scholarly journals Design of English Intelligent Simulated Paper Marking System

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Lina Yang ◽  
Wei Liu
Keyword(s):  
Normal Distribution ◽  
Support Vector ◽  
Skewed Data ◽  
Examination System ◽  
Penalty Factor ◽  
Fitting Algorithm ◽  
Teachers And Students ◽  
Depth Study ◽  
Enhancement Method ◽  
Standard Support

In this paper, we analyze the intelligent subdesign of the simulated marking system through an in-depth study of it. This paper proposes a correlation analysis-based quantification of N-element sense values and a rationality enhancement-based scoring fitting algorithm for English essays. This paper also extracts word features, sentence features, and chapter structure features in essays to fit English composition scores. Since not all students can complete the essays according to the topic requirements, a triage scoring model is used to separate the normal essays from the low-scoring essays. Statistically, it was found that the essay scores also showed a certain normal distribution. The standard support vector regression algorithm is prone to data skewing problems, so this paper addresses this problem by using a rationality enhancement method that gives a corresponding penalty factor according to the distribution of the dataset. The results show that the English essay scoring fitting algorithm proposed in this paper can well improve the prediction accuracy of some data and solve the problem of skewed data where the scores show a normal distribution. This paper designs and implements an online mock examination system that incorporates an intelligent scoring system for essays, enabling it to meet the needs of teachers and students for online examinations and intelligent scoring.

scholarly journals Crop Classification Based on a Novel Feature Filtering and Enhancement Method

2019 ◽  
Vol 11 (4) ◽  
pp. 455 ◽  
Author(s):  
Limin Wang ◽  
Qinghan Dong ◽  
Lingbo Yang ◽  
Jianmeng Gao ◽  
Jia Liu
Keyword(s):  
Normal Distribution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Northeastern China ◽  
Lookup Table ◽  
Support Vector ◽  
Enhancement Method ◽  
Feature Filtering ◽  
Crop Classification

Vegetation indices, such as the normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI) derived from remote sensing images, are widely used for crop classification. However, vegetation index profiles for different crops with a similar phenology lead to difficulties in discerning these crops both spectrally and temporally. This paper proposes a feature filtering and enhancement (FFE) method to map soybean and maize, two major crops widely cultivated during the summer season in Northeastern China. Different vegetation indices are first calculated and the probability density functions (PDFs) of these indices for the target classes are established based on the hypothesis of normal distribution; the vegetation index images are then filtered using the PDFs to obtain enhanced index images where the pixel values of the target classes are ”enhanced”. Subsequently, the minimum Gini index of each enhanced index image is computed, generating at the same time the weight for every index. A composite enhanced feature image is produced by summing all indices with their weights. Finally, a classification is made from the composite enhanced feature image by thresholding, which is derived automatically based on the samples. The efficiency of the proposed FFE method is compared with the maximum likelihood classification (MLC), support vector machine (SVM), and random forest (RF) in a mapping operation to determine the soybean and maize distribution in a county in Northeastern China. The classification accuracies resulting from this comparison show that the FFE method outperforms MLC, and its accuracies are similar to those of SVM and RF, with an overall accuracy of 0.902 and a kappa coefficient of 0.846. This indicates that the FFE method is an appropriate method for crop classification to distinguish crops with a similar phenology. Our research also shows that when the sample size reaches a certain level (e.g., 2000), the mean and standard deviation of the sample are very close to the actual values, which leads to high classification accuracy. In a case where the condition of normal distribution is not fulfilled, the PDF of the vegetation index can be created by a lookup table. Furthermore, as the method is rather simple and explicit, and convenient in terms of computing, it can be used as the backbone for automatic crop mapping operations.

