  
Vol 1044-1045 ◽  
pp. 1484-1488
Author(s):  
Yue Kun Fan ◽  
Xin Ye Li ◽  
Meng Meng Cao
Keyword(s):  
Collaborative Filtering ◽  
Social Relationships ◽  
Digital Libraries ◽  
Social Relationship ◽  
Nearest Neighbor ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Personalized Recommendation ◽  
Recommendation Algorithm

Currently collaborative filtering is widely used in e-commerce, digital libraries and other areas of personalized recommendation service system. Nearest-neighbor algorithm is the earliest proposed and the main collaborative filtering recommendation algorithm, but the data sparsity and cold-start problems seriously affect the recommendation quality. To solve these problems, A collaborative filtering recommendation algorithm based on users' social relationships is proposed. 0n the basis of traditional filtering recommendation technology, it combines with the interested objects of user's social relationship and takes the advantage of the tags to projects marked by users and their interested objects to improve the methods of recommendation. The experimental results of MAE ((Mean Absolute Error)) verify that this method can get better quality of recommendation.

Crop Disease Detection Using Data Science Techniques

2021 ◽  
pp. 80-97
Author(s):  
Shakti Kumar
Keyword(s):  
Data Science ◽  
Nearest Neighbor ◽  
Plant Diseases ◽  
K Nearest Neighbor ◽  
Classification Feature ◽  
Crop Disease ◽  
Using Data ◽  
Agricultural Yields ◽  
Day By Day

Plant disease is a mutilation of the normal state of a plant that changes its essential quality and prevents a plant from performing to its actual potential. Due to drastic environment changes, plant diseases are growing day by day, which results the higher losses in quantity of agricultural yields. To prevent the loss in the crop yield, the timely disease identification is necessary. Monitoring the plant diseases without any digital mean makes it difficult to identify the disease correctly and timely. It requires more amounts of work, time, and great experience in the plant diseases. Automatic approach of image processing and applying the different data science techniques to classify the disease correctly is a good idea for this which includes acquisition, classification, feature extraction, pre-processing, and segmentation all are performed on the leaf images. This chapter will briefly discuss the data science techniques used for the classification of the images like SVM, k-nearest neighbor, decision tree, ANN, and convolutional neural network (CNN).

scholarly journals A Comparison of Models for Estimating Solar Radiation from Sunshine Duration in Croatia

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Tihomir Betti ◽  
Ivana Zulim ◽  
Slavica Brkić ◽  
Blanka Tuka
Keyword(s):  
Mean Absolute Error ◽  
Sunshine Duration ◽  
Model Performance ◽  
Absolute Error ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Mean Square ◽  
Using Data ◽  
Statistical Indicators

The performance of seventeen sunshine-duration-based models has been assessed using data from seven meteorological stations in Croatia. Conventional statistical indicators are used as numerical indicators of the model performance: mean absolute percentage error (MAPE), mean bias error (MBE), mean absolute error (MAE), and root-mean-square error (RMSE). The ranking of the models was done using the combination of all these parameters, all having equal weights. The Rietveld model was found to perform the best overall, followed by Soler and Dogniaux-Lemoine monthly dependent models. For three best-performing models, new adjusted coefficients are calculated, and they are validated using separate dataset. Only the Dogniaux-Lemoine model performed better with adjusted coefficients, but across all analysed locations, the adjusted models showed improvement in reduced maximum percentage error.

scholarly journals Fault Diagnosis Method for Rolling Bearings Based on Composite Multiscale Fluctuation Dispersion Entropy

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 290 ◽  
Author(s):  
Xiong Gan ◽  
Hong Lu ◽  
Guangyou Yang
Keyword(s):  
Fault Diagnosis ◽  
Nearest Neighbor ◽  
Coarse Grained ◽  
K Nearest Neighbor ◽  
Rolling Bearings ◽  
Entropy Estimation ◽  
Fault Recognition ◽  
Knn Classifier ◽  
Diagnosis Method ◽  
The Stability

This paper proposes a new method named composite multiscale fluctuation dispersion entropy (CMFDE), which measures the complexity of time series under different scale factors and synthesizes the information of multiple coarse-grained sequences. A simulation validates that CMFDE could improve the stability of entropy estimation. Meanwhile, a fault recognition method for rolling bearings based on CMFDE, the minimum redundancy maximum relevancy (mRMR) method, and the k nearest neighbor (kNN) classifier (CMFDE-mRMR-kNN) is developed. For the CMFDE-mRMR-kNN method, the CMFDE method is introduced to extract the fault characteristics of the rolling bearings. Then, the sensitive features are obtained by utilizing the mRMR method. Finally, the kNN classifier is used to recognize the different conditions of the rolling bearings. The effectiveness of the proposed CMFDE-mRMR-kNN method is verified by analyzing the standard experimental dataset. The experimental results show that the proposed fault diagnosis method can effectively classify the conditions of rolling bearings.

scholarly journals Some Concerns when Using Data from the Cooperative Weather Station Networks: A Nebraska Case Study

