A hybrid model for student grade prediction using support vector machine and neural network

2020 ◽  
pp. 1-11
Author(s):  
Jianjun Miao
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Machine Learning Algorithms ◽  
Experimental Comparison ◽  
Support Vector ◽  
Advantages And Disadvantages ◽  
Grade Prediction ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Classification Prediction

It is difficult for the intelligent teaching system in colleges to effectively predict student grade, which makes it difficult to formulate follow-up teaching strategies. In order to improve the effect of student grade prediction, this study improves the neural network algorithm, combines support vector machines to build a student grade prediction model, and uses PCA to reduce the dimensionality of the sample data. The specific operation is realized by SPSS software. Moreover, this study removes redundant information inside the input vector and compresses multiple features into a few typical features as much as possible. In addition, the research set a control experiment to analyze the performance of the research model and compare the advantages and disadvantages of the classification prediction effect of traditional machine learning algorithms and neural network algorithms. Through experimental comparison, we can see that the model constructed in this paper has certain advantages in all aspects of parameter performance, and the prediction model proposed in this study has certain effects.

scholarly journals Application of Machine Learning Algorithms for Solving Problems in the Oil and Gas Sector

2021 ◽  
Vol 19 (3) ◽  
pp. 55-64
Author(s):  
K. N. Maiorov ◽  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Reinforcement Learning ◽  
Machine Learning Algorithms ◽  
Design Stage ◽  
Learning Methods ◽  
Field Development ◽  
Advantages And Disadvantages ◽  
Machine Learning Methods ◽  
The Neural Network

The paper examines the life cycle of field development, analyzes the processes of the field development design stage for the application of machine learning methods. For each process, relevant problems are highlighted, existing solutions based on machine learning methods, ideas and problems are proposed that could be effectively solved by machine learning methods. For the main part of the processes, examples of solutions are briefly described; the advantages and disadvantages of the approaches are identified. The most common solution method is feed-forward neural networks. Subject to preliminary normalization of the input data, this is the most versatile algorithm for regression and classification problems. However, in the problem of selecting wells for hydraulic fracturing, a whole ensemble of machine learning models was used, where, in addition to a neural network, there was a random forest, gradient boosting and linear regression. For the problem of optimizing the placement of a grid of oil wells, the disadvantages of existing solutions based on a neural network and a simple reinforcement learning approach based on Markov decision-making process are identified. A deep reinforcement learning algorithm called Alpha Zero is proposed, which has previously shown significant results in the role of artificial intelligence for games. This algorithm is a decision tree search that directs the neural network: only those branches that have received the best estimates from the neural network are considered more thoroughly. The paper highlights the similarities between the tasks for which Alpha Zero was previously used, and the task of optimizing the placement of a grid of oil producing wells. Conclusions are made about the possibility of using and modifying the algorithm of the optimization problem being solved. Аn approach is proposed to take into account symmetric states in a Monte Carlo tree to reduce the number of required simulations.

scholarly journals How to Guarantee Food Safety via Grain Storage? An Approach to Improve Management Effectiveness by Machine Learning Algorithms

2021 ◽  
Vol 2 (8) ◽  
pp. 675-684
Author(s):  
Jin Wang ◽  
Youjun Jiang ◽  
Li Li ◽  
Chao Yang ◽  
Ke Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Grain Storage ◽  
Management Effectiveness

The purpose of grain storage management is to dynamically analyze the quality change of the reserved grains, adopt scientific and effective management methods to delay the speed of the quality deterioration, and reduce the loss rate during storage. At present, the supervision of the grain quality in the reserve mainly depends on the periodic measurements of the quality of the grains and the milled products. The data obtained by the above approach is accurate and reliable, but the workload is too large while the frequency is high. The obtained conclusions are also limited to the studied area and not applicable to be extended into other scenarios. Therefore, there is an urgent need of a general method that can quickly predict the quality of grains given different species, regions and storage periods based on historical data. In this study, we introduced Back-Propagation (BP) neural network algorithm and support vector machine algorithm into the quality prediction of the reserved grains. We used quality index, temperature and humidity data to build both an intertemporal prediction model and a synchronous prediction model. The results show that the BP neural network based on the storage characters from the first three periods can accurately predict the key storage characters intertemporally. The support vector machine can provide precise predictions of the key storage characters synchronously. The average predictive error for each of wheat, rice and corn is less than 15%, while the one for soybean is about 20%, all of which can meet the practical demands. In conclusion, the machine learning algorithms are helpful to improve the management effectiveness of grain storage.

