Automatic Recognition of Rock Images Based on Convolutional Neural Network and Discrete Cosine Transform

Li, Yan; Shi, Danling; Bu, Fengju

doi:10.18280/ts.360512

Cited by 15 publications

(3 citation statements)

References 14 publications

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“…Deep learning is an artificial neural network algorithm that uses data as input and processes it with a hidden layer. Furthermore, a non-linear transformation of the input data is carried out to calculate the output value (Li et al, 2019).…”

Section: Deep Learningmentioning

confidence: 99%

Heart Disease Prediction based on Physiological Parameters Using Ensemble Classifier and Parameter Optimization

Muliawan,

Rizal,

Hadiyoso

2023

JAETS

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This study describes the prediction of heart disease using ensemble classifiers with parameter optimization. As input, a public dataset was taken from UCI machine learning repository, which refers to the dataset at UCI Machine learning. The dataset consists of 13 variables that are considered to influence heart disease. Particle swarm optimization (PSO) was used for feature selection and principal component analysis (PCA) for feature extraction to reduce the features' dimensions. The application of parameter optimization on several machine learning methods such as SVM (Radial Basis Function), Deep learning, and Ensemble Classifier (bagging and boosting) to get the highest accuracy comparison. The results of this study using PSO dimensionality reduction in the public dataset of heart disease resulted in the slightest accuracy compared to PCA. In contrast, the highest accuracy was obtained from optimizing Deep Learning parameters with an accuracy of 84.47% and optimization of SVM RBF parameters with an accuracy of 83.56%. The highest accuracy in the ensemble classifier using bagging on SVM of 83.51%, with a difference of 0.5% from SVM without using bagging.

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Section: Deep Learningmentioning

confidence: 99%

Heart Disease Prediction based on Physiological Parameters Using Ensemble Classifier and Parameter Optimization

Muliawan,

Rizal,

Hadiyoso

2023

JAETS

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“…With the rapid development of computational intelligence (AI) and data mining, data-driven intelligent methods have become popular in the prediction of shortterm passenger flow [9]. e novel intelligent methods include long short-term memory (LSTM) [10][11][12], neural network (NN) [13][14][15][16][17][18][19][20], random forest (RF) [21,22], support vector machine (SVM) [23,24], fusion convolutional LSTM (FCL Net) [25], agent-based model (ABM) [26], and Bayesian network [27].…”

Section: Introductionmentioning

confidence: 99%

Forecast and Early Warning of Regional Bus Passenger Flow Based on Machine Learning

Liu

Tan

2020

Mathematical Problems in Engineering

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This paper mainly forecasts the short-term passenger flow of regional bus stations based on the integrated circuit (IC) card data of bus stations and puts forward an early warning model for regional bus passenger flow. Firstly, the bus stations were aggregated into virtual regional bus stations. Then, the short-term passenger flow of regional bus stations was predicted by the machine learning (ML) method of support vector machine (SVM). On this basis, the early warning model for regional bus passenger flow was developed through the capacity analysis of regional bus stations. The results show that the prediction accuracy of short-term passenger flow could be improved by replacing actual bus stations with virtual regional bus stations because the passenger flow of regional bus stations is more stable than that of a single bus station. The accurate prediction and early warning of regional bus passenger flow enable urban bus dispatchers to maintain effective control of urban public transport, especially during special and large-scale activities.

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“…Considering that the evaluation for the cultivation and improvement of college students' innovation ability is a complex systematic project, it will involve the comprehensive effect of many factors, and there is also some uncertain information to be handled. Traditional system engineering analysis methods, such as neural networks and genetic algorithms etc., have been well applied in the field of nonlinear engineering [11,12]. Zagrebina et al [13] predicted and analyzed power consumption based on recurrent neural network models.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation Model for the Cultivation and Improvement of the Innovation Ability of College Students

Wang

Yan

2020

Int. J. Emerg. Technol. Learn.

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In modern times, the innovation ability of college students is a focus of college education. However, the cultivation and improvement of this ability are constrained by many factors, and the ability is too complex to be evaluated accurately. To solve the problems, this paper puts forward an extension model suitable for evaluating the cultivation and improvement of the said ability. Firstly, the research problem was defined clearly as the evaluation of the cultivation and improvement for the innovation ability of college students. Based on the extension theory, an evaluation model was established for the cultivation and improvement of the said ability, the workflow of model implementation was explained, and the corresponding evaluation algorithm was designed in details. Finally, the proposed model and algorithm were proved operable through case analysis. The research results provide support to the solution of complex systematic decision-making problems.

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Automatic Recognition of Rock Images Based on Convolutional Neural Network and Discrete Cosine Transform

Cited by 15 publications

References 14 publications

Heart Disease Prediction based on Physiological Parameters Using Ensemble Classifier and Parameter Optimization

Heart Disease Prediction based on Physiological Parameters Using Ensemble Classifier and Parameter Optimization

Forecast and Early Warning of Regional Bus Passenger Flow Based on Machine Learning

An Evaluation Model for the Cultivation and Improvement of the Innovation Ability of College Students

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