2005 ◽  
Vol 22 (5) ◽  
pp. 592-602 ◽  
Author(s):  
Hong Wu ◽  
Kenneth G. Hubbard ◽  
Jinsheng You
Keyword(s):  
Mean Absolute Error ◽  
Absolute Error ◽  
Daily Temperature ◽  
Weather Data ◽  
Observation Error ◽  
Climate Data ◽  
Temperature And Precipitation ◽  
Using Data ◽  
Observation Times

Abstract In this study, daily temperature and precipitation amounts that are observed by the Cooperative Observer Program (COOP) were compared among geographically close stations. Hourly observations from nearby Automatic Weather Data Network (AWDN) stations were utilized to resolve the discrepancies between the observations during the same period. The statistics of maximum differences in temperature and precipitation between COOP stations were summarized. In addition, the quantitative measures of the deviations between COOP and AWDN stations were expressed by root-mean-square error, mean absolute error, and an index of agreement. The results indicated that significant discrepancies exist among the daily observations between some paired stations because of varying observation times, observation error, sensor error, and differences in microclimate exposure. The purpose of this note is to bring attention to the problem and offer guidance on the use of daily observations in the comparison and creation of weather maps. In addition, this study demonstrates approaches for identifying the sources of the discrepancies in daily temperature and precipitation observations. The findings will be useful in the quality assurance (QA) procedures of climate data.

scholarly journals Klasifikasi Penentuan Pengajuan Kartu Kredit Menggunakan K-Nearest Neighbor

2020 ◽  
Vol 22 (1) ◽  
pp. 73-82
Author(s):  
Yogiek Indra Kurniawan ◽  
Tiyssa Indah Barokah
Keyword(s):  
Credit Card ◽  
Nearest Neighbor ◽  
Credit Cards ◽  
K Nearest Neighbor ◽  
The Status ◽  
Using Data

A credit card is a device payment issued by the bank certain made of plastic and useful as a tool payment on credit carried out by the owner of the card or in accordance with the name of listed in a credit card is on when making purchases goods or services. The problems facing in giving a credit cards to customers bank that have signed up is difficult to determine the category of a credit cards in accordance with the customer bank. By doing this research is expected to facilitate the bank or the analysis to determine the category of a credit card to customers bank right. The research used is by applying methods K-Nearest Neighbor to classify prospective customers in the making a credit card in accordance with the category of  customers by using data customers at the Bank BNI Syariah Surabaya. A method K-Nearest Neighbor used to seek patterns on the data customers so established variable as factors supporters in the form of gender, the status of the house, the status, the number of dependants (children), a profession and revenue annually. The results of this research shows that an average of the value of precision of 92%, the value of recall of 83%, and the value of accuracy of 93%. Thus, this application is effective to help analyst credit cards in classifying customers to get credit cards that appropriate criteria.

scholarly journals Detect Frauds in Credit Card using Data Mining Techniques

2019 ◽  
Vol 9 (2) ◽  
pp. 4891-4895
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Nearest Neighbor ◽  
Hidden Markov ◽  
K Nearest Neighbor ◽  
Detection Systems ◽  
Important Concern ◽  
On Line ◽  
Using Data ◽  
The Individual

In today era credit card are extensively used for day to day business as well as other transactions. Ascent within the variety of transactions through master card has junction rectifier to rise in the dishonest activities. In trendy day's fraud is one in every of the most important concern within the monetary loses not solely to the merchants however additionally to the individual purchasers. Data processing had competed a commanding role within the detection of credit card in on-line group action. Our aim is to first of all establish the categories of the fraud secondly, the techniques like K-nearest neighbor, Hidden Markov model, SVM, logistic regression, decision tree and neural network. So fraud detection systems became essential for the banks to attenuate their loses. In this paper we have research about the various detecting techniques to identify and detect the fraud through varied techniques of data mining

scholarly journals Data Mining Classification Of Filing Credit Customers Without Collateral With K-Nearest Neighbor Algorithm (Case study: PT. BPR Diori Double)

2020 ◽  
Vol 2 (2) ◽  
pp. 204-210
Author(s):  
Jeprianto Sinaga ◽  
Bosker Sinaga
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Savings And Loans ◽  
Long Time ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
The Many

Unsecured loans are the community's choice for lending to banks that provide Reviews These services. PT. RB Diori Ganda is a regional private banking company that serves savings and loans and loans without collateral for the community. Submission of unsecured loans must go through an assessor team to process the analysis of the attributes that Affect the customer's classification so that credit can be approved, the which is then submitted to the commissioner for credit approval. But what if Reviews those who apply for credit on the same day in large amounts, of course this will the make the process of credit analysis and approval will take a long time. If it is seen from the many needs of the community to apply for loans without collateral, a classification application is needed, in order to Facilitate the work of the assessor team in the process of analyzing the attributes that Affect customer classification. To find out the classification of customers who apply for unsecured loans for using data mining with the K-Nearest Neighbor algorithm. The result of this research is the classification of problematic or non-performing customers for credit applications without collateral.

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