Elaborate Ligand-Based Modeling Coupled with Multiple Linear Regression and k Nearest Neighbor QSAR Analyses Unveiled New Nanomolar mTOR Inhibitors

2013 ◽  
Vol 53 (10) ◽  
pp. 2587-2612 ◽  
Author(s):  
Mohammad A. Khanfar ◽  
Mutasem O. Taha
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Nearest Neighbor ◽  
Mtor Inhibitors ◽  
K Nearest Neighbor

scholarly journals Analysis and Prediction of CET4 Scores Based on Data Mining Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hongyan Wang
Keyword(s):  
Data Mining ◽  
Linear Regression ◽  
Test Score ◽  
Nearest Neighbor ◽  
Classification Model ◽  
Data Mining Algorithm ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm ◽  
Classification Efficiency

This paper presents the concept and algorithm of data mining and focuses on the linear regression algorithm. Based on the multiple linear regression algorithm, many factors affecting CET4 are analyzed. Ideas based on data mining, collecting history data and appropriate to transform, using statistical analysis techniques to the many factors influencing the CET-4 test were analyzed, and we have obtained the CET-4 test result and its influencing factors. It was found that the linear regression relationship between the degrees of fit was relatively high. We further improve the algorithm and establish a partition-weighted K-nearest neighbor algorithm. The K-weighted K nearest neighbor algorithm and the partition algorithm are used in the CET-4 test score classification prediction, and the statistical method is used to study the relevant factors that affect the CET-4 test score, and screen classification is performed to predict when the comparison verification will pass. The weight K of the input feature and the adjacent feature are weighted, although the allocation algorithm of the adjacent classification effect has not been significantly improved, but the stability classification is better than K-nearest neighbor algorithm, its classification efficiency is greatly improved, classification time is greatly reduced, and classification efficiency is increased by 119%. In order to detect potential risk graduating students earlier, this paper proposes an appropriate and timely early warning and preschool K-nearest neighbor algorithm classification model. Taking test scores or make-up exams and re-learning as input features, the classification model can effectively predict ordinary students who have not graduated.

Use of Local Linear Regression Model for Short-Term Traffic Forecasting

2003 ◽  
Vol 1836 (1) ◽  
pp. 143-150 ◽  
Author(s):  
Hongyu Sun ◽  
Henry X. Liu ◽  
Heng Xiao ◽  
Rachel R. He ◽  
Bin Ran
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Nearest Neighbor ◽  
Time Of Day ◽  
Local Linear Regression ◽  
K Nearest Neighbor ◽  
Short Term ◽  
Traffic Forecasting ◽  
Local Linear

The traffic-forecasting model, when considered as a system with inputs of historical and current data and outputs of future data, behaves in a nonlinear fashion and varies with time of day. Traffic data are found to change abruptly during the transition times of entering and leaving peak periods. Accurate and real-time models are needed to approximate the nonlinear time-variant functions between system inputs and outputs from a continuous stream of training data. A proposed local linear regression model was applied to short-term traffic prediction. The performance of the model was compared with previous results of nonparametric approaches that are based on local constant regression, such as the k-nearest neighbor and kernel methods, by using 32-day traffic-speed data collected on US-290, in Houston, Texas, at 5-min intervals. It was found that the local linear methods consistently showed better performance than the k-nearest neighbor and kernel smoothing methods.

scholarly journals k-NEAREST NEIGHBOR AND LINEAR REGRESSION IN THE PREDICTION OF THE ARTIFICIAL FORM FACTOR

FLORESTA ◽  
2020 ◽  
Vol 50 (3) ◽  
pp. 1669
Author(s):  
Deivison Venicio Souza ◽  
Júlio Cesar Nievola ◽  
Ana Paula Dalla Corte ◽  
Carlos Roberto Sanquetta
Keyword(s):  
Form Factor ◽  
Linear Regression ◽  
Nearest Neighbor ◽  
K Nearest Neighbor

scholarly journals Graduate Admission Prediction Using Machine Learning

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Linear Regression ◽  
Multilayer Perceptron ◽  
Nearest Neighbor ◽  
Educational Institutions ◽  
Master's Program ◽  
Learning Models ◽  
K Nearest Neighbor ◽  
Machine Learning Models

Student admission problem is very important in educational institutions. This paper addresses machine learning models to predict the chance of a student to be admitted to a master’s program. This will assist students to know in advance if they have a chance to get accepted. The machine learning models are multiple linear regression, k-nearest neighbor, random forest, and Multilayer Perceptron. Experiments show that the Multilayer Perceptron model surpasses other models.