Analysis for Incremental and Decremental Standard Support Vector Machine

2014 ◽  
Vol 24 (7) ◽  
pp. 1601-1613 ◽  
Author(s):  
Bin GU ◽  
Guan-Sheng ZHENG ◽  
Jian-Dong WANG
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Standard Support Vector Machine ◽  
Standard Support

scholarly journals Critical Examination of the Parametric Approaches to Analysis of the Non-Verbal Human Behavior: A Case Study in Facial Pre-Touch Interaction

2020 ◽  
Vol 10 (11) ◽  
pp. 3817
Author(s):  
Soheil Keshmiri ◽  
Masahiro Shiomi ◽  
Kodai Shatani ◽  
Takashi Minato ◽  
Hiroshi Ishiguro
Keyword(s):  
Normal Distribution ◽  
Human Subjects ◽  
Critical Examination ◽  
Reliable Estimate ◽  
Support Vector ◽  
Face Area ◽  
Behavioral Studies ◽  
Touch Interaction ◽  
Ordinary Regression ◽  
Simplifying Assumptions

A prevailing assumption in many behavioral studies is the underlying normal distribution of the data under investigation. In this regard, although it appears plausible to presume a certain degree of similarity among individuals, this presumption does not necessarily warrant such simplifying assumptions as average or normally distributed human behavioral responses. In the present study, we examine the extent of such assumptions by considering the case of human–human touch interaction in which individuals signal their face area pre-touch distance boundaries. We then use these pre-touch distances along with their respective azimuth and elevation angles around the face area and perform three types of regression-based analyses to estimate a generalized facial pre-touch distance boundary. First, we use a Gaussian processes regression to evaluate whether assumption of normal distribution in participants’ reactions warrants a reliable estimate of this boundary. Second, we apply a support vector regression (SVR) to determine whether estimating this space by minimizing the orthogonal distance between participants’ pre-touch data and its corresponding pre-touch boundary can yield a better result. Third, we use ordinary regression to validate the utility of a non-parametric regressor with a simple regularization criterion in estimating such a pre-touch space. In addition, we compare these models with the scenarios in which a fixed boundary distance (i.e., a spherical boundary) is adopted. We show that within the context of facial pre-touch interaction, normal distribution does not capture the variability that is exhibited by human subjects during such non-verbal interaction. We also provide evidence that such interactions can be more adequately estimated by considering the individuals’ variable behavior and preferences through such estimation strategies as ordinary regression that solely relies on the distribution of their observed behavior which may not necessarily follow a parametric distribution.

Support Vector Data Description Based Multivariate Cumulative Sum Control Chart

2011 ◽  
Vol 314-316 ◽  
pp. 2482-2485 ◽  
Author(s):  
Shu Guang He ◽  
Chuan Yan Zhang
Keyword(s):  
Normal Distribution ◽  
Numerical Experiments ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Distributed Data ◽  
Vector Data ◽  
Cumulative Sum ◽  
Data Description ◽  
Cumulative Sum Control ◽  
Better Than

A SVDD (Support Vector Data Description) based MCUSUM (Multivariate Cumulative Sum) chart is proposed and referred as S-MCUSUM chart, which has an advantage of distribution free. Numerical experiments on the performance of the S-MCUSUM chart is compared to the COT (Cumulative of T) chart. The results show that the COT chart is somewhat better than the S-MCUSUM chart for multivariate normally distributed data. However, the S-MCUSUM chart is much better than the COT chart for banana-shaped distributed data which is a typical non-normal distribution.

scholarly journals Gas Outburst Prediction Model Using Improved Entropy Weight Grey Correlation Analysis and IPSO-LSSVM

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Haibo Liu ◽  
Yujie Dong ◽  
Fuzhong Wang
Keyword(s):  
Correlation Analysis ◽  
Prediction Model ◽  
Least Square ◽  
Support Vector ◽  
Entropy Weight ◽  
Grey Correlation ◽  
Gas Outburst ◽  
Penalty Factor ◽  
Kernel Parameter ◽  
Grey Correlation Analysis