scholarly journals Research on Forecasting of China's Monetary Policy Based on Random Forest Algorithm.pdf

2020 ◽  
Author(s):  
chuanxin qiu
Keyword(s):  
Neural Network ◽  
Monetary Policy ◽  
Random Forest ◽  
Prediction Accuracy ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Random Forest Algorithm ◽  
Bank Of China ◽  
Macroeconomic Indicators ◽  
Neural Network Algorithm

This paper uses the random forest algorithm model to quantify and predict the monetary policy of the People's Bank of China under the input of 16 indicators macroeconomic indicators. It is compared with three other machine learning algorithms (CART decision tree, support vector machine and neural network algorithm), discrete selection model and combined prediction model. The results show that the random forest algorithm shows better prediction accuracy in predicting the direction of the central bank's monetary policy.

scholarly journals Research on Forecasting of China's Monetary Policy Based on Random Forest Algorithm.pdf

2020 ◽  
Author(s):  
chuanxin qiu
Keyword(s):  
Neural Network ◽  
Monetary Policy ◽  
Random Forest ◽  
Prediction Accuracy ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Random Forest Algorithm ◽  
Bank Of China ◽  
Macroeconomic Indicators ◽  
Neural Network Algorithm

This paper uses the random forest algorithm model to quantify and predict the monetary policy of the People's Bank of China under the input of 16 indicators macroeconomic indicators. It is compared with three other machine learning algorithms (CART decision tree, support vector machine and neural network algorithm), discrete selection model and combined prediction model. The results show that the random forest algorithm shows better prediction accuracy in predicting the direction of the central bank's monetary policy.

scholarly journals Analysis of Sports Performance Prediction Model Based on GA-BP Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jinjuan Wang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Performance Prediction ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Training Model ◽  
Sports Performance ◽  
Advantages And Disadvantages ◽  
Network Algorithm ◽  
Neural Network Algorithm

There are many factors that affect athletes’ sports performance in sports competitions. The traditional sports performance prediction method is difficult to obtain more accurate sports performance prediction results and corresponding data analysis in a short time, which is not conducive for coaches to formulate targeted and scientific training sprint plans for athletes’ problems. Therefore, based on GA-BP neural network algorithm, this paper constructs a sports performance prediction model and carries out experiments and analysis. The experimental results show that GA-BP neural network algorithm has a faster convergence speed than BP neural network and can achieve the expected error accuracy in a shorter time, which overcomes the problems of the BP neural network. At the same time, different from the previous models, GA-BP neural network algorithm can get the athlete training model according to the relationship between quality training indicators and special sports training results, which can more intuitively show the advantages and disadvantages of athletes. In the final sports performance prediction results, GA-BP neural network prediction results have higher accuracy, better stability, better prediction effect, and higher application value than BP neural network.

scholarly journals Research on Forecasting of China's Monetary Policy Based on Random Forest Algorithm.pdf

2020 ◽  
Author(s):  
chuanxin qiu
Keyword(s):  
Neural Network ◽  
Monetary Policy ◽  
Random Forest ◽  
Prediction Accuracy ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Random Forest Algorithm ◽  
Bank Of China ◽  
Macroeconomic Indicators ◽  
Neural Network Algorithm

This paper uses the random forest algorithm model to quantify and predict the monetary policy of the People's Bank of China under the input of 16 indicators macroeconomic indicators. It is compared with three other machine learning algorithms (CART decision tree, support vector machine and neural network algorithm), discrete selection model and combined prediction model. The results show that the random forest algorithm shows better prediction accuracy in predicting the direction of the central bank's monetary policy.

scholarly journals An Enhanced Novel GA-based Malware Detection in End Systems Using Structured and Unstructured Data by Comparing Support Vector Machine and Neural Network

2021 ◽  
Vol 11 (2) ◽  
pp. 1514-1525
Author(s):  
Sai Tejeshwar Reddy T
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Malware Detection ◽  
Unstructured Data ◽  
Support Vector ◽  
Network Algorithms ◽  
Android Malware ◽  
Android Malware Detection ◽  
The Neural Network ◽  
Neural Network Algorithm

Aim: The aim of the work is to perform android malware detection using Structured and Unstructured data by comparing Neural Network algorithms and SVM. Materials and Methods: consider two groups such as Support Vector Machine and Neural Network. For each algorithm take N=10 samples from the dataset collected and perform two iterations on each algorithm to identify the Malware Detection. Result: The accuracy results of the Neural Network model has potential up to (82.91%) and the Support Vector Machine algorithm has an accuracy of (79.67%) for Android malware detection with the significance value of (p=0.007). Conclusion: classification of android malware detection using Neural Network algorithm shows better accuracy than SVM.

