A machine learning prediction of academic performance of secondary school students using radial basis function neural network

The effectiveness evaluation of industry education integration model for applied universities under back propagation neural network

As the education field continues to advance, industry-education integration has become a crucial strategy for enhancing teaching quality in applied universities. This study investigates how artificial intelligence, specifically the back propagation neural network (BPNN), can be applied within an industry-education integration framework to strengthen students' skills and employability. A series of experiments were conducted to assess the model's effectiveness in linking theoretical learning with practical experience, as well as in improving students' hands-on and innovative abilities. Results demonstrate that the BPNN-optimized model substantially boosts students' overall competencies. For instance, the average academic score of students in the experimental group rose from 78.5 to 85.2, practical assessment scores increased from 76.8 to 88.4, and innovation scores improved from 74.2 to 82.5. Additionally, the employment rate for the experimental group reached 94%, surpassing the control group's 76%, with significant gains in job satisfaction and career planning skills. These findings highlight that the BPNN-based industry-education integration model effectively strengthens students' theoretical knowledge, practical skills, and employability, offering a valuable framework for enhanced university-industry collaboration.

Prediction of soil heavy metal contents in urban residential areas and the strength of deep learning: A case study of Beijing

Predicting soil heavy metal (SHM) content is crucial for understanding SHM pollution levels in urban residential areas and guide efforts to reduce pollution. However, current research indicates low SHM prediction accuracy in urban areas. Therefore, we employed a deep learning method (fully connected deep neural network) alongside four other methods (muti-layer perceptron, radial basis function neural network, multiple stepwise linear regression, and Kriging interpolation) to predict SHM content in the urban residential areas of Beijing and demonstrated the strength of deep learning in improving prediction accuracy. We found the contents of the evaluated heavy metals (Cd, Cu, Pb, and Zn) exhibited significant correlations with numerous other soil physicochemical properties and environmental factors. The prediction accuracy for Cu, Pb, and Zn contents was relatively high across different methods. Notably, deep learning showed considerable strength in predicting the contents of the four heavy metals, with the R2 for the test set of the model ranging from 0.75 to 0.91. Compared to other methods, deep learning achieved markedly higher prediction accuracy according to different accuracy evaluation indicators (e.g., deep learning showed increases in the cumulative R2 of the four heavy metals ranging from 53.16 % to 187.36 % compared to other methods). Our study indicates that deep learning can significantly improve SHM content prediction accuracy in urban areas and is highly applicable in urban residential areas with complex environmental influences.

Student course grade prediction using the random forest algorithm: Analysis of predictors' importance

BACKGROUND

Universities need to find strategies for improving student retention rates. Predicting student academic performance enables institutions to identify underachievers and take appropriate actions to increase student completion and lower dropout rates.

METHOD

In this work, we proposed a model based on random forest methodology to predict students' course performance using seven input predictors and find their relative importance in determining the course grade. Seven predictors were derived from transcripts and recorded data from 650 undergraduate computing students.

RESULTS

Our findings indicate that grade point average and high school score were the two most significant predictors of a course grade. The course category and class attendance percentage have equal importance. Course delivery mode does not have a significant effect.

CONCLUSION

Our findings show that courses students at risk find challenging can be identified, and appropriate actions, procedures, and policies can be taken.

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

Artificial neural networks (ANNs) ability to learn, correct errors, and transform a large amount of raw data into beneficial medical decisions for treatment and care has increased in popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients' healthcare decisions and efficient disease diagnosis. We study different types of ANNs in the existing literature that advance ANNs' adaptation for complex applications. Specifically, we investigate ANNs' advances for predicting viral, cancer, skin, and COVID-19 diseases. Furthermore, we propose a deep convolutional neural network (CNN) model called ConXNet, based on chest radiography images, to improve the detection accuracy of COVID-19 disease. ConXNet is trained and tested using a chest radiography image dataset obtained from Kaggle, achieving more than 97% accuracy and 98% precision, which is better than other existing state-of-the-art models, such as DeTraC, U-Net, COVID MTNet, and COVID-Net, having 93.1%, 94.10%, 84.76%, and 90% accuracy and 94%, 95%, 85%, and 92% precision, respectively. The results show that the ConXNet model performed significantly well for a relatively large dataset compared with the aforementioned models. Moreover, the ConXNet model reduces the time complexity by using dropout layers and batch normalization techniques. Finally, we highlight future research directions and challenges, such as the complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications.

Design of Customized Garments Towards Sustainable Fashion Using 3D Digital Simulation and Machine Learning-Supported Human–Product Interactions

This paper put forward a new interactive design approach for customized garments towards sustainable fashion using machine learning techniques, including radial basis function artificial neural network (RBF ANN), genetic algorithms (GA), probabilistic neural network (PNN), and support vector regression (SVR). First, RBF ANNs were employed to estimate the detailed human body dimensions to fulfill consumers’ ergonomics requirements. Next, the GA-based models were developed to generate the formalized design solutions following the consumer profiles (demands). Afterwards, the evaluation model was established to quantitatively characterize the relations between consumer profiles and garment profiles from the generated design solutions. The design solutions would be digitally demonstrated and recommended to the consumer following the evaluation results in descending order. Meanwhile, the PNN-based models were created to predict garment fitness based on virtual try-on. Moreover, the SVR-based self-adjustment mechanism was built to estimate and control garment design parameters according to the consumer’s feedback. Based on these mathematical models, the approach enhances the interactions among digital garment demonstration, the designer’s professional knowledge and the user’s perception to find out the most relevant design solution. The effectiveness of the new approach was verified by a real application case of leisure pants customization. The results show that the proposed method can powerfully support the designers’ quality personalized design solutions for consumers more accurately, fast, intelligently, and sustainably, compared with the existing approaches. More importantly, it also establishes an effective and reliable communication channel and mechanism among consumers, fashion designers, pattern designer, and garment producer.