This paper investigates the problem of gas outburst prediction in the working face of coal mine. Firstly, based on a comprehensive analysis of influence factors of gas outburst, an improved entropy weight algorithm is introduced into a grey correlation analysis algorithm; thus, the reasonable weights and correlation order of the influencing factors are obtained to improve the objectivity of the evaluation. The main controlling factors obtained are used as the input of the prediction model. Secondly, by utilizing the improved particle swarm optimization (IPSO), the penalty factor and kernel parameter of least square support vector machine (LSSVM) are optimized to enhance the global search ability and avoid the occurrence of the local optimal solutions, and a new prediction model of gas outburst based on IPSO-LSSVM is established. At last, the prediction model is applied in the tunneling heading face 14141 of Jiuli Hill mine in Jiaozuo City, China. The case study demonstrates that the prediction accuracy of the proposed model is 92%, which is improved compared with that of the SVM model and GA-LSSVM model.

Optimization of diesel substitution rate based on piston maximum temperature pattern recognition in dual-fuel engine

2016 ◽  
Vol 231 (10) ◽  
pp. 1388-1401 ◽  
Author(s):  
Youyao Fu ◽  
Bing Xiao
Keyword(s):  
Pattern Recognition ◽  
Substitution Rate ◽  
Closed Loop ◽  
Maximum Temperature ◽  
Support Vector ◽  
Dual Fuel ◽  
Temperature Pattern ◽  
Closed Loop Control ◽  
Loop Control ◽  
Penalty Factor

The diesel and natural gas dual-fuel engine has gained increasing interest in recent years because of its excellent power and economy. However, the diesel substitution rate cannot be controlled optimally, owing to the lack of a feedback indicator reflecting the cylinder combustion process, which easily leads to a serious thermal load problem. This paper presents a closed-loop control with feedback from a piston maximum temperature (PMT) pattern to regulate the diesel substitution rate in real time. A v-support vector machine ( v-SVM) is proposed to train classifiers for online recognition of the PMT pattern. Nitrogen oxide (NOx) emission levels, excess air coefficient, engine speed and inlet pressure are chosen as feature variables. The PMTs, calculated by finite element analysis in ANSYS, are utilized to determine the labels of feature data. Moreover, 10-fold cross-validation is employed to choose the optimal kernel function, kernel parameters and penalty factor. A synthetic minority oversampling technique (SMOTE) is introduced to remedy the class imbalance problem in training classifiers. Furthermore, a timer-based debouncing mechanism is employed to alleviate the dynamic process influence on the PMT pattern recognition. Experiment revealed that the classifiers yield desirable predictions, with classification accuracies higher than 90%. Meanwhile, the diesel substitution rates are regulated to appropriate values through the closed-loop control algorithm, which guarantees that the dual-fuel engine runs in its safe region and maintains its excellent economy.

scholarly journals An Optimal SVM with Feature Selection Using Multiobjective PSO

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Iman Behravan ◽  
Oveis Dehghantanha ◽  
Seyed Hamid Zahiri ◽  
Nasser Mehrshad
Keyword(s):  
Kernel Function ◽  
Optimization Problem ◽  
Recognition Score ◽  
Computational Time ◽  
Support Vector ◽  
Feature Subset ◽  
Risk Minimization ◽  
Minimization Principle ◽  
Penalty Factor ◽  
Mlp Neural Networks

Support vector machine is a classifier, based on the structured risk minimization principle. The performance of the SVM depends on different parameters such as penalty factor, C, and the kernel factor, σ. Also choosing an appropriate kernel function can improve the recognition score and lower the amount of computation. Furthermore, selecting the useful features among several features in dataset not only increases the performance of the SVM, but also reduces the computational time and complexity. So this is an optimization problem which can be solved by heuristic algorithm. In some cases besides the recognition score, the reliability of the classifier’s output is important. So in such cases a multiobjective optimization algorithm is needed. In this paper we have got the MOPSO algorithm to optimize the parameters of the SVM, choose appropriate kernel function, and select the best feature subset simultaneously in order to optimize the recognition score and the reliability of the SVM concurrently. Nine different datasets, from UCI machine learning repository, are used to evaluate the power and the effectiveness of the proposed method (MOPSO-SVM). The results of the proposed method are compared to those which are achieved by single SVM, RBF, and MLP neural networks.

