A Comparative Theoretical and Empirical Analysis of Machine Learning Algorithms

Gupta, Shailja; Kaur, Manpreet; Lakra, Sachin; Dixit, Yogesh Babu

doi:10.14704/web/v17i1/web17011

Cited by 10 publications

(5 citation statements)

References 35 publications

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“…Even in the normal working condition due to the uncertainty of the hardware, data processing process of unreasonable and other factors will also generate part of the abnormal sample points, but in the normal working condition, the continuity and frequency of the abnormal sample points are much lower than the continuity and frequency of the abnormal sample points under the fault condition. Based on the above fact, a predetermined value can be set in the practical application [ 21 ]. If the frequency of the current outlier sample is lower than this value, the recognition result of the outlier sample will be output as “reject recognition” to avoid false alarm, while if the frequency is higher than the predetermined value, it can be judged that “fault exists in the system” to achieve fault detection.…”

Section: Analysis Of Music Intelligence Marketing Strategies For Variational Fuzzy Neural Network Algorithmsmentioning

confidence: 99%

Variational Fuzzy Neural Network Algorithm for Music Intelligence Marketing Strategy Optimization

Sun

2022

Computational Intelligence and Neuroscience

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In this paper, we use a variational fuzzy neural network algorithm to conduct an in-depth analysis and research on the optimization of music intelligent marketing strategy. The music recommendation system proposed in this paper includes a user modelling module, audio feature extraction module, and recommendation algorithm module. The basic idea of the recommendation algorithm is as follows: firstly, the historical behavioural information of music users is collected, and the user preference model is constructed by using the method of matrix decomposition of the hidden semantic model; then, the audio resources in the system are preprocessed and the spectrum map that can represent the music features is extracted; the similarity between the user’s preferred features and the music potential features are calculated to generate recommendations for the target user. The user-music dataset for model training and testing is constructed in-house, and the network model structure used for system experiments is designed based on a typical convolutional neural network model, while the model training tuning parameters are compared and selected. Finally, the model is trained and tested in this paper, and the system is evaluated in terms of both prediction rating accuracy and recommendation list generation accuracy using root mean square error, accuracy, recall, and F1 value as recommendation quality evaluation metrics. The experimental results show that the recommendation algorithm in this paper has certain feasibility and effectiveness. Compared with other traditional music recommendation algorithms, this paper makes full use of the powerful advantage of deep neural networks to automatically extract features and obtain higher-level music feature representations from the audio content, while incorporating the historical behavioural information of user interactions with music, which can effectively alleviate the problems such as cold start in recommendation systems.

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Section: Analysis Of Music Intelligence Marketing Strategies For Variational Fuzzy Neural Network Algorithmsmentioning

confidence: 99%

Variational Fuzzy Neural Network Algorithm for Music Intelligence Marketing Strategy Optimization

Sun

2022

Computational Intelligence and Neuroscience

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“…e evaluation of the college students' mental health status is essentially a nonlinear classification problem. Since the characteristics of everyone's mental state data are multidimensional and these characteristics involve many nonlinear factors and have the characteristics of multilevel, multivariable, nonlinear, and strong coupling, it is difficult to describe quantitatively by traditional mathematical models or methods [15]. To improve the accuracy of the evaluation of the mental health status of college students, it is very necessary to establish a more scientific and reasonable evaluation model of the mental health status of college students.…”

Section: Svm Multidimensional State Data Reduction Evaluationmentioning

confidence: 99%

[Retracted] Multidimensional State Data Reduction and Evaluation of College Students’ Mental Health Based on SVM

Peiqing

2022

Journal of Mathematics

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In response to the shortcomings of the traditional methods for evaluating the mental health status of college students in terms of computational complexity and low accuracy, a method for evaluating the mental health status of college students based on data reduction and support vector machines was proposed. A model experiment containing internal and external personality tendency classification, anxiety, and depression dichotomy was designed using logistic regression analysis, information entropy, and SVM algorithm to construct the feature dimensions of the network behavior data, combined with the labeled data of mental state to derive the sample data set for model experiments. In the experimental process, to reflect the difference in the effect of different models, various types of mathematical models were constructed for horizontal comparison; at the same time, to reflect the influence of the parameters of the same type of model, different combinations of parameters were constructed using a grid search algorithm to vertically compare the difference in the effect. The average accuracy of the dichotomous model for anxiety and depression in the sample of 1433 students was 0.80 or higher. The experiments show that the method of predicting students’ psychological status through their online behavioral data is feasible, and the mathematical classification model can be used to grasp students’ psychological status in real time and to warn students with abnormal psychological status, thus helping school counselors to intervene and prevent them promptly.

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“…Yuan and Li [ 15 ] studied the gradient changes of the parallel self-organizing network. The experimental results show that the prediction performance of the classic RBF network has been improved [ 16 ]. The researchers proposed a cross-validation subspace method to select the optimal number of hidden layer nodes.…”

Section: Related Workmentioning

confidence: 99%

Score Prediction of Sports Events Based on Parallel Self-Organizing Nonlinear Neural Network

Ling

2022

Computational Intelligence and Neuroscience

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This paper introduces the basic concepts and main characteristics of parallel self-organizing networks and analyzes and predicts parallel self-organizing networks through neural networks and their hybrid models. First, we train and describe the law and development trend of the parallel self-organizing network through historical data of the parallel self-organizing network and then use the discovered law to predict the performance of the new data and compare it with its true value. Second, this paper takes the prediction and application of chaotic parallel self-organizing networks as the main research line and neural networks as the main research method. Based on the summary and analysis of traditional neural networks, it jumps out of inertial thinking and first proposes phase space. Reconstruction parameters and neural network structure parameters are unified and optimized, and then, the idea of dividing the phase space into multiple subspaces is proposed. The multi-neural network method is adopted to track and predict the local trajectory of the chaotic attractor in the subspace with high precision to improve overall forecasting performance. During the experiment, short-term and longer-term prediction experiments were performed on the chaotic parallel self-organizing network. The results show that not only the accuracy of the simulation results is greatly improved but also the prediction performance of the real data observed in reality is also greatly improved. When predicting the parallel self-organizing network, the minimum error of the self-organizing difference model is 0.3691, and the minimum error of the self-organizing autoregressive neural network is 0.008, and neural network minimum error is 0.0081. In the parallel self-organizing network prediction of sports event scores, the errors of the above models are 0.0174, 0.0081, 0.0135, and 0.0381, respectively.

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A Comparative Theoretical and Empirical Analysis of Machine Learning Algorithms

Cited by 10 publications

References 35 publications

Variational Fuzzy Neural Network Algorithm for Music Intelligence Marketing Strategy Optimization

Variational Fuzzy Neural Network Algorithm for Music Intelligence Marketing Strategy Optimization

[Retracted] Multidimensional State Data Reduction and Evaluation of College Students’ Mental Health Based on SVM

Score Prediction of Sports Events Based on Parallel Self-Organizing Nonlinear Neural Network

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