scholarly journals Analysis and Prediction of Childhood Pneumonia Deaths using Machine Learning Algorithms

2021 ◽  
Author(s):  
Felipe A. L. Soares ◽  
Efrem E. O. Lousada ◽  
Tiago B. Silveira ◽  
Raquel A. F. Mini ◽  
Luis E. Zárate ◽  
...  
Keyword(s):  
Neural Network ◽  
Community Acquired Pneumonia ◽  
Machine Learning Algorithms ◽  
Cardiac Insufficiency ◽  
Network Algorithms ◽  
Childhood Pneumonia ◽  
The Neural Network ◽  
Scatter Plots ◽  
Neural Network Algorithm ◽  
Tract Infections

Acute Respiratory Tract Infections are among the leading causes of child mortality worldwide. Specifically, community-acquired pneumonia has different causes, such as: passive smoking, air pollution, poor hygiene, cardiac insufficiency, oropharyngeal colonization, nutritional deficiency, immunosuppression, and environmental, economic and social factors. Due to the variation of these causes, knowledge discovery in this area of health has been a great challenge for researchers. Thus, this paper presents the steps for the construction of a database and evaluation results applied to the analysis and prediction of potential deaths caused by childhood pneumonia using the Pictorea method. For this, the Random Forest and Artificial Neural Network algorithms were used, and after comparison, the Neural Network algorithm showed higher accuracy by up to 87.57%. This algorithm was used to analyze and predict the number of deaths from pneumonia in children up to 5 years old, and the results were presented using Root Mean Square Error and scatter plots. A domain specialist validated the results and defined that the pattern found is relevant for future studies in the medical field, helping to analyze the behavior of countries and predict future scenarios.

The Optimization of Wire Bonding Parameters and Quality Prediction Model Based on Neural Network

2012 ◽  
Vol 542-543 ◽  
pp. 976-980 ◽  
Author(s):  
Xiao Dan Guan ◽  
Gang Chen ◽  
Wan Lei Liang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Structural Parameters ◽  
Wire Bonding ◽  
Quality Prediction ◽  
Bonding Quality ◽  
Bonding Parameters ◽  
The Neural Network ◽  
Neural Network Algorithm

In this article, the parameters affecting the quality of wire bonding are analyzed by orthogonal testing with the methods of variance analysis and F tests. By analyzing the results, parameters that have a major impact on the quality of wire bonding are optimized. Because the relationship is complicated and non-linear between the impacting parameters and bonding quality, this article introduces a neural network algorithm of BPNN to build a model describing it. The structural parameters of the neural network are identified and a quality prediction model of wire bonding is established in this article. The model is validated, the results show that this proposed model has higher precision and it can accurately reflect the trends of the bonding quality indicators.

scholarly journals Wolf is Coming— Dynamic Classification Prediction Model of Vespa Mandarinia

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Yang Yue ◽  
Haomiao Niu ◽  
Jiao Liu
Keyword(s):  
Neural Network ◽  
Cellular Automata ◽  
Prediction Model ◽  
Diffusion Model ◽  
Classification Accuracy ◽  
Propagation Process ◽  
The Neural Network ◽  
Natural Domain ◽  
Classification Prediction ◽  
Set Up

Given the threat of Vespa mandarinia invasion to ecological balance, according to the data and information provided, the dynamic reproduction model of Vespa mandarinia is established by using natural domain interpolation, and the variation law of total bumblebee with time, latitude, and longitude is obtained. At the same time, we established theclassification prediction model by using a neural network and established the mapping relationship between time and space to evaluation grade.we meshed the area provided by the title, assigned values to the location of Vespa mandarinia(VM), and established a VM diffusion model with natural neighborhood interpolation. Its propagation process is simulated by cellular automata. It is determined that VM spreads in a circular shape centered at (122.93174°W, 48.93457°N) and (122.57376°W, 49.07848°N) in the Washington area, with the farthest distance being 1184.4 km and 985 km respectively.we set up a classification prediction model for better classification. According to the image upload time and location, SVM and neural network are used for classification prediction, and the classification accuracy is 74.26% and 97.60%, respectively, and the neural network has higher classification accuracy. So we choose the neural network. 

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