Parameters of Support Vector Machines Model Optimized Method Based on Genetic Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 1873-1876
Author(s):  
Yu Zhen Xie ◽  
Zhao Gang Wang ◽  
Xiao Wei Dai
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Support Vector Machine Model ◽  
Optimization Problems ◽  
Support Vector ◽  
Machine Model ◽  
Penalty Factor ◽  
Vector Machines ◽  
Cross Validation Error ◽  
Function Selection

In order to obtain more accurate parameters of support vector machine model, using genetic algorithm to optimize the parameters is an effective method. This paper analyzes the principle of support vector machine for regression, support vector machine kernel function selection, kernel parameters, penalty factor selection and adjustment methods, taking into account genetic algorithm is effective in solving optimization problems, proposed a method using genetic algorithm to optimize the parameters of support vector machine, which uses genetic algorithms to make cross-validation error minimized. The simulation results demonstrate the effectiveness of this method.

scholarly journals INJUSTICE REPRESENTATION IN THE POETRY ANTHOLOGY NYANYIAN AKAR RUMPUT BY WIJI THUKUL (An Analysis Study of Critical Literacy)

2019 ◽  
Vol 18 (1) ◽  
pp. 15-32
Author(s):  
Fetti Astrini Rishanjani ◽  
Zainal Rafli ◽  
Zuriyati Zuriyati
Keyword(s):  
Qualitative Research ◽  
Critical Literacy ◽  
Descriptive Analysis ◽  
Literary Study ◽  
Analysis Method ◽  
Learning Materials ◽  
Land Expropriation ◽  
Analysis Study ◽  
Teachers And Students ◽  
Depth Study

ABSTRACT The aim of this research is to do an in-depth study of the representation of injustice cointaned in the anthology of poetry Nyanyian Akar Rumput written by Wiji Thukul and its implication in learning Indonesian language. This research is a qualitative research using descriptive analysis method. The research concludes that the analyzed poems represent acts of injustice, such as commutative and recreative injustice. The results of this research showed: (1) Representation of commutative injustice was found in the poem entitled “Tanah” that revealed the people’s demand of justice caused by land expropriation;(2) Representation of recreative injustice was found in the poem entitled “Batas Panggung” that revealed the opposition caused by the lack of freedom in voicing their aspirations;(3) The implication of this research can be used by teachers and students as learning materials for literary study. Keywords: representation of injustice, poetry, critical literacy

scholarly journals An Extensive Online Examination System With Automatic Assessment Technique

2020 ◽  
Vol 6 (1) ◽  
pp. 54-59
Author(s):  
S Kabir ◽  
MP Hossain ◽  
K Mallik ◽  
M Rahman ◽  
MJ Islam ◽  
...  
Keyword(s):  
Relative Error ◽  
Online Environment ◽  
Automatic Assessment ◽  
Examination System ◽  
Science And Engineering ◽  
Assessment Technique ◽  
Frequency Matching ◽  
Global Pandemic ◽  
Online Examination ◽  
Teachers And Students

An online examination system is a software solution, which allows any industry or institute to arrange, conduct, and manage examinations via an online environment. Online Examination is an essential ingredient in electronic and interactive learning; both teachers and students are benefited from this. It’s very much useful during the current situation of the global pandemic Novel Corona Virus (COVID-19). In this paper, we proposed a system with automatic assessment technique is generated. The algorithms for calculations word frequency, matching keywords, analyzing linguistics, generating grades are proposed in this system. The system is implemented by using PhpStrom and MySQL. The performances of the system is evaluated with a large number of students and questions as well as answers, and we found the absolute (about 0.3%) and relative error (about 3.57%) which is quite satisfactory. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 6(1), Dec 2019 P 54-59

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