scholarly journals KOMPARASI ALGORITMA LR, K-NN DAN SVM UNTUK ESTIMASI AREA KEBAKARAN HUTAN

2018 ◽  
Vol 3 (2) ◽  
pp. 103
Author(s):  
Fitriyani Fitriyani ◽  
Rangga Sanjaya
Keyword(s):  
Support Vector Machine ◽  
Linear Regression ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor

Kebakaran hutan menimbulkan berbagai permasalahan seperti asap yang dapat mengganggu sistem pernapasan, kerusakan lingkungan dan bencana lainnya. Kebakaran hutan juga dapat berdampak pada biaya yang akan dikeluarkan untuk menyelesaikan masalah yang timbul akibat kebakaran hutan, sehingga diperlukan penelitian untuk mengukur tingkat radiasi api pada area yang terbakar. Algoritma LR (Linear Regression), K-NN (K-Nearest Neighbor) dan SVM (Support Vector Machine) merupakan metode untuk regresi dan klasifikasi. Pada penelitian ini dilakukan perbandingan atau komparasi untuk mendapatkan algoritma terbaik dalam estimasi area kebakaran hutan.

scholarly journals Classification of The NTEV Problems on The Commercial Building

2018 ◽  
Vol 9 (2) ◽  
pp. 380 ◽  
Author(s):  
Mohd Abdul Talib Mat Yusoh ◽  
Saidatul Habsah Asman ◽  
Zuhaila Mat Yasin ◽  
Ahmad Farid Abidin
Keyword(s):  
Linear Regression ◽  
Nearest Neighbor ◽  
Confusion Matrix ◽  
Commercial Building ◽  
K Nearest Neighbor ◽  
City Block ◽  
S Transform ◽  
Processing Signal ◽  
Block Variables

Neutral to Earth Voltage (NTEV) is one of power quality (PQ) problems in the commercial building that need to be resolved.  The classification of the NTEV problems is a method to identify the source types of disturbance in alleviating the problems.  This paper presents the classification of NTEV source in the commercial building which is known as the harmonic, loose termination, and lightning.  The Euclidean, City block, and Chebyshev variables for K-Nearest Neighbor (K-NN) classifying are being utilized in order to identify the best performance for classifying the NTEV problems.  Then, S-Transform (ST) is applied as a pre-processing signal to extract the desired features of NTEV problem for classifier input.  Furthermore, the performance of K-NN variables is validated by using the confusion matrix and linear regression.  The classification results show that all the K-NN variables capable to identify the NTEV problems. While the K-NN results show that the Euclidean and City block variables are well performed rather than the Chebyshev variable.  However, the Chebyshev variable is still reliable as the confusion matrix shows minor misclassification. Then, the linear regression outperformed the percentage close to a perfect value which is hundred percent.

Pengaruh Motivasi Kerja Terhadap Kinerja Pegawai Kantor Sekretariat Daerah Kabupaten Gunungkidul

2017 ◽  
Vol 23 (2) ◽  
pp. 121-137
Author(s):  
Ary Sutrischastini ◽  
Agus Riyanto
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Sampling Method ◽  
Work Motivation ◽  
Probability Sampling ◽  
Random Probability

This paper will discuss the effect of work motivation (incentives, motives and expectations) on the performance of the staff of the Regional Secretariat Gunungkidul. The purpose of this paper is: 1) Determine the effect of incentives on the performance of the staff of the Regional Secretariat Gunungkidul, 2) Determine the effect of motive on the performance of the staff of the Regional Secretariat Gunungkidul, 3) To know the effect of expectations on the performance of the staff of the Regional Secretariat Gunungkidul, 4)To know the effect of incentives, motives and expectations on the performance of the staff of the Regional Secretariat Gunungkidul.Research sites in the Regional Secretariat Gunungkidul and the population is 162entire employee in the Regional Secretariat Gunungkidul. Samples amounted to 116 respondents taken with simple random probability sampling method. Data were analyzed using multiple linear regression. Results obtained: (1) incentives positive and significant effect on the performance of, (2) motif positive and significant effect on the performance of, (3) expectations positive and significant impact on the performance of , and (4) incentives, motives and expectations of positive and significant impact on the performance of the staff of the Regional Secretariat Gunungkidul.

Sign in / Sign up

Export Citation Format